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Everything you need to know about Computed Tomography (CT) & CT Scanning

Pancreas: Radiomics Imaging Pearls - Educational Tools | CT Scanning | CT Imaging | CT Scan Protocols - CTisus
Imaging Pearls ❯ Pancreas ❯ Radiomics

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  • “Rigiroli et al provide an important advance in the struggle to select appropriate surgical candidates with pancreatic ductal adenocarcinoma based on preoperative CT imaging. With the increased use of neoadjuvant therapy, this article is particularly relevant given the known challenges in assessing vascular involvement after chemotherapy. However, the reality of neoadjuvant therapy is that the treatment landscape is evolving rapidly, with new drug and external beam radiation trials maturing every year. A persistent challenge in bringing radiomics to clinical practice in patients with cancer is the generalizability of predictive models that are derived from a subset of treatment regimens that may no longer be relevant over time.”
    Radiomics for CT Assessment of Vascular Contact in Pancreatic Adenocarcinoma  
    Richard K.G.Do, Avinash Kambadakone
    Radiology 2021; 00:1–2 • https://doi.org/10.1148/radiol.2021211635
  • Background: Current imaging methods for prediction of complete margin resection (R0) in patients with pancreatic ductal adeno- carcinoma (PDAC) are not reliable.  
    Purpose: To investigate whether tumor-related and perivascular CT radiomic features improve preoperative assessment of arterial involvement in patients with surgically proven PDAC.  
    Conclusion: A model based on tumor-related and perivascular CT radiomic features improved the detection of superior mesenteric artery involvement in patients with pancreatic ductal adenocarcinoma.  
    CT Radiomic Features of Superior Mesenteric Artery Involvement in Pancreatic Ductal Adenocarcinoma: A Pilot Study  
    Francesca Rigiroli et al.
    Radiology 2021; 000:1–13 • https://doi.org/10.1148/radiol.2021210699 
  • • In a retrospective study of 194 patients with pancreatic ductal adenocarcinoma, CT radiomic features demonstrated sensitivity of 62% (33 of 53 patients) and specificity of 77% (108 of 141 patients) in the detection of superior mesenteric artery involvement in patients undergoing surgery for pancreatic ductal adenocarcinoma.  
    • The radiomic model results outperformed the assessment made by expert radiologists in consensus during a multidisciplinary meeting, yielding areas under the curve of 0.71 and 0.54, respectively (P , .001).  
    CT Radiomic Features of Superior Mesenteric Artery Involvement in Pancreatic Ductal Adenocarcinoma: A Pilot Study  
    Francesca Rigiroli et al.
    Radiology 2021; 000:1–13 • https://doi.org/10.1148/radiol.2021210699 
  • “In conclusion, our results suggest that the analysis of tu- mor-related and perivascular radiomic features improves pre- operative assessment of tumor involvement of the superior mesenteric artery in patients with pancreatic ductal adenocar- cinoma, a highly challenging task for even experienced multi- disciplinary teams, particularly after neoadjuvant therapy. To ensure our model is valid and unbiased, it should be validated in a separate independent data set. Future work may also in- corporate more sophisticated modeling techniques, including unsupervised machine learning frameworks and deep learning algorithms that fuse radiomics data with other types of clinical data.”
    CT Radiomic Features of Superior Mesenteric Artery Involvement in Pancreatic Ductal Adenocarcinoma: A Pilot Study  
    Francesca Rigiroli et al.
    Radiology 2021; 000:1–13 • https://doi.org/10.1148/radiol.2021210699 
  • “In our study, the radiomic model showed higher negative predictive value than the multidisciplinary assessment in ruling out SMA tumoral involvement defined by a clearance of 1 mm. Our results and previous studies have shown that predicting margin status using only standard CT criteria is challenging. Recent investigations have emphasized the need for optimal identification of patients with high likelihood of margin- negative resection, such as with a tumor more than 1 mm from the margin, because it yields a better prognosis compared with patients with positive surgical margin (tumor ≤1 mm to the margin or direct involvement). Despite being limited to assessment of the SMA margin, the application of our radiomic model in a clinical setting could help to guide radiologists in predicting margin status.”
    CT Radiomic Features of Superior Mesenteric Artery Involvement in Pancreatic Ductal Adenocarcinoma: A Pilot Study  
    Francesca Rigiroli et al.
    Radiology 2021; 000:1–13 • https://doi.org/10.1148/radiol.2021210699 
  • 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 tu- mor 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 im- proves survival prediction in patients with PDAC.  
    CT Radiomics–Based Preoperative Survival Prediction in Patients With Pancreatic Ductal Adenocarcinoma  
    Seyoun P, Kawamoto S, Fishman EK, Chu LC et al.
    AJR:217, November 2021 (in press)  
  • “Survival time after the surgical resection was used to stratify patients into a low- risk group (survival time > 3 years) and a high-risk group (survival time < 1 year). The 3D volume of the whole pancreatic tumor and background pancreas were manually seg- mented. A total of 478 radiomics features were extracted from tumors and 11 extra features were computed from pancreas boundaries. The 10 most relevant features were selected by feature reduction. Survival analysis was performed on the basis of clinical parameters both with and without the addition of the selected features. Survival status and time were estimated by a random survival forest algorithm. Concordance index (C-index) was used to evaluate performance of the survival prediction model.”
    CT Radiomics–Based Preoperative Survival Prediction in Patients With Pancreatic Ductal Adenocarcinoma  
    Seyoun P, Kawamoto S, Fishman EK, Chu LC et al.
    AJR:217, November 2021 (in press) 
  • "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 tech- nique that describes spatial variations in pixel intensities in images for quantitative assessment. 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 P, Kawamoto S, Fishman EK, Chu LC et al.
    AJR:217, November 2021 (in press) 
  •  “The entire 3D volume of the pancreas was segmented based on thin-slice venous phase images. The 3D volume of the whole tumor and background pancreas was manually segmented by four trained researchers using a commercial annotation software (Velocity, Varian Medical Systems). The boundaries were verified by three abdominal radiologists with 5–30 years of experience. Each case was randomly assigned to one of the researchers and a radiologist. The researcher and radiologist had face-to-face sessions to review each case. Any disagreement or errors identified during this review were corrected.”
    CT Radiomics–Based Preoperative Survival Prediction in Patients With Pancreatic Ductal Adenocarcinoma  
    Seyoun P, Kawamoto S, Fishman EK, Chu LC et al.
    AJR:217, November 2021 (in press) 
  • "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 P, Kawamoto S, Fishman EK, Chu LC et al.
    AJR:217, November 2021 (in press) 

  • CT Radiomics–Based Preoperative Survival Prediction in Patients With Pancreatic Ductal Adenocarcinoma  
    Seyoun P, Kawamoto S, Fishman EK, Chu LC et al.
    AJR:217, November 2021 (in press) 
  • "The 10 most relevant radiomics features were selected to distinguish the high- and low-risk groups and are listed in Table 4. To determine the classification power of the selected features, binary classification was performed using a random forest. Among 90 patients, 45 (50.0%, 22 low-risk and 23 high-risk) randomly select- ed patients were included in the training set, and the remaining 45 (50.0%, 23 low-risk and 22 high-risk) were included in the validation set. The overall accuracy of classification of patients into high- and low-risk groups based on selected image features was 82.2%. The high-risk group showed a higher classification performance, with 86.4% accuracy, compared with the low-risk group, with 78.3% accuracy.”
    CT Radiomics–Based Preoperative Survival Prediction in Patients With Pancreatic Ductal Adenocarcinoma  
    Seyoun P, Kawamoto S, Fishman EK, Chu LC et al.
    AJR:217, November 2021 (in press) 
  • "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 P, Kawamoto S, Fishman EK, Chu LC et al.
    AJR:217, November 2021 (in press) 

  • CT Radiomics–Based Preoperative Survival Prediction in Patients With Pancreatic Ductal Adenocarcinoma  
    Seyoun P, Kawamoto S, Fishman EK, Chu LC et al.
    AJR:217, November 2021 (in press) 
  • “Pancreatic schwannoma is a slowly growing, encapsulated, benign neoplasm that typically arises in the peripheral epineurium of either the sympathetic or parasympathetic autonomic fibers or branches of vagus nerve that extend to the pancreas. Pancreatic schwannomas most frequently involve the pancreas head (40%), followed by body (21%), neck (6%), tail (15%), and uncinate process (13%), respectively.”
    Abdominal schwannomas: review of imaging findings and pathology.
    Lee NJ, Hruban RH, Fishman EK.Abdom Radiol (NY).
    2017 Jul;42(7):1864-1870
  • "The features of pancreatic schwannomas on CT scan include low-density and/or cystic degenerative areas. MR imaging usually shows hypointensity on T1-weighted images and hyperintensity on T2-weighted images but like the CT features, these findings are nonspecific. Two-thirds of pancreatic schwannomas undergo degenerative changes such as cyst formation, necrosis, calcification, and hemorrhage, and these changes can mimic pancreatic cystic tumors.”
    Abdominal schwannomas: review of imaging findings and pathology.
    Lee NJ, Hruban RH, Fishman EK.Abdom Radiol (NY).
    2017 Jul;42(7):1864-1870
  • Background: or Purpose: Pancreatic ductal adenocarcinoma (PDAC) is a leading cause of mortality in the world with the overall 5-year survival rate of 6%. The survival of patients with PDAC is closely related to recurrence and therefore it is necessary to identify the risk factors for recurrence. This study uses artificial intelligence approaches and multi-center registry data to analyze the recurrence of pancreatic cancer after surgery and its major determinants.  
    Results: Based on variable importance from the random forest, major predictors of disease-free survival after surgery were tumor size (0.00310), tumor grade (0.00211), TNM stage (0.00211), T stage (0.00146) and lymphovascular invasion (0.00125). The coefficients of these variables were statistically significant in the Cox model (p < 0.05). The C-Index averages of the random forest and the Cox model were 0.6805 and 0.7738, respectively.  
    Conclusions: This is the first artificial-intelligence study with multi-center registry data to predict disease-free survival after the surgery of pancreatic cancer. The findings of this methodological study demonstrate that artificial intelligence can provide a valuable decision-support system for treating patients undergoing surgery for pancreatic cancer. However, at present, further studies are needed to demonstrate the actual benefit of applying machine learning algorithms in clinical practice.  
    Usefulness of artificial intelligence for predicting recurrence following surgery for pancreatic cancer: Retrospective cohort study  
    Kwang-Sig Lee et al.
    International Journal of Surgery 93 (2021) 106050 
  • Results: Based on variable importance from the random forest, major predictors of disease-free survival after surgery were tumor size (0.00310), tumor grade (0.00211), TNM stage (0.00211), T stage (0.00146) and lymphovascular invasion (0.00125). The coefficients of these variables were statistically significant in the Cox model (p < 0.05). The C-Index averages of the random forest and the Cox model were 0.6805 and 0.7738, respectively.  
    Conclusions: This is the first artificial-intelligence study with multi-center registry data to predict disease-free survival after the surgery of pancreatic cancer. The findings of this methodological study demonstrate that artificial intelligence can provide a valuable decision-support system for treating patients undergoing surgery for pancreatic cancer. However, at present, further studies are needed to demonstrate the actual benefit of applying machine learning algorithms in clinical practice.  
    Usefulness of artificial intelligence for predicting recurrence following surgery for pancreatic cancer: Retrospective cohort study  
    Kwang-Sig Lee et al.
    International Journal of Surgery 93 (2021) 106050 
  • “Pancreatic ductal adenocarcinoma (PDAC) is a leading cause of mortality among all cancers. It ranked fifth among all cancers in terms of mortality and its overall 5-year survival rate was just 6% in Korea for 2015. Surgical resection is essential for its cure but only a small proportion of its cases are found at an early stage enough for the procedure. Moreover, its recurrence rate after surgery is estimated to be 50%–60%, while its 5-year survival rate after surgery is reported to be just 20%–30% . The mean disease-free period in imaging studies is 267 ± 158 d with negative surgical margins, but 72 ± 47 d with positive margins. Therefore the survival of patients with PDAC is closely related to recurrence, and recurrence after surgery is one of the typical characteristics of PDAC .”
    Usefulness of artificial intelligence for predicting recurrence following surgery for pancreatic cancer: Retrospective cohort study  
    Kwang-Sig Lee et al.
    International Journal of Surgery 93 (2021) 106050 
  • "It is very important to prevent the recurrence of pancreatic cancer after surgery and there has been strong endeavor to identify major predictors of its disease-free survival after surgery. However, the results of existing literature were inconsistent and predictors in these studies were unmodifiable in general. Predictive nomograms were developed to combine and visualize the findings of traditional statistical models such as logistic regression and the Cox model regarding the recurrence of pancreatic cancer after surgery. But the predictive nomograms still require unrealistic assumptions of the traditional statistical models, i.e., ceteris paribus, “all the other variables staying constant”. In this context, this study used the random forest and multi-center registry data to analyze the recurrence of pancreatic cancer after surgery and its major determinants.”
    Usefulness of artificial intelligence for predicting recurrence following surgery for pancreatic cancer: Retrospective cohort study  
    Kwang-Sig Lee et al.
    International Journal of Surgery 93 (2021) 106050 
  • ”Secondly, it was beyond the scope of this study to combine deep learning and the Cox model for predicting the recurrence of pancreatic cancer after surgery. Deep learning can be defined as “a sub-group of the artificial neural network whose number of hidden layers is larger than five, e.g., ten”. The last three years have seen the emergence of new strands of research to combine the Cox model with different types of its deep-learning counterparts. The continued development and application of these cutting-edge approaches would break new ground and bring more profound clinical insights regarding the recurrence of pancreatic cancer after surgery and its major determinants.”
    Usefulness of artificial intelligence for predicting recurrence following surgery for pancreatic cancer: Retrospective cohort study  
    Kwang-Sig Lee et al.
    International Journal of Surgery 93 (2021) 106050 
  • "Chakraborty et al. utilized radiomics features extracted from pre- surgical CT images, as markers for assessment of malignancy risk of BD- IPMNs. Similar to the previous studies, they categorized their cohort of 103 patients into low-risk and high-risk IPMNs based on final pathological findings after cyst resection. They extracted four new radio- graphically inspired features (enhanced boundary fraction, enhanced inside fraction, filled largest connected component fraction and average weighted eccentricity), along with intensity and orientation-based texture features from the CT images.”
    Radiomics in stratification of pancreatic cystic lesions: Machine learning in action  
    Vipin Dalala et al.
    Cancer Letters,Volume 469,2020,Pages 228-237
  • "This has led to an increased interest in radiomics, a high-throughput extraction of comprehensible data from standard of care images. Radiomics can be used as a diagnostic and prognostic tool in personalized medicine. It utilizes quantitative image analysis to extract features in conjunction with machine learning and artificial intelligence (AI) methods like support vector machines, random forest, and convolutional neural network for feature se- lection and classification. Selected features can then serve as imaging biomarkers to predict high-risk PCLs. Radiomics studies conducted heretofore on PCLs have shown promising results.”
    Radiomics in stratification of pancreatic cystic lesions: Machine learning in action  
    Vipin Dalala et al.
    Cancer Letters,Volume 469,2020,Pages 228-237
  • "IPMNs and MCNs are the only radiographically identifiable precursors of pancreatic cancer. Consequently, accurate assessment of the malignant potential of these cystic lesions may allow early detection of resectable PCLs prior to oncogenesis. The latest guidelines propose a practical approach for their management and surveillance, yet the clinical management of these mucinous cystic lesions remains challenging. The variable risk of malignant transformation combined with elevated risks associated with pancreatic surgery have led to conflicting recommendations for the management of mucinous cystic lesions.”
    Radiomics in stratification of pancreatic cystic lesions: Machine learning in action  
    Vipin Dalala et al.
    Cancer Letters,Volume 469,2020,Pages 228-237
  • "Although only a few studies describing the use of radiomics in risk stratification of PCLs have been published, these studies have demonstrated that radiomics can be utilized to non-invasively discriminate between low-risk and high-risk PCLs before resection. This cost-effective approach would enable us to accurately recommend lifesaving surgery for individuals with malignant cysts and spare those with benign lesions the morbidity, mortality and high costs associated with pancreatic surgeries. Consequently, more studies are warranted to develop these imaging biomarkers which can be used to differentiate between benign and malignant PCLs.”
    Radiomics in stratification of pancreatic cystic lesions: Machine learning in action  
    Vipin Dalala et al.
    Cancer Letters,Volume 469,2020,Pages 228-237
  • Background: To identify preoperative computed tomography radiomics texture features which correlate with resection margin status and prognosis in resected pancreatic head adenocarcinoma.
    Methods: Improved prognostication methods utilizing novel non-invasive radiomic techniques may accurately predict resection margin status preoperatively. In an ongoing concerning pancreatic head adenocarcinoma, the venous enhanced CT images of 86 patients who underwent pancreaticoduodenectomy were selected, and the resection margin (>1 mm or ≤1 mm) was identified by pathological examination. Three regions of interests (ROIs) were then taken from superior to inferior facing the superior mesenteric vein and artery. Subsequent Laplacian-Dirichlet based texture analysis methods extracting algorithm of texture features within ROIs were analyzed and assessed in relation to patient prognosis.
    Radiomics Signatures of Computed Tomography Imaging for Predicting Resection Margin Status in Pancreatic Head Adenocarcinoma
    Jinheng Liu et al.
    BMC Surgery (in press)
  • “Radiomic texture analysis of pre-operative enhanced CT images can be used for accurate preoperative assessment of resection margins in patients with pancreatic ahead adenocarcinoma providing clinicians alongside patients a more non-invasive means of perioperative prognostication to guide management.”
    Radiomics Signatures of Computed Tomography Imaging for Predicting Resection Margin Status in Pancreatic Head Adenocarcinoma
    Jinheng Liu et al.
    BMC Surgery (in press)

  • Radiomics Signatures of Computed Tomography Imaging for Predicting Resection Margin Status in Pancreatic Head Adenocarcinoma
    Jinheng Liu et al.
    BMC Surgery (in press)

  • Prediagnostic Image Data, Artificial Intelligence, and Pancreatic Cancer A Tell-Tale Sign to Early Detection
    Matthew R. Young et al.
    Pancreas 2020;49: 882–886

  • Prediagnostic Image Data, Artificial Intelligence, and Pancreatic Cancer A Tell-Tale Sign to Early Detection
    Matthew R. Young et al.
    Pancreas 2020;49: 882–886
  • “The AI-driven diagnostic software has the potential to trans- form early detection of pancreatic cancer by improving accuracy and consistency of interpretation of radiologic imaging scans and related patient data. The development of reproducible AI systems requires access to current, large, diverse, and multisite data sets, which are subject to numerous data sharing limitations. Fu- ture efforts are likely to involve alternative data sharing solutions to enable the development of both public and private AI-ready data resources. Early detection of pancreatic cancer represents an attractive AI use case, well matched to benefit from the MTD challenge approach. This approach will significantly expand the use of sensitive data to improve early detection of pancreatic cancer and lay the foundation for the development of federated architectures for real-world medical data in general.”
    Prediagnostic Image Data, Artificial Intelligence, and Pancreatic Cancer A Tell-Tale Sign to Early Detection
    Matthew R. Young et al.
    Pancreas 2020;49: 882–886
  • “PDAC is the most common pancreatic malig- nancy, accounting for more than 85% of pancreatic tumors. It is typically a disease of elderly patients, with a mean age at presentation of 68 years and a male-to-female ratio of 1.6:1. After colorectal cancer, it is the second most common cancer of the digestive system in the United States, and its incidence is rising sharply.The development of pancreatic cancer is strongly related to smoking, family history, obesity, long-standing diabetes, and chronic pancreatitis. Early stages of PDAC are clinically silent. Abdominal pain is the most frequently reported clinical symptom, even when the tumor is small (<2 cm).”
    Pancreatic Ductal Adenocarcinoma and Its Variants: Pearls and Perils
    Schawkat K et al.
    RadioGraphics 2020; 40:0000–0000
  • 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 
  • Objectives: The primary aim of this study was to determine if computed tomographic (CT) texture analysis measurements of the tumor are independently associated with progression-free survival (PFS) and overall survival (OS) in patients with unresectable pancreatic ductal adenocarcinoma (PDAC), including both unresectable locally advanced and metastatic PDAC, who were treated with chemotherapy.
    Conclusions: Pretreatment CT quantitative imaging biomarkers from texture analysis are associated with PFS and OS in patients with unresectable PDAC who were treated with chemotherapy, and the combination of pre- treatment texture parameters and tumor size have the potential to perform better in survival models than imaging biomarker alone.
    Unresectable pancreatic ductal adenocarcinoma: Role of CT quantitative T imaging biomarkers for predicting outcomes of patients treated with chemotherapy
    S.-H. Cheng, et al.
    European Journal of Radiology 113 (2019) 188–197
  • "CT texture analysis, a novel imaging post-processing tool, can reflect tumor heterogeneity through analyzing the distribution of pixel intensities in CT images and identifying relationships among those intensities. This may reveal subtle differences imperceptible to the naked eye, thereby compensating for the limitations of conventional CT.”
    Unresectable pancreatic ductal adenocarcinoma: Role of CT quantitative T imaging biomarkers for predicting outcomes of patients treated with chemotherapy
    S.-H. Cheng, et al.
    European Journal of Radiology 113 (2019) 188–197
  • “CT texture analysis relies on objective computer-aided evaluation of gray-level patterns within lesions to assess tumor heterogeneity quantitatively in terms of numerous relevant parameters, which has been used in the prediction of various cancer prognosis . In locally advanced rectal cancer, CT texture features have been associated with better neoadjuvant chemoradiotherapy response and higher disease-free survival . In pancreatic adenocarcinoma, CT-derived texture features of dissimilarity and inverse normalized differences may be promising prognostic imaging biomarkers of overall survival in patients undergoing surgical resection with curative intent.”
    Unresectable pancreatic ductal adenocarcinoma: Role of CT quantitative T imaging biomarkers for predicting outcomes of patients treated with chemotherapy
    S.-H. Cheng, et al.
    European Journal of Radiology 113 (2019) 188–197

  • Unresectable pancreatic ductal adenocarcinoma: Role of CT quantitative T imaging biomarkers for predicting outcomes of patients treated with chemotherapy
    S.-H. Cheng, et al.
    European Journal of Radiology 113 (2019) 188–197
  • "Notably, texture analysis was performed in the portal phase of the contrast enhanced CT in our present study according to previous studies. Although Bronstein et al. proved that the pancreatic phase was preferred to the portal phase, the quantitative assessment of McNulty et al. found that tumor conspicuity is equivalent in the pancreatic and portal phases. Furthermore, during the portal phase, the progressive accumulation of contrast medium within the tumor might provide more comprehensive information of the biological character- istics of tumors. Thus, the above reasons might explain why portal phase was chosen by previous studies for texture analysis.”
    Unresectable pancreatic ductal adenocarcinoma: Role of CT quantitative T imaging biomarkers for predicting outcomes of patients treated with chemotherapy
    S.-H. Cheng, et al.
    European Journal of Radiology 113 (2019) 188–197
  • "In this study, CT texture analysis was only performed on a single image which represent the largest area of the lesion. This may not exactly and comprehensively reflect disease characteristics, although prior studies reported that comparison of 2D vs. 3D measurements of single lesions showed fairly comparable results.”
    Unresectable pancreatic ductal adenocarcinoma: Role of CT quantitative T imaging biomarkers for predicting outcomes of patients treated with chemotherapy
    S.-H. Cheng, et al.
    European Journal of Radiology 113 (2019) 188–197
  • “In conclusion, instead of post-chemotherapy texture parameters or Δ value, pre-chemotherapy could provide more information about tumor biology. Therefore, using pre-chemotherapy texture of unresectable PDAC to predict survival is more accurate and reliable. Furthermore, texture analysis as a noninvasive image-processing tool has the potential to select patients with good prognosis before therapy, indicating a promising prospect of clinical application in the future.”
    Unresectable pancreatic ductal adenocarcinoma: Role of CT quantitative T imaging biomarkers for predicting outcomes of patients treated with chemotherapy
    S.-H. Cheng, et al.
    European Journal of Radiology 113 (2019) 188–197
  • 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
  • Objective To develop and validate a radiomics-based nomogram for preoperatively predicting grade 1 and grade 2/3 tumors in patients with pancreatic neuroendocrine tumors (PNETs).
    Results The fusion radiomic signature has significant association with histologic grade (p < 0.001). The nomogram integrating independent clinical risk factor tumor margin and fusion radiomic signature showed strong discrimination with an area under the curve (AUC) of 0.974 (95% CI 0.950–0.998) in the training cohort and 0.902 (95% CI 0.798–1.000) in the validation cohort with good calibration. Decision curve analysis verified the clinical usefulness of the predictive nomogram.
    Conclusion We proposed a comprehensive nomogram consisting of tumor margin and fusion radiomic signature as a powerful tool to predict grade 1 and grade 2/3 PNET preoperatively and assist the clinical decision-making for PNET patients.
    CT radiomics may predict the grade of pancreatic neuroendocrine tumors: a multicenter study
    Gu D et al.
    European Radiology June 2019: https://doi.org/10.1007/s00330-019-06176-x
  • Key Points
    • Radiomic signature has strong discriminatory ability for the histologic grade of PNETs.
    • Arterial and portal venous phase CT imaging are complementary for the prediction of PNET grading.
    • The comprehensive nomogram outperformed clinical factors in assisting therapy strategy in PNET patients.
    CT radiomics may predict the grade of pancreatic neuroendocrine tumors: a multicenter study
    Gu D et al.
    European Radiology June 2019: https://doi.org/10.1007/s00330-019-06176-x
  • ”Thus, in this multicenter study, we build a radiomic-based predictive model to noninvasively and operatively achieve PNET grading using CT images. Meanwhile, we would also explore the predictive value of clinical and radiological variables, as comparisons with the radiomic signature. A final combined model integrating both radiomic and clinical factors is expected to accurately classify PNET grading.”
    Can Artificial Intelligence Fix the Reproducibility Problem of Radiomics?
    Park CM
    Radiology 2019; 00:1–2 • https://doi.org/10.1148/radiol.2019191154

  • Can Artificial Intelligence Fix the Reproducibility Problem of Radiomics?
    Park CM
    Radiology 2019; 00:1–2 • https://doi.org/10.1148/radiol.2019191154
  • ”For the fusion radiomic signature, we build a multivariable logistic regression model using the two single-phase radiomic signatures. The fusion radiomic signature outperformed either of the single-phase radiomic signatures. Potential reasons for this finding may be that the combination of the two phases could show the vascularity of PNETs more accurately than only one phase. The fusion signature could also provide more textural information in the tumor microenvironments since the most effective features from the two phases in this study were texture features.”
    Can Artificial Intelligence Fix the Reproducibility Problem of Radiomics?
    Park CM
    Radiology 2019; 00:1–2 • https://doi.org/10.1148/radiol.2019191154
  • Objectives: The primary aim of this study was to determine if computed tomographic (CT) texture analysis measurements of the tumor are independently associated with progression-free survival (PFS) and overall survival (OS) in patients with unresectable pancreatic ductal adenocarcinoma (PDAC), including both unresectable locally advanced and metastatic PDAC, who were treated with chemotherapy.
    Conclusions: Pretreatment CT quantitative imaging biomarkers from texture analysis are associated with PFS and OS in patients with unresectable PDAC who were treated with chemotherapy, and the combination of pre- treatment texture parameters and tumor size have the potential to perform better in survival models than imaging biomarker alone.
    Unresectable pancreatic ductal adenocarcinoma: Role of CT quantitative T imaging biomarkers for predicting outcomes of patients treated with chemotherapy
    Cheng, Si-Hang et al.
    European Journal of Radiology, Volume 113, 188 - 197
  • “This may reveal subtle differences imperceptible to the naked eye, thereby compensating for the limitations of conventional CT . CT texture analysis relies on objective computer-aided evaluation of gray-level patterns within lesions to assess tumor heterogeneity quantitatively in terms of numerous relevant parameters, which has been used in the prediction of various cancer prognosis.”
    Unresectable pancreatic ductal adenocarcinoma: Role of CT quantitative T imaging biomarkers for predicting outcomes of patients treated with chemotherapy
    Cheng, Si-Hang et al.
    European Journal of Radiology, Volume 113, 188 - 197
  • In conclusion, instead of post-chemotherapy texture parameters or Δ value, pre-chemotherapy could provide more information about tumor biology. Therefore, using pre-chemotherapy texture of unresectable PDAC to predict survival is more accurate and reliable. Furthermore, texture analysis as a noninvasive image-processing tool has the potential to select patients with good prognosis before therapy, indicating a promising prospect of clinical application in the future.
    Unresectable pancreatic ductal adenocarcinoma: Role of CT quantitative T imaging biomarkers for predicting outcomes of patients treated with chemotherapy
    Cheng, Si-Hang et al.
    European Journal of Radiology, Volume 113, 188 - 197
  • “In our study, SD has been demonstrated to be closely associated with both PFS and OS, and higher SD, which indicated higher intratumoral heterogeneity, predicted better survival outcome in patients with unresectable PDAC. However, in many cancers, increased tumor heterogeneity is associated with worse outcomes. Hypoxia and necrosis, correlated with impaired response to chemotherapy and radiotherapy, are likely to occur in tumors with low levels of angiogenesis, which were closely associated with SD value. In addition, tumor necrosis, which can reflect the presence of hypoxia, was in- vestigated by previous study to verify its significant value in predicting outcome in patients with PDAC, and multivariate survival analysis showed that necrosis was an independent predictor of poor outcome in terms of both disease-free survival (DFS) and disease-specific survival.”
    Unresectable pancreatic ductal adenocarcinoma: Role of CT quantitative T imaging biomarkers for predicting outcomes of patients treated with chemotherapy
    Cheng, Si-Hang et al.
    European Journal of Radiology, Volume 113, 188 - 197
  • ”The CT texture parameter measured in this study included (1) mean gray-level intensity (Mean, brightness); (2) standard deviation (SD, spread of distribution); (3) entropy (irregularity or complexity of pixel intensity in space); (4) mean of positive pixels (MPP); (5) skewness (symmetry of the pixel intensity distribution); (6) kurtosis (sharpness or pointedness of the pixel intensity distribution).”
    Unresectable pancreatic ductal adenocarcinoma: Role of CT quantitative T imaging biomarkers for predicting outcomes of patients treated with chemotherapy
    Cheng, Si-Hang et al.
    European Journal of Radiology, Volume 113, 188 - 197
  • Purpose: To develop and validate an effective model to differentiate NF-pNET from PDAC.
    Conclusion: The integrated model outperformed the model based on clinicoradiological features alone and was comparable to the model based on the radiomic signature alone with respect to the differential diagnosis of atypical NF-pNET and PDAC. The nomogram achieved an optimal preoperative, noninvasive differential diagnosis between atypical pNET and PDAC, which can better inform therapeutic choice in clinical practice.
    Differentiation of atypical non-functional pancreatic neuroendocrine tumor T and pancreatic ductal adenocarcinoma using CT based radiomics
    Ming He et al.
    European Journal of Radiology 117 (2019) 102–111
  • “The therapeutic strategies and prognoses differ significantly between these two major pancreatic solid lesion subtypes, which make the correct differentiation of PDAC from pNET a major issue in clinical practice, especially for atypical cases. For pNET, enucleation is possible, and patients with liver metastasis and with preoperative vascular abutment or invasion can still benefit from surgical resection. For PDAC, more radical surgery is needed, which entails higher post-operative complications and risks; surgery is contraindicated for pa- tients with liver metastasis or vascular invasion.”
    Differentiation of atypical non-functional pancreatic neuroendocrine tumor T and pancreatic ductal adenocarcinoma using CT based radiomics
    Ming He et al.
    European Journal of Radiology 117 (2019) 102–111
  • In the present study, our hypothesis was that a radiomics-based model represented with a nomogram that integrated clinicoradiological features and the radiomic signature would improve the differential diagnostic performance between atypical NF-pNET and PDAC, which is difficult to achieve in clinical practice. Therefore, we aimed to develop and validate an effective model and represent it with a nomogram to differentiate NF-pNET from PDAC.
    Differentiation of atypical non-functional pancreatic neuroendocrine tumor T and pancreatic ductal adenocarcinoma using CT based radiomics
    Ming He et al.
    European Journal of Radiology 117 (2019) 102–111

  • Differentiation of atypical non-functional pancreatic neuroendocrine tumor T and pancreatic ductal adenocarcinoma using CT based radiomics
    Ming He et al.
    European Journal of Radiology 117 (2019) 102–111
  • In conclusion, the integrated model outperformed the model based on the clinicoradiological features alone and performed comparably to the model based on the radiomic signature alone in the differential diagnosis of atypical NF-pNET versus PDAC. The nomogram achieved an optimal preoperative, noninvasive differential diagnosis between atypical pNET and PDAC, which facilitates informed therapeutic choices in clinical practice.
    Differentiation of atypical non-functional pancreatic neuroendocrine tumor T and pancreatic ductal adenocarcinoma using CT based radiomics
    Ming He et al.
    European Journal of Radiology 117 (2019) 102–111
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