Imaging Pearls ❯ Deep Learning ❯ Radiomics
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- “The combined model showed improved performance compared to both the clinical and radiomics models in the test group, with an AUC of 0.844, accuracy of 0.767, sensitivity of 0.806, and specificity of 0.667. Subsequently, DCA of the combined model indicated optimal clinical value for predicting PNI status. Machine learning radiomics models can accurately predict PNI status in patients with pancreatic ductal adenocarcinoma. The combined model, which incorporates clinical and radiomics features, enhances preoperative diagnostic performance and aids in the selection of treatment methods.”
Preoperative Prediction of Perineural Invasion in Pancreatic Ductal Adenocarcinoma Using Machine Learning Radiomics Based on Contrast‑Enhanced CT Imaging
Wenzheng Lu · Yanqi Zhong · Xifeng Yang ·et al.
Journal of Imaging Informatics in Medicine https://doi.org/10.1007/s10278-024-01325-1 - “Multivariate logistic regression was employed to identify independent predictors and establish clinical models. A combined model was constructed by integrating clinical and radiomics features. Model performances were assessed by receiver operating characteristic curves (ROCs) and decision curve analyses (DCAs). A total of 788 radiomics features were extracted from CECT images, of which 14 were identified as significant through the three-step selection process. Among the machine learning models, the SVM radiomics model exhibited the highest predictive performance in the test group, achieving an area under the curve (AUC) of 0.831, accuracy of 0.698, sensitivity of 0.677, and specificity of 0.750. After logistic regression screening, the clinical model was established using carbohydrate antigen 19–9 (CA199) levels as one independent predictor. In the test group, the clinical model demonstrated an AUC of 0.644, accuracy of 0.744, sensitivity of 0.871, and specificity of 0.417.”
Preoperative Prediction of Perineural Invasion in Pancreatic Ductal Adenocarcinoma Using Machine Learning Radiomics Based on Contrast‑Enhanced CT Imaging
Wenzheng Lu · Yanqi Zhong · Xifeng Yang ·et al.
Journal of Imaging Informatics in Medicine https://doi.org/10.1007/s10278-024-01325-1 - “In fact, clinical factors and imaging features also need to be considered in the clinic. Our study demonstrated that the combined models surpassed the performance of models based solely on radiomics signatures or clinical features in the test group, achieving an AUC of 0.844. Lee et al. integrated clinical and radiomics features to develop a hybrid model for predicting early recurrence of pancreatic ductal adenocarcinoma, achieving the highest diagnostic power in the test set (AUC, 0.830), which aligned with our research findings. This demonstrated that creating a combined model was more beneficial for improving diagnostic performance.”
Preoperative Prediction of Perineural Invasion in Pancreatic Ductal Adenocarcinoma Using Machine Learning Radiomics Based on Contrast‑Enhanced CT Imaging
Wenzheng Lu · Yanqi Zhong · Xifeng Yang ·et al.
Journal of Imaging Informatics in Medicine https://doi.org/10.1007/s10278-024-01325-1 - “This study had several limitations. Firstly, as a retrospective study, it is inherently subject to biases such as selection bias and potential confounders. In the future, a larger sample size multi-center retrospective study or a prospective study is suggested to improve the robustness of the model. Secondly, ROIs of tumors were delineated manually, and the next step will be to try to use automated or semi-automated segmentation methods for automatic segmentation and delineation of the tumors to improve and provide more consistent and objective results. Thirdly, our study classified PNI as positive or negative; however, the extent of PNI may be a more valuable prognostic factor. Radiomics models could offer an efficient method for differentiating between various grades of PNI, which should be further explored and validated infuture studies.”
Preoperative Prediction of Perineural Invasion in Pancreatic Ductal Adenocarcinoma Using Machine Learning Radiomics Based on Contrast‑Enhanced CT Imaging
Wenzheng Lu · Yanqi Zhong · Xifeng Yang ·et al.
Journal of Imaging Informatics in Medicine https://doi.org/10.1007/s10278-024-01325-1
- Objectives: This research aimed to assess the value of radiomics combined with multiple machine learning algorithms in the diagnosis of pancreatic ductal adenocarcinoma (PDAC) lymph node (LN) metastasis, which is expected to provide clinical treatment strategies. Conclusions: Combining radiomics and machine learning algorithms demonstrated the potential for identifying the LN metastasis of PDAC. As a non-invasive and efficient preoperative prediction tool, it can be beneficial for decision-making in clinical practice.
Contrast-enhanced CT radiomics combined with multiple machine learning algorithms for preoperative identification of lymph node metastasis in pancreatic ductal adenocarcinoma
Yue Huang et al.
Front. Oncol. 14:1342317. doi: 10.3389/fonc.2024.1342317 - Methods: A total of 128 patients with pathologically confirmed PDAC and who underwent surgical resection were randomized into training (n=93) and validation (n=35) groups. This study incorporated a total of 13 distinct machine learning algorithms and explored 85 unique combinations of these algorithms. The area under the curve (AUC) of each model was computed. The model with the highest mean AUC was selected as the best model which was selected to determine the radiomics score (Radscore). The clinical factors were examined by the univariate and multivariate analysis, which allowed for the identification of factors suitable for clinical modeling. The multivariate logistic regression was used to create a combined model using Radscore and clinical variables. The diagnostic performance was assessed by receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA).
Contrast-enhanced CT radiomics combined with multiple machine learning algorithms for preoperative identification of lymph node metastasis in pancreatic ductal adenocarcinoma
Yue Huang et al.
Front. Oncol. 14:1342317. doi: 10.3389/fonc.2024.1342317 - Radiomics provides a large amount of medical image information that can reveal hidden features of diseases that are invisible to the naked eye. Previous research has demonstrated the efficacy of integrating radiomics and machine learning (ML) techniques in many applications. Several researchers have constructed multiphasic contrast-enhanced CT (CECT) radiomics models to evaluate the preoperative LN status of PDAC. These studies indicated that radiomics models have significant potential in predicting pancreatic cancer with lymph node metastasis (LNM). However, the researchers predominantly chose modeling algorithms based on their preferences and limitations in knowledge. It is imperative that evidence be provided to select appropriate models for solving clinical problems. To the best of our knowledge, no study has explored the use of radiomics combined with multiple ML algorithms in thepreoperative identification of the LN status in PDAC
Contrast-enhanced CT radiomics combined with multiple machine learning algorithms for preoperative identification of lymph node metastasis in pancreatic ductal adenocarcinoma
Yue Huang et al.
Front. Oncol. 14:1342317. doi: 10.3389/fonc.2024.1342317 - “Although CT is the most commonly used method to evaluate pancreatic cancer resectability, Imai et al. indicated that loworder data such as LN diameter and volume measured from CT image cannot reflect obvious differences between patients with pancreatic cancer with and without LN metastasis. This may explain why visual assessment of CT scans has a low efficiency for detecting LN metastases among patients with pancreatic cancer. Radiomics, also referred to as a “whole-tumor virtual biopsy technique,” enables the extraction of numerous image features from the entire tumor, reflecting its heterogeneity and characteristics. A biopsy examines only a portion of the tumor tissue, which cannot comprehensively assess the intratumor heterogeneity before surgery. In contrast, radiomics can non-invasively reflect comprehensive information about the entire tumor. This approach has promise for enhancing diagnostic capabilities and facilitating the development of personalized treatment approaches.”
Contrast-enhanced CT radiomics combined with multiple machine learning algorithms for preoperative identification of lymph node metastasis in pancreatic ductal adenocarcinoma
Yue Huang et al.
Front. Oncol. 14:1342317. doi: 10.3389/fonc.2024.1342317 - “In summary, of the 233 radiomics models examined, the model built by applying AP+VP radiomics features and a combination of Lasso-Logistic algorithm had the most favorable performance in both the training and validation cohorts. Our investigation showed that integrating the AP+VP-Radscore with clinical parameters yielded the best performance. This combined model has the potential to serve as an accurate and non-invasive instrument for forecasting LN metastasis of PDAC, hence facilitating clinical decision-making.”
Contrast-enhanced CT radiomics combined with multiple machine learning algorithms for preoperative identification of lymph node metastasis in pancreatic ductal adenocarcinoma
Yue Huang et al.
Front. Oncol. 14:1342317. doi: 10.3389/fonc.2024.1342317
- Background. PanNETs are a rare group of pancreatic tumors that display heterogeneous histopathological and clinical behavior. Nodal disease has been established as one of the strongest predictors of patient outcomes in PanNETs. Lack of accurate preoperative assessment of nodal disease is a major limitation in the management of these patients, in particular those with small (< 2 cm) low-grade tumors. The aim of the study was to evaluate the ability of radiomic features (RF) to preoperatively predict the presence of nodal disease in pancreatic neuroendocrine tumors (PanNETs).
Preoperative Prediction of Lymph Node Metastases in Nonfunctional Pancreatic Neuroendocrine Tumors Using a Combined CT Radiomics-Clinical Model.
Ahmed TM, Zhu Z, Yasrab M, Blanco A, Kawamoto S, He J, Fishman EK, Chu L, Javed AA
Ann Surg Oncol. 2024 Aug 23. doi: 10.1245/s10434-024-16064-4. Epub ahead of print - Results. Of the 320 patients included in the study, 92 (28.8%) had nodal disease based on histopathological assessment of the surgical specimen. A radiomic signature based on ten selected RF was developed. Clinicopathological characteristics predictive of nodal disease included tumor grade and size. Upon internal validation the combined radiomics and clinical feature model demonstrated adequate performance (AUC 0.80) in identifying nodal disease. The modelmaccurately identified nodal disease in 85% of patients with small tumors (< 2 cm).
Conclusions. Non-invasive preoperative assessment of nodal disease using RF and clinicopathological characteristics is feasible.
Preoperative Prediction of Lymph Node Metastases in Nonfunctional Pancreatic Neuroendocrine Tumors Using a Combined CT Radiomics-Clinical Model.
Ahmed TM, Zhu Z, Yasrab M, Blanco A, Kawamoto S, He J, Fishman EK, Chu L, Javed AA
Ann Surg Oncol. 2024 Aug 23. doi: 10.1245/s10434-024-16064-4. Epub ahead of print - ”In conclusion, this study presents a novel tool on the basis of RFs and clinical factors for accurate prediction of nodal disease in NF-PanNETs. Furthermore, we demonstrated that addition of RFs to clinical factors can make this model more robust and accurate. Further validation of this model is required to assess its performance in external cohorts. If validated, this tool could allow for non-invasive serial assessment of nodal disease in patients with well differentiated nonfunctioning PanNETs to tailor management plans and provide precise therapy on the basis of each patient’s disease biology.”
Preoperative Prediction of Lymph Node Metastases in Nonfunctional Pancreatic Neuroendocrine Tumors Using a Combined CT Radiomics-Clinical Model.
Ahmed TM, Zhu Z, Yasrab M, Blanco A, Kawamoto S, He J, Fishman EK, Chu L, Javed AA
Ann Surg Oncol. 2024 Aug 23. doi: 10.1245/s10434-024-16064-4. Epub ahead of print 
- Purpose: The purpose of this study was to develop a radiomics-based algorithm to identify small pancreatic neuroendocrine tumors(PanNETs) on CT and evaluate its robustness across manual and automated segmentations, exploring the feasibility of automated screening.
Conclusion: Radiomics features identify small PanNETs, with stable performance when extracted using automated segmentations. These models demonstrate high sensitivity, complementing the high specificity of radiologists, and could serve as opportunistic screeners.
Radiomics machine learning algorithm facilitates detection of small pancreatic neuroendocrine tumors on CT.
Lopez-Ramirez F, Soleimani S, Azadi JR, Sheth S, Kawamoto S, Javed AA, Tixier F, Hruban RH, Fishman EK, Chu LC.
Diagn Interv Imaging. 2024 Sep 14:S2211-5684(24)00172-4. doi: 10.1016/j.diii.2024.08.003. Epub ahead of print. PMID: 39278763 . - Results:A total of 135 patients with 142 PanNETs,and135 healthy controls were included.There were 168 women and 102 men,with a mean age of 55.4 +/-11.6(standard deviation) years(range:20−85years). Median PanNET size was 1.3cm (Q1,1.0;Q3,1.5;range:0.5−1.9).The arterial phase Light GBM model achieved the best performance in the test set,with 90% sensitivity (95% confidence interval[CI]:80−98),76% specificity (95%CI:62−88)and an AUC of 0.87 (95% CI:0.79−0.94).Using features from the automated segmentations,this model achieved an AUC of 0.86(95%CI:0.79−0.93). In comparison, the radiologists achieved a mean 50% sensitivity and 100% specificity using arterial phase CT images.
Radiomics machine learning algorithm facilitates detection of small pancreatic neuroendocrine tumors on CT.
Lopez-Ramirez F, Soleimani S, Azadi JR, Sheth S, Kawamoto S, Javed AA, Tixier F, Hruban RH, Fishman EK, Chu LC.
Diagn Interv Imaging. 2024 Sep 14:S2211-5684(24)00172-4. doi: 10.1016/j.diii.2024.08.003. Epub ahead of print. 
Radiomics machine learning algorithm facilitates detection of small pancreatic neuroendocrine tumors on CT.
Lopez-Ramirez F, Soleimani S, Azadi JR, Sheth S, Kawamoto S, Javed AA, Tixier F, Hruban RH, Fishman EK, Chu LC.
Diagn Interv Imaging. 2024 Sep 14:S2211-5684(24)00172-4. doi: 10.1016/j.diii.2024.08.003. Epub ahead of print.
Radiomics machine learning algorithm facilitates detection of small pancreatic neuroendocrine tumors on CT.
Lopez-Ramirez F, Soleimani S, Azadi JR, Sheth S, Kawamoto S, Javed AA, Tixier F, Hruban RH, Fishman EK, Chu LC.
Diagn Interv Imaging. 2024 Sep 14:S2211-5684(24)00172-4. doi: 10.1016/j.diii.2024.08.003. Epub ahead of print.
Radiomics machine learning algorithm facilitates detection of small pancreatic neuroendocrine tumors on CT.
Lopez-Ramirez F, Soleimani S, Azadi JR, Sheth S, Kawamoto S, Javed AA, Tixier F, Hruban RH, Fishman EK, Chu LC.
Diagn Interv Imaging. 2024 Sep 14:S2211-5684(24)00172-4. doi: 10.1016/j.diii.2024.08.003. Epub ahead of print.- “In conclusion, this study demonstrates the feasibility of radiomics-based tools, with features extracted from manual and automatic pancreas segmentations, to detect small PanNETs with higher sensitivity than two experienced radiologists.These preliminary results suggest that radiomics could potentially serve as a second reader or opportunist screener for detection of low-stage disease and ultimately impact clinical care.”
Radiomics machine learning algorithm facilitates detection of small pancreatic neuroendocrine tumors on CT.
Lopez-Ramirez F, Soleimani S, Azadi JR, Sheth S, Kawamoto S, Javed AA, Tixier F, Hruban RH, Fishman EK, Chu LC.
Diagn Interv Imaging. 2024 Sep 14:S2211-5684(24)00172-4. doi: 10.1016/j.diii.2024.08.003. Epub ahead of print.
- Background: We identified computed tomography (CT)-derived radiomic features predictive of tumor progression within three months, then examined their ability to prognosticate overall survival (OS) along with clinical features in pancreatic cancer. We evaluated these features in patients with unresected pancreatic cancer who underwent stereotactic body radiation therapy (SBRT) in sequence with chemotherapy, but not surgery.
Conclusions: CT-derived radiomic features predict rapid tumor progression following SBRT, confer nearly a twofold increase in mortality risk, and, along with patient age, enhance the identification of patients with stage II-IIIpancreatic cancer with poor OS.
Utility of radiomic features in predicting clinical outcomes in stage II-III pancreatic cancer.
Haruka Itakura,et al
American Society of Clinical Oncology Sept 2024 (abstract) - “In this retrospective study, we examined a cohort of 101 patients with stage II-III pancreatic cancer who underwent SBRT with sequential chemotherapy at a single institution (Stanford Health Care) between 1999- 2020. From their pre-SBRT contrast-enhanced CT images with segmented tumors, delineating regions-of-interest, we extracted 900 radiomic (quantitative pixel-level imaging characteristic) features. In the first phase, we identified radiomic features that predicted rapid tumor progression within three months following SBRT.”
Utility of radiomic features in predicting clinical outcomes in stage II-III pancreatic cancer.
Haruka Itakura,et al
American Society of Clinical Oncology Sept 2024 (abstract) - Background: When determining the initial chemotherapy regimen for advanced or metastatic pancreatic cancer, various factors must be evaluated. The decision between FOLFIRINOX and Gemcitabine/Nab-paclitaxel (GnP) is challenging, as patient survival hinges on the efficacy and toxicity profiles of these treatments, alongside individual patient characteristics and vulnerabilities. This study aims to guide the selection of an appropriate first-line chemotherapy regimen for advanced or metastatic pancreatic cancer by leveraging machine learning (ML) methods to predict survival outcomes. Conclusions: We developed the ML models that can compute the probability of OS based on the routinely collected data from patients with advanced or metastatic pancreatic cancer. To the best of our knowledge, this is the first ML solution aimed at aiding clinicians in the selection of the first-line chemotherapy regimen for pancreatic cancer.
Predicting chemotherapy response in patients with advanced or metastatic pancreatic cancer using machine learning.
Moonho Kim, Gyucheol Choi, Jamin Koo
American Society of Clinical Oncology. Sept 2024 - Results: The median age of the patients was 66 years, with 62.3% being male. The MLnmodels achieved the ROC-AUC of 0.81 when predicting OS after 12 months following the initial administration of FOLFIRINOX (n=61) or GnP (n=47). Five (peritoneal metastases, other metastases, bilirubin level, white blood cell counts, and retroperitoneal lymph node metastases) or four (age, tumor location, sex, and metastatic status) covariates were used to achieve the predictive accuracy for the two regimens, respectively. The median OS of the high versus low risk groups of FOLFIRINOX predicted by the ML models were significantly different (7 vs 18 months, P, 0.01), recording the hazard ratio (HR) of 2.79 (95% CI, 1.47-5.26). Similarly, the median OS of the high and low risk groups of GnP were significantly different (8 vs 16 months,P , 0.001, HR 3.50, 95% CI, 1.60-7.66).
Predicting chemotherapy response in patients with advanced or metastatic pancreatic cancer using machine learning.
Moonho Kim, Gyucheol Choi, Jamin Koo
American Society of Clinical Oncology. Sept 2024
- Background To develop and compare machine learning models based on triphasic contrast-enhanced CT (CECT) for distinguishing between benign and malignant renal tumors.
Materials and Methods In total, 427 patients were enrolled from two medical centers: Center 1 (serving as the training set) and Center 2 (serving as the external validation set). First, 1781 radiomic features were individually extracted from corticomedullary phase (CP), nephrographic phase (NP), and excretory phase (EP) CECT images, after which 10 features were selected by the minimum redundancy maximum relevance method. Second, random forest (RF) models were constructed from single-phase features (CP, NP, and EP) as well as from the combination of features from all three phases (TP). Third,the RF models were assessed in the training and external validation sets. Finally, the internal prediction mechanisms of the models were explained by the SHapley Additive exPlanations (SHAP) approach.
Interpretable multiphasic CT‑based radiomic analysis for preoperatively differentiating benign and malignant solid renal tumors: a multicenter study
Yaohai Wu et al.
Abdominal Radiology (2024) 49:3096–3106 - Results A total of 266 patients with renal tumors from Center 1 and 161 patients from Center 2 were included. In the training set, the AUCs of the RF models constructed from the CP, NP, EP, and TP features were 0.886, 0.912, 0.930, and 0.944, respectively. In the external validation set, the models achieved AUCs of 0.860, 0.821, 0.921, and 0.908, respectively. The “original_shape_Flatness” feature played the most important role in the prediction outcome for the RF model based on EP features according to the SHAP method.
Conclusions The four RF models efficiently differentiated benign from malignant solid renal tumors, with the EP feature based RF model displaying the best performance.
Interpretable multiphasic CT‑based radiomic analysis for preoperatively differentiating benign and malignant solid renal tumors: a multicenter study
Yaohai Wu et al.
Abdominal Radiology (2024) 49:3096–3106 - “In summary, all four RF models efficiently differentiated benign from malignant solid renal tumors, and the RF model based on EP features displayed the best performance. The feature “original_shape_Flatness” played the greatest role in predicting the outcome of RF model based on EP image features.”
Interpretable multiphasic CT‑based radiomic analysis for preoperatively differentiating benign and malignant solid renal tumors: a multicenter study
Yaohai Wu et al.
Abdominal Radiology (2024) 49:3096–3106
- Objectives: To evaluate the robustness of radiomics features among photon-counting detector CT (PCD-CT) and dual energy CT (DECT) systems.
Conclusion: The radiomics features were non-reproducible with significant variability in values among different CT techniques.
Clinical relevance statement: Radiomics features are non-reproducible with significant variability in values among photon-counting detector CT and dual-energy CT systems, necessitating careful attention to improve the cross-system generalizability of radiomic features before implementation of radiomics analysis in clinical routine.
Robustness of radiomics among photoncounting detector CT and dual-energy CT systems: a texture phantom study
Lan Zhu, Haipeng Dong, Jing Sun, et al.
European Radiology https://doi.org/10.1007/s00330-024-10976-1 - “To conclude, this study outlined the variability of radiomics features derived from VMIs generated using one PCD-CT system and four traditional energy integrating DECT systems, despite using comparable protocols. Different radiation dose levels did not substantially change radiomics features, while the repeatability of radiomics features was heterogeneous across CT techniques. Radiomics analysis based on one CT technique should not be directly transferred to others without validation. Future investigations are encouraged to mitigate radiomics variability due to CT techniques.”
Robustness of radiomics among photoncounting detector CT and dual-energy CT systems: a texture phantom study
Lan Zhu, Haipeng Dong, Jing Sun, et al.
European Radiology https://doi.org/10.1007/s00330-024-10976-1
- “The purpose of this study was to systematically create a radiomics dataset of normal abdominal and pelvic radiomics that can be used for model development and validation. Young adults without any previously known disease, aged > 17 and ≤ 36 years old, were retrospectively included. All patients had undergone CT scanning for emergency indications. In case abnormal findings were identified, the relevant anatomical structures were excluded. Deep learning was used to automatically segment the majority of visible anatomical structures with the TotalSegmentator model as applied in 3DSlicer. Radiomics features including first order, texture, wavelet, and Laplacian of Gaussian transformed features were extracted with PyRadiomics. A Github repository was created to host the resulting dataset. Radiomics data were extracted from a total of 531 patients with a mean age of 26.8 +/- 5.19 years, including 250 female and 281 male patients. A maximum of 53 anatomical structures were segmented and used for subsequent radiomics data extraction. Radiomics features were derived from a total of 526 non-contrast and 400 contrast-enhanced (portal venous) series. The dataset is publicly available for model development and validation purposes.”
Developing a Radiomics Atlas Dataset of normal Abdominal and Pelvic computed Tomography (RADAPT)
Elisavet Kapetanou et al.
Journal of Imaging Informatics in Medicine https://doi.org/10.1007/s10278-024-01028-7 - “Organs and anatomic structures of the reproductive system not detected by TotalSegmentator have not been included. Segmentation included abdominal organs (the liver, spleen, pancreas, adrenals, kidneys, gallbladder), muscles (paraspinal muscles, gluteal muscles, iliopsoas muscles), bones (lower ribs included in abdominal images, lower thoracic vertebrae, lumbar vertebrae, pelvic bones, and proximal femurs), and vessels (aorta and common iliac arteries, portal vein, inferior vena cava, and common iliac veins).”
Developing a Radiomics Atlas Dataset of normal Abdominal and Pelvic computed Tomography (RADAPT)
Elisavet Kapetanou et al.
Journal of Imaging Informatics in Medicine https://doi.org/10.1007/s10278-024-01028-7 - “There are some inherent limitations in this study. The dataset is derived from a specific age group of young adults limited to ≥ 17 and ≤ 36 years old, which may not be entirely representative of the broader population. Age-related changes in organ morphology and tissue characteristics might introduce variability when applying this radiomics reference atlas to older populations. Nonetheless, this specific age range was chosen by design with the goal of representing healthy tissues and organs. Another limitation is that there is a possibility that an undiagnosed/ undocumented underlying disease could be present in some of our patients. Nonetheless, a comprehensive analysis of all available patient data was done to ensure that no relevant disease was on record and that no visible abnormal imaging finding was included.”
Developing a Radiomics Atlas Dataset of normal Abdominal and Pelvic computed Tomography (RADAPT)
Elisavet Kapetanou et al.
Journal of Imaging Informatics in Medicine https://doi.org/10.1007/s10278-024-01028-7
- Objectives To develop and validate a contrast-enhanced computed tomography (CECT)–based radiomics nomogram for the preoperative evaluation of Ki-67 proliferation status in pancreatic ductal adenocarcinoma (PDAC).
Conclusions The radiomics-clinical nomogram is an effective and non-invasive computer-aided tool to predict the Ki-67 expression status in patients with PDAC.
Clinical relevance statement The radiomics-clinical nomogram is an effective and non-invasive computer-aided tool to predict the Ki-67 expression status in patients with pancreatic ductal adenocarcinoma.
Development of a CT radiomics nomogram for preoperative prediction of Ki‑67 index in pancreatic ductal adenocarcinoma:a two‑center retrospective study
Qian Li et al.
European Radiology 2023 https://doi.org/10.1007/s00330-023-10393-w - Ki-67, as a nuclear protein, is expressed in the proliferative stage except for the G0 phase of cells, which can accurately reflect proliferative status of tumor cells. Regarding PDAC, some previous studies have reported that higher Ki-67 expression may be indicative of higher tumor grade, which could reflect the aggressiveness of the tumor. Ki-67 expression > 50% is associated with poor disease-free survival, disease-specific survival, and overall survival. It is an independent indicator for postoperative early recurrence, which may prompt neoadjuvant therapy instead of upfront surgery.
Development of a CT radiomics nomogram for preoperative prediction of Ki‑67 index in pancreatic ductal adenocarcinoma:a two‑center retrospective study
Qian Li et al.
European Radiology 2023 https://doi.org/10.1007/s00330-023-10393-w - The increasedKi-67 index had bad impact on prognosis, so the high Ki-67 group was more prone to have elevated CA19-9 levels. Another important finding was that CTreportedpositive LN status was more prevalent in the high Ki-67 group. Some previous studies had found the PDAC patients with high Ki-67 index were more likely to present LN metastasis, and CT-reported LN status was significantly related to the pathological LN status. Therefore, we think it may explain why CT-reported LN status could predict Ki-67 expression status.
Development of a CT radiomics nomogram for preoperative prediction of Ki‑67 index in pancreatic ductal adenocarcinoma:a two‑center retrospective study
Qian Li et al.
European Radiology 2023 https://doi.org/10.1007/s00330-023-10393-w - “In conclusion, this study used CECT radiomics features to develop an effective and non-invasive radiomicsclinical nomogram that showed favorable performance in preoperative evaluation of the Ki-67 expression status for patients with PDA.”
Development of a CT radiomics nomogram for preoperative prediction of Ki‑67 index in pancreatic ductal adenocarcinoma:a two‑center retrospective study
Qian Li et al.
European Radiology 2023 https://doi.org/10.1007/s00330-023-10393-w
- CONCLUSIONS: Radiomics-based ML models can detect PDAC from normal pancreas when it is beyond human interrogation capability at a substantial lead time before clinical diagnosis. Prospective validation and integration of such models with complementary fluid-based biomarkers has the potential for PDAC detection at a stage when surgical cure is a possibility.
Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis
Sovanlal Mukherjee et al.
Gastroenterology 2022;163:1435–1446 - BACKGROUND & AIMS: Our purpose was to detect pancreatic ductal adenocarcinoma (PDAC) at the prediagnostic stage (3– 36 months before clinical diagnosis) using radiomics-based machine-learning (ML) models, and to compare performance against radiologists in a case-control study.
RESULTS: Median (range) time between prediagnostic CTs of the test subset and PDAC diagnosis was 386 (97–1092) days. SVM had the highest sensitivity (mean; 95% confidence interval) (95.5; 85.5–100.0), specificity (90.3; 84.3–91.5), F1- score (89.5; 82.3–91.7), area under the curve (AUC) (0.98; 0.94–0.98), and accuracy (92.2%; 86.7–93.7) for classification of CTs into prediagnostic versus normal. All 3 other ML models, KNN, RF, and XGBoost, had comparable AUCs (0.95, 0.95, and 0.96, respectively). The high specificity of SVM wasgeneralizable to both the independent internal (92.6%) and the National Institutes of Health dataset (96.2%). In contrast, interreader radiologist agreement was only fair (Cohen’s kappa 0.3) and their mean AUC (0.66; 0.46–0.86) was lower than each of the 4 ML models (AUCs: 0.95–0.98) (P < .001).Radiologists also recorded false positive indirect findings of PDAC in control subjects (n . 83) (7% R4, 18% R5).
Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis
Sovanlal Mukherjee et al.
Gastroenterology 2022;163:1435–1446 
- BACKGROUND & AIMS: The aims of our case-control study were (1) to develop an automated 3-dimensional (3D) Convolutional Neural Network (CNN) for detection of pancreatic ductal adenocarcinoma (PDA) on diagnostic computed tomography scans (CTs), (2) evaluate its generalizability on multiinstitutional public data sets, (3) its utility as a potential screening tool using a simulated cohort with high pretest probability, and (4) its ability to detect visually occult preinvasive cancer on prediagnostic CTs.
CONCLUSIONS: This automated artificial intelligence model trained on a large and diverse data set shows high accuracy and generalizable performance for detection of PDA on diagnostic CTs as well as for visually occult PDA on prediagnostic CTs. Prospective validation with blood-based biomarkers is warranted to assess the potential for early detection of sporadic PDA in high-risk individuals.
Automated Artificial Intelligence Model Trained on a Large Data Set Can Detect Pancreas Cancer on Diagnostic Computed Tomography Scans As Well As Visually Occult Preinvasive Cancer on Prediagnostic Computed Tomography Scans
Panagiotis Korfiatis et al.
Gastroenterology 2023 (in press) - “In summary, the automated AI model shows high accuracy and generalizable performance for detection of PDA on standard-of-care diagnostic CTs as well as for detection of preinvasive visually occult PDA on prediagnostic CTs at a substantial lead time before clinical diagnosis. Despite being trained on larger tumors, the model had a high sensitivity for stage T1 and isodense tumors as well as high specificity for control CTs. The model’s performance was consistent across variations in patient demographics and image acquisition parameters and was generalizable on multiinstitutional public data sets. The model also showed promising potential in a bootstrapped population with a case-control distribution that matches high-risk groups such as glycemically defined NOD. Further optimization and prospective evaluation in combination with emerging blood based biomarkers is warranted to assess the potential for early detection of sporadic PDA in high-risk cohorts.”
Automated Artificial Intelligence Model Trained on a Large Data Set Can Detect Pancreas Cancer on Diagnostic Computed Tomography Scans As Well As Visually Occult Preinvasive Cancer on Prediagnostic Computed Tomography Scans
Panagiotis Korfiatis et al.
Gastroenterology 2023 (in press)
- Purpose: The purpose of this study was to develop a radiomics-signature using computed tomography (CT) data for the preoperative prediction of grade of nonfunctional pancreatic neuroendocrine tumors (NF-PNETs).
Conclusion: Non-invasive assessment of tumor grade in patients with PNETs using the proposed radiomic-signature demonstrated high accuracy. Prospective validation and optimization could overcome the commonly experienced diagnostic uncertainty in the assessment of tumor grade in patients with PNETs and could facilitate clinical decision-making.
Accurate non-invasive grading of nonfunctional pancreatic neuroendocrine tumors with a CT derived radiomics signature.
Javed AA, Zhu Z, Kinny-Köster B, Habib JR, Kawamoto S, Hruban RH, Fishman EK, Wolfgang CL, He J, Chu LC.
Diagn Interv Imaging. 2023 Aug 17:S2211-5684(23)00155-9. doi: 10.1016/j.diii.2023.08.002. Epub ahead of print. PMID: 37598013. - •A radiomic signature derived from CT data was developed to preoperatively predict tumor grade of pancreatic neuroendocrine tumors.
•This study demonstrates that non-invasive assessment of tumor grade using the proposed radiomic-signature is feasible and highly accurate.
•Accurate prediction of pancreatic neuroendocrine tumor grade via radiomic signature could allow timely tailoring of patient management and allow non-invasive serial assessment in patients on surveillance.
Accurate non-invasive grading of nonfunctional pancreatic neuroendocrine tumors with a CT derived radiomics signature.
Javed AA, Zhu Z, Kinny-Köster B, Habib JR, Kawamoto S, Hruban RH, Fishman EK, Wolfgang CL, He J, Chu LC.
Diagn Interv Imaging. 2023 Aug 17:S2211-5684(23)00155-9. doi: 10.1016/j.diii.2023.08.002. Epub ahead of print. PMID: 37598013. - “In conclusion, we developed a multianalyte model based on primary tumor radiomic features and clinicopathological characteristics that can preoperatively predict tumor grade in PNETs. The developed model offers a higher sensitivity of detecting G2/G3 PNETs as compared to EUS-FNA which is the current gold standard for preoperative assessment of PNETs. This non-invasive tool can help in assessment of grade at diagnosis to determine need for resection as well as serial assessment of patients on surveillance to identify potential changes in tumor biology.”
Accurate non-invasive grading of nonfunctional pancreatic neuroendocrine tumors with a CT derived radiomics signature.
Javed AA, Zhu Z, Kinny-Köster B, Habib JR, Kawamoto S, Hruban RH, Fishman EK, Wolfgang CL, He J, Chu LC.
Diagn Interv Imaging. 2023 Aug 17:S2211-5684(23)00155-9. doi: 10.1016/j.diii.2023.08.002. Epub ahead of print. PMID: 37598013.
- ”Radiomics is changing the world of medicine and more specifically the world of oncology. Early diagnosis and treatment improve the prognosis of patients with cancer. After treatment, the evaluation of the response will determine future treatments. In oncology, every change in treatment means a loss of ther- apeutic options and this is key in pancreatic cancer. Radiomics has been developed in oncology in the early diagnosis and differential diagnosis of benign and malignant lesions, in the evaluation of response, in the prediction of possible side effects, marking the risk of recurrence, survival and prognosis of the disease. Some studies have validated its use to differentiate normal tissues from tumor tissues with high sensitivity and specificity, and to differentiate cystic lesions and pancreatic neuroendocrine tumor grades with texture parameters. In addition, these parameters have been related to survival in patients with pancreatic cancer and to response to radiotherapy and chemotherapy. This review aimed to establish the current status of the use of radiomics in pancreatic cancer and future perspectives.”
Radiomics in pancreatic cancer for oncologist: Present and future
Carolina de la Pinta
Hepatobiliary & Pancreatic Diseases International 21 (2022) 356–361 - “Chu et al. used radiomic features of CT images to differentiate pancreatic adenocarcinoma and normal pancreatic tissues in a series of patients with a radiological and pathological diagnosis, and the study included a training cohort and a validation cohort. Accuracy, sensitivity and specificity were calculated. Patients were classified with a sensitivity of 100% and a specificity of 98.5%. This would allow a more precise definition of tumor areas, which is very important to local treatment strategies.”
Radiomics in pancreatic cancer for oncologist: Present and future
Carolina de la Pinta
Hepatobiliary & Pancreatic Diseases International 21 (2022) 356–361 - “Dmitriev et al. differentiated four types of cysts by com- bining demographic variables with radiomic characteristics of in- tensity and shape, achieving differentiation of 84% of the lesions. Wei et al. analyzed cyst images in preoperative tests to differentiate SCNs from other pancreatic cystic lesions (PCLs) includ- ing 17 intensity and texture features (T-range, wavelet intensity, T-median, and wavelet neighbourhood gray-tone difference matrix busyness) and clinical features. Adequate classification was achieved in 76% of patients and 84% in a validation cohort of 60 patients. Yang et al. evaluated variable slice images, 2 and 5 mm, without affecting feature extraction. In the validation group the accuracy was 74% in patients with 2-mm slice and 83% in 5- mm slice. ”
Radiomics in pancreatic cancer for oncologist: Present and future
Carolina de la Pinta
Hepatobiliary & Pancreatic Diseases International 21 (2022) 356–361 - “Yamashita et al. demonstrated that differences in contrast- enhanced CT acquisition affected the results of the radiomic study leading to changes in segmentation and its reproducibility and comparability between series . The study did not demonstrate statistically significant differences in CT model, pixel spacing, and contrast administration ratio. The study suggests that radiologists are more or less sensitive to CT acquisition parameters, demonstrating the importance of adjusting for these variables to established protocols. Furthermore, this study support the hypothesis of the usefulness of a semi-automated segmentation tool previously trained by several radiologists that can homogenize these varia- tions. Standardization of protocols is therefore important, in addition to external validation. Also many of the comparisons between diagnostic entities using radiomics are subjective and not clinically applicable. For example, the distinction between pancreatic adenocarcinoma and pancreatic neuroendocrine tumors alone.”
Radiomics in pancreatic cancer for oncologist: Present and future
Carolina de la Pinta
Hepatobiliary & Pancreatic Diseases International 21 (2022) 356–361 - ”Radiomics is a promising non-invasive tool for the diagnosis and clinical management of pancreatic tumors. The usefulness of radiomics has been studied in the differential diagnosis of benign, premalignant and malignant lesions in the pancreas. In addition, in patients with neoadjuvant pancreatic cancer, it can help in the more precise definition of lesions for radiotherapy and assessment of response. Radiomics provides a more adequate and reproducible measurement of the tumor than other methods. In addition, the combination of radiomics and genomics has a promising future. However, image acquisition protocols and radiomic analysis sys- tems need to be standardized and validation cohorts are needed. Further studies are needed to consolidate the available data.”
Radiomics in pancreatic cancer for oncologist: Present and future
Carolina de la Pinta
Hepatobiliary & Pancreatic Diseases International 21 (2022) 356–361
- “This scoping review has provided evidence that 12 artificial intelligence-based machine learning models have sufficient capacity to evaluate the risk of malignancy in IPMN. However, the methodological quality of the included studies is inadequate, and the clinical value of the proposed models has not been proven. As a result, caution should be advised when interpreting these results, and the findings must be corroborated by additional high-quality studies. Future research should focus on developing rigorous models and investigating their usefulness in clinical practice to ensure that they are dependable tools for assessing the risk of malignancy in IPMN.”
Artificial intelligence-based models to assess the risk of malignancy on radiological imaging in patients with intraductal papillary mucinous neoplasm of the pancreas: scoping review
Alberto Balduzzi et al.
Br J Surg. 2023 Jul 4:znad201. doi: 10.1093/bjs/znad201. (in press) - “Most patients diagnosed with IPMN will be kept under surveillance, aimed at monitoring progression of the cyst, which may require surgical resection in highly selected patients. Still, the risk of clinicians missing IPMN progression to malignancy is concerning5, with burdensome consequences for the patient. This concern must be balanced against the risk of complications after major pancreatic surgery. Therefore, patient selection is crucial both to avoid unnecessary surgery for benign lesions, and to continue surveillance safely. Typically, diagnostic imaging plays a central role in guiding patient selection for, and the timing of, surgery. However, current imaging approaches fall short for optimal decision-making.”
Artificial intelligence-based models to assess the risk of malignancy on radiological imaging in patients with intraductal papillary mucinous neoplasm of the pancreas: scoping review
Alberto Balduzzi et al.
Br J Surg. 2023 Jul 4:znad201. doi: 10.1093/bjs/znad201. (in press) - “Future research should concentrate on developing methodologically sound, generalizable, and clinically validated models. Multiple methodological elements are frequently missed or ignored, as is evident from the mRQS scores of the research included. Once robust and generalizable models have been constructed, their performance and value should be validated in clinical settings. Currently available studies have focused on assessing the discriminative performance of machine learning models for malignant IPMNs. However, ideally, models would exclude the presence of malignancy with a high negative predictive value and ‘safely’ advise surveillance in patients who would have been selected for surgical treatment according to current criteria. This would represent a true added value to current clinical practice.”
Artificial intelligence-based models to assess the risk of malignancy on radiological imaging in patients with intraductal papillary mucinous neoplasm of the pancreas: scoping review
Alberto Balduzzi et al.
Br J Surg. 2023 Jul 4:znad201. doi: 10.1093/bjs/znad201. (in press)
- “Mukherjee et al. [1] explored the utility of quantitative CT radiomic features of the pancreas in identifying patients who would develop pancreatic cancer in the subsequent 3–36 months. They found that their radiomics-based model had good predictive capacity, achieving sensitivity of 95% and specificity of 90% in a validation sample. They found performance robustness across CT scanners and slice thicknesses, and the model outperformed radiologists in identifying cases of pancreatic cancer. These findings add to the growing body of evidence that the indirect effects of pancreatic cancer, including endocrine and exocrine dysfunction and, now, whole-organ radiomic changes, may precede the diagnosis of cancer and serve as early detection biomarkers.”
Beyond the AJR: CT Radiomic Features of the Pancreas Predict Development of Pancreatic Cancer
Michael H. Rosenthal,Khoschy Schawkat
AJR 2023; 220:763 - “In this study, the most predictive radiomic features were measures of local pancreatic gland heterogeneity. These features are not discernible in visual assessment and do not meet the threshold for radiologists to report but are biologically plausible given existing evidence that pancreatic cancer affects broader pancreatic function early in the disease process.”
Beyond the AJR: CT Radiomic Features of the Pancreas Predict Development of Pancreatic Cancer
Michael H. Rosenthal, Khoschy Schawkat
AJR 2023; 220:763 - “These automated CT biomarkers could be deployed as part of the radiologist’s clinical workflow, allowing prospective risk profiling in practice. In pancreatic cancer, opportunistic screening could identify individuals at sufficiently high risk to warrant active screening, as is currently performed for families at high risk. Such an approach, however, would generate a high rate of false-positives for every true-positive. To be clinically useful, the approach will likely have to be integrated with other risk markers.”
Beyond the AJR: CT Radiomic Features of the Pancreas Predict Development of Pancreatic Cancer
Michael H. Rosenthal, Khoschy Schawkat
AJR 2023; 220:763 - “Machine learning–based radiomic analyses may be a novel strategy to screen for pancreatic cancer by revealing changes in the pancreas that precede the development of pancreatic cancer and the emergence of a radiologically detectable mass.”
Beyond the AJR: CT Radiomic Features of the Pancreas Predict Development of Pancreatic Cancer
Michael H. Rosenthal, Khoschy Schawkat
AJR 2023; 220:763 - “In conclusion, we detected and quantified the imaging signature of early pancreatic carcinogenesis from volumetrically segmented normal pancreas on standard-of-care CTs. The radiomics-based ML classifiers had high discrimination accuracy for classification of pancreas into prediagnostic for PDAC vs normal. The high accuracy of the SVM model was validated on CTs from external institutions. Its high specificity was generalizable on an independent internal cohort and on an external public dataset. In contrast, radiologist readers had low interreader agreement, sensitivity, and discrimination accuracy, which shows that novel AI-based approaches can detect PDAC at a subclinical stage when it is beyond the scope of the human interrogation.”
Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis.
Mukherjee S, et al.
Gastroenterology. 2022 Nov;163(5):1435-1446.e3 - “Prospective validation of these ML models and their integration with complementary blood and other fluid-based biomarkers has the potential to further improve cancer prediction capabilities at the prediagnostic or symptom-free stage. Such models also have the potential to elucidate the longitudinal changes of carcinogenesis that precede the clinical diagnosis of PDAC. Finally, such models can be deployed to detect early cancer in ongoing clinical trials such as the Early Detection Initiative that seeks to evaluate outcomes of a screening strategy by using clinical risk-prediction models and CT in cohorts at high risk for PDAC.”
Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis.
Mukherjee S, et al.
Gastroenterology. 2022 Nov;163(5):1435-1446.e3 - “CONCLUSIONS: Radiomics-based ML models can detect PDAC from normal pancreas when it is beyond human interrogation capability at a substantial lead time before clinical diagnosis. Prospective validation and integration of such models with complementary fluid-based biomarkers has the potential for PDAC detection at a stage when surgical cure is a possibility.”
Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis.
Mukherjee S, et al.
Gastroenterology. 2022 Nov;163(5):1435-1446.e3 - The median (range) CT slice thickness was 3 (0.5–5) mm. CTs were acquired on CT systems from 4 different vendors (81 Siemens, Munich, Germany [52.3%]; 41 GE, Boston, MA [26.4%]; 25 Toshiba, Tokyo, Japan [16.1%]; and 8 Philips, Amsterdam, The Netherlands [5.2%]). All these CTs had been previously interpreted to be negative for PDAC during routine clinical interpretation at our institution. Each CT was rereviewed by 1 of 3 radiologists (R1, R2, R3 with 2–4 years of post-radiology residency experience) who confirmed optimal image quality (eg, lack of motion artifacts compromising visual interpretation), portal venous phase of enhancement, absence of pancreatitis, focal solid or cystic lesion, and biliary or pancreatic duct stent. In addition, radiologists in consensus evaluated each prediagnostic CT for any indirect or secondary imaging signs of early PDAC, such as focal contour abnormality or attenuation difference, biliary or pancreatic duct dilatation or cutoff, and focal parenchymal atrophy.
Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis.
Mukherjee S, et al.
Gastroenterology. 2022 Nov;163(5):1435-1446 - Conclusion The current twelve published IBSI-compliant PDAC radiomic studies show high variability and often incomplete methodology resulting in low robustness and reproducibility. Clinical relevance statement Radiomics research requires IBSI compliance, data harmonization, and reproducible feature extraction methods for non-invasive imaging biomarker discoveries to be valid. This will ensure a successful clinical implementation and ultimately an improvement of patient outcomes as part of precision and personalised medicine.
Current state of radiomic research in pancreatic cancer: focusing on study design and reproducibility of findings
James Alex Malcolm et al.
European Radiology 2023 (in press) - “Radiomics is a computational method of extracting features (RF) from medical images that has the potential to develop non-invasive imaging biomarkers aiding in improved PDAC delineation and treatment. To date, there is limited PDAC CT radiomics primary research data available.”
Current state of radiomic research in pancreatic cancer: focusing on study design and reproducibility of findings
James Alex Malcolm et al.
European Radiology 2023 (in press) - “Our analysis revealed several challenges associated with the use of retrospective data from heterogeneous small to moderately sized cohorts. Furthermore, there was a lack agreement of RFs deemed significant. Additionally, variations in CT techniques and lack of consistency in RF segmentation and selection further hindered reproducibility of findings. However, it is noteworthy that GLCM-associated RFs were observed in 6 of the 12 studies reviewed, albeit without any consistent subcategories. The median cohort size was 106 patients, with 7 out of the 12 studies having such small cohort sizes rendering a validation process impossible. Training-validation-ratio of cohorts is an important factor in ensuring a robust prognostic model.”
Current state of radiomic research in pancreatic cancer: focusing on study design and reproducibility of findings
James Alex Malcolm et al.
European Radiology 2023 (in press)
- Methods: In this IRB-approved retrospective single-institution study, patients with surgically resected pancreatic cysts who underwent preoperative abdominal CT from 2003 to 2016 were identified. Pancreatic cyst(s) and background pancreas were manually segmented, and 488 radiomics features were extracted. Random forest classification based on radiomics features, age, and gender was evaluated with fourfold cross-validation. An academic radiologist blinded to the final pathologic diagnosis reviewed each case and provided the most likely diagnosis.
Results: 214 patients were included (64 intraductal papillary mucinous neoplasms, 33 mucinous cystic neoplasms, 60 serous cystadenomas, 24 solid pseudopapillary neoplasms, and 33 cystic neuroendocrine tumors). The radiomics-based machine learning approach showed AUC of 0.940 in pancreatic cyst classification, compared with AUC of 0.895 for the radiologist.
Conclusion: Radiomics-based machine learning achieved equivalent performance as an experienced academic radiologist in the classification of pancreatic cysts. The high diagnostic accuracy can potentially maximize the efficiency of healthcare utilization by maximizing detection of high-risk lesions.
Classification of pancreatic cystic neoplasms using radiomic feature analysis is equivalent to an experienced academic radiologist: a step toward computer‑augmented diagnostics for radiologists
Linda C. Chu et al.
Abdominal Radiology (2022) 47:4139–4150 - Conclusion: Radiomics-based machine learning achieved equivalent performance as an experienced academic radiologist in the classification of pancreatic cysts. The high diagnostic accuracy can potentially maximize the efficiency of healthcare utilization by maximizing detection of high-risk lesions.
Classification of pancreatic cystic neoplasms using radiomic feature analysis is equivalent to an experienced academic radiologist: a step toward computer‑augmented diagnostics for radiologists
Linda C. Chu et al.
Abdominal Radiology (2022) 47:4139–4150 
Classification of pancreatic cystic neoplasms using radiomic feature analysis is equivalent to an experienced academic radiologist: a step toward computer‑augmented diagnostics for radiologists
Linda C. Chu et al.
Abdominal Radiology (2022) 47:4139–4150
Classification of pancreatic cystic neoplasms using radiomic feature analysis is equivalent to an experienced academic radiologist: a step toward computer‑augmented diagnostics for radiologists
Linda C. Chu et al.
Abdominal Radiology (2022) 47:4139–4150- In this study, the performance of the radiomics feature based classification achieved AUC of 0.940 in distinguishing among five types of pancreatic cystic neoplasms. The performance was similar to previous studies with multi-class pancreatic cyst classifications that included three or four cyst types, with accuracy of 79.6–83.6%. Previous studies on radiomics-based pancreatic cyst classification did not include a direct comparison with a radiologist, therefore, it was difficult to assess if the radiomics-based classification reported provided any added value relative to the standard of care. The current study showed that the radiomics-based pancreatic cyst classification achieved equivalent performance as an academic radiologist with more than 25 years of experience. These results indicate that radiomics- based classification could be valuable in improving the current standard of care.
Classification of pancreatic cystic neoplasms using radiomic feature analysis is equivalent to an experienced academic radiologist: a step toward computer‑augmented diagnostics for radiologists
Linda C. Chu et al.
Abdominal Radiology (2022) 47:4139–4150 - This study showed that a radiomics-based model can achieve equivalent performance as an experienced academic radiologist in the classification of a wide array of pancreatic cysts with variable malignant potential. This model has the potential to refine pancreatic cyst management by improving diagnostic accuracy of cystic lesions, which can minimize healthcare utilization while maximizing detection of malignant lesions. This study confirms the ability of a radiomic based model to accurately classify pancreatic cystic neoplasms. Further validation and clinical integration of this model could help optimize management of pancreatic cysts by maximizing the rate of detection of malignant lesions while reducing healthcare utilization.
Classification of pancreatic cystic neoplasms using radiomic feature analysis is equivalent to an experienced academic radiologist: a step toward computer‑augmented diagnostics for radiologists
Linda C. Chu et al.
Abdominal Radiology (2022) 47:4139–4150
- Objective: To develop and validate an effective model for identifying patients with postoperative local disease recurrence of pancreatic ductal adenocarcinoma (PDAC).
Methods: A total of 153 patients who had undergone surgical resection of PDAC with regular postoperative follow-up were consecutively enrolled and randomly divided into training (n = 108) and validation (n = 45) cohorts. The postoperative soft-tissue biopsy results or clinical follow-up results served as the reference diagnostic criteria. Radiomics analysis of the postoperative soft-tissue was performed on a commercially available prototype software using portal vein phase image. Three models were built to characterize postoperative soft tissue: computed tomography (CT)-based radiomics, clinicoradiological, and their combination. The area under the receiver operating characteristic curves (AUC) was used to evaluate the differential diagnostic performance. A nomogram was used to select the final model with best performance. One radiologist’s diagnostic choices that were made with and without the nomogram’s assistance were evaluated.
Computed Tomography-based Radiomics Evaluation of Postoperative Local Recurrence of Pancreatic Ductal Adenocarcinoma
Ming He et al.
Acad Radiol 2022;&:1–9 - Results: A seven-feature combined radiomics signature was constructed as a predictor of postoperative local recurrence. The nomogram model combining the radiomics signature with postoperative CA 19-9 elevation showed the best performance (training cohort, AUC = 0.791 [95%CI: 0.707, 0.876]; validation cohort, AUC = 0.742 [95%CI: 0.590, 0.894]). In the validation cohort, the AUC for differential diagnosis was significantly improved for the combined model relative to that for postoperative CA 19-9 elevation (AUC = 0.742 vs. 0.533, p < 0.001). The calibration curve and decision curve analysis demonstrated the clinical usefulness of the proposed nomogram. The diagnostic performance of the radiologist was not significantly improve by using the proposed nomogram (AUC = 0.742 vs. 0.670, p = 0.17).
Conclusion: The combined model using CT radiomic features and CA 19-9 elevation effectively characterized postoperative soft tissue and potentially may improve treatment strategies and facilitate personalized treatment for PDAC after surgical resection.
Computed Tomography-based Radiomics Evaluation of Postoperative Local Recurrence of Pancreatic Ductal Adenocarcinoma
Ming He et al.
Acad Radiol 2022;&:1–9 - “In the present study, we obtained encouraging data when using radiomics to analyze enhanced CT scan images recorded 3 months after surgery. The resulting nomogram, which combines the radiomics signatures and postoperative elevation of CA 19-9, is expected to serve as a reference indicator for clinicians planning postoperative follow-up strategies. Patients for whom the nomogram shows a high probability of postoperative local recurrence may be better candidates for regular follow-ups, facilitating earlier confirmation of recurrence and prompt treatment. In patients for whom the nomogram indicates a relatively low probability of recurrence, a symptom-driven follow-up strategy can be used to alleviate the patients’ financial and psychological burdens.”
Computed Tomography-based Radiomics Evaluation of Postoperative Local Recurrence of Pancreatic Ductal Adenocarcinoma
Ming He et al.
Acad Radiol 2022;&:1–9 - “In the present study, the sensitivity and specificity of radiomics analysis for characterizing postoperative soft tissue were 70.8% and 63.3%, respectively, in the validation cohort; both of these values were significantly higher than those of the postoperative CA 19-9 (54.2% and 52.4%), respectively, (p < 0.05, both). Furthermore, the combination of radiomics signature and clinicoradiological features further improved the sensitivity and specificity to 76.3% and 66.7%, respectively, in the validation cohort. The combined model (postoperative elevation of CA 19-9 combined with the radiomics signatures) performed well both in the primary and validation cohort, showing its robustness and reliability for early diagnosis of postoperative local recurrence.”
Computed Tomography-based Radiomics Evaluation of Postoperative Local Recurrence of Pancreatic Ductal Adenocarcinoma
Ming He et al.
Acad Radiol 2022;&:1–9
- Background: Radiomics and radiogenomics are two words that recur often in language of radiologists, nuclear doctors and medical physicists especially in oncology field. Radiomics is the technique of medical images analysis to extract quantitative data that are not detected by human eye.
Results: Several studies showed that radiomics is very promising. However, there were some critical issues: poor standardization and generalization of radiomics results, data-quality control, repeatability, reproducibility, database balancing and issues related to model overfitting.
Conclusions: Radiomics procedure should made considered all pitfalls and challenges to obtain robust and reproducible results that could be generalized in other patients cohort.
Radiomics in medical imaging: pitfalls and challenges in clinical management
Roberta Fusco et al.
Japanese Journal of Radiology https://doi.org/10.1007/s11604-022-01271-4 
Radiomics in medical imaging: pitfalls and challenges in clinical management
Roberta Fusco et al.
Japanese Journal of Radiology https://doi.org/10.1007/s11604-022-01271-4- "The features can be morphological, of first, second and higher statistical orders. The morphological metrics describe the shape of segmented volume of interest and its geometric characteristics such as volume, largest diameter in different orthogonal directions, surface, compactness and sphericity. First-order statistics features describe the distribution of individual voxel values such as mean, median, maximum, minimum values, skewness (asymmetry), kurtosis (flatness), uniformity, and entropy. Second-order statistics features are obtained calculating the statistical inter-relationships between neighboring voxels providing a measure of spatial arrangement and of intra-lesion heterogeneity.”
Radiomics in medical imaging: pitfalls and challenges in clinical management
Roberta Fusco et al.
Japanese Journal of Radiology https://doi.org/10.1007/s11604-022-01271-4 - "Radiomics is the technique of medical images analysis to extract quantitative data that are not detected by human eye. In practice, visual analysis manages to extract only about 10% of the information contained in a digital medical image. If, on the other hand, these images are analyzed in detail by powerful computers through complex mathematical algorithms, it is possible to obtain objective quantitative data, capable of providing information on the underlying patho-physiological phenomena, inaccessible to simple visual analysis. Therefore, in the field of medicine, radiomics is a method that extracts a large number of features from medical images using data-characterization algorithms.”
Radiomics in medical imaging: pitfalls and challenges in clinical management
Roberta Fusco et al.
Japanese Journal of Radiology https://doi.org/10.1007/s11604-022-01271-4 - "Although several studies showed as Radiomic is very promising, there has been poor standardization and generalization of radiomics results, which limit the translation of this method into clinical setting. Clear limitations of this field are emerging, especially with regard to data-quality control, repeatability, reproducibility, generalizability of results, and issues related to model overfitting.”
Radiomics in medical imaging: pitfalls and challenges in clinical management
Roberta Fusco et al.
Japanese Journal of Radiology https://doi.org/10.1007/s11604-022-01271-4
- “One must ask, then, what is the burden of value a radiology AI product must provide to justify purchase? The answer depends on many factors including the health care setting and its purchasing structure, the health care payer system, and patient distribution, but a nearly universal thread is that the software must provide financial return on investment. The challenge is matching the “return” to the “investor.” When these two parties are mismatched, the cost justification for one group’s investment for another group’s benefit rarely occurs.”
The Business of Artificial Intelligence in Radiology Has Little to Do With Radiologists
Trivedi H
J Am Coll Radiol. 2022 Feb 15:S1546-1440(22)00113-2. - “If an AI model can increase the throughput for ex- amination interpretation, the practice can now absorb this additional volume without an additional radiolo- gist hire. Similarly, models that triage low complexity or negative studies can be used to route these exami- nations to physician extenders and reduce costs for a group by over 75% while maintaining imaging revenue. Conversely, AI models that close the loop on patient follow-up or detect incidental findings have no financial benefit for a private practice and therefore are less likely to be paid for by the radiology group.”
The Business of Artificial Intelligence in Radiology Has Little to Do With Radiologists
Trivedi H
J Am Coll Radiol. 2022 Feb 15:S1546-1440(22)00113-2. - "Similarly, driving additional outpatient referrals into the radiology department (ie, additional examinations) can also be significant revenue generators. For example, closing the loop on an incidental adrenal nodule can result in an additional triple-phase CT or MRI examinations and thousands of dollars in additional revenue while providing standard of care for the patient. Models that provide opportunistic screening such as scoring of coronary artery calcium on routine nongated chest CT can identify high-risk patients for cardiology referral, some of whom may ultimately receive advanced interventions. Capture or retention of a patient into the health system provides significant revenue streams, and in each of these cases the financial incentive and champions for adoption of these models are outside of the radiology department. Ultimately, these models have little impact on radiology workflow or the finances of a radiology practice.”
The Business of Artificial Intelligence in Radiology Has Little to Do With Radiologists
Trivedi H
J Am Coll Radiol. 2022 Feb 15:S1546-1440(22)00113-2. - "In the emergency setting in which community hospitals may not have 24-7 radiologist coverage, AI models can help increase patient throughput and lead to significant cost savings. Many emergency pro- viders must currently choose the lesser of two evils—have patients wait overnight for examination re- ports or independently interpret examinations to guide patient disposition. Deployment of AI models in these settings can increase confidence in discharging patients for negative examinations or help quickly flag emergent findings that require immediate intervention.”
The Business of Artificial Intelligence in Radiology Has Little to Do With Radiologists
Trivedi H
J Am Coll Radiol. 2022 Feb 15:S1546-1440(22)00113-2. - "Nevertheless, at least two companies have recently secured additional CPT codes for use of AI software—one for detection of vertebral compression fracture on CT (www.zebramedical.com) and another for scoring trabecular bone health on bone densitometry examinations (www.nanox.vision) to improve risk stratification for osteopenic and osteoporotic patients. However, it is important to note that reimbursements for AI software may have unintended consequences on the reimbursement for radiology examination interpretation, particularly for cases in which AI software reduces the average interpretation time of the examination.”
The Business of Artificial Intelligence in Radiology Has Little to Do With Radiologists
Trivedi H
J Am Coll Radiol. 2022 Feb 15:S1546-1440(22)00113-2. - "Lastly, although the majority of care in the United States is based on fee-for-service, there are a few domestic (eg, Veterans Affairs Hospitals) and many more international examples of vertical payment models in which there are in- centives to improve quality as a means to reduce cost. In these set- tings, a win-win-win is possible for patients, payers, and physicians. For example, in a fee-for-service system, a model that reduces unnecessary biopsies in screening mammography is good for patients and payers but may face barriers to adoption because it decreases hospital and practice revenue. However, the same model is a single-payer system is a win-win-win: patients receive better care, physicians have decreased workload, and payers significantly reduce costs. For this reason, many AI companies have seen wider adoption in Europe and Asia as compared with the United States.”
The Business of Artificial Intelligence in Radiology Has Little to Do With Radiologists
Trivedi H
J Am Coll Radiol. 2022 Feb 15:S1546-1440(22)00113-2. - “In summary, the path to adoption of radiology AI is complex but must be viewed through a realistic lens that considers the economic truths of the health care system. Software that improves quality alone without a secondary benefit in efficiency, referrals, or another revenue stream is difficult to justify in a fee-for-service model, but these same models are being actively adopted within single-payer systems domestically and internationally. Commercial radiology AI vendors should consider these dynamics when developing models for various markets and tailor their value propositions to the needs of the potential customer. Ultimately, this will increase AI adoption and transform radiology AI into a financially sustainable tool for radiologists, hospitals systems, and most importantly patients.”
The Business of Artificial Intelligence in Radiology Has Little to Do With Radiologists
Trivedi H
J Am Coll Radiol. 2022 Feb 15:S1546-1440(22)00113-2.
- Background: The translation of radiomic models into clinical practice is hindered by the limited reproducibility of features across software and studies. Standardization is needed to accelerate this process and to bring radiomics closer to clinical deployment.
Purpose: To assess the standardization level of seven radiomic software programs and investigate software agreement as a func- tion of built-in image preprocessing (eg, interpolation and discretization), feature aggregation methods, and the morphological characteristics (ie, volume and shape) of the region of interest (ROI).
Conclusion: The agreement of radiomic software varied in relation to factors that had already been standardized (eg, interpolation and discretization methods) and factors that need standardization. Both dependences must be resolved to ensure the reproducibility of radiomic features and to pave the way toward the clinical adoption of radiomic models.
A Novel Benchmarking Approach to Assess the Agreement among Radiomic Tools
Andrea Bettinelli et al.
Radiology 2022; 000:1–9 (in press) - Summary
A novel approach was used to assess radiomic software agreement based on Italian multicenter Shared Understanding of Radiomic Extractors phantoms and a systematic feature extraction, finding that discrepancies were still present among standardized radiomic programs.
A Novel Benchmarking Approach to Assess the Agreement among Radiomic Tools
Andrea Bettinelli et al.
Radiology 2022; 000:1–9 (in press) - Key Results
- On Image Biomarker Standardization Initiative (IBSI) phantoms, radiomic software programs were able to compute different percentages (range, 21%–100%) of the IBSI benchmark values, although they were all highly standardized regarding feature definition; when considering preprocessing, “matching” values with the IBSI benchmark fell by up to 30% for the individual program.
- On Italian multicenter Shared Understanding of Radiomic Extractors phantoms, software agreement was significantly dependent (a = .05; Bonferroni-adjusted a = .00009) on discretization and aggregation methods, as well as on newly investigated factors (ie, region of interest shape and volume). - "In conclusion, we designed a new investigation scenario in which we demonstrated that, despite the ongoing efforts of both the Image Biomarker Standardization Initiative and software developers to standardize radiomic tools, additional efforts are needed to achieve full concordance. This would hasten the use of radiomic models in clinical practice and their application to improve cancer prognosis.”
A Novel Benchmarking Approach to Assess the Agreement among Radiomic Tools
Andrea Bettinelli et al.
Radiology 2022; 000:1–9 (in press) - Background: Radiomics is a progressing field of research that deals with the extraction of quantitative metrics from medical images. Radiomic features detention indirectly tissue features such as heterogeneity and shape and can, alone or in combination with demographic, histological, genomic, or proteomic data, be used for decision support system in clinical setting.
Methods: This article is a narrative review on Radiomics in Primary Liver Cancers. Particularly, limitations and future perspectives are discussed.
Conclusions: Although several studies have shown that this analysis is very promising, there is little standardization and generalization of the results, which limits the translation of this method into the clinical context. The limitations are mainly related to the evaluation of data quality, repeatability, reproducibility, overfitting of the model.
An update on radiomics techniques in primary liver cancers
Vincenza Granata et al.
Granata et al. Infectious Agents and Cancer (2022) 17:6 https://doi.org/10.1186/s13027-022-00422-6 
An update on radiomics techniques in primary liver cancers
Vincenza Granata et al.
Granata et al. Infectious Agents and Cancer (2022) 17:6 https://doi.org/10.1186/s13027-022-00422-6- “Radiomics is a rapidly evolving field of research that deals with the extraction of quantitative metrics within medical images that capture tissue and lesion characteristics such as heterogeneity and shape and which can, alone or in combination with demographic, histological, genomic or proteomic data, to be used for the resolution of clinical problems. In oncology, the assessment of tissue heterogeneity is of particular interest: genomic analyzes have shown that the degree of tumour heterogeneity is a prognostic determinant of survival. Although many studies have shown that radiomics to be very promising, there has been little standardization and generalization of radiomic findings, which limit the use of this method into the clinical practice. Clear limitations especially regard to data quality control, repeatability, reproducibility, generalizability of results and issues related to model overfitting.”
An update on radiomics techniques in primary liver cancers
Vincenza Granata et al.
Granata et al. Infectious Agents and Cancer (2022) 17:6 https://doi.org/10.1186/s13027-022-00422-6
- “ Texture analysis has arisen as a tool to explore the amount of data contained in images that cannot be explored by humans visually. Radiomics is a method that extracts a large number of features from radiographic medical images using data-characterization algorithms. These features, termed radiomic features, have the potential to uncover disease characteristics. The goal of both radiomics and texture analysis is to go beyond size or human- eye based semantic descriptors, to enable the non-invasive extraction of quantitative radiological data to correlate them with clinical outcomes or pathological characteristics.”
Texture analysis imaging “what a clinical radiologist needs to know”
Giuseppe Corrias et al.
European Journal of Radiology 146 (2022) 110055 - “Texture analysis represents some specific features of radiomics, a term borrowed from material science which defines the measure of the variation of a surface. In medical imaging, texture analysis defines the measure of variation of pixel intensities on a given image, region-of-interest, or volume. A rough-textured image would have a high rate of change in the high and low pixel intensity, compared with a smooth-textured material. Texture analysis as such is a subfield used in the radiomic setting. A typical example of radiomics performed used texture analysis is using it to correlate molecular and histological features of diffuse high-grade gliomas.”
Texture analysis imaging “what a clinical radiologist needs to know”
Giuseppe Corrias et al.
European Journal of Radiology 146 (2022) 110055 - "Radiomics offers a nearly unlimited source of imaging biomarkers that could support cancer detection, diagnosis, assessment of prognosis, prediction of response to treatment, and monitoring of disease status. For a clinical radiologist, radiomics has the prospective to help with the diagnosis of both common and rare tumors. Visualization of tumor heterogeneity may be crucial in the assessment of tumor aggressiveness and prognosis.”
Texture analysis imaging “what a clinical radiologist needs to know”
Giuseppe Corrias et al.
European Journal of Radiology 146 (2022) 110055 - "However, it should be noted that radiomic and radiogenomic analyses can be used to identify correlations, but not causes; thus, they are not expected to enable definitive assessment of genetic or other bio- markers through imaging alone. However, correlation of radiomic data with genomic or otheromic data could inform not only the decision about whether to test for certain gene alterations in biopsy samples but also the choice of biopsy sites. It also could provide information to support histopathologic findings. This is important, as it is estimated the error rate of cancer histopathology can be as high as 23%.”
Texture analysis imaging “what a clinical radiologist needs to know”
Giuseppe Corrias et al.
European Journal of Radiology 146 (2022) 110055 - "A typical radiomic analysis workflow consists of five main steps: 1) image acquisition, 2) segmentation, 3) computation of radiomic features within the segmented region, 4) feature selection, model building and classification, 5) statistical analysis. Radiomic methods are not only designed to predict early overall survival or to identify predictive pathological characteristics such as microvascular invasion, they may also predict liver tumor response to treatment. For example, there is early evidence that pre-treatment CT-derived signatures can predict survival in many types of diseases, for example in patients with Hepatocellular Carcinoma (HCC) or advanced HCC treated with sorafenib.”
Texture analysis imaging “what a clinical radiologist needs to know”
Giuseppe Corrias et al.
European Journal of Radiology 146 (2022) 110055 - "Radiomic features can be divided into two important groups (Table 1): “semantic” and “agnostic” features (the texture analysis itself). Semantic features are those that are commonly used in the radiology lexicon to describe regions of interest (i. e. shape, location, vascularity, necrosis, etc). Agnostic features are those that attempt to capture lesion heterogeneity through quantitative mathematical descriptors (i.e. histograms, wavelets, etc). extracted from filtered images, and fractal features.”
Texture analysis imaging “what a clinical radiologist needs to know”
Giuseppe Corrias et al.
European Journal of Radiology 146 (2022) 110055 - "Semantic features are commonly and qualitatively used by radiologists to analyze lesions, but in radiomics a computer quantitatively analyzes them. This is a crucial step in the “historical” development of radiomics: one of the first articles which has done so comes from Segal et al. , probably the first example of radiogenomics; they used a finite series of radiologist-scored quantitative features to predict gene expression patterns in hepatocellular carcinoma.”
Texture analysis imaging “what a clinical radiologist needs to know”
Giuseppe Corrias et al.
European Journal of Radiology 146 (2022) 110055 - "Agnostic or texture features can be divided into three groups: statistical (first order and second order features); transform-based, structural-based analysis. The mathematical definitions of these features are independent of imaging modality. The most known texture descriptors of the image are: kurtosis, skewness, intensity histogram, descriptors of the relationships between image voxels (e.g. gray-level co-occurrence matrix (GLCM), run length matrix (RLM), size zone matrix (SZM), and neighborhood gray tone difference matrix (NGTDM), derived textures, textures extracted from filtered images, and fractal features.”
Texture analysis imaging “what a clinical radiologist needs to know”
Giuseppe Corrias et al.
European Journal of Radiology 146 (2022) 110055 
Texture analysis imaging “what a clinical radiologist needs to know”
Giuseppe Corrias et al.
European Journal of Radiology 146 (2022) 110055- "Radiomics and texture analysis are innovative techniques in the field of radiology. Their development as an objective, quantifiable and reproductible technique is crucial, and it has been the more criticized aspect in the latest years. However, all the conducted studies, even if lacking of reproducibility, showed that there are many benefits of these techniques: they can provide an objective non-invasive assessment of different aspects of diseases, the more studied and accepted being the heterogeneity (of a lesion, of an organ) with use of routine imaging data, as opposed to subjective current visual analysis. Oncological applications have been the more extendedly studied since it is proved tumoral heterogeneity is one of the most important aspects of aggressiveness of almost every type of cancer. The further development of the technique, with more reproducible approaches, will help translation of its use into both clinical and research trials as well as into the development of clinical decision support systems.”
Texture analysis imaging “what a clinical radiologist needs to know”
Giuseppe Corrias et al.
European Journal of Radiology 146 (2022) 110055
- OBJECTIVE. Pancreatic ductal adenocarcinoma (PDAC) is often a lethal malignancy with limited preoperative predictors of long-term survival. The purpose of this study was to evaluate the prognostic utility of preoperative CT radiomics features in predict- ing postoperative survival of patients with PDAC.
RESULTS. The mean age of patients with PDAC was 67 ± 11 (SD) years. The mean tumor size was 3.31 ± 2.55 cm. The 10 most relevant radiomics features showed 82.2% ac- curacy in the classification of high-risk versus low-risk groups. The C-index of survival prediction with clinical parameters alone was 0.6785. The addition of CT radiomics features improved the C-index to 0.7414.
CONCLUSION. Addition of CT radiomics features to standard clinical factors improves survival prediction in patients with PDAC.
CT Radiomics–Based Preoperative Survival Prediction in Patients With Pancreatic Ductal Adenocarcinoma
Seyoun Park et al.
AJR 2021; 217:1104–1112 - “Tumors are spatially heterogeneous structures that can be characterized at a macro scale, and normal parenchyma can also be affected by the growth of the tumor. Texture analysis using medical images, especially radiomics approaches, is an established technique that describes spatial variations in pixel intensities in images for quantitative assess- ment. Whereas radiologists may qualitatively describe PDAC enhancement patterns as, for example, homogeneously isoattenuating or heterogeneously hypoattenuating, tex- ture analysis can capture more subtle underlying differences that may reflect important pathologic differences and thereby help predict patient survival.”
CT Radiomics–Based Preoperative Survival Prediction in Patients With Pancreatic Ductal Adenocarcinoma
Seyoun Park et al.
AJR 2021; 217:1104–1112 - “A total 489 radiomics features from the whole seg- mented 3D tumor and from the remaining pancreatic parenchyma were extracted to express pancreas and tumor phenotypes of patients with PDAC. Radiomics features used in this study included 478 features from the whole 3D tumor and 11 features from the pancreatic parenchyma not involved by the tumor. The tumor features include 14 first-order statistics of the volumet- ric CT intensities, eight shape features of the target structure, 33 texture features from a gray-level co-occurrence matrix (GLCM) and a gray-level run-length matrix, and 368 texture features from the eight volumes filtered by wavelets.”
CT Radiomics–Based Preoperative Survival Prediction in Patients With Pancreatic Ductal Adenocarcinoma
Seyoun Park et al.
AJR 2021; 217:1104–1112 - “Based on the selected radiomics features, a random survival forest was applied for survival prediction in a multivariate dataset with missing variables. Each decision node was divided until three unique deaths (d = 3) remained in the leaf node. Ten thousand trees were built by the training set using the AUC for the split of internal nodes. Each end node stored the survival sta- tus (dead or alive), survival time, and a Cox proportional hazard function of the assigned cases. The survival time and survival status predictions in the validation cohort were determined by majority voting based on the trained trees.”
CT Radiomics–Based Preoperative Survival Prediction in Patients With Pancreatic Ductal Adenocarcinoma
Seyoun Park et al.
AJR 2021; 217:1104–1112 
CT Radiomics–Based Preoperative Survival Prediction in Patients With Pancreatic Ductal Adenocarcinoma
Seyoun Park et al.
AJR 2021; 217:1104–1112- "The accuracy of survival prediction using only preoperative clinical variables was 0.6785 in terms of the C-index. This value improved to 0.7321 when the 10 selected image features from the tumor phenotype were added and further improved to 0.7414 with additional pancreatic image features. Figure 6 shows the Kaplan-Meier curves for the overall survival prediction of the validation cohort and for the ground-truth prediction.”
CT Radiomics–Based Preoperative Survival Prediction in Patients With Pancreatic Ductal Adenocarcinoma
Seyoun Park et al.
AJR 2021; 217:1104–1112 - "Preoperative CT scans show the global status of pancreatic parenchyma and tumor texture. The overall survival of patients who underwent surgical resection was explored in this study using preoperative CT radiomics features. However, information on different types of therapies (e.g., adjuvant chemotherapy) that each patient received can be combined to analyze the response of each therapy. Even though we found no clear evidence of differences in adjuvant therapy between high-risk and low-risk groups in our dataset, the correlation with detailed types of adjuvant therapy could be further studied with a proper study design. In addition, postoperative clinical parameters can be also considered for better prediction of overall survival.”
CT Radiomics–Based Preoperative Survival Prediction in Patients With Pancreatic Ductal Adenocarcinoma
Seyoun Park et al.
AJR 2021; 217:1104–1112 - "We found that radiomics features extracted from tumors and from the nonneoplastic pancreas can be used to improve survival prediction models of patients who underwent surgery for PDAC. This algorithm could be combined with other pathologic and genetic biomarkers.”
CT Radiomics–Based Preoperative Survival Prediction in Patients With Pancreatic Ductal Adenocarcinoma
Seyoun Park et al.
AJR 2021; 217:1104–1112 
CT Radiomics–Based Preoperative Survival Prediction in Patients With Pancreatic Ductal Adenocarcinoma
Seyoun Park et al.
AJR 2021; 217:1104–1112
CT Radiomics–Based Preoperative Survival Prediction in Patients With Pancreatic Ductal Adenocarcinoma
Seyoun Park et al.
AJR 2021; 217:1104–1112
CT Radiomics–Based Preoperative Survival Prediction in Patients With Pancreatic Ductal Adenocarcinoma
Seyoun Park et al.
AJR 2021; 217:1104–1112- “Radiomics is the high throughput extraction of large sets of quantitative data from imaging studies that can be used to characterize healthy and pathological tissues to inform diagnosis and prognosis. Texture analysis, a subtype of radiomics, quantifies gray-level pixels and voxels in a frequency histogram and their spatial relationships to describe lesion heterogeneity within a 2-dimensional region of interest (ROI) or 3-dimensional volume of interest (VOI). Computed tomography (CT) texture analysis has dem- onstrated promise in diagnosing and risk-stratifying patients with PCs. Predictive ability of radiomics models can be enhanced by integrating clinical features in pancreas and non-pancreas tissues.”
Machine learning principles applied to CT radiomics to predict mucinous pancreatic cysts
Adam M. Awe et al.
Abdominal Radiology https://doi.org/10.1007/s00261-021-03289-0 - "This retrospective analysis demonstrated that machine learn- ing principles applied to radiomics, clinical parameters, and surgical pathology can be used to create a mucinous classifier of PCs. The machine learning mucinous classifiers out- performed the baseline mucinous classifiers on G-mean and AUC scoring metrics, which we believe are the metrics best suited to assess the model quality and potential for useful predictions. Performance was comparable between XGBoost texture feature only and combined models. Shapely additive explanation analysis demonstrated that trends in important model-building variables can be identified. However, overall this remains a challenging task with only moderate performance of the best model.”
Machine learning principles applied to CT radiomics to predict mucinous pancreatic cysts
Adam M. Awe et al.
Abdominal Radiology https://doi.org/10.1007/s00261-021-03289-0 - “In conclusion, our study demonstrates that machine learning principles can be applied to radiomics data of PCs to help detect mucinous phenotypes. While this information does not obviate the need for other diagnostic testing, it may help risk stratify patients with PCs. We also demonstrate that integration of radiologic and clinical fea- tures with texture feature radiomics data does not improve performance of our mucinous classifier. However, unique radiomic, radiologic, and clinical features were important in building our machine learning mucinous classifiers. These results highlight the potential of machine learning algorithms applied to high-throughput PC radiomics features in helping to detect mucinous cyst phenotype in patients and deserves further study to improve and validate such models.”
Machine learning principles applied to CT radiomics to predict mucinous pancreatic cysts
Adam M. Awe et al.
Abdominal Radiology https://doi.org/10.1007/s00261-021-03289-0
- “Radiomics refers to the extraction of mineable data from medi- cal imaging and has been applied within oncology to improve diagnosis, prognostication, and clinical decision support, with the goal of delivering precision medicine.The authors provide a practi- cal approach for successfully implementing a radiomic workflow from planning and conceptualization through manuscript writing. Applications in oncology typically are either classification tasksthat involve computing the probability of a sample belonging to a category, such as benign versus malignant, or prediction of clinical events with a time-to-event analysis, such as overall survival.The radiomic workflow is multidisciplinary, involving radiologists and data and imaging scientists, and follows a stepwise process involving tumor segmentation, image preprocessing, feature extraction, model development, and validation."
Radiomics in Oncology: A Practical Guide
Joshua D. Shur, et al.
RadioGraphics 2021; 41:1717–1732 - “Images are curated and processed before segmentation, which can be performed on tumors, tumor subregions, or peritumoral zones. Extracted features typically describe the distribution of signal intensities and spatial relationship of pixels within a region of interest.To improve model performance and reduce overfitting, redundant and nonreproducible features are removed.Validation is essential to estimate model performance in new data and can be performed iteratively on samples of the dataset (cross-validation) or on a separate hold-out dataset by using internal or external data. A variety of noncommercial and commercial radiomic software applications can be used. Guidelines and artifi- cial intelligence checklists are useful when planning and writing up radiomic studies. Although interest in the field continues to grow, radiologists should be familiar with potential pitfalls to ensure that meaningful conclusions can be drawn.”
Radiomics in Oncology: A Practical Guide
Joshua D. Shur, et al.
RadioGraphics 2021; 41:1717–1732 - "Radiomics refers to the extraction of mineable high-dimensional data from radiologic images and has been applied within oncol- ogy to improve diagnosis and prognostication with the aimof delivering precision medicine.The premise is that imaging data convey meaningful information about tumor biology, behavior, and pathophysiology and may reveal information that is not otherwise apparent to current radiologic and clinical interpretation.”
Radiomics in Oncology: A Practical Guide
Joshua D. Shur, et al.
RadioGraphics 2021; 41:1717–1732 - - Radiomics refers to the extraction of mineable high-dimen- sional data from radiologic images and has been applied within oncology to improve diagnosis and prognostication with the aim of delivering precision medicine.
- Radiomic studies in oncology are usually either (a) classifica- tion tasks or (b) prediction of clinical outcomes by using a time-to-event analysis.
- As with any research study, a radiomic study should have a testable hypothesis that should address a relevant clinical question, usually with the aim of meeting an unfulfilled need in cancer management.
- Radiomic features are “handcrafted” in that the algorithms used to generate them are designed or chosen by the data scientist rather than being learned directly from the images, as is found with deep learning approaches.
Radiomics in Oncology: A Practical Guide
Joshua D. Shur, et al.
RadioGraphics 2021; 41:1717–1732 
Radiomics in Oncology: A Practical Guide
Joshua D. Shur, et al.
RadioGraphics 2021; 41:1717–1732
Radiomics in Oncology: A Practical Guide
Joshua D. Shur, et al.
RadioGraphics 2021; 41:1717–1732
Radiomics in Oncology: A Practical Guide
Joshua D. Shur, et al.
RadioGraphics 2021; 41:1717–1732- “Once clinical and radiomic data are collected and curated, statistical models are fitted to predict study endpoints, such as tumor type or survival time. A typical model uses input features (including the radiomic features described previously and clinical features such as tumor markers or lymph node status) in addition to target data that the model aims to predict, such as benign versus malignant or risk of recurrence.The final performance and generalizability of models discovered from a radiomic analysis is determined by vali- dating the model on new test data.”
Radiomics in Oncology: A Practical Guide
Joshua D. Shur, et al.
RadioGraphics 2021; 41:1717–1732 - "Radiomic applications in oncology include diagnosis, prognostication, and prediction of clinical outcomes. It is a multidisciplinary field, encompassing radiologists and data and imaging scientists. A variety of challenges exist, including a need for standardization across all stages of the workflow and prospective validation across multiple sites using real-world heterogeneous datasets. This article provides multiple learning points to improve study design and execution and to enhance translation of radiomics into clinical practice.”
Radiomics in Oncology: A Practical Guide
Joshua D. Shur, et al.
RadioGraphics 2021; 41:1717–1732
- “Radiomics analysis extracts a large number of features from conventional radiological cross-sectional images that were traditionally undetectable by the naked human eye. It identifies tumor heterogeneity in a comprehensive and noninvasive way, reflecting the biological behaviour of lesions, and thus assists in clinical diagnosis and treatment evaluation. This review describes the radiomics approach and its uses in the evaluation of pancreatic ductal adenocarcinoma (PDAC). This discipline holds the potential to characterize lesions more accurately, assesses the primary tumour and predicts the response to therapy and prognosis in PDAC. Existing studies have provided significant insights into the application of radiomics in managing the PDAC. However, a variety of challenges, including data quality and quantity, imaging segmentation, and the standardization of the radiomics process need to be solved before its widespread clinical implementation.”
Radiomics in pancreatic ductal adenocarcinoma: a state of art review
Ming He et al.
Journal of Pancreatology (2020) 3:4 
Radiomics in pancreatic ductal adenocarcinoma: a state of art review
Ming He et al.
Journal of Pancreatology (2020) 3:4- "The application of radiomics in PDAC mainly includes the following 3 aspects: lesion characterization, primary tumour assessment and response to therapy and prognosis. It has also been used in other nononcologic conditions associated with PDAC. The initial results of radiomics related to PDAC are promising. However, there are still many problems and challenges that need to be solved, including data quality and quantity, imaging segmentation and the standardization of the radiomics process. Radiologists need to work closely with researchers such as information scientists to establish the standardized process of radiomics analysis. Multi-centre data sharing and public database establishment would provide more high-quality data for radiomics analysis. With the development of the radiomics in PDAC, it has a considerable potential to be a useful assistant in the clinical workflow for PDAC’s personalized medicine.”
Radiomics in pancreatic ductal adenocarcinoma: a state of art review
Ming He et al.
Journal of Pancreatology (2020) 3:4 - “The procedure of the radiomics analysis should be carefully evaluated and standardized in every step to eliminate the potential bias and confounding factors. Extensive disclosure of the imaging protocols, evaluation criteria, reproducibility and/ or clinical utility is of great significance. Multiple studies had a limitation of unclear description about the detailed process of radiomics performed pre-processing, reconstruction, variations in feature nomenclature, mathematical definition, methodology, and software implementation of the applied feature extraction algorithms. The process of feature reduction and/or exclusion should be described clearly in the future. designs and a head-to-head comparison of quantitative features against standard diagnostic radiologist assessment are needed in the future.”
Radiomics in pancreatic ductal adenocarcinoma: a state of art review
Ming He et al.
Journal of Pancreatology (2020) 3:4
- Purpose: The purpose of this study is to evaluate diagnostic performance of a commercially available radiomics research prototype vs. an in-house radiomics software in the binary classification of CT images from patients with pancreatic ductal adenocarcinoma (PDAC) vs. healthy controls.
Results: When 40 radiomics features were used in the random forest classification, in-house software achieved superior sensitivity (1.00) and accuracy (0.992) compared to the commercially available research prototype (sensitivity = 0.950, accuracy = 0.968). When the number of features was reduced to five features, diagnostic performance of the in-house soft- ware decreased to sensitivity (0.950), specificity (0.923), and accuracy (0.936). Diagnostic performance of the commercially available research prototype was unchanged.
Conclusion: Commercially available and in-house radiomics software achieve similar diagnostic performance, which may lower the barrier of entry for radiomics research and allow more clinician-scientists to perform radiomics research.
Diagnostic performance of commercially available vs. in‐house radiomics software in classification of CT images from patients with pancreatic ductal adenocarcinoma vs. healthy controls
Linda C. Chu · Berkan Solmaz · Seyoun Park · Satomi Kawamoto · Alan L. Yuille · Ralph H. Hruban · Elliot K. Fishman
Abdominal Radiology (2020) 45:2469–2475 - “This study showed that a commercially available radiomics software may be able to achieve similar diagnostic performance as an in-house radiomics software. The results obtained from one radiomics software may be transferrable to another system. Availability of commercial radiomics software may lower the barrier of entry for radiomics research and allow more researchers to engage in this exciting area of research.”
Diagnostic performance of commercially available vs. in‐house radiomics software in classification of CT images from patients with pancreatic ductal adenocarcinoma vs. healthy controls
Linda C. Chu · Berkan Solmaz · Seyoun Park · Satomi Kawamoto · Alan L. Yuille · Ralph H. Hruban · Elliot K. Fishman
Abdominal Radiology (2020) 45:2469–2475
- Purpose: To enhance clinician’s decision-making by diagnosing hepatocellular carcinoma (HCC) in cirrhotic patients with indeterminate liver nodules using quantitative imaging features extracted from triphasic CT scans.
Conclusion: A signature using a single feature was validated in a multicenter retrospective cohort to diagnose HCC in cirrhotic patients with indeterminate liver nodules. Artificial intelligence could enhance clinicians’ decision by identifying a subgroup of patients with high HCC risk.
Radiomics machine-learning signature for diagnosis of hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules
Fatima-Zohra Mokrane et al.
European Radiology (2020) 30:558–570 - "Our study assembled the largest radiomics dataset of indeterminate cirrhotic liver nodules to date and offers a proof of concept that machine-learning-based radiomics signature using change in quantitative CT features across the arterial and portal venous phases can allow a non-invasive accurate diagnosis of HCCs in cirrhotic patients with indeterminate nodules. This signature would allow for identification of high HCC–risk patients, who should be prioritized for therapy, allowing thus clinically significant benefits.”
Radiomics machine-learning signature for diagnosis of hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules
Fatima-Zohra Mokrane et al.
European Radiology (2020) 30:558–570 - Results: The reliability of radiomic features calculated by the different software platforms was only excellent (ICC > 0.9) for 4/17 radiomic features when comparing all four platforms. Reliability improved to ICC > 0.9 for 15/17 radiomic features when analysis was restricted to the three IBSI-compliant platforms. Failure to harmonize calculation settings resulted in poor reliability, even across the IBSI-compliant platforms. Software platform version also had a marked effect on feature reliability in CERR and LIFEx. Features identified as having significant relationship to survival varied between platforms, as did the direction of hazard ratios.
Conclusion: IBSI compliance, user-defined calculation settings and choice of platform version all influence the statistical reli- ability and corresponding performance of prognostic models in radiomics.
Reliability and prognostic value of radiomic features are highly dependent on choice of feature extraction platform
Isabella Fornacon-Wood et al.
European Radiology https://doi.org/10.1007/s0330-020-06957-9 - • Reliability of radiomic features varies between feature calculation platforms and with choice of software version.
• Image Biomarker Standardisation Initiative (IBSI) compliance improves reliability of radiomic features across platforms, but only when calculation settings are harmonised.
• IBSI compliance, user-defined calculation settings and choice of platform version collectively affect the prognostic value of features.
Reliability and prognostic value of radiomic features are highly dependent on choice of feature extraction platform
Isabella Fornacon-Wood et al.
European Radiology https://doi.org/10.1007/s0330-020-06957-9
- Purpose: The purpose of this study was to determine whether computed tomography (CT)-based machine learning of radiomics features could help distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC).
Results: The pancreas was diffusely involved in 37 (37/89; 41.6%) patients with AIP and not diffusely in 52(52/89; 58.4%) patients. Using machine learning, 95.2% (59/62; 95% confidence interval [CI]: 89.8–100%),83.9% (52:67; 95% CI: 74.7–93.0%) and 77.4% (48/62; 95% CI: 67.0–87.8%) of the 62 test patients werecorrectly classified as either having PDAC or AIP with thin-slice venous phase, thin-slice arterial phase, and thick-slice venous phase CT, respectively. Three of the 29 patients with AIP (3/29; 10.3%) were incorrectly classified as having PDAC but all 33 patients with PDAC (33/33; 100%) were correctly classified with thin-slice venous phase with 89.7% sensitivity (26/29; 95% CI: 78.6–100%) and 100% specificity (33/33;95% CI: 93–100%) for the diagnosis of AIP, 95.2% accuracy (59/62; 95% CI: 89.8–100%) and area under the curve of 0.975 (95% CI: 0.936–1.0).
Conclusions: Radiomic features help differentiate AIP from PDAC with an overall accuracy of 95.2%. - Purpose: The purpose of this study was to determine whether computed tomography (CT)-based machine learning of radiomics features could help distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC).
Conclusions: Radiomic features help differentiate AIP from PDAC with an overall accuracy of 95.2%.
Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features
S. Park, L.C. Chu, R.H. Hruban, Vogelstein, K.W. Kinzler, A.L. Yuille, Fouladi, S. Shayesteh, S. Ghandili, C.L. Wolfgang, R. Burkhart, J. He, E.K. Fishman, S. Kawamoto
Diagnostic and Interventional Imaging (in press) - •CT radiomics differentiates AIP from PDAC with 89.7% sensitivity and 100% specificity.
•Thin slice CT radiomics better differentiates AIP from PDAC than thick slice CT radiomics.
•Venous phase CT radiomics better differentiates AIP from PDAC than arterial phase radiomics.
Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features
S. Park, L.C. Chu, R.H. Hruban, Vogelstein, K.W. Kinzler, A.L. Yuille, Fouladi, S. Shayesteh, S. Ghandili, C.L. Wolfgang, R. Burkhart, J. He, E.K. Fishman, S. Kawamoto
Diagnostic and Interventional Imaging (in press) - “AIP has clinical and imaging features that overlap with those of pancreatic ductal adenocarcinoma (PDAC) and can pose a significant diagnostic dilemma even for experienced radiologists . The management of these two conditions is markedly different. Patients with AIP are initially treated with oral corticosteroids, while patients with PDAC are treated with a combination of surgical resection and chemotherapy. The most common presentation of AIP is obstructive jaundice and pancreatic enlargement, which mimics that of PDAC and 2–6% of patients undergoing surgical resection for suspected pancreatic cancer are actually diagnosed with AIP upon histopathological analysis. Computed tomography (CT) plays an important role in the evaluation of suspected pancreatic cancer, and is often the initial diagnostic imaging modality. It is of utmost importance to correctly differentiate AIP from PDAC early in the disease process so as to administer the proper treatment and avoid unnecessary pancreatic resections in patients with AIP.”
Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features
S. Park, L.C. Chu, R.H. Hruban, Vogelstein, K.W. Kinzler, A.L. Yuille, Fouladi, S. Shayesteh, S. Ghandili, C.L. Wolfgang, R. Burkhart, J. He, E.K. Fishman, S. Kawamoto
Diagnostic and Interventional Imaging (in press) 
Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features
S. Park, L.C. Chu, R.H. Hruban, Vogelstein, K.W. Kinzler, A.L. Yuille, Fouladi, S. Shayesteh, S. Ghandili, C.L. Wolfgang, R. Burkhart, J. He, E.K. Fishman, S. Kawamoto
Diagnostic and Interventional Imaging (in press)- "In conclusion, radiomics analysis of CT images is reasonably accurate in differentiating AIP from PDAC. Using such features, in combination with clinical and standard radiologic analyses, may improve the accuracy of AID diagnosis and spare patients’ unnecessary surgical procedure.”
Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features
S. Park, L.C. Chu, R.H. Hruban, Vogelstein, K.W. Kinzler, A.L. Yuille, Fouladi, S. Shayesteh, S. Ghandili, C.L. Wolfgang, R. Burkhart, J. He, E.K. Fishman, S. Kawamoto
Diagnostic and Interventional Imaging (in press) - "Our results showed that by combining radiomics features, AIP could be distinguished from PDAC with a sensitivity of 89.7% and a specificity of 100%, and an overall accuracy of 95.2%. Among 3 patients with focal AIP were falsely classified as PDAC using radiomics features, two patients had focal AIP in the head with a plastic stent in the common bile duct, which can sensitively affect to the quantitative feature computation. In our study, the accuracy was higher than that in a previous study that evaluated CT to differentiate AIP from PDAC based on morphological features. In that study, the mean accuracies for diagnosing AIP and PDAC were 68% and 83%, respectively. In our study, AIP was considered as a diagnosis or differential diagnosis by the radiologists in only in 67% of patients with AIP not already suspected to be AIP at the time of CT examination.”
Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features
S. Park, L.C. Chu, R.H. Hruban, Vogelstein, K.W. Kinzler, A.L. Yuille, Fouladi, S. Shayesteh, S. Ghandili, C.L. Wolfgang, R. Burkhart, J. He, E.K. Fishman, S. Kawamoto
Diagnostic and Interventional Imaging (in press) - “We found that radiomics features were better at distinguishing AIP from PDAC using venous phase CT images than using arterial phase images. We also performed radiomics analysis on both thin- and thick-slice reconstructions. We found that thin-slice CT based radiomics signature had better diagnostic performance than thick-slice, as reported in pulmonary nodules and lung cancer in prior studies.”
Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features
S. Park, L.C. Chu, R.H. Hruban, Vogelstein, K.W. Kinzler, A.L. Yuille, Fouladi, S. Shayesteh, S. Ghandili, C.L. Wolfgang, R. Burkhart, J. He, E.K. Fishman, S. Kawamoto
Diagnostic and Interventional Imaging (in press) - Purpose: The purpose of this study is to evaluate diagnostic performance of a commercially available radiomics research prototype vs. an in-house radiomics software in the binary classification of CT images from patients with pancreatic ductal adenocarcinoma (PDAC) vs. healthy controls.
Conclusion: Commercially available and in-house radiomics software achieve similar diagnostic performance, which may lower the barrier of entry for radiomics research and allow more clinician-scientists to perform radiomics research.
Diagnostic performance of commercially available vs. in‐house radiomics software in classification of CT images from patients with pancreatic ductal adenocarcinoma vs. healthy controls
Linda C. Chu · Berkan Solmaz · Seyoun Park · Satomi Kawamoto · Alan L. Yuille · Ralph H. Hruban · Elliot K. Fishman
Abdominal Radiology https://doi.org/10.1007/s00261-020-02556-w - “Results: When 40 radiomics features were used in the random forest classification, in-house software achieved superior sensitivity (1.00) and accuracy (0.992) compared to the commercially available research prototype (sensitivity = 0.950, accuracy = 0.968). When the number of features was reduced to five features, diagnostic performance of the in-house soft- ware decreased to sensitivity (0.950), specificity (0.923), and accuracy (0.936). Diagnostic performance of the commercially available research prototype was unchanged.”
Diagnostic performance of commercially available vs. in‐house radiomics software in classification of CT images from patients with pancreatic ductal adenocarcinoma vs. healthy controls
Linda C. Chu · Berkan Solmaz · Seyoun Park · Satomi Kawamoto · Alan L. Yuille · Ralph H. Hruban · Elliot K. Fishman
Abdominal Radiology https://doi.org/10.1007/s00261-020-02556-w - “Radiomics has the potential to generate imaging biomarkers for classification and prognostication. Technical parameters from image acquisition to feature extraction and analysis have the potential to affect radiomics features. The current study used the same CT images with manual segmentation on both a commercially available research prototype and in-house radiomics software to control for any variability at the image acquisition step and compared the diagnostic performance of the two programs. Both programs achieved similar diagnostic performance in the binary classification of CT images from patients with PDAC and healthy control subjects, despite differences in the radiomics fea-tures they employed (854 features in commercial program vs. 478 features in in-house program).”
Diagnostic performance of commercially available vs. in‐house radiomics software in classification of CT images from patients with pancreatic ductal adenocarcinoma vs. healthy controls
Linda C. Chu · Berkan Solmaz · Seyoun Park · Satomi Kawamoto · Alan L. Yuille · Ralph H. Hruban · Elliot K. Fishman
Abdominal Radiology https://doi.org/101007/s00261-020-02556-w - "This is reassuring that even though there may be variations in the computed values for radiomics features, the differences do not seem to significantly impact the overall diagnostic performance of the constellation of radiomics features. This is important for the broader implementation of radiomics research. Currently, many radiomics studies have been performed using proprietary in-house software, which requires in-house expertise in computer science, a luxury that only a few academic centers can afford. The results of this study show that commercially available radiomics software may be a viable alternative to in-house computer science expertise, which can lower the barrier of entry for radiomics research and allow clinicians to validate findings of the published studies with their own local datasets.”
Diagnostic performance of commercially available vs. in‐house radiomics software in classification of CT images from patients with pancreatic ductal adenocarcinoma vs. healthy controls
Linda C. Chu · Berkan Solmaz · Seyoun Park · Satomi Kawamoto · Alan L. Yuille · Ralph H. Hruban · Elliot K. Fishman
Abdominal Radiology https://doi.org/101007/s00261-020-02556-w - “This study showed that a commercially available radiomics software may be able to achieve similar diagnostic performance as an in-house radiomics software. The results obtained from one radiomics software may be transferrable to another system. Availability of commercial radiom ics software may lower the barrier of entry for radiomics research and allow more researchers to engage in this exciting area of research.”
Diagnostic performance of commercially available vs. in‐house radiomics software in classification of CT images from patients with pancreatic ductal adenocarcinoma vs. healthy controls
Linda C. Chu · Berkan Solmaz · Seyoun Park · Satomi Kawamoto · Alan L. Yuille · Ralph H. Hruban · Elliot K. Fishman
Abdominal Radiology https://doi.org/101007/s00261-020-02556-w
Assessing Radiology Research on Artificial Intelligence:
A Brief Guide for Authors, Reviewers, and Readers—From the Radiology Editorial Board
David A.Bluemke et al.
Radiology 2019; (in press) https://doi.org/10.1148/radiol.2019192515- “As an interim step, the Radiology editorial board has developed a list of nine key considerations that help us evaluate AI research (Table). The goal of these considerations is to improve the soundness and applicability of AI research in diagnostic imaging. These considerations are enumerated for the authors, but manuscript reviewers and readers may also find these points to be helpful.”
Assessing Radiology Research on Artificial Intelligence: A Brief Guide for Authors, Reviewers, and Readers—From the Radiology Editorial Board
David A.Bluemke et al.
Radiology 2019; (in press) https://doi.org/10.1148/radiol.2019192515 - “1. Carefully define all three image sets (training, validation, and test sets of images) of the AI experiment. As sum- marized by Park and Han, the AI algorithm is trained on an initial set of images according to a standard of reference. The training algorithm is tuned and validated on a separate set of im- ages. Finally, an independent “test” set of images is used to report final statistical results of the AI. Ideally, each of the three sets of images should be independent, without overlap. Also, the inclusion and exclusion criteria for the dataset, in addition to the justification for removing any outlier, should be explained.”
Assessing Radiology Research on Artificial Intelligence: A Brief Guide for Authors, Reviewers, and Readers—From the Radiology Editorial Board
David A.Bluemke et al.
Radiology 2019; (in press) https://doi.org/10.1148/radiol.2019192515 - “ 2. Use an external test set for final statistical reporting. ML/AI models are very prone to overfitting, meaning that they work well only for images on which they were trained. Ideally, an outside set of images (eg, from another institution, the external test set) is used for final assessment to determine if the ML/AI model will generalize.”
Assessing Radiology Research on Artificial Intelligence: A Brief Guide for Authors, Reviewers, and Readers—From the Radiology Editorial Board
David A.Bluemke et al.
Radiology 2019; (in press) https://doi.org/10.1148/radiol.2019192515 - “3. Use multivendor images, preferably for each phase of the AI evaluation (training, validation, test sets). Radiologists are aware that MRI scans from one vendor do not look like those from another vendor. Such differences are detected by radiomics and AI algorithms. Vendor-specific algorithms are of much less interest than multivendor AI algorithms.”
Assessing Radiology Research on Artificial Intelligence: A Brief Guide for Authors, Reviewers, and Readers—From the Radiology Editorial Board
David A.Bluemke et al.
Radiology 2019; (in press) https://doi.org/10.1148/radiol.2019192515 - 4. Justify the size of the training, validation, and test sets. The number of images required to train an AI algorithm depends on the application. For example, an AI model may learn image segmentation after only a few hundred images, while thousands of chest radiographs may simultaneously be needed to detect lung nodules or multiple abnormalities. In their work classifying chest radiographs as normal or abnormal, Dunnmon et al began with 200000 chest images; however, their AI algo- rithm showed little benefit for improved performance after just 20000 chest radiographs. For many applications, the “correct” number of images may be unknown at the start of the research. The research team should evaluate the relationship between the number of training images versus model performance. For the test set, traditional sample size statistical considerations can be applied to determine the minimum number of images needed.
Assessing Radiology Research on Artificial Intelligence: A Brief Guide for Authors, Reviewers, and Readers—From the Radiology Editorial Board
David A.Bluemke et al.
Radiology 2019; (in press) https://doi.org/10.1148/radiol.2019192515 - 5. Train the AI algorithm using a standard of reference that is widely accepted in our field. For chest radiographs, a panel of expert radiologists interpreting the chest radiograph is an inferior standard of reference compared with the chest CT. Similarly, the radiology report is considered an inferior standard of reference relative to dedicated “research readings” of the chest CT scans. Although surprising to nonradiologists, this journal and other high-impact journals in our field do not consider the clinical report to be a high-quality standard of reference for any research study in our field, including AI. Clinical reports often have nuanced conclusions and are generated for patient care and not for research purposes. For instance, degenerative spine disease may have little significance at 80 years old but could be critical at age 15.
Assessing Radiology Research on Artificial Intelligence: A Brief Guide for Authors, Reviewers, and Readers—From the Radiology Editorial Board
David A.Bluemke et al.
Radiology 2019; (in press) https://doi.org/10.1148/radiol.2019192515 - 6. Describe any preparation of images for the AI algorithm. For coronary artery disease on CT angiograms, did the AI interpret all 300 source images? Or did the authors manu- ally select relevant images or crop images to a small field of view around the heart? Such preparation and annotation of images greatly affects radiologist understanding of the AI model. Manual cropping of tumor features is standard in radiomics studies; such studies should always evaluate the relationship of the size and reproducibility of the cropped volume to the final statistical result.
Assessing Radiology Research on Artificial Intelligence: A Brief Guide for Authors, Reviewers, and Readers—From the Radiology Editorial Board
David A.Bluemke et al.
Radiology 2019; (in press) https://doi.org/10.1148/radiol.2019192515 - 7. Benchmark the AI performance to radiology experts. For computer scientists working on AI, competitions and leader boards for the “best” AI are common. Results frequently com- pare one AI to another based on the area under the receiver op- erating characteristic curve (AUC). However, to treat a patient, physicians are much more interested in the comparison of the AI algorithm to expert readers but not just any readers. Experienced radiologist readers are preferred to benchmark an algorithm de- signed to detect radiologic abnormalities. For example, when evaluating an AI algorithm to detect stroke on CT scans, expert neuroradiologists (rather than generalists or neurologists) are known to have the highest performance.
Assessing Radiology Research on Artificial Intelligence: A Brief Guide for Authors, Reviewers, and Readers—From the Radiology Editorial Board
David A.Bluemke et al.
Radiology 2019; (in press) https://doi.org/10.1148/radiol.2019192515 - 8. Demonstrate how the AI algorithm makes decisions. As indicated above, computer scientists conducting imaging research often summarize their results as a single AUC value. That AUC is compared with the competitor, the prior best al- gorithm. Unfortunately, the AUC value alone has little relation- ship to clinical medicine. Even a high AUC value of 0.95 may include an operating mode where 99 of 100 abnormalities are missed. To help clinicians understand the AI performance, many research teams overlay colored probability maps from the AI on the source images.
Assessing Radiology Research on Artificial Intelligence: A Brief Guide for Authors, Reviewers, and Readers—From the Radiology Editorial Board
David A.Bluemke et al.
Radiology 2019; (in press) https://doi.org/10.1148/radiol.2019192515 - 9. The AI algorithm should be publicly available so that claims of performance can be verified. Just like MRI or CT scanners, AI algorithms need independent validation. Commercial AI products may work in the computer laboratory but have poor function in the reading room. “Trust but verify” is essential for AI that may ultimately be used to help prescribe therapy for our patients. All AI algorithms should be made publicly available via a website such as GitHub. Commercially available algorithms are considered publicly available.
Assessing Radiology Research on Artificial Intelligence: A Brief Guide for Authors, Reviewers, and Readers—From the Radiology Editorial Board
David A.Bluemke et al.
Radiology 2019; (in press) https://doi.org/10.1148/radiol.2019192515
Assessing Radiology Research on Artificial Intelligence:
A Brief Guide for Authors, Reviewers, and Readers—From the Radiology Editorial Board
David A.Bluemke et al.
Radiology 2019; (in press) https://doi.org/10.1148/radiol.2019192515- “As an interim step, the Radiology editorial board has developed a list of nine key considerations that help us evaluate AI research (Table). The goal of these considerations is to improve the soundness and applicability of AI research in diagnostic imaging. These considerations are enumerated for the authors, but manuscript reviewers and readers may also find these points to be helpful.”
Assessing Radiology Research on Artificial Intelligence: A Brief Guide for Authors, Reviewers, and Readers—From the Radiology Editorial Board
David A.Bluemke et al.
Radiology 2019; (in press) https://doi.org/10.1148/radiol.2019192515 - “1. Carefully define all three image sets (training, validation, and test sets of images) of the AI experiment. As sum- marized by Park and Han, the AI algorithm is trained on an initial set of images according to a standard of reference. The training algorithm is tuned and validated on a separate set of im- ages. Finally, an independent “test” set of images is used to report final statistical results of the AI. Ideally, each of the three sets of images should be independent, without overlap. Also, the inclusion and exclusion criteria for the dataset, in addition to the justification for removing any outlier, should be explained.”
Assessing Radiology Research on Artificial Intelligence: A Brief Guide for Authors, Reviewers, and Readers—From the Radiology Editorial Board
David A.Bluemke et al.
Radiology 2019; (in press) https://doi.org/10.1148/radiol.2019192515 - “ 2. Use an external test set for final statistical reporting. ML/AI models are very prone to overfitting, meaning that they work well only for images on which they were trained. Ideally, an outside set of images (eg, from another institution, the external test set) is used for final assessment to determine if the ML/AI model will generalize.”
Assessing Radiology Research on Artificial Intelligence: A Brief Guide for Authors, Reviewers, and Readers—From the Radiology Editorial Board
David A.Bluemke et al.
Radiology 2019; (in press) https://doi.org/10.1148/radiol.2019192515 - “3. Use multivendor images, preferably for each phase of the AI evaluation (training, validation, test sets). Radiologists are aware that MRI scans from one vendor do not look like those from another vendor. Such differences are detected by radiomics and AI algorithms. Vendor-specific algorithms are of much less interest than multivendor AI algorithms.”
Assessing Radiology Research on Artificial Intelligence: A Brief Guide for Authors, Reviewers, and Readers—From the Radiology Editorial Board
David A.Bluemke et al.
Radiology 2019; (in press) https://doi.org/10.1148/radiol.2019192515 - 4. Justify the size of the training, validation, and test sets. The number of images required to train an AI algorithm depends on the application. For example, an AI model may learn image segmentation after only a few hundred images, while thousands of chest radiographs may simultaneously be needed to detect lung nodules or multiple abnormalities. In their work classifying chest radiographs as normal or abnormal, Dunnmon et al began with 200000 chest images; however, their AI algo- rithm showed little benefit for improved performance after just 20000 chest radiographs. For many applications, the “correct” number of images may be unknown at the start of the research. The research team should evaluate the relationship between the number of training images versus model performance. For the test set, traditional sample size statistical considerations can be applied to determine the minimum number of images needed.
Assessing Radiology Research on Artificial Intelligence: A Brief Guide for Authors, Reviewers, and Readers—From the Radiology Editorial Board
David A.Bluemke et al.
Radiology 2019; (in press) https://doi.org/10.1148/radiol.2019192515 - 5. Train the AI algorithm using a standard of reference that is widely accepted in our field. For chest radiographs, a panel of expert radiologists interpreting the chest radiograph is an inferior standard of reference compared with the chest CT. Similarly, the radiology report is considered an inferior standard of reference relative to dedicated “research readings” of the chest CT scans. Although surprising to nonradiologists, this journal and other high-impact journals in our field do not consider the clinical report to be a high-quality standard of reference for any research study in our field, including AI. Clinical reports often have nuanced conclusions and are generated for patient care and not for research purposes. For instance, degenerative spine disease may have little significance at 80 years old but could be critical at age 15.
Assessing Radiology Research on Artificial Intelligence: A Brief Guide for Authors, Reviewers, and Readers—From the Radiology Editorial Board
David A.Bluemke et al.
Radiology 2019; (in press) https://doi.org/10.1148/radiol.2019192515 - 6. Describe any preparation of images for the AI algorithm. For coronary artery disease on CT angiograms, did the AI interpret all 300 source images? Or did the authors manu- ally select relevant images or crop images to a small field of view around the heart? Such preparation and annotation of images greatly affects radiologist understanding of the AI model. Manual cropping of tumor features is standard in radiomics studies; such studies should always evaluate the relationship of the size and reproducibility of the cropped volume to the final statistical result.
Assessing Radiology Research on Artificial Intelligence: A Brief Guide for Authors, Reviewers, and Readers—From the Radiology Editorial Board
David A.Bluemke et al.
Radiology 2019; (in press) https://doi.org/10.1148/radiol.2019192515 - 7. Benchmark the AI performance to radiology experts. For computer scientists working on AI, competitions and leader boards for the “best” AI are common. Results frequently com- pare one AI to another based on the area under the receiver op- erating characteristic curve (AUC). However, to treat a patient, physicians are much more interested in the comparison of the AI algorithm to expert readers but not just any readers. Experienced radiologist readers are preferred to benchmark an algorithm de- signed to detect radiologic abnormalities. For example, when evaluating an AI algorithm to detect stroke on CT scans, expert neuroradiologists (rather than generalists or neurologists) are known to have the highest performance.
Assessing Radiology Research on Artificial Intelligence: A Brief Guide for Authors, Reviewers, and Readers—From the Radiology Editorial Board
David A.Bluemke et al.
Radiology 2019; (in press) https://doi.org/10.1148/radiol.2019192515 - 8. Demonstrate how the AI algorithm makes decisions. As indicated above, computer scientists conducting imaging research often summarize their results as a single AUC value. That AUC is compared with the competitor, the prior best al- gorithm. Unfortunately, the AUC value alone has little relation- ship to clinical medicine. Even a high AUC value of 0.95 may include an operating mode where 99 of 100 abnormalities are missed. To help clinicians understand the AI performance, many research teams overlay colored probability maps from the AI on the source images.
Assessing Radiology Research on Artificial Intelligence: A Brief Guide for Authors, Reviewers, and Readers—From the Radiology Editorial Board
David A.Bluemke et al.
Radiology 2019; (in press) https://doi.org/10.1148/radiol.2019192515 - 9. The AI algorithm should be publicly available so that claims of performance can be verified. Just like MRI or CT scanners, AI algorithms need independent validation. Commercial AI products may work in the computer laboratory but have poor function in the reading room. “Trust but verify” is essential for AI that may ultimately be used to help prescribe therapy for our patients. All AI algorithms should be made publicly available via a website such as GitHub. Commercially available algorithms are considered publicly available.
Assessing Radiology Research on Artificial Intelligence: A Brief Guide for Authors, Reviewers, and Readers—From the Radiology Editorial Board
David A.Bluemke et al.
Radiology 2019; (in press) https://doi.org/10.1148/radiol.2019192515
State‐of‐the‐art in radiomics of hepatocellular carcinoma: a review of basic principles, applications, and limitations
Santos JM et al.
Abdominal Radiology 2019 (in press)- “Radiomics is characterized by the extraction of quantitative imaging features from conventional imaging modalities using computer based algorithms and the correlation of these features with relevant clinical endpoints, such as pathology, therapeutic response, and survival. These quantitative data are called radiomics features, of which texture features are a subset.”
State‐of‐the‐art in radiomics of hepatocellular carcinoma: a review of basic principles, applications, and limitations
Santos JM et al.
Abdominal Radiology 2019 (in press) - Radiomics is an emerging field that converts medical imaging into high‐dimensional mineable features, providing a quantitative assessment of the image. These features can then be associated to clinical endpoints, such as pathology, therapeutic response, and survival. With the quantitative analysis of digital imaging, radiomics can potentially detect specific characteristics of a disease that otherwise could not be accessed visually with a potential to inform future precision medicine.”
State‐of‐the‐art in radiomics of hepatocellular carcinoma: a review of basic principles, applications, and limitations
Santos JM et al.
Abdominal Radiology 2019 (in press) - “Radiomics is a new field in medical imaging with the potential of changing medical practice. Radiomics is characterized by the extraction of several quantitative imaging features which are not visible to the naked eye from conventional imaging modalities, and its correlation with specific relevant clinical endpoints, such as pathology, therapeutic response, and survival. Several studies have evaluated the use of radiomics in patients with hepatocellular carcinoma (HCC) with encouraging results, particularly in the pretreatment prediction of tumor biological characteristics, risk of recurrence, and survival.”
State‐of‐the‐art in radiomics of hepatocellular carcinoma: a review of basic principles, applications, and limitations
Santos JM et al.
Abdominal Radiology 2019 (in press) - OBJECTIVE. The purpose of this study was to systematically review the radiomics literature on renal mass characterization in terms of reproducibility and validation strategies.
CONCLUSION. Workflow characteristics described in the radiomics literature about renal mass characterization are heterogeneous. To bring radiomics from a mere research area to clinical use, the field needs many more papers that consider the reproducibility of radiomic features and include independent or external validation in their workflow.
Radiomics of Renal Masses: Systematic Review of Reproducibility and Validation Strategies
Kocak B et al.
AJR 2020; 214:129–136 - “Fifth, regarding segmentation styles, the majority of the studies were conducted with single-slice without multiple sampling or 3D approaches with almost similar rates. It is noteworthy that some authors used segmen- tation as a data augmentation technique to increase and balance the number of labeled slices, which in this study we call the single- slice with multiple sampling style.”
Radiomics of Renal Masses: Systematic Review of Reproducibility and Validation Strategies
Kocak B et al.
AJR 2020; 214:129–136 - "In this study, we systematically reviewed the radiomics literature about renal mass characterization with a particular focus on two important methodologic quality issues: feature reproducibility and model validation strategies. Our qualitative synthesis showed that these strategies varied to a large extent. Despite the well-known reproducibility problem of radiomics, approximately one-half of the papers had no reproducibility analysis in their workflow.”
Radiomics of Renal Masses: Systematic Review of Reproducibility and Validation Strategies
Kocak B et al.
AJR 2020; 214:129–136 - "To bring the field of radiomics of renal masses from mere research to the clinical stage, future research should be designed with independent or external validation. The systematic work conducted for this study should provide guidance for researchers and reviewers in this ever-developing research area about what has been done to date and what needs to be done for the future of this realm.”
Radiomics of Renal Masses: Systematic Review of Reproducibility and Validation Strategies
Kocak B et al.
AJR 2020; 214:129–136 - Background: Texture analysis of medical images has been reported to be a reliable method for differential diagnosis of neoplasms. This study was to investigate the performance of textural features and the combined performance of textural features and morphological characteristics in the differential diagnosis of pancreatic serous and mucinous cystadenomas.
Conclusions: In conclusion, our preliminary results highlighted the potential of CT texture analysis in discriminating pancreatic serous cystadenoma from mucinous cystadenoma. Furthermore, the combination of morphological characteristics and textural features can significantly improve the diagnostic performance, which may provide a reliable method for selecting patients with surgical intervention indications in consideration of the different treatment principles of the two diseases.
Differential diagnosis of pancreatic serous cystadenoma and mucinous cystadenoma: utility of textural features in combination with morphological characteristics
Jing Yang et al.
BMC Cancer (2019) 19:1223 https://doi.org/10.1186/s12885-019-6421-7 - “In conclusion, our preliminary results highlighted the potential of CT texture analysis to discriminate pancreatic serous cystadenoma and mucinous cystadenoma. Furthermore, the combination of morphological characteristics and textural features can significantly improve differential diagnostic performance, which may provide a reliable method for selecting pancreatic cystadenoma patients who need surgical intervention.”
Differential diagnosis of pancreatic serous cystadenoma and mucinous cystadenoma: utility of textural features in combination with morphological characteristics
Jing Yang et al.
BMC Cancer (2019) 19:1223 https://doi.org/10.1186/s12885-019-6421-7 - "Thus, surgical intervention should be proposed in a minority of patients with serous cystadenoma, and only for those who had uncertain diagnosis after systemic examinations or had symptoms. Given the risk of invasive disease and the relatively young age at diagnosis, surgical management is recommended for all mucinous cystadenoma patients who are medically fit for the surgery. Therefore, the differential diagnosis of the two diseases is clinically crucial for the choice of treatment regimen.”
Differential diagnosis of pancreatic serous cystadenoma and mucinous cystadenoma: utility of textural features in combination with morphological characteristics
Jing Yang et al.
BMC Cancer (2019) 19:1223 https://doi.org/10.1186/s12885-019-6421-7
- Radiomics: extraction of features from diagnostic images, the final product of which is a quantitative feature/parameter, measurable and mineable from images. A Radiomics analysis can extract over 400 features from a region of interest in a CT, MRI, or PET study, and correlate these features with each other and other data, far beyond the capability of the human eye or brain to appreciate. Such features may be used to predict prognosis and response to treatment . AI can support analysis of radiomics features and help in the correlation between radiomics and other data (proteomics, genomics, liquid biopsy, etc.) by building patients’ signatures.
What the radiologist should know about artificial intelligence – an ESR white paper
Insights into Imaging (2019) 10:44 https://doi.org/10.1186/s13244-019-0738-2
- Radiomics: extraction of features from diagnostic images, the final product of which is a quantitative feature/parameter, measurable and mineable from images. A Radiomics analysis can extract over 400 features from a region of interest in a CT, MRI, or PET study, and correlate these features with each other and other data, far beyond the capability of the human eye or brain to appreciate. Such features may be used to predict prognosis and response to treatment . AI can support analysis of radiomics features and help in the correlation between radiomics and other data (proteomics, genomics, liquid biopsy, etc.) by building patients’ signatures.
What the radiologist should know about artificial intelligence – an ESR white paper
Insights into Imaging (2019) 10:44 https://doi.org/10.1186/s13244-019-0738-2
- “Results: Radiomics features extracted from contrast-enhanced CT were able to discriminate pancreatic mucinous cystadenomas from serous cystadenomas in both the training group (slice thickness of 2 mm, AUC 0.77, sensitivity 0.95, specificity 0.83, accuracy 0.85; slice thickness of 5 mm, AUC 0.72, sensitivity 0.90, specificity 0.84, accuracy 0.86) and the validation group (slice thickness of 2 mm, AUC 0.66, sensitivity 0.86, specificity 0.71, accuracy 0.74; slice thickness of 5 mm, AUC 0.75, sensitivity 0.85, specificity 0.83, accuracy 0.83).
Discrimination of Pancreatic Serous Cystadenomas From Mucinous Cystadenomas With CT Textural Features: Based on Machine Learning
Yang J et al.
Front. Oncol., 12 June 2019 /doi.org/10.3389/fonc.2019.00494 - The parameters derived from texture analysis and several clinicopathological characteristic (age, gender, size, location of lesions, enhancement of peripheral wall, mural nodules, and calcification of lesions) were analyzed using random forest and Least Absolute Shrinkage and Selection Operator (LASSO) methods. In the group of 2 mm slice thickness, 22 parameters were obtained using the random forest analysis and 12 parameters were obtained using LASSO method; 5 overlapping parameters were discovered. In the group of 5 mm slice thickness, 18 parameters were obtained using the random forest analysis and 14 parameters were obtained using LASSO method; 4 overlapping parameters were discovered. Those selected textural parameters were given as mean ± standard deviation. Statistical differences of textural parameters were analyzed using independent sample t-test. A p-value of <0.05 was considered significant.
Discrimination of Pancreatic Serous Cystadenomas From Mucinous Cystadenomas With CT Textural Features: Based on Machine Learning
Yang J et al.
Front. Oncol., 12 June 2019 /doi.org/10.3389/fonc.2019.00494 - Given the benign nature of pancreatic serous cystadenomas and malignant potential of mucinous cystadenomas, resection is not suggested for most of the patients with serous cystadenoma while surgical treatment is recommended for all surgical fit patients with mucinous cystadenoma . Therefore, preoperative differential diagnosis is critical. Currently, cross- sectional imaging, endoscopic ultrasound (EUS), fine-needle aspiration (FNA) biopsy and cyst fluid analysis were frequently employed to assist in the differential diagnosis. EUS with cyst fluid analysis is the most important mean to distinguish pancreatic mucinous cystadenomas from serous cystadenomas. A cyst fluid carcinoembryonic antigen (CEA) level > 192 ng/mL has been reported to be useful for identification of mucinous cystadenomas, with a sensitivity of 73% and specificity of 84%. However, cyst fluid analysis is limited by its invasiveness
Discrimination of Pancreatic Serous Cystadenomas From Mucinous Cystadenomas With CT Textural Features: Based on Machine Learning
Yang J et al.
Front. Oncol., 12 June 2019 /doi.org/10.3389/fonc.2019.00494 - In general, the heterogeneity of tissue is composed of multiple texture patterns, so a single textural parameter cannot fully display the gross textural characteristics of tumor. In the preliminary analysis of this study, we also tried to analyze individual factors, and the results were not satisfactory. In consideration of this, a complex of integrated different textural parameters is required to represent gross texture of tumor more comprehensively. Random forest model, a powerful machine-learning approach, has proved successful in classifying subjects into the correct group. Previous studies have also indicated that random forest model could be used in the analysis of textural features. In this study, random forest model was able to discriminate between pancreatic mucinous cystadenomas and serous cystadenomas.
Discrimination of Pancreatic Serous Cystadenomas From Mucinous Cystadenomas With CT Textural Features: Based on Machine Learning
Yang J et al.
Front. Oncol., 12 June 2019 /doi.org/10.3389/fonc.2019.00494 - In conclusion, our study provided preliminary evidence that analysis texture of lesions in CT images was a reliable method to differentiate diagnosis of pancreatic mucinous cystadenomas and serous cystadenomas, which may provide a convenient, non-invasive and repeatable approach to determine whether surgery is needed in clinical practice. However, multicentre studies with larger sample size are needed to confirm these results.
Discrimination of Pancreatic Serous Cystadenomas From Mucinous Cystadenomas With CT Textural Features: Based on Machine Learning
Yang J et al.
Front. Oncol., 12 June 2019 /doi.org/10.3389/fonc.2019.00494
- “Results: Radiomics features extracted from contrast-enhanced CT were able to discriminate pancreatic mucinous cystadenomas from serous cystadenomas in both the training group (slice thickness of 2 mm, AUC 0.77, sensitivity 0.95, specificity 0.83, accuracy 0.85; slice thickness of 5 mm, AUC 0.72, sensitivity 0.90, specificity 0.84, accuracy 0.86) and the validation group (slice thickness of 2 mm, AUC 0.66, sensitivity 0.86, specificity 0.71, accuracy 0.74; slice thickness of 5 mm, AUC 0.75, sensitivity 0.85, specificity 0.83, accuracy 0.83).
Discrimination of Pancreatic Serous Cystadenomas From Mucinous Cystadenomas With CT Textural Features: Based on Machine Learning
Yang J et al.
Front. Oncol., 12 June 2019 /doi.org/10.3389/fonc.2019.00494 - The parameters derived from texture analysis and several clinicopathological characteristic (age, gender, size, location of lesions, enhancement of peripheral wall, mural nodules, and calcification of lesions) were analyzed using random forest and Least Absolute Shrinkage and Selection Operator (LASSO) methods. In the group of 2 mm slice thickness, 22 parameters were obtained using the random forest analysis and 12 parameters were obtained using LASSO method; 5 overlapping parameters were discovered. In the group of 5 mm slice thickness, 18 parameters were obtained using the random forest analysis and 14 parameters were obtained using LASSO method; 4 overlapping parameters were discovered. Those selected textural parameters were given as mean ± standard deviation. Statistical differences of textural parameters were analyzed using independent sample t-test. A p-value of <0.05 was considered significant.
Discrimination of Pancreatic Serous Cystadenomas From Mucinous Cystadenomas With CT Textural Features: Based on Machine Learning
Yang J et al.
Front. Oncol., 12 June 2019 /doi.org/10.3389/fonc.2019.00494 - Given the benign nature of pancreatic serous cystadenomas and malignant potential of mucinous cystadenomas, resection is not suggested for most of the patients with serous cystadenoma while surgical treatment is recommended for all surgical fit patients with mucinous cystadenoma . Therefore, preoperative differential diagnosis is critical. Currently, cross- sectional imaging, endoscopic ultrasound (EUS), fine-needle aspiration (FNA) biopsy and cyst fluid analysis were frequently employed to assist in the differential diagnosis. EUS with cyst fluid analysis is the most important mean to distinguish pancreatic mucinous cystadenomas from serous cystadenomas. A cyst fluid carcinoembryonic antigen (CEA) level > 192 ng/mL has been reported to be useful for identification of mucinous cystadenomas, with a sensitivity of 73% and specificity of 84%. However, cyst fluid analysis is limited by its invasiveness
Discrimination of Pancreatic Serous Cystadenomas From Mucinous Cystadenomas With CT Textural Features: Based on Machine Learning
Yang J et al.
Front. Oncol., 12 June 2019 /doi.org/10.3389/fonc.2019.00494 - In general, the heterogeneity of tissue is composed of multiple texture patterns, so a single textural parameter cannot fully display the gross textural characteristics of tumor. In the preliminary analysis of this study, we also tried to analyze individual factors, and the results were not satisfactory. In consideration of this, a complex of integrated different textural parameters is required to represent gross texture of tumor more comprehensively. Random forest model, a powerful machine-learning approach, has proved successful in classifying subjects into the correct group. Previous studies have also indicated that random forest model could be used in the analysis of textural features. In this study, random forest model was able to discriminate between pancreatic mucinous cystadenomas and serous cystadenomas.
Discrimination of Pancreatic Serous Cystadenomas From Mucinous Cystadenomas With CT Textural Features: Based on Machine Learning
Yang J et al.
Front. Oncol., 12 June 2019 /doi.org/10.3389/fonc.2019.00494 - In conclusion, our study provided preliminary evidence that analysis texture of lesions in CT images was a reliable method to differentiate diagnosis of pancreatic mucinous cystadenomas and serous cystadenomas, which may provide a convenient, non-invasive and repeatable approach to determine whether surgery is needed in clinical practice. However, multicentre studies with larger sample size are needed to confirm these results.
Discrimination of Pancreatic Serous Cystadenomas From Mucinous Cystadenomas With CT Textural Features: Based on Machine Learning
Yang J et al.
Front. Oncol., 12 June 2019 /doi.org/10.3389/fonc.2019.00494
- “Texture analysis has a potential role in distinguishing benign from malignant adrenal nodules on CECT and may decrease the need for additional imaging studies in the workup of incidentally discovered adrenal nodules.”
Can Texture Analysis Be Used to Distinguish Benign From Malignant Adrenal Nodules on Unenhanced CT, Contrast-Enhanced CT, or In- Phase and Opposed-Phase MRI?
Ho LM et al.
AJR 2019; 212:554–561 - “Current imaging methods can diagnose lipid-rich adenomas with the use of either unenhanced CT or chemical-shift MRI and can diagnose lipid-poor adenomas on the basis of calculation of the percentage washout on contrast-enhanced CT (CECT).”
Can Texture Analysis Be Used to Distinguish Benign From Malignant Adrenal Nodules on Unenhanced CT, Contrast-Enhanced CT, or In- Phase and Opposed-Phase MRI?
Ho LM et al.
AJR 2019; 212:554–561 - “Image-based texture analysis is a quantitative technique that provides a measure of lesion heterogeneity on the basis of local variations in image brightness. First-order statistics- based texture analysis evaluates the number of pixels that have a particular gray-level value within a defined ROI. First-order texture analysis does not account for the location of the pixels within the ROI. Second-order statistics- based texture analysis evaluates the location and spatial interrelationship s between pixels of variable gray-level values.”
Can Texture Analysis Be Used to Distinguish Benign From Malignant Adrenal Nodules on Unenhanced CT, Contrast-Enhanced CT, or In- Phase and Opposed-Phase MRI?
Ho LM et al.
AJR 2019; 212:554–561 - For example, first-order texture analysis can determine how many pixels have attenuation of 0 HU within an adrenal nodule. Second-order texture analysis can determine whether those pixels with an attenuation of 0 HU within an adrenal nodule are distributed evenly or are clustered in groups.
Can Texture Analysis Be Used to Distinguish Benign From Malignant Adrenal Nodules on Unenhanced CT, Contrast-Enhanced CT, or In- Phase and Opposed-Phase MRI?
Ho LM et al.
AJR 2019; 212:554–561 - “Texture analysis of CECT images showed higher diagnostic performance for the diagnosis of malignancy, compared with CECT attenuation. The performance of select individual CECT texture features (long-run high gray-level emphasis, entropy, and short-run low gray-level emphasis) were comparable to unenhanced attenuation on CT and the SII on MRI, which are the standard diagnostic imaging tests used to distinguish adrenal adenomas from metastases in clinical practice.”
Can Texture Analysis Be Used to Distinguish Benign From Malignant Adrenal Nodules on Unenhanced CT, Contrast-Enhanced CT, or In- Phase and Opposed-Phase MRI?
Ho LM et al.
AJR 2019; 212:554–561 - Increased tumor heterogeneity is the most likely reason for the ability of texture analysis to predict adrenal malignancy on CECT. As is seen in Figure 2, lipid-poor adenomas appeared homogeneous on CECT, compared with malignant lesions, which appeared heterogeneous. We speculate that the administration of contrast material may make lipid- poor adenomas appear more homogeneous because both lipid-rich and lipid-poor areas will have uptake of contrast medium.
Can Texture Analysis Be Used to Distinguish Benign From Malignant Adrenal Nodules on Unenhanced CT, Contrast-Enhanced CT, or In- Phase and Opposed-Phase MRI?
Ho LM et al.
AJR 2019; 212:554–561 - “Malignant adrenal lesions become more heterogeneous after contrast material administration because of tumor angiogenesis and increased conspicuity of tumor necrosis. In support of our theory, a recent study by Sasaguri et al. showed that adrenal metastases from renal carcinoma showed visibly higher internal heterogeneity, compared with benign adrenal masses on CECT.”
Can Texture Analysis Be Used to Distinguish Benign From Malignant Adrenal Nodules on Unenhanced CT, Contrast-Enhanced CT, or In- Phase and Opposed-Phase MRI?
Ho LM et al.
AJR 2019; 212:554–561 - Another limitation of the present study is the retrospective nature of the data acquisition. Because this is an observational study, the type of scanner used for each patient was not controlled. One cannot underestimate the potential impact of variation in CT and MR image quality on the results of texture analysis. This factor alone represents a major challenge when one considers the robustness of applying texture analysis in the clinical setting.
Can Texture Analysis Be Used to Distinguish Benign From Malignant Adrenal Nodules on Unenhanced CT, Contrast-Enhanced CT, or In- Phase and Opposed-Phase MRI?
Ho LM et al.
AJR 2019; 212:554–561 - “The introduction of radiomics has brought with it the vast expansion of the promise of quantitative and objective assessment of images. Interpretations are no longer limited to features like area, volume, and histogram-derived metrics; they can include hundreds of different features including shape, gray-level run-length matrices, Haralick texture, het- erogeneity, coarseness, or busyness.”
How Can Radiomics Be Consistently Applied across Imagers and Institutions?
Steiger P, Sood R
Radiology (in press) - It allows for correction of radiomic measurements on the basis of their distribution and knowledge of covariates. The authors tested their method with one publicly available phantom data set and two patient data sets from patients with lung cancer. They convincingly showed that their method reduced im- ager-induced variability without sacrificing diagnostic sensitivity. Their article explains the method clearly and pro- vides all the references needed to replicate the work. This should encourage others to apply this method and test it in other radiomics studies and applications.
How Can Radiomics Be Consistently Applied across Imagers and Institutions?
Steiger P, Sood R
Radiology (in press) - “In summary, the results of the present study indicate that the use of texture analysis for evaluation of adrenal nodules works best with CECT. This finding suggests that CT texture analysis may have a potential role in distinguishing benign lipid-poor ad- enomas from adrenal malignancy on single- phase CECT. Furthermore, the application of texture analysis may potentially decrease the need for additional imaging studies to workup incidentally discovered adrenal nodules.”
Can Texture Analysis Be Used to Distinguish Benign From Malignant Adrenal Nodules on Unenhanced CT, Contrast-Enhanced CT, or In- Phase and Opposed-Phase MRI?
Ho LM et al.
AJR 2019; 212:554–561 - “Radiomics holds the promise to become a tool at the disposal of the radiologist to expand the qualitative interpretation of the image, with additional quantitative information that can provide functional and prospective information not evident from the image alone. More studies are needed to fulfill this promise. The proposed algorithm has been shown to be effective in both thin- and thick-section CT images.”
How Can Radiomics Be Consistently Applied across Imagers and Institutions?
Steiger P, Sood R
Radiology (in press) - “Therefore, the general success of radiomics in lung cancer and oncology will in part depend on the development and adoption of tailored image acquisition techniques for quantitative feature analysis. Radiomics will benefit from an extension of efforts already underway to standardize quantitative imaging, spearheaded by the Quantitative Imaging Biomarkers Alliance.”
How Can Radiomics Be Consistently Applied across Imagers and Institutions?
Steiger P, Sood R
Radiology (in press) - ”Substantial hurdles remain until radiomics can become a routine tool in the radiology reading room of the future, as eloquently explained by Gillies et al. Among them is the need to validate any radiomics biomarkers in prospective multicenter studies. The variability introduced by the wide variety of avail- able equipment and imaging protocols must be controlled to allow these radiomic biomarkers to be used in a broader manner. The method presented by Orlhac et al. may have an important role in this research.”
How Can Radiomics Be Consistently Applied across Imagers and Institutions?
Steiger P, Sood R
Radiology (in press)
- “Substantial hurdles remain until radiomics can become a routine tool in the radiology reading room of the future, as eloquently explained by Gillies et al (1). Among them is the need to validate any radiomics biomarkers in prospective multicenter studies. The variability introduced by the wide variety of available equipment and imaging protocols must be controlled to allow these radiomic biomarkers to be used in a broader manner. The method presented by Orlhac et al (2) may have an important role in this research.”
How Can Radiomics Be Consistently Applied across Imagers and Institutions?
Peter Steiger, Rohit Sood
Radiology 2019; 00:1–2 - “The introduction of radiomics has brought with it the vast expansion of the promise of quantitative and objective assessment of images. Interpretations are no longer limited to features like area, volume, and histogram-derived metrics; they can include hundreds of different features including shape, gray-level run-length matrices, Haralick texture, heterogeneity, coarseness, or busyness (1). Putting such higher dimension image characteristics into the context of increasingly accessible clinical information about patients holds promise for evidence-based clinical decision support.”
How Can Radiomics Be Consistently Applied across Imagers and Institutions?
Peter Steiger, Rohit Sood
Radiology 2019; 00:1–2 - In this issue of Radiology, Orlhac et al (2) adapt a method originally used in genomics to correct variations in radiomic measurements caused by different imagers and imaging protocols (2). The proposed method is based on a statistical method called ComBat, which is readily available in the open-source R statistical programming language (R Foundation for Statistical Computing, Vienna, Austria). Unlike other previously published methods, this approach does not require images to be modified.
How Can Radiomics Be Consistently Applied across Imagers and Institutions?
Peter Steiger, Rohit Sood
Radiology 2019; 00:1–2 
- Background: Radiomics extracts features from medical images more precisely and more accurately than visual assessment. However, radiomics features are affected by CT scanner parameters such as reconstruction kernel or section thickness, thus obscuring underlying biologically important texture features.
Conclusion: Image compensation successfully realigned feature distributions computed from different CT imaging protocols and should facilitate multicenter radiomic studies.
Validation of A Method to Compensate Multicenter Effects Affecting CT Radiomics
Orlhac F et al.
Radiology 2019; 00:1–7 - “Radiomics extracts features from medical images that quantify tumor shape, intensity histogram, and texture of the lesions more precisely and more accurately than visual assessment by a radiologist to build models that involve features to assist patient treat- ment. In particular, texture analysis from CT images has led to promising results to distinguish between tumor lesions with different histopathologic characteristics and to predict treatment response or patient survival.”
Validation of A Method to Compensate Multicenter Effects Affecting CT Radiomics
Orlhac F et al.
Radiology 2019; 00:1–7 - Key points
* Radiomic feature values obtained by using different CT imaging protocols or scanners can be corrected for the protocol or scanner effect by using the proposed compensation method.
* The use of realigned features will enable multicentric studies to pool data from different sites to build reliable radiomic models based on large databases.
* The proposed compensation method is easily available, fast, and requires neither phantom acquisition nor feature recalculation.
Validation of A Method to Compensate Multicenter Effects Affecting CT Radiomics
Orlhac F et al.
Radiology 2019; 00:1–7 - “Nonbiological differences related to CT scanner type can be removed from radiomic feature values, allowing radiomics features to be combined in multicenter or multivendor studies.”
Validation of A Method to Compensate Multicenter Effects Affecting CT Radiomics
Orlhac F et al.
Radiology 2019; 00:1–7 - “To correct for differences in features caused by the various imaging protocols, we used the ComBat function (https://github.com/Jfortin1/ComBatHarmonization) compensation method.”
Validation of A Method to Compensate Multicenter Effects Affecting CT Radiomics
Orlhac F et al.
Radiology 2019; 00:1–7 - “In conclusion, ComBat makes it possible to pool radiomic features from different CT protocols. This method appears promising to address the center effect in multicenter radiomic studies and to possibly raise the statistical power of those studies. ComBat is data driven, which means that the transformations identified by ComBat to set all data in a common space should be estimated for each study involving data from different cen- ters and protocols.”
Validation of A Method to Compensate Multicenter Effects Affecting CT Radiomics
Orlhac F et al.
Radiology 2019; 00:1–7
- “Moreover, radiomics-predicted lymph node metastasis emerged as a preoperative predictor of both disease-specific survival and recurrence-free survival after curative intent resection of biliary tract cancers (hazard ratios, 3.37 and 1.98, respectively). Overall, there was important personalized information for medical decision support.”
CT-based Radiomics for Biliary Tract Cancer: A Possible Solution for Predicting Lymph Node Metastases
Laghi A, Voena C
Radiology 2019; 290:99–100 - There are limitations. Although the model was built with rigorous methodologic structure, a multicentric study collecting a larger number of patients would be necessary to check for the generalizability of the radiomics signature. The influence of different CT parameters (eg, kilovolt, milliampere-seconds, and reconstruction filters) on extraction of radiomics features was not among the objectives of this study, although this is a relevant variable that might affect data consistency and limit the extensive use of the model.
CT-based Radiomics for Biliary Tract Cancer: A Possible Solution for Predicting Lymph Node Metastases
Laghi A, Voena C
Radiology 2019; 290:99–100 - A correlation with genomic profile of biliary tract cancers may have been desirable, especially in the era of target therapy where specific genomic profiles are associated with either response or resistance to a specific drug. Nevertheless, radiomics approaches seem to have a bright future, especially if collaborative multidisciplinary teams are involved. Ultimately, to achieve personalized medicine, personalized imaging must be involved.
CT-based Radiomics for Biliary Tract Cancer: A Possible Solution for Predicting Lymph Node Metastases
Laghi A, Voena C
Radiology 2019; 290:99–100 - “Ultimately, to achieve personalized medicine, personalized imaging must be involved."
CT-based Radiomics for Biliary Tract Cancer: A Possible Solution for Predicting Lymph Node Metastases
Laghi A, Voena C
Radiology 2019; 290:99–100 - “The further goal of radiomics analytics is to develop decision support tools, such as predictive models, by incorporating radiomics signature and other morphologic features. Radiomics models providing individualized risk estimation of LN metastasis have been developed and validated in studies focused on esophageal, colorectal, and bladder cancers with good results."
CT-based Radiomics for Biliary Tract Cancer: A Possible Solution for Predicting Lymph Node Metastases
Laghi A, Voena C
Radiology 2019; 290:99–100
- "Radiomics uses advanced image-processing techniques to extract a large number of quantitative parameters from imaging data, and its potential to improve diagnostic accuracy is increasingly being studied . Initial studies have reported promising performance of radiomics with and without the use of machine learning in the prediction of the prostate cancer Gleason score."
Radiomic Machine Learning for Characterization of Prostate Lesions with MRI: Comparison to ADC Values
Bonekamp D et al.
Radiology 2018 (in press) - "In conclusion, this study compared the use of mean ADC and radiomics with machine learning for the characterization of lesions that were prospectively detected during routine clinical interpretation.
Quantitative assessment of the mean ADC was more accurate than qualitative PI-RADS assessment in classifying a lesion as clinically significant prostate cancer. Radiomics provided additional data that ADC metrics (including mean ADC) were more valuable than other MRI features. In fact, at the current cohort size, no added benefit of the radiomic approach was found, and mean ADC is suggested as the best choice for quantitative prostate assessment."
Radiomic Machine Learning for Characterization of Prostate Lesions with MRI: Comparison to ADC Values
Bonekamp D et al.
Radiology 2018 (in press) - Purpose: To determine the feasibility of using a deep learning approach to detect cartilage lesions (including cartilage softening, fibrillation, fissuring, focal defects, diffuse thinning due to cartilage degeneration, and acute cartilage injury) within the knee joint on MR images.
Conclusion: is study demonstrated the feasibility of using a fully automated deep learning–based cartilage lesion detection system to evaluate the articular cartilage of the knee joint with high diagnostic performance and good intraobserver agreement for detecting cartilage degeneration and acute cartilage injury.
Deep Learning Approach for Evaluating Knee MR Images: Achieving High Diagnostic Performance for Cartilage Lesion Detection
FangLiu et al.
Radiology 2018 (in press)
- “Radiomics is a process that extracts a large number of quantitative features from medical images. It can potentially be applied to any medical condition, but it is currently applied mostly in oncology for quantification of tumour phenotype and for development of decision support tools. Deep learning and convolutional neural networks have the potential to automatically extract the significant features from images to help predict an important outcome (eg, cancer-specific mortality).”
Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology An Tang et al. Canadian Association of Radiologists Journal 69 (2018) 120e135 - “With technological advances in computer science, it is anticipated that an increasing number of repetitive tasks will be automated over time. The PACS of all hospitals contain large imaging datasets with matching descriptions within radiology reports that can be used to perform ML on very large scale. The interactions between radiology images and their reports have been used to train ML for automated detection of disease in images [56]. Of note, a recent review of deep learning revealed that many recent applications in medical image analysis focus on 2D convolutional neural networks which do not directly leverage 3D information [57]. While 3D convolutional neural networks are emerging for analysis of multiplanar imaging (eg, CT), further research will be required to analyze multiparametric imaging examinations (eg, MRI).”
Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology An Tang et al. Canadian Association of Radiologists Journal 69 (2018) 120e135 - “AI techniques have been steadily developed since 1955 but recently have undergone a resurgence due to breakthrough performance arising from a combination of factors: wide availability of labeled data, advances in neural network architectures, and availability of parallel computing hardware. In radiology, AI applications currently focus on anomaly detection, segmentation, and classification of images. Familiarity with the terminology and key concepts in this field will allow the radiology community to critically analyze the opportunities, pitfalls, and challenges associated with the introduction of these new tools. Radiologists should become actively involved in research and development in collaboration with key stakeholders, scientists, and industrial partners to ensure radiologist oversight in the definition of use cases and validation process, and in the clinical application for patient care. Residency programs should integrate health informatics and computer science courses in AI in their curriculum.”
Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology An Tang et al. Canadian Association of Radiologists Journal 69 (2018) 120e135
- “Radiomics (or radiogenomics) is the correlation between the imaging appearance of cancer and the genomics of such. Advances in traditional machine learning and more novel deep learning approaches in this area have shown promising results. Moreover, deep learning techniques has achieved state-of-the-art results in biomedical image segmentation, which can be used to automatically segment and extract volumes of organs, specific tissues, and regions of interest. The radiology report of the future may automatically include such quantitative information, which could be used to assess disease and guide treatment decisions.”
Machine Learning in Radiology: Applications Beyond Image Interpretation Paras Lakhani et al. J Am Coll Radiol (in press) - “Ultimately, machine learning has the potential to dramatically improve patient care. Importantly for radiologists, machine learning algorithms can help address many problems in current-day radiology practices that do not involve image interpretation. Although much of the attention in the machine learning space has focused on the ability of machines to classify image findings, there are many other useful applications of machine learning that will improve efficiency and utilization of radiology practices today.”
Machine Learning in Radiology: Applications Beyond Image Interpretation Paras Lakhani et al. J Am Coll Radiol (in press)