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

August 2019 Imaging Pearls - Educational Tools | CT Scanning | CT Imaging | CT Scan Protocols - CTisus
Imaging Pearls ❯ August 2019

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3D and Workflow

  • “Three-dimensional (3D) imaging is now a useful tool to routinely obtain important information and not only a cosmetic adjunct to two-dimensional images. Studies that are now 30-year-old have demonstrated that 3D imaging techniques provide additional data unattainable with other imaging modalities that improve the preoperative assessment of the resectability of hepatic metastases and allow planning a safer surgical approach. 3D imaging has also been used to better understand the complex anatomy of a variety of intra-abdominal or intra-thoracic organs and to identify anatomical variations. Currently, 3D is part of daily routine and is shared by many specialties.”
    Cinematic rendering: When virtuality comes true
    P.Soyer
    Diagnostic and Interventional Imaging (in press July 2019)
  • “Cinematic rendering needs isotropic voxels from volumetric CT data similar to those used for MIP, volume rendering and surface rendering visualizations. Basically, cinematic rendering is very similar to volume rendering, although it utilizes a more complex global lighting model. The global lighting model produces high degrees of surface detail and shadowing effects that generate depth in the 3D visualizations and give a photorealistic quality to the images. Preliminary works show that cinematic rendering produces photorealistic images with enhanced detail by comparison with other 3D visualization methods .”
    Cinematic rendering: When virtuality comes true
    P.Soyer
    Diagnostic and Interventional Imaging (in press July 2019)
  • In this issue of Diagnostic and Interventional Imaging, three articles are devoted to the application of cinematic rendering to CT data to obtain breath-taking 3D images. These three articles nicely illustrate how cinematic rendering shows promise in improving the visualization of enhancement pattern and internal architecture of abdominal lesions, local tumor extension, and global disease burden, which may be helpful in a variety of diseases for lesion characterization and pretreatment planning and also the potential applications in forensic imaging. Although the utility of cinematic rendering in terms of diagnostic capability improvement has not yet been fully established, these articles should suggest future directions for researchers to pursue.
    Cinematic rendering: When virtuality comes true
    P.Soyer
    Diagnostic and Interventional Imaging (in press July 2019)

  • Cinematic rendering of focal liver masses
    L.C.Chu, S.P.Rowe E.K.Fishman
    Diagnostic and Interventional Imaging (In Press)

  • Cinematic rendering of focal liver masses
    L.C.Chu, S.P.Rowe E.K.Fishman
    Diagnostic and Interventional Imaging (In Press)
  • 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
  • Objectives: To assess whether radiomics signature can identify aggressive behavior and predict recurrence of hepatocellular carcinoma (HCC) after liver transplantation.
    Results: The stable radiomics features associated with the recurrence of HCC were simply found in arterial phase and portal phase. The prediction model based on the radiomics features extracted from the arterial phase showed better prediction performance than the portal vein phase or the fusion signature combining both of arterial and portal vein phase. A radiomics nomogram based on combined model consisting of the radiomics signature and clinical risk factors showed good predictive performance for RFS with a C-index of 0.785 (95% confidence interval [CI]: 0.674-0.895) in the training dataset and 0.789 (95% CI: 0.620-0.957) in the validation dataset. The calibration curves showed agreement in both training (p = 0.121) and validation cohorts (p = 0.164).
    Conclusions: Radiomics signature extracted from CT images may be a potential imaging biomarker for liver cancer invasion and enable accurate prediction of HCC recurrence after liver transplantation.
    Radiomics analysis enables recurrence prediction for hepatocellular carcinoma after liver transplantation
    Donghui Guo et al.
    European Journal of Radiology 117 (2019) 33–40
  • “Microvascular invasion (MVI) and consequently, early tumor recurrence, is noted in 20% of patients undergoing liver transplantation, reducing the 5-year survival from 80% to 40% after liver transplantation . Recurrence has been the main factor that affects the curative effect of HCC after liver transplantation. Prospective identification of recurrence in patients with HCC thus has direct implications for organ allocation, surgical techniques, prognosis, and public policy.”
    Radiomics analysis enables recurrence prediction for hepatocellular carcinoma after liver transplantation
    Donghui Guo et al.
    European Journal of Radiology 117 (2019) 33–40
  • “Previous reports have indicated that some radiomic features extracted from tumor regions of CT images, especially characteristics of arterial and portal phases, can surpass traditional indicators such as Barcelona Clinic Liver Cancer (BCLC) for assessing the efficacy of HCC hepatectomy. These high-throughput characteristics related to MVI (Microvascular invasion) or prognosis of HCC may be potential independent predictors of HCC recurrence after liver transplantation.”
    Radiomics analysis enables recurrence prediction for hepatocellular carcinoma after liver transplantation
    Donghui Guo et al.
    European Journal of Radiology 117 (2019) 33–40
  • Radiomics signature was built with the rad-score of arterial phase. A combined predictive model was built by incorporating the clinical risk factors and radiomics signature with multivariable Cox regression model. Patients were finally stratified into high-risk and low-risk groups based on the combined model with cut-off values at the median of the training dataset in arterial phase. The Log- rank test was computed to compare the two separated KM survival curves.”
    Radiomics analysis enables recurrence prediction for hepatocellular carcinoma after liver transplantation
    Donghui Guo et al.
    European Journal of Radiology 117 (2019) 33–40
  • “A multi-feature-based radiomics signature was identified to be an effective biomarker for the prediction of HCC recurrence after liver transplantation in this study, with potential prognosis value for individualized RFS. The radiomics signature successfully stratified patients with HCC into high-risk and low-risk groups, with significant differences in RFS. Furthermore, the radiomics nomogram based on the combined model which integrates effective clinical characteristics and radiomics signature showed good discrimination and prominent predictive performance for RFS in HCC patients after liver transplantation.”
    Radiomics analysis enables recurrence prediction for hepatocellular carcinoma after liver transplantation
    Donghui Guo et al.
    European Journal of Radiology 117 (2019) 33–40
  • “For patients with high recurrence, liver transplantation is not recommended, so that the scarce donor liver resources can be allocated to patients who urgently need liver transplantation and have a good prognosis.”
    Radiomics analysis enables recurrence prediction for hepatocellular carcinoma after liver transplantation
    Donghui Guo et al.
    European Journal of Radiology 117 (2019) 33–40
  • “ It is conducive to fully exploit potential images using artificial intelligence and big data technology to overcome limitations of traditional evaluation methods, improve the predictive performance of HCC recurrence after liver transplantation, facilitate the rational distribution of donors in clinical practice, and conduct neces- sary intraoperative or postoperative prophylactic treatment for patients with high recurrence.”
    Radiomics analysis enables recurrence prediction for hepatocellular carcinoma after liver transplantation
    Donghui Guo et al.
    European Journal of Radiology 117 (2019) 33–40
  • Although the usefulness of the proposed nomogram lacks external validation, calibration curve analysis demonstrated that the radiomics nomogram was consistent with actual observations for the probability of tumor recurrence at 1, 2, or 3 years. This indicates that radiomics signature has the potential to be used as a biomarker for recurrence in HCC patients after liver transplantation. Further, radiomics can be used to predict MVI and histopathological differentiation closely related to prognosis of HCC. Radiomics technique is expected to become an important cornerstone of accurate diagnosis and treatment of HCC.
    Radiomics analysis enables recurrence prediction for hepatocellular carcinoma after liver transplantation
    Donghui Guo et al.
    European Journal of Radiology 117 (2019) 33–40
  • Purpose The aim of this study was to investigate the relationship between CT imaging phenotypes and genetic and biological characteristics in pancreatic ductal adenocarcinoma (PDAC).
    Conclusions In this study, we demonstrate that in PDAC SMAD4 status and tumor stromal content can be predicted using radiomic analysis of preoperative CT imaging. These data show an association between resectable PDAC imaging features and underlying tumor biology and their potential for future precision medicine.
    CT radiomics associations with genotype and stromal content in pancreatic ductal adenocarcinoma
    Marc A. Attiyeh et al.
    Abdominal Radiology (in press) https://doi.org/10.1007/s00261-019-02112-1
  • “In this study, we identified radiomic features associated with PDAC genetic alterations and stromal content. These associations show the potential of using noninvasive imaging on pre-surgical pancreas cancer patients for precision medicine. Linking radiomic features to underlying tumor biology is an area of great interest, given the ubiquity of diagnostic imaging and the challenges and costs in performing molecular analyses. Recent DNA/RNA sequencing studies have provided in-depth insights into individual tumor’s genetic makeup and have demonstrated some prognostic power for survival that may guide personalized treatment.”
    CT radiomics associations with genotype and stromal content in pancreatic ductal adenocarcinoma
    Marc A. Attiyeh et al.
    Abdominal Radiology (in press) https://doi.org/10.1007/s00261-019-02112-1
  • “In conclusion, we demonstrate that radiomic features extracted from clinical CT images are associated with genotype, the number of altered genes, and stromal content in PDAC. These associations may underlie the observation that PDAC imaging features are associated with survival. Further studies will be needed to increase sample size and perform external validation."
    CT radiomics associations with genotype and stromal content in pancreatic ductal adenocarcinoma
    Marc A. Attiyeh et al.
    Abdominal Radiology (in press) https://doi.org/10.1007/s00261-019-02112-1
  • OBJECTIVE. The purpose of this study is to develop and evaluate an unenhanced CT–based radiomics model to predict brain metastasis (BM) in patients with category T1 lung adenocarcinoma.
    CONCLUSION. A CT-based radiomics model presented good predictive performance and great potential for predicting BM in patients with category T1 lung adenocarcinoma. As a promising adjuvant tool, it can be helpful for guiding BM screening and thus benefiting personalized surveillance for patients with lung cancer.
    CT-Based Radiomics Model for Predicting Brain Metastasis in Category T1 Lung Adenocarcinoma
    Chen A et al.
    AJR 2019; 213:134–139
  • “Non–small cell lung cancer (NSCLC) accounts for approximately 80–85% of all lung cancers. Adenocarcinoma (ADC) is the most common histologic subtype of NSCLC, accounting for almost 50% of all lung cancer. The prognosis of lung cancer mostly depends on the TNM system, which may determine therapy. The brain is one of the most frequent sites of extrathoracic metastases, and brain metastasis (BM) is a very important prognostic factor for the M category.”
    CT-Based Radiomics Model for Predicting Brain Metastasis in Category T1 Lung Adenocarcinoma
    Chen A et al.
    AJR 2019; 213:134–139
  • “With the rapid development of radiomics in oncology, many predictive radiomics models have been developed to predict the preoperative stage and prognosis of various malignant tumors, and all these radiomics signatures implied superior prediction po- tential. To our knowledge, the association between CT-based radiomics features and the risk factors of BM in category T1 ADC has not been established.”
    CT-Based Radiomics Model for Predicting Brain Metastasis in Category T1 Lung Adenocarcinoma
    Chen A et al.
    AJR 2019; 213:134–139
  • “In conclusion, the CT-based radiomics model had a powerful predictive performance and great potential for predicting BM in patients with category T1 lung ADC. As a promising adjuvant tool, it could guide therapeutic strategies for and personalized surveillance of patients.”
    CT-Based Radiomics Model for Predicting Brain Metastasis in Category T1 Lung Adenocarcinoma
    Chen A et al.
    AJR 2019; 213:134–139
  • Background: Intratumor heterogeneity in lung cancer may influence outcomes. CT radiomics seeks to assess tumor features to provide detailed imaging features. However, CT radiomic features vary according to the reconstruction kernel used for image generation.
    Purpose: To investigate the effect of different reconstruction kernels on radiomic features and assess whether image conversion using a convolutional neural network (CNN) could improve reproducibility of radiomic features between different kernels.
    Conclusion: Chest CT image conversion using a convolutional neural network effectively reduced the effect of two different reconstruction kernels and may improve the reproducibility of radiomic features in pulmonary nodules or masses.
    Deep Learning–based Image Conversion of CT Reconstruction Kernels Improves Radiomics Reproducibility for Pulmonary Nodules or Masses
    Choe J et al.
    Radiology 2019; 00:1–9 • https://doi.org/10.1148/radiol.2019181960
  • * For evaluation of pulmonary nodules and masses, reproducibility of radiomic features using different CT reconstruction kernels is low (concordance correlation coefficient [CCC], 0.38). Only 107 of 702 features (15.2%; by excluding wavelet features, 16 of 78 [20.5%]) showed a CCC of 0.85 or higher.
    * Texture and wavelet features are more affected than tumor intensity features by different kernels (CCC, 0.82 6 0.17 for tumor intensity features, 0.61 6 0.17 for texture features, and 0.35 6 0.33 for wavelet features) because these features are sensitive to changes in image spatial and density resolutions.
    Deep Learning–based Image Conversion of CT Reconstruction Kernels Improves Radiomics Reproducibility for Pulmonary Nodules or Masses
    Choe J et al.
    Radiology 2019; 00:1–9 • https://doi.org/10.1148/radiol.2019181960

  • Deep Learning–based Image Conversion of CT Reconstruction Kernels Improves Radiomics Reproducibility for Pulmonary Nodules or Masses
    Choe J et al.
    Radiology 2019; 00:1–9 • https://doi.org/10.1148/radiol.2019181960
  • “Radiomics converts medical images such as CT scans into high-throughput quantitative data that can be used to improve diagnostic, prognostic, and predictive accuracy. Indeed, radiomics can provide objective and comprehensive information regarding whole or subregions of cancers in a noninvasive and repeatable way. Radiomic signatures have been demonstrated to reflect intratumoral heterogeneity and to be associated with gene-expression profiles, both of which can serve as important prognostic factors.”
    Can Artificial Intelligence Fix the Reproducibility Problem of Radiomics?
    Park CM
    Radiology 2019; 00:1–2 • https://doi.org/10.1148/radiol.2019191154
  • Radiomics: Feature Variability can be due to;
    - image reconstruction algorithms
    - Slice thickness (.75mm vs 3mm vs 5mm)
    - Use of iodinated contrast agent
    - Delivery of iodinated contrast agent ( injection rate, contrast volume and timing of acquisition)
  • Although we previously believed that different reconstruction kernels could not be used interchangeably in radiomics research, Choe and colleagues showed that it may be possible to compare radiomics features from CT images with different reconstruction kernels using this novel CNN-based kernel- conversion algorithm.”
    Can Artificial Intelligence Fix the Reproducibility Problem of Radiomics?
    Park CM
    Radiology 2019; 00:1–2 • https://doi.org/10.1148/radiol.2019191154
  • “In conclusion, a convolutional neural network–based kernel-conversion algorithm dramatically improved the similarity of CT radiomic features obtained using different reconstruction kernels. Deep learning–based approaches are expected to substantially contribute to the applicability of radiomics. The study by Choe et al will serve as one of the first steps toward bigger strides in radiomics.
    Can Artificial Intelligence Fix the Reproducibility Problem of Radiomics?
    Park CM
    Radiology 2019; 00:1–2 • https://doi.org/10.1148/radiol.2019191154
  • 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.”
    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

  • 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
  • ”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.”
    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
  • 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
  • ”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
  • 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
  • Purpose: Grades of pancreatic neuroendocrine neoplasms (PNENs) are associated with the choice of treatment strategies. Texture analysis has been used in tumor diagnosis and staging evaluation. In this study, we aim to evaluate the potential ability of texture parameters in differentiation of PNENs grades.
    Results: There were significant differences in tumor margin, pancreatic duct dilatation, lymph nodes invasion, size, portal enhancement ratio (PER), arterial enhancement ratio (AER), mean grey-level intensity, kurtosis, entropy, and uniformity among G1, G2, and pancreatic neuroendocrine carcinoma (PNEC) G3 (p < 0.01).
    Conclusions: Our data indicated that texture parameters have potential in grading PNENs, in particular in differentiating PNEC G3 from PNETs G1/G2.
    Textural analysis on contrast-enhanced CT in pancreatic neuroendocrine neoplasms: association with WHO grade
    Chuangen Guo et al.
    Abdom Radiol (2019) 44:576–585
  • “The texture parameters included mean grey-level inten- sity, kurtosis, skewness, entropy, and uniformity which had been used in previous reports. The mean of each parameter was calculated automatically. The mathematical expression and means of texture parame- ters were reported in the previous study. The inter- observer agreements in ROIs were assessed with Conger’s kappa test. For those cases with interobserver disagreement, texture analysis was not performed until the two readers reach a consensus.”
    Textural analysis on contrast-enhanced CT in pancreatic neuroendocrine neoplasms: association with WHO grade
    Chuangen Guo et al.
    Abdom Radiol (2019) 44:576–585

  • Textural analysis on contrast-enhanced CT in pancreatic neuroendocrine neoplasms: association with WHO grade
    Chuangen Guo et al.
    Abdom Radiol (2019) 44:576–585
  • “The higher standard deviations in texture parameters were present in PNET G2 and PNEC. PNET G2 and PNEC showed more heterogeneous than PNET G1 on contrast-enhanced imaging [12]. PNET G2 usually showed marked enhancement. Mild enhancement was also found in PNETs G2. PNEC usually showed mild enhancement. However, marked enhancement also can be found in PNEC. Therefore, the standard deviation is higher in PNET G2/PNEC than PNET G1.”
    Textural analysis on contrast-enhanced CT in pancreatic neuroendocrine neoplasms: association with WHO grade
    Chuangen Guo et al.
    Abdom Radiol (2019) 44:576–585
  • In conclusion, our data indicate that tumor size, pancreatic duct dilation, local invasion/metastases, AER, and PER have potential for differentiating PNEC G3 from PNET G1/G2. Moreover, our data indicate that texture analysis on contrast-enhanced CT images may represent as promising, non-invasive biomarkers to evaluate the pathologic grade of PNENs.”
    Textural analysis on contrast-enhanced CT in pancreatic neuroendocrine neoplasms: association with WHO grade
    Chuangen Guo et al.
    Abdom Radiol (2019) 44:576–585
  • Objectives: This study was designed to estimate the performance of textural features derived from contrast- enhanced CT in the differential diagnosis of pancreatic serous cystadenomas and pancreatic mucinous cystadenomas.
    Conclusions: In conclusion, our study provided preliminary evidence that textural features derived from CT images were useful in differential diagnosis of pancreatic mucinous cystadenomas and serous cystadenomas, which may provide a non-invasive 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
    Jing Yang et al.
    Front Oncol. 2019 Jun 12;9:494. doi: 10.3389/fonc.2019.00494
  • Methods: Fifty-three patients with pancreatic serous cystadenoma and 25 patients with pancreatic mucinous cystadenoma were included. Textural parameters of the pancreatic neoplasms were extracted using the LIFEx software, and were analyzed using random forest and Least Absolute Shrinkage and Selection Operator (LASSO) methods. Patients were randomly divided into training and validation sets with a ratio of 4:1; random forest method was adopted to constructed a diagnostic prediction model. Scoring metrics included sensitivity, specificity, accuracy, and AUC.
    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
    Jing Yang et al.
    Front Oncol. 2019 Jun 12;9:494. doi: 10.3389/fonc.2019.00494

  • Discrimination of Pancreatic Serous Cystadenomas From Mucinous Cystadenomas With CT Textural Features: Based on Machine Learning
    Jing Yang et al.
    Front Oncol. 2019 Jun 12;9:494. doi: 10.3389/fonc.2019.00494
  • “The textural parameters were obtained using Local Image features Extraction (LIFEx) software in the portal vein phase CT images. A two-dimensional region of interest (ROI) was delineated around the boundary of tumor lesion in each layer of transaxial CT images to form a three-dimensional ROI. ROIs were drawn independently by two radiologists, who were unaware of the diagnosis of patients. Minimum, maximum, mean, and standard deviation of the density values inside the ROI were calculated. From these primary calculations, geometry based and histogram based features, the Gray-level co- occurrence matrix (GLCM), the Neighborhood gray-level different matrix (NGLDM), the Gray level run length matrix (GLRLM) and the Gray level zone length matrix (GLZLM) were obtained.”
    Discrimination of Pancreatic Serous Cystadenomas From Mucinous Cystadenomas With CT Textural Features: Based on Machine Learning
    Jing Yang et al.
    Front Oncol. 2019 Jun 12;9:494. doi: 10.3389/fonc.2019.00494
Deep Learning

  • "If computers continue to obey Moore's Law, doubling their speed and memory capacity every eighteen months, the result is that computers are likely to over​take humans in intelligence at some point in the next hundred years. When an artificial intelligence (AI) becomes better than humans at AI design, so that it can recursively improve itself without human help, we may face an intelligence explosion that ultimately results in machines whose intelligence exceeds ours by more than ours exceeds that of snails. When that happens, we will need to ensure that the computers have goals aligned with ours. It's tempting to dismiss the notion of highly intelligent machines as mere science fiction, but this would be a mistake, and potentially our worst mistake ever.
    Brief Answers to the Big Questions
    Stephen Hawking
  • "For the last twenty years or so, AI has been focused on the problems surrounding the construction of intelligent agents, systems that perceive and act in a particular environment. In this context, intelligence is related to statistical and economic notions of rationality -- that is, colloquially, the ability to make good decisions, plans or inferences. As a result of this recent work, there has been a large degree of integration and cross-fertilisation among Al, machine- learning, statis​tics, control theory, neuroscience and other fields. The establishment of shared theoretical frameworks, combined with the availability of data and processing power, has yielded remarkable successes in various component tasks, such as speech recognition, image classification, autonomous vehicles, machine transla​tion, legged locomotion and question-answering systems.
    Brief Answers to the Big Questions
    Stephen Hawking
  • “AI can augment our existing intelligence to open up advances in every area of science and society. However, it will also bring dangers. While primitive forms of artificial intelligence developed so far have proved very useful, I fear the consequences of creating something that can match or surpass humans. The concern is that AI would take off on its own and redesign itself at an ever- increasing rate. Humans, who are limited by slow biological evolution, couldn't compete and would be superseded. And in the future AI could develop a will of its own, a will that is in conflict with ours. Others believe that humans can command the rate of tech​nology for a decently long time, and that the potential of AI to solve many of the world's problems will be realised. Although I am well known as an optimist regarding the human race, I am not so sure."
    Brief Answers to the Big Questions
    Stephen Hawking
  • “Compounding the challenge is deep-learning models’ voracious demand for data. Most models have been developed in controlled settings using available, and often narrow, data sets — and the results algorithms produce are only as robust as the data used to develop them. AI models can be brittle, working well with data from the environment in which they were developed but faltering when applied to data generated at other locations with different patient populations, imaging machines and techniques. For example, in a November, 2018 study published in PLOS Medicine, researchers at the Icahn School of Medicine and other institutions showed that the performance of a deep learning model used to diagnose pneumonia on chest X-rays was significantly lower when used to evaluate X- rays from other hospitals.”
    What AI “App Stores” Will Mean for Radiology
    Kim W, Holzberger K
    Harvard Business Review June 2019
  • AI marketplaces are already creating collaborative communities of healthcare developers and users. For example, the University of Rochester is using an FDA-cleared application developed by Aidoc that analyzes CT exams for a suspected intracranial hemorrhage, then prioritizes them on the radiologist’s worklist for immediate attention when time-to- treatment is critical. For longer-term patient care, the University of Pennsylvania is using an application developed by Aidence and eUnity to assist radiologists in the time-consuming task of detecting and characterizing lung nodules for making follow-up comparisons and reporting.
    What AI “App Stores” Will Mean for Radiology
    Kim W, Holzberger K
    Harvard Business Review June 2019
  • “The second is by improving the speed and quality of radiology reporting. These algorithms can automate repetitive tasks and act as virtual residents, pre-processing images to highlight potentially important findings, making measurements and comparisons, and automatically adding data and clinical intelligence to the report for the radiologist’s review. Algorithms also can provide quality checks, for example by detecting errors in laterality or patient sex and to ensure report accuracy and assist with billing and coding, all of which can reduce clinicians’ stress.”
    What AI “App Stores” Will Mean for Radiology
    Kim W, Holzberger K
    Harvard Business Review June 2019
  • “Despite broad awareness of these trends, medical education continues to be largely information based, as if physicians are still the only source of medical knowledge. The reality of this web-enabled era is different. Patients readily garner more information, both correct and incorrect, to bring to clinical encounters and expect meaningful discussions with their physicians. These expectations challenge physicians not only to keep current but also to be able to communicate options to patients in a language that speaks meaningfully to their individual concerns and preferences.”
    Reimagining Medical Education in the Age of AI
    Steven A. Wartman, and C. Donald Combs
    AMA Journal of Ethics February 2019, Volume 21, Number 2: E146-152
  • In addition, the skills required of practicing physicians will increasingly involve facility in collaborating with and managing artificial intelligence (AI) applications that aggregate vast amounts of data, generate diagnostic and treatment recommendations, and assign confidence ratings to those recommendations. The ability to correctly interpret probabilities requires mathematical sophistication in stochastic processes, something current medical curricula address inadequately. In part, the need for more sophisticated mathematical understanding is driven by the analytics of precision and personalized medicine, which rely on AI to predict which treatment will work for a particular disease in a particular subgroup of patients.
    Reimagining Medical Education in the Age of AI
    Steven A. Wartman, and C. Donald Combs
    AMA Journal of Ethics February 2019, Volume 21, Number 2: E146-152
  • “As we pointed out earlier, the increasing incongruence between the organizing and retention capacities of the human mind and medicine’s growing complexity should compel significant re-engineering of medical school curricula. Curricula should shift from a focus on information acquisition to an emphasis on knowledge management and communication. Nothing manifests this need for change better than the observation that every patient is becoming a big data challenge. For clinicians, the need to understand probabilities—such as confidence ratings for diagnostic or therapeutic recommendations generated by an AI clinical decision support system—will likely increase as personalized medicine continues to enlarge its role in practice.”
    Reimagining Medical Education in the Age of AI
    Steven A. Wartman, and C. Donald Combs
    AMA Journal of Ethics February 2019, Volume 21, Number 2: E146-152
  • Accordingly, we advocate new curricula that respond to the challenges of AI while being less detrimental to learners’ mental health. These curricula should emphasize 4 major features: Knowledge capture, not knowledge retention; Collaboration with and management of AI applications; A better understanding of probabilities and how to apply them meaningfully in clinical decision making with patients and families; and The cultivation of empathy and compassion.
    Reimagining Medical Education in the Age of AI
    Steven A. Wartman, and C. Donald Combs
    AMA Journal of Ethics February 2019, Volume 21, Number 2: E146-152
  • * Knowledge capture, not knowledge retention
    * Collaboration with and management of AI applications
    * A better understanding of probabilities and how to apply them meaningfully in clinical decision making with patients and families; and
    * The cultivation of empathy and compassion.
    Reimagining Medical Education in the Age of AI
    Steven A. Wartman, and C. Donald Combs
    AMA Journal of Ethics February 2019, Volume 21, Number 2: E146-152
Liver

  • Objectives: To assess whether radiomics signature can identify aggressive behavior and predict recurrence of hepatocellular carcinoma (HCC) after liver transplantation.
    Results: The stable radiomics features associated with the recurrence of HCC were simply found in arterial phase and portal phase. The prediction model based on the radiomics features extracted from the arterial phase showed better prediction performance than the portal vein phase or the fusion signature combining both of arterial and portal vein phase. A radiomics nomogram based on combined model consisting of the radiomics signature and clinical risk factors showed good predictive performance for RFS with a C-index of 0.785 (95% confidence interval [CI]: 0.674-0.895) in the training dataset and 0.789 (95% CI: 0.620-0.957) in the validation dataset. The calibration curves showed agreement in both training (p = 0.121) and validation cohorts (p = 0.164).
    Conclusions: Radiomics signature extracted from CT images may be a potential imaging biomarker for liver cancer invasion and enable accurate prediction of HCC recurrence after liver transplantation.
    Radiomics analysis enables recurrence prediction for hepatocellular carcinoma after liver transplantation
    Donghui Guo et al.
    European Journal of Radiology 117 (2019) 33–40
  • “Microvascular invasion (MVI) and consequently, early tumor recurrence, is noted in 20% of patients undergoing liver transplantation, reducing the 5-year survival from 80% to 40% after liver transplantation . Recurrence has been the main factor that affects the curative effect of HCC after liver transplantation. Prospective identification of recurrence in patients with HCC thus has direct implications for organ allocation, surgical techniques, prognosis, and public policy.”
    Radiomics analysis enables recurrence prediction for hepatocellular carcinoma after liver transplantation
    Donghui Guo et al.
    European Journal of Radiology 117 (2019) 33–40
  • “Previous reports have indicated that some radiomic features extracted from tumor regions of CT images, especially characteristics of arterial and portal phases, can surpass traditional indicators such as Barcelona Clinic Liver Cancer (BCLC) for assessing the efficacy of HCC hepatectomy. These high-throughput characteristics related to MVI (Microvascular invasion) or prognosis of HCC may be potential independent predictors of HCC recurrence after liver transplantation.”
    Radiomics analysis enables recurrence prediction for hepatocellular carcinoma after liver transplantation
    Donghui Guo et al.
    European Journal of Radiology 117 (2019) 33–40
  • Radiomics signature was built with the rad-score of arterial phase. A combined predictive model was built by incorporating the clinical risk factors and radiomics signature with multivariable Cox regression model. Patients were finally stratified into high-risk and low-risk groups based on the combined model with cut-off values at the median of the training dataset in arterial phase. The Log- rank test was computed to compare the two separated KM survival curves.”
    Radiomics analysis enables recurrence prediction for hepatocellular carcinoma after liver transplantation
    Donghui Guo et al.
    European Journal of Radiology 117 (2019) 33–40
  • “A multi-feature-based radiomics signature was identified to be an effective biomarker for the prediction of HCC recurrence after liver transplantation in this study, with potential prognosis value for individualized RFS. The radiomics signature successfully stratified patients with HCC into high-risk and low-risk groups, with significant differences in RFS. Furthermore, the radiomics nomogram based on the combined model which integrates effective clinical characteristics and radiomics signature showed good discrimination and prominent predictive performance for RFS in HCC patients after liver transplantation.”
    Radiomics analysis enables recurrence prediction for hepatocellular carcinoma after liver transplantation
    Donghui Guo et al.
    European Journal of Radiology 117 (2019) 33–40
  • “For patients with high recurrence, liver transplantation is not recommended, so that the scarce donor liver resources can be allocated to patients who urgently need liver transplantation and have a good prognosis.”
    Radiomics analysis enables recurrence prediction for hepatocellular carcinoma after liver transplantation
    Donghui Guo et al.
    European Journal of Radiology 117 (2019) 33–40
  • “ It is conducive to fully exploit potential images using artificial intelligence and big data technology to overcome limitations of traditional evaluation methods, improve the predictive performance of HCC recurrence after liver transplantation, facilitate the rational distribution of donors in clinical practice, and conduct neces- sary intraoperative or postoperative prophylactic treatment for patients with high recurrence.”
    Radiomics analysis enables recurrence prediction for hepatocellular carcinoma after liver transplantation
    Donghui Guo et al.
    European Journal of Radiology 117 (2019) 33–40
  • Although the usefulness of the proposed nomogram lacks external validation, calibration curve analysis demonstrated that the radiomics nomogram was consistent with actual observations for the probability of tumor recurrence at 1, 2, or 3 years. This indicates that radiomics signature has the potential to be used as a biomarker for recurrence in HCC patients after liver transplantation. Further, radiomics can be used to predict MVI and histopathological differentiation closely related to prognosis of HCC. Radiomics technique is expected to become an important cornerstone of accurate diagnosis and treatment of HCC.
    Radiomics analysis enables recurrence prediction for hepatocellular carcinoma after liver transplantation
    Donghui Guo et al.
    European Journal of Radiology 117 (2019) 33–40
Pancreas

  • Purpose: Grades of pancreatic neuroendocrine neoplasms (PNENs) are associated with the choice of treatment strategies. Texture analysis has been used in tumor diagnosis and staging evaluation. In this study, we aim to evaluate the potential ability of texture parameters in differentiation of PNENs grades.
    Results: There were significant differences in tumor margin, pancreatic duct dilatation, lymph nodes invasion, size, portal enhancement ratio (PER), arterial enhancement ratio (AER), mean grey-level intensity, kurtosis, entropy, and uniformity among G1, G2, and pancreatic neuroendocrine carcinoma (PNEC) G3 (p < 0.01).
    Conclusions: Our data indicated that texture parameters have potential in grading PNENs, in particular in differentiating PNEC G3 from PNETs G1/G2.
    Textural analysis on contrast-enhanced CT in pancreatic neuroendocrine neoplasms: association with WHO grade
    Chuangen Guo et al.
    Abdom Radiol (2019) 44:576–585
  • “The texture parameters included mean grey-level inten- sity, kurtosis, skewness, entropy, and uniformity which had been used in previous reports. The mean of each parameter was calculated automatically. The mathematical expression and means of texture parame- ters were reported in the previous study. The inter- observer agreements in ROIs were assessed with Conger’s kappa test. For those cases with interobserver disagreement, texture analysis was not performed until the two readers reach a consensus.”
    Textural analysis on contrast-enhanced CT in pancreatic neuroendocrine neoplasms: association with WHO grade
    Chuangen Guo et al.
    Abdom Radiol (2019) 44:576–585

  • Textural analysis on contrast-enhanced CT in pancreatic neuroendocrine neoplasms: association with WHO grade
    Chuangen Guo et al.
    Abdom Radiol (2019) 44:576–585
  • “The higher standard deviations in texture parameters were present in PNET G2 and PNEC. PNET G2 and PNEC showed more heterogeneous than PNET G1 on contrast-enhanced imaging [12]. PNET G2 usually showed marked enhancement. Mild enhancement was also found in PNETs G2. PNEC usually showed mild enhancement. However, marked enhancement also can be found in PNEC. Therefore, the standard deviation is higher in PNET G2/PNEC than PNET G1.”
    Textural analysis on contrast-enhanced CT in pancreatic neuroendocrine neoplasms: association with WHO grade
    Chuangen Guo et al.
    Abdom Radiol (2019) 44:576–585
  • In conclusion, our data indicate that tumor size, pancreatic duct dilation, local invasion/metastases, AER, and PER have potential for differentiating PNEC G3 from PNET G1/G2. Moreover, our data indicate that texture analysis on contrast-enhanced CT images may represent as promising, non-invasive biomarkers to evaluate the pathologic grade of PNENs.”
    Textural analysis on contrast-enhanced CT in pancreatic neuroendocrine neoplasms: association with WHO grade
    Chuangen Guo et al.
    Abdom Radiol (2019) 44:576–585
  • Objectives: This study was designed to estimate the performance of textural features derived from contrast- enhanced CT in the differential diagnosis of pancreatic serous cystadenomas and pancreatic mucinous cystadenomas.
    Conclusions: In conclusion, our study provided preliminary evidence that textural features derived from CT images were useful in differential diagnosis of pancreatic mucinous cystadenomas and serous cystadenomas, which may provide a non-invasive 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
    Jing Yang et al.
    Front Oncol. 2019 Jun 12;9:494. doi: 10.3389/fonc.2019.00494
  • Methods: Fifty-three patients with pancreatic serous cystadenoma and 25 patients with pancreatic mucinous cystadenoma were included. Textural parameters of the pancreatic neoplasms were extracted using the LIFEx software, and were analyzed using random forest and Least Absolute Shrinkage and Selection Operator (LASSO) methods. Patients were randomly divided into training and validation sets with a ratio of 4:1; random forest method was adopted to constructed a diagnostic prediction model. Scoring metrics included sensitivity, specificity, accuracy, and AUC.
    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
    Jing Yang et al.
    Front Oncol. 2019 Jun 12;9:494. doi: 10.3389/fonc.2019.00494

  • Discrimination of Pancreatic Serous Cystadenomas From Mucinous Cystadenomas With CT Textural Features: Based on Machine Learning
    Jing Yang et al.
    Front Oncol. 2019 Jun 12;9:494. doi: 10.3389/fonc.2019.00494
  • “The textural parameters were obtained using Local Image features Extraction (LIFEx) software in the portal vein phase CT images. A two-dimensional region of interest (ROI) was delineated around the boundary of tumor lesion in each layer of transaxial CT images to form a three-dimensional ROI. ROIs were drawn independently by two radiologists, who were unaware of the diagnosis of patients. Minimum, maximum, mean, and standard deviation of the density values inside the ROI were calculated. From these primary calculations, geometry based and histogram based features, the Gray-level co- occurrence matrix (GLCM), the Neighborhood gray-level different matrix (NGLDM), the Gray level run length matrix (GLRLM) and the Gray level zone length matrix (GLZLM) were obtained.”
    Discrimination of Pancreatic Serous Cystadenomas From Mucinous Cystadenomas With CT Textural Features: Based on Machine Learning
    Jing Yang et al.
    Front Oncol. 2019 Jun 12;9:494. doi: 10.3389/fonc.2019.00494
  • Purpose The aim of this study was to investigate the relationship between CT imaging phenotypes and genetic and biological characteristics in pancreatic ductal adenocarcinoma (PDAC).
    Conclusions In this study, we demonstrate that in PDAC SMAD4 status and tumor stromal content can be predicted using radiomic analysis of preoperative CT imaging. These data show an association between resectable PDAC imaging features and underlying tumor biology and their potential for future precision medicine.
    CT radiomics associations with genotype and stromal content in pancreatic ductal adenocarcinoma
    Marc A. Attiyeh et al.
    Abdominal Radiology (in press) https://doi.org/10.1007/s00261-019-02112-1
  • “In this study, we identified radiomic features associated with PDAC genetic alterations and stromal content. These asso- ciations show the potential of using noninvasive imaging on pre-surgical pancreas cancer patients for precision medicine. Linking radiomic features to underlying tumor biology is an area of great interest, given the ubiquity of diagnostic imaging and the challenges and costs in performing molec- ular analyses. Recent DNA/RNA sequencing studies have provided in-depth insights into individual tumor’s genetic makeup and have demonstrated some prognostic power for survival that may guide personalized treatment.”
    CT radiomics associations with genotype and stromal content in pancreatic ductal adenocarcinoma
    Marc A. Attiyeh et al.
    Abdominal Radiology (in press) https://doi.org/10.1007/s00261-019-02112-1
  • “In conclusion, we demonstrate that radiomic features extracted from clinical CT images are associated with genotype, the number of altered genes, and stromal content in PDAC. These associations may underlie the observation that PDAC imaging features are associated with survival. Further studies will be needed to increase sample size and perform external validation."
    CT radiomics associations with genotype and stromal content in pancreatic ductal adenocarcinoma
    Marc A. Attiyeh et al.
    Abdominal Radiology (in press) https://doi.org/10.1007/s00261-019-02112-1 
  • 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
Small Bowel

  • Neuroendocrine Tumors of the Duodenum: Facts
    Duodenal neuroendocrine tumors (NETs) comprise 2–3% of all GI endocrine tumors and are increasing in frequency. These include gastrinomas, somatostatinomas, nonfunctional NETs, gangliocytic paragangliomas, and poorly differentiated NE carcinomas. Although, the majority are nonfunctional, these tumors are a frequent cause of Zollinger-Ellison syndrome and can cause other clinical hormonal syndromes (carcinoid, Cushing's, etc.).
  • “Duodenal carcinoid tumors commonly appear as an enhancing mass in either the arterial or venous phases. If a primary tumor is not seen in the duodenum, adjacent enhancing lymphadenopathy can be a clue to the presence of a duodenal carcinoid tumor.”
    Duodenal neuroendocrine tumors: retrospective evaluation of CT imaging features and pattern of metastatic disease on dual-phase MDCT with pathologic correlation.
    Tsai SD, Kawamoto S, Wolfgang CL, Hruban RH, Fishman EK
    Abdom Imaging. 2015 Jun;40(5):1121-30
  • The incidence of neuroendocrine tumors of the GI tract has increased in the last few decades which may in part be due to the increased detection of tumors with wider availability of thin-section multi-detector computed tomography (CT) and endoscopy. For instance, a study based on a national population-based cancer registry in England found the incidence rate of neuroendocrine tumors in the GI tract increased 3- to 4-fold from 1971 to 2006 with an increase of fivefold in the duodenum in men and 6.7-fold in the duodenum in women. This underscores the importance of imaging tests in the primary diagnosis and staging of GI neuroendocrine tumors.
    Duodenal neuroendocrine tumors: retrospective evaluation of CT imaging features and pattern of metastatic disease on dual-phase MDCT with pathologic correlation.
    Tsai SD, Kawamoto S, Wolfgang CL, Hruban RH, Fishman EK
    Abdom Imaging. 2015 Jun;40(5):1121-30
  • Most duodenal carcinoids are sporadic but may be associated with clinical syndromes such as multiple endocrine neoplasia type 1 (MEN-1) and neurofibromatosis type 1(NF-1) . Two-thirds of duodenal neuroendocrine tumors are gastrinomas and one-third of these are functioning tumors manifesting as Zollinger–Ellison syndrome (ZES). The next most common type (20%) of duodenal neuroendocrine tumors is somatostatinomas. Other more rare types of neuroendocrine tumors are nonfunctioning serotonin-, gastrin-, or calcitonin-producing tumors and gangliocytic paragangliomas. Somatostatinomas are strongly associated with NF-1 as up to 50% of patients with somatostatinomas have NF-1. Somatostatinomas associated with NF-1 are usually found around the ampulla, and they histologically often contain psamomma bodies.
    Duodenal neuroendocrine tumors: retrospective evaluation of CT imaging features and pattern of metastatic disease on dual-phase MDCT with pathologic correlation.
    Tsai SD, Kawamoto S, Wolfgang CL, Hruban RH, Fishman EK
    Abdom Imaging. 2015 Jun;40(5):1121-30
  • Our results show that duodenal carcinoid tumors enhance during the arterial phase of intravenous-contrasted enhanced CT and although they do lose contrast enhancement during the venous phase (30.4%) as has often been previously reported; however, in a significant percentage (60.9%), there was an increase in contrast enhancement during the venous phase and no change in contrast enhancement in the venous phase in 8.7% of patients. Early-phase arterial enhancement pattern is an important criterion in distinguishing a duodenal carcinoid tumor from other duodenal masses such as adenocarcinoma which is usually hypovascular, adenomas, or other peri-ampullary masses.
    Duodenal neuroendocrine tumors: retrospective evaluation of CT imaging features and pattern of metastatic disease on dual-phase MDCT with pathologic correlation.
    Tsai SD, Kawamoto S, Wolfgang CL, Hruban RH, Fishman EK
    Abdom Imaging. 2015 Jun;40(5):1121-30
  • In conclusion, carcinoid tumors of the duodenum most often present as a focal polypoid mass, but may present as an area of wall thickening or intramural mass with the primary tumor not well defined. Regional lymphadenopathy may be more pronounced than the primary lesion in the duodenum. The CT features of an enhancing duodenal mass can be suggestive of a carcinoid tumor. Duodenal carcinoid tumors are most common in the proximal duodenum and may present with metastatic disease as evidenced by regional enhancing lymphadenopathy or hypervascular liver lesions.
    Duodenal neuroendocrine tumors: retrospective evaluation of CT imaging features and pattern of metastatic disease on dual-phase MDCT with pathologic correlation.
    Tsai SD, Kawamoto S, Wolfgang CL, Hruban RH, Fishman EK
    Abdom Imaging. 2015 Jun;40(5):1121-30
© 1999-2019 Elliot K. Fishman, MD, FACR. All rights reserved.