Imaging Pearls ❯ Pancreas ❯ Radiomics
|
-- OR -- |
|
- “The WHO grading of pancreatic neuroendocrine neoplasms (PanNENs) is essential in patient management and an independent prognostic factor for patient survival. Radiomics features from CE-CT images hold promise for the outcome and tumor grade prediction. However, variations in reconstruction parameters can impact the predictive value of radiomics. 127 patients with histopathologically confirmed PanNENs underwent CT scans with filtered back projection (B20f) and iterative (I26f) reconstruction kernels. 3190 radiomic features were extracted from tumors and pancreatic volumes. Wilcoxon paired tests assessed the impact of reconstruction kernels and ComBat harmonization efficiency. SVM models were employed to predict tumor grade using the entire set of radiomics features or only those identified as harmonizable. The models’ performance was assessed on an independent dataset of 36 patients. Significant differences, after correction for multiple testing, were observed in 69% of features in the pancreatic volume and 51% in the tumor volume with B20f and I26f kernels. SVM models demonstrated accuracy ranging from 0.67 (95%CI: 0.50–0.81) to 0.83 (95%CI: 0.69–0.94) in distinguishing grade 1 cases from higher grades. Reconstruction kernels alter radiomics features and iterative kernel models trended towards higher performance. ComBat harmonization mitigates kernel impacts but addressing this effect is crucial in studies involving data from different kernels.”
Can CT Image Reconstruction Parameters Impact the Predictive Value of Radiomics Features in Grading Pancreatic Neuroendocrine Neoplasms?
Tixier F, Lopez-Ramirez F, Blanco A, Yasrab M, Javed AA, Chu LC, Fishman EK, Kawamoto S
Bioengineering (Basel). 2025 Jan 16;12(1):80. doi: 10.3390/bioengineering12010080. PMID: 39851354; PMCID: PMC11763079. - “ The models’ performance was assessed on an independent dataset of 36 patients. Significant differences, after correction for multiple testing, were observed in 69% of features in the pancreatic volume and 51% in the tumor volume with B20f and I26f kernels. SVM models demonstrated accuracy ranging from 0.67 (95%CI: 0.50–0.81) to 0.83 (95%CI: 0.69–0.94) in distinguishing grade 1 cases from higher grades. Reconstruction kernels alter radiomics features and iterative kernel models trended towards higher performance. ComBat harmonization mitigates kernel impacts but addressing this effect is crucial in studies involving data from different kernels.”
Can CT Image Reconstruction Parameters Impact the Predictive Value of Radiomics Features in Grading Pancreatic Neuroendocrine Neoplasms?
Tixier F, Lopez-Ramirez F, Blanco A, Yasrab M, Javed AA, Chu LC, Fishman EK, Kawamoto S
Bioengineering (Basel). 2025 Jan 16;12(1):80. doi: 10.3390/bioengineering12010080. PMID: 39851354; PMCID: PMC11763079. - “Radiomics analysis has emerged as a valuable tool in constructing prognostic and predictive models in oncology, leveraging the capability of radiomic features to capture underlying biological characteristics. Machine learning models based on radiomics features have demonstrated valuable clinical applications, supported by a growing body of evidence. Notably, these models have proven to be effective in applications such as predicting the histological grade of PanNENs in computed tomography (CT) images , offering guidance for follow-up and clinical decision-making. Preoperative tumor grading is essential for the effective clinical management of patients with PanNEN. However, biopsy-based techniques, while commonly used, are not ideal due to their invasive nature and the risk of misclassification due to tumor heterogeneity.”
Can CT Image Reconstruction Parameters Impact the Predictive Value of Radiomics Features in Grading Pancreatic Neuroendocrine Neoplasms?
Tixier F, Lopez-Ramirez F, Blanco A, Yasrab M, Javed AA, Chu LC, Fishman EK, Kawamoto S
Bioengineering (Basel). 2025 Jan 16;12(1):80. doi: 10.3390/bioengineering12010080. PMID: 39851354; PMCID: PMC11763079. - “However, the integration of radiomics into clinical practice necessitates a concerted effort to standardize reconstruction algorithms. This task is particularly challenging given the rapid advancements in scanner technologies, such as photon counting CT, which introduce new complexities for achieving harmonization in radiomics. Nevertheless, these technological shifts also present opportunities to enhance the utility of radiomics.”
Can CT Image Reconstruction Parameters Impact the Predictive Value of Radiomics Features in Grading Pancreatic Neuroendocrine Neoplasms?
Tixier F, Lopez-Ramirez F, Blanco A, Yasrab M, Javed AA, Chu LC, Fishman EK, Kawamoto S
Bioengineering (Basel). 2025 Jan 16;12(1):80. doi: 10.3390/bioengineering12010080. PMID: 39851354; PMCID: PMC11763079. - “Image reconstruction represents one of the numerous challenges for the clinical use of radiomics. To facilitate the translation into clinical practice, it is essential to provide a detailed description of all image processing steps, from data acquisition to modeling, and follow already established guidelines such as those from the IBSI In multicenter studies, various parameters, including CT manufacturer and acquisition settings, can vary and impact radiomic features. These additional sources of variability should be considered and must be carefully managed to harmonize images or radiomics features prior to modeling. Furthermore, when interpreting and generalizing radiomics findings across different centers, it is essential to understand precisely how data vary from the datasets used to develop the models.”
Can CT Image Reconstruction Parameters Impact the Predictive Value of Radiomics Features in Grading Pancreatic Neuroendocrine Neoplasms?
Tixier F, Lopez-Ramirez F, Blanco A, Yasrab M, Javed AA, Chu LC, Fishman EK, Kawamoto S
Bioengineering (Basel). 2025 Jan 16;12(1):80. doi: 10.3390/bioengineering12010080. PMID: 39851354; PMCID: PMC11763079. - “In this paper, we explored the influence of two soft tissue reconstruction kernels (I26f and B20f) on radiomics features and their predictive value for determining PanNET grades. Our findings indicate that a substantial number of features are biased by the reconstruction kernel, and I26f showed more promise than B20f for predicting PanNET grades. For studies employing mixed data arising from different reconstruction kernels, it is imperative to address this effect through harmonization techniques, such as ComBat, and by being cautious if using features not identified as harmonizable.”
Can CT Image Reconstruction Parameters Impact the Predictive Value of Radiomics Features in Grading Pancreatic Neuroendocrine Neoplasms?
Tixier F, Lopez-Ramirez F, Blanco A, Yasrab M, Javed AA, Chu LC, Fishman EK, Kawamoto S
Bioengineering (Basel). 2025 Jan 16;12(1):80. doi: 10.3390/bioengineering12010080. PMID: 39851354; PMCID: PMC11763079. - The tumor grade based on the WHO classification system is an independent prognostic factor for survival in patients with PanNENs. Also, the low-grade small PanNETs are indolent tumors with a good prognosis, and patients with small nonfunctioning PanNETs may undergo active surveillance or surgical resection. Therefore, pretreatment prediction of the PanNENs pathological tumor grade is important in determining prognosis and helps to guide the management of patients.
Can CT Image Reconstruction Parameters Impact the Predictive Value of Radiomics Features in Grading Pancreatic Neuroendocrine Neoplasms?
Tixier F, Lopez-Ramirez F, Blanco A, Yasrab M, Javed AA, Chu LC, Fishman EK, Kawamoto S.
Bioengineering (Basel). 2025 Jan 16;12(1):80. - “The 2017 WHO classification system describes two categories of PanNENs: well-differentiated pancreatic neuroendocrine tumors (PanNETs) and poorly differentiated pancreatic neuroendocrine carcinoma (PanNECs). PanNETs are well-differentiated tumors with minimal to moderate atypia and lack of necrosis and express intense synaptophysin or chromogranin. A positivity is classified as grade 1, 2, or 3 based on the mitotic index and the Ki-67 index PanNECs are tumors with high mitotic index and Ki-67 index and are characterized by poorly differentiated tumors consisting of atypical cells with substantial necrosis that are faintly positive for neuroendocrine markers. The tumor grade based on the WHO classification system is an independent prognostic factor for survival in patients with PanNENs. Also, the low-grade small PanNETs are indolent tumors with a good prognosis, and patients with small nonfunctioning PanNETs may undergo active surveillance or surgical resection. Therefore, pretreatment prediction of the PanNENs pathological tumor grade is important in determining prognosis and helps to guide the management of patient.”
Can CT Image Reconstruction Parameters Impact the Predictive Value of Radiomics Features in Grading Pancreatic Neuroendocrine Neoplasms?
Tixier F, Lopez-Ramirez F, Blanco A, Yasrab M, Javed AA, Chu LC, Fishman EK, Kawamoto S.
Bioengineering (Basel). 2025 Jan 16;12(1):80. - “In this study, we assessed the impact of soft tissue image reconstruction kernels on the radiomics features, explored the possibility of correcting for this effect using ComBat harmonization, and evaluated the predictive value of the radiomic features from images reconstructed with B20f and I26f to distinguish between WHO grade 1 and higher grade PanNENs, including grade 2 or 3 PanNETs and PanNECs. The primary objective was to investigate the reconstruction variability to provide valuable insights to improve the generalizability of PanNENs grading models based on radiomics. However, the results on feature robustness to reconstruction kernel and ComBat harmonization should extend to other radiomic models based on contrast CT features obtained from images reconstructed with iterative or filtered back projection soft tissue reconstruction kernels.”
Can CT Image Reconstruction Parameters Impact the Predictive Value of Radiomics Features in Grading Pancreatic Neuroendocrine Neoplasms?
Tixier F, Lopez-Ramirez F, Blanco A, Yasrab M, Javed AA, Chu LC, Fishman EK, Kawamoto S.
Bioengineering (Basel). 2025 Jan 16;12(1):80.
- Background: As pancreatic cystic neoplasms (PCN) differ in current standard of care, and these treatments can affect quality of life to varying degrees, a definitive preoperative diagnosis must be reliable. Current diagnostic approaches, specifically traditional cross- sectional imaging techniques, face certain limitations. But radiomics has been shown to have high diagnostic accuracy across a range of diseases. Objective to conduct a comprehensive review of the literature on the use of radiomics to differentiate Mucinous Cystic Neoplasm (MCN) from Serous Cystic Neoplasm (SCN).
Methods: This study was comprehensively searched in Pubmed, Scopus and Web of Science databases for meta-analysis of studies that used radiomics to distinguish MCN from SCN. Risk of bias was assessed using the diagnostic accuracy study quality assessment method and combined with sensitivity, specificity, diagnostic odds ratio, and summary receiver operating characteristic (SROC)curve analysis.
Radiomics for differentiating pancreatic Mucinous Cystic Neoplasm from Serous Cystic Neoplasm: Systematic Review and Meta-Analysis
Longjia Zhang, Boyu Diao, Zhiyao Fan, Hanxiang Zhan
Academic Radiology 2025 (in press) - Results: A total of 884 patients from 8 studies were included in this analysis, including 365 MCN and 519 SCN. The Meta-analysis found that radiomics identified MCN and SCN with high sensitivity and specificity, with combined sensitivity and specificity of 0.84(0.82–0.87) and 0.82(0.79–0.84). The positive likelihood ratio (PLR) and the negative likelihood ratio (NLR) are 5.61(3.72, 8.47) and 0.14(0.09–0.26). In addition, the area under the SROC curve (AUC) was drawn at 0.93. No significant risk of publication bias was detected through the funnel plot analysis. The performances of feature extraction from the volume of interest (VOI) or Using AI classifier in the radiomics models were superior to those of protocols employing region of interest (ROI) or absence of AI classifier.
Conclusion: This meta-analysis demonstrates that radiomics exhibits high sensitivity and specificity in distinguishing between MCN and SCN, and has the potential to become a reliable diagnostic tool for their identification.
Radiomics for differentiating pancreatic Mucinous Cystic Neoplasm from Serous Cystic Neoplasm: Systematic Review and Meta-Analysis
Longjia Zhang, Boyu Diao, Zhiyao Fan, Hanxiang Zhan
Academic Radiology 2025 (in press) - ”Radiomics plays a pivotal role in advancing the field of precision medicine, When radiomics is compared with traditional diagnostic imaging, it is clear that the former significantly improves efficiency while improving the accuracy of disease diagnosis and prediction. Radiomics analysis provides a powerful tool for modern medicine by utilizing complex image analysis tools and the rapid development and validation of medical imaging data, using image-based features for accurate diagnosis and treatment. Despite its excellent performance in diagnosing tumors, this approach is limited by the fact that the sensitivity and specificity of imaging rarely exceed 95%.”
Radiomics for differentiating pancreatic Mucinous Cystic Neoplasm from Serous Cystic Neoplasm: Systematic Review and Meta-Analysis
Longjia Zhang, Boyu Diao, Zhiyao Fan, Hanxiang Zhan
Academic Radiology 2025 (in press) - “Our study discovered that radiomics possesses a high degree of accuracy in distinguishing PCNs, particularly in detecting MCN and SCN, making it worthy of promotion for broader application. However, we require additional high-quality research to further validate these findings, and its practical efficiency and clinical applicability also need to be explored further. Moreover, we anticipate the establishment of standardized radiomics guidelines in the future to provide more evidence for the diagnosis and treatment of pancreatic cystic neoplasms.”
Radiomics for differentiating pancreatic Mucinous Cystic Neoplasm from Serous Cystic Neoplasm: Systematic Review and Meta-Analysis
Longjia Zhang, Boyu Diao, Zhiyao Fan, Hanxiang Zhan
Academic Radiology 2025 (in press)
- Background: We identified computed tomography (CT)-derived radiomic features predictive of tumor progression within three months, then examined their ability to prognosticate overall survival (OS) along with clinical features in pancreatic cancer. We evaluated these features in patients with unresected pancreatic cancer who underwent stereotactic body radiation therapy (SBRT) in sequence with chemotherapy, but not surgery.
Conclusions: CT-derived radiomic features predict rapid tumor progression following SBRT, confer nearly a twofold increase in mortality risk, and, along with patient age, enhance the identification of patients with stage II-IIIpancreatic cancer with poor OS.
Utility of radiomic features in predicting clinical outcomes in stage II-III pancreatic cancer.
Haruka Itakura,et al
American Society of Clinical Oncology Sept 2024 (abstract) - “In this retrospective study, we examined a cohort of 101 patients with stage II-III pancreatic cancer who underwent SBRT with sequential chemotherapy at a single institution (Stanford Health Care) between 1999- 2020. From their pre-SBRT contrast-enhanced CT images with segmented tumors, delineating regions-of-interest, we extracted 900 radiomic (quantitative pixel-level imaging characteristic) features. In the first phase, we identified radiomic features that predicted rapid tumor progression within three months following SBRT.”
Utility of radiomic features in predicting clinical outcomes in stage II-III pancreatic cancer.
Haruka Itakura,et al
American Society of Clinical Oncology Sept 2024 (abstract) - Background: When determining the initial chemotherapy regimen for advanced or metastatic pancreatic cancer, various factors must be evaluated. The decision between FOLFIRINOX and Gemcitabine/Nab-paclitaxel (GnP) is challenging, as patient survival hinges on the efficacy and toxicity profiles of these treatments, alongside individual patient characteristics and vulnerabilities. This study aims to guide the selection of an appropriate first-line chemotherapy regimen for advanced or metastatic pancreatic cancer by leveraging machine learning (ML) methods to predict survival outcomes. Conclusions: We developed the ML models that can compute the probability of OS based on the routinely collected data from patients with advanced or metastatic pancreatic cancer. To the best of our knowledge, this is the first ML solution aimed at aiding clinicians in the selection of the first-line chemotherapy regimen for pancreatic cancer.
Predicting chemotherapy response in patients with advanced or metastatic pancreatic cancer using machine learning.
Moonho Kim, Gyucheol Choi, Jamin Koo
American Society of Clinical Oncology. Sept 2024 - Results: The median age of the patients was 66 years, with 62.3% being male. The MLnmodels achieved the ROC-AUC of 0.81 when predicting OS after 12 months following the initial administration of FOLFIRINOX (n=61) or GnP (n=47). Five (peritoneal metastases, other metastases, bilirubin level, white blood cell counts, and retroperitoneal lymph node metastases) or four (age, tumor location, sex, and metastatic status) covariates were used to achieve the predictive accuracy for the two regimens, respectively. The median OS of the high versus low risk groups of FOLFIRINOX predicted by the ML models were significantly different (7 vs 18 months, P, 0.01), recording the hazard ratio (HR) of 2.79 (95% CI, 1.47-5.26). Similarly, the median OS of the high and low risk groups of GnP were significantly different (8 vs 16 months,P , 0.001, HR 3.50, 95% CI, 1.60-7.66).
Predicting chemotherapy response in patients with advanced or metastatic pancreatic cancer using machine learning.
Moonho Kim, Gyucheol Choi, Jamin Koo
American Society of Clinical Oncology. Sept 2024
- Purpose Delay in diagnosis can contribute to poor outcomes in pancreatic ductal adenocarcinoma (PDAC), and new tools for early detection are required. Recent application of artificial intelligence to cancer imaging has demonstrated great potential in detecting subtle early lesions. The aim of the study was to evaluate global and local accuracies of deep neural network (DNN) segmentation of normal and abnormal pancreas with pancreatic mass. Methods Our previously developed and reported residual deep supervision network for segmentation of PDAC was applied to segment pancreas using CT images of potential renal donors (normal pancreas) and patients with suspected PDAC (abnormal pancreas). Accuracy of DNN pancreas segmentation was assessed using DICE simulation coefficient (DSC), average symmetric surface distance (ASSD), and Hausdorff distance 95% percentile (HD95) as compared to manual segmentation. Furthermore, two radiologists semi-quantitatively assessed local accuracies and estimated volume of correctly segmented pancreas.
Deep neural network-based segmentation of normal and abnormal pancreas on abdominal CT: evaluation of global and local accuracies.
Kawamoto S, Zhu Z, Chu LC, Javed AA, Kinny-Köster B, Wolfgang CL, Hruban RH, Kinzler KW, Fouladi DF, Blanco A, Shayesteh S, Fishman EK.
Abdom Radiol (NY). 2024 Feb;49(2):501-511 - Methods Our previously developed and reported residual deep supervision network for segmentation of PDAC was applied to segment pancreas using CT images of potential renal donors (normal pancreas) and patients with suspected PDAC (abnormal pancreas). Accuracy of DNN pancreas segmentation was assessed using DICE simulation coefficient (DSC), average symmetric surface distance (ASSD), and Hausdorff distance 95% percentile (HD95) as compared to manual segmentation. Furthermore, two radiologists semi-quantitatively assessed local accuracies and estimated volume of correctly segmented pancreas.
Deep neural network-based segmentation of normal and abnormal pancreas on abdominal CT: evaluation of global and local accuracies.
Kawamoto S, Zhu Z, Chu LC, Javed AA, Kinny-Köster B, Wolfgang CL, Hruban RH, Kinzler KW, Fouladi DF, Blanco A, Shayesteh S, Fishman EK.
Abdom Radiol (NY). 2024 Feb;49(2):501-511 - Results Forty-two normal and 49 abnormal CTs were assessed. Average DSC was 87.4 ± 3.1% and 85.5 ± 3.2%, ASSD 0.97 ± 0.30 and 1.34 ± 0.65, HD95 4.28 ± 2.36 and 6.31 ± 6.31 for normal and abnormal pancreas, respectively. Semiquantitatively, ≥95% of pancreas volume was correctly segmented in 95.2% and 53.1% of normal and abnormal pancreas by both radiologists, and 97.6% and 75.5% by at least one radiologist. Most common segmentation errors were made on pancreatic and duodenal borders in both groups, and related to pancreatic tumor including duct dilatation, atrophy, tumor infiltration and collateral vessels. Conclusion Pancreas DNN segmentation is accurate in a majority of cases, however, minor manual editing may be necessary; particularly in abnormal pancreas.
Deep neural network-based segmentation of normal and abnormal pancreas on abdominal CT: evaluation of global and local accuracies.
Kawamoto S, Zhu Z, Chu LC, Javed AA, Kinny-Köster B, Wolfgang CL, Hruban RH, Kinzler KW, Fouladi DF, Blanco A, Shayesteh S, Fishman EK.
Abdom Radiol (NY). 2024 Feb;49(2):501-511 - “Pancreas DNN segmentation is accurate in a majority of cases, however, minor manual editing may be necessary; particularly in abnormal pancreas.” .
Deep neural network-based segmentation of normal and abnormal pancreas on abdominal CT: evaluation of global and local accuracies.
Kawamoto S, Zhu Z, Chu LC, Javed AA, Kinny-Köster B, Wolfgang CL, Hruban RH, Kinzler KW, Fouladi DF, Blanco A, Shayesteh S, Fishman EK.
Abdom Radiol (NY). 2024 Feb;49(2):501-511 - “Development of the algorithms using deep learning to automatically detect the pancreas and PDAC on CT scans is dependent on the quality of data input and therefore, it is vital to have high-quality annotated data to maximize their performance and clinical utility. The accuracy of manual segmenting the pancreas on CT images is one factor that can affect performance and reproducibility. Segmentation of the pancreas and other abdominal organs for supervised learning in particular via the manual approach is tedious, time consuming, and requires experienced radiologists. Furthermore, it is operator dependent with inter-observer and intra-observer variability being recognized as issues for manual segmentation.”
Deep neural network-based segmentation of normal and abnormal pancreas on abdominal CT: evaluation of global and local accuracies.
Kawamoto S, Zhu Z, Chu LC, Javed AA, Kinny-Köster B, Wolfgang CL, Hruban RH, Kinzler KW, Fouladi DF, Blanco A, Shayesteh S, Fishman EK.
Abdom Radiol (NY). 2024 Feb;49(2):501-511 
Deep neural network-based segmentation of normal and abnormal pancreas on abdominal CT: evaluation of global and local accuracies.
Kawamoto S, Zhu Z, Chu LC, Javed AA, Kinny-Köster B, Wolfgang CL, Hruban RH, Kinzler KW, Fouladi DF, Blanco A, Shayesteh S, Fishman EK.
Abdom Radiol (NY). 2024 Feb;49(2):501-511
Deep neural network-based segmentation of normal and abnormal pancreas on abdominal CT: evaluation of global and local accuracies.
Kawamoto S, Zhu Z, Chu LC, Javed AA, Kinny-Köster B, Wolfgang CL, Hruban RH, Kinzler KW, Fouladi DF, Blanco A, Shayesteh S, Fishman EK.
Abdom Radiol (NY). 2024 Feb;49(2):501-511
Deep neural network-based segmentation of normal and abnormal pancreas on abdominal CT: evaluation of global and local accuracies.
Kawamoto S, Zhu Z, Chu LC, Javed AA, Kinny-Köster B, Wolfgang CL, Hruban RH, Kinzler KW, Fouladi DF, Blanco A, Shayesteh S, Fishman EK.
Abdom Radiol (NY). 2024 Feb;49(2):501-511- ”Deep neural network segmentation of the pancreas is more difficult compared to other abdominal organs including liver, spleen, kidneys, and gallbladder. This difficulty may be related to poor boundary and low contrast of pancreas from adjacent organs (e.g., duodenum, vessels) and large variation of its shape and size compared to other organs. For example, pancreas with fatty infiltration with scattered fat within and along the surface of the pancreas is difficult to manually segment accurately due to irregularly lobulated contour. It is also difficult to accurately segment pancreas border with poor contrast organs such as the duodenum particularly in thin patients with poor fat planes.”
Deep neural network-based segmentation of normal and abnormal pancreas on abdominal CT: evaluation of global and local accuracies.
Kawamoto S, Zhu Z, Chu LC, Javed AA, Kinny-Köster B, Wolfgang CL, Hruban RH, Kinzler KW, Fouladi DF, Blanco A, Shayesteh S, Fishman EK.
Abdom Radiol (NY). 2024 Feb;49(2):501-511 - Deep neural network segmentation of abnormal pancreas was more challenging. In abnormal pancreata with a variety of pancreatic masses, deep neural network prediction of the pancreas was less accurate than normal pancreas with average DSC of 85.5%. It is likely due to various size, shape, and locations of pancreatic masses, and unpredicted changes in shape and geometry in the pancreas upstream from the pancreatic masses .In our cases, however, only minor errors were observed in many cases. By visual assessment using the scores, 53.1% of cases were score 9 or higher by both radiologists, and 75.5% by at least one radiologist in which more than 95% of volume of the pancreas and pancreatic mass together was correctly segmented.
Deep neural network-based segmentation of normal and abnormal pancreas on abdominal CT: evaluation of global and local accuracies.
Kawamoto S, Zhu Z, Chu LC, Javed AA, Kinny-Köster B, Wolfgang CL, Hruban RH, Kinzler KW, Fouladi DF, Blanco A, Shayesteh S, Fishman EK.
Abdom Radiol (NY). 2024 Feb;49(2):501-511 - In conclusion, our study found that segmentation of the pancreas using deep neural network is accurate and can be applied for AI based volumetric analyses in the majority of the cases. Minor manual editing may be necessary, more commonly in cases with pancreatic pathology. Further study using a larger number of cases with different CT equipment and protocol variation is needed to generalize the pancreatic segmentation model that would be used to further improvement of algorithms.
Deep neural network-based segmentation of normal and abnormal pancreas on abdominal CT: evaluation of global and local accuracies.
Kawamoto S, Zhu Z, Chu LC, Javed AA, Kinny-Köster B, Wolfgang CL, Hruban RH, Kinzler KW, Fouladi DF, Blanco A, Shayesteh S, Fishman EK.
Abdom Radiol (NY). 2024 Feb;49(2):501-511
- “Radiomics of pancreatic CT and MR images has enhanced pancreatic ductal adenocarcinoma detection and its differentiation from pancreatic neuroendocrine tumors, mass-forming chronic pancreatitis, or autoimmune pancreatitis. Radiomics can further help to better characterize incidental pancreatic cystic lesions, accurately discriminating benign from malignant intrapancreatic mucinous neoplasms.”
Radiomics in CT and MR imaging of the liver and pancreas: tools with potential for clinical application
M. .lvaro Berbis et al.
Abdominal Radiology (in press) - “This step consists of the generation of the ROI or VOI that defines the area of the image in which the radiomics features will be calculated (Fig. 3). In liver and pancreas images, this step is frequently performed via semi-automatic or manual methods. Several open-source applications (Slicer [11], MITK Workbench [10], CERR [12]) and freeware(LifEx[13]) allow the semi-automatic segmentation of lesions. Conversely, manual segmentation tools or tools that require the intervention of a specialist are limited by the bias they can potentially introduce in the model, as well as their need for considerable time and resources [14]. In these cases, inter-subject reproducibility must be evaluated and the characteristics that are not reproducible should be eliminated, as shown in the following sections.”
Radiomics in CT and MR imaging of the liver and pancreas: tools with potential for clinical application
M. lvaro Berbis et al.
Abdominal Radiology (in press) - “Regarding pancreatic lesions, RF-based models have been trained to distinguish between pancreatic cancer (PC) and healthy tissue. Models based on RF, adaptive boosting, support vector machine (SVM), or extremely randomized trees have also been used to classify PC and pancreatitis. In intraductal papillary mucinous neoplasm (IPNM), logistic regression has been used to generate models to detect cancer. Cas. et al. reviewed all these works, concluding that radiomics may be a valid approach to apply in the diagnosis, risk stratification, prediction of biological or genomic status, or treatment response assessment in cases of PC.”
Radiomics in CT and MR imaging of the liver and pancreas: tools with potential for clinical application
M. lvaro Berbis et al.
Abdominal Radiology (in press) - “With the arrival of higher-resolution cross-sectional imaging techniques, incidental pancreatic cystic lesions (PCL) have been increasingly discovered. PCLs are defined and classified according to the WHO criteria. Among the different types of PCLs, some kinds of mucinous lesions present a risk of malignant transformation to PDAC. Their differential diagnosis includes those of non-neoplastic (pseudocysts) and neoplastic origin (pancreatic cystic neoplasms). The latter can be benign neoplasms (serous cystic neoplasm, SCN) or cystic neoplasms with the potential to become malignant (main duct and brunch duct intraductal papillary mucinous neoplasms [IPMN] and mucinous cystic neoplasms).”
Radiomics in CT and MR imaging of the liver and pancreas: tools with potential for clinical application
M. lvaro Berbis et al.
Abdominal Radiology (in press) - “Radiomics and AI are promising techniques that allow the development and validation of cancer biomarkers and the building of predictive models. However, although the analysis of radiomics features by ML algorithms has been demonstrated to be useful to find patterns and to serve as predictive markers for patient outcomes in lung, kidney, or colorectal cancer, there is still limited information regarding the pancreas.”
Radiomics in CT and MR imaging of the liver and pancreas: tools with potential for clinical application
M. lvaro Berbis et al.
Abdominal Radiology (in press) - “The extraction of quantitative CT texture features allows us to distinguish PDAC from PNET. Moreover, PNET grade can be predicted based on texture analysis. Radiomics analysis by five different ML models (distance correlation, AdaBoost, gradient boosting decision tree, least absolute shrinkage and selection operator, XGBoost, and RF) in a retrospective study conducted on 82 patients was able to differentiate between pathological grades G1, G2, and G3 in PNET patients. Interestingly, Gu et al. developed a radiomics signature with a solid ability to discriminate different PNET histological grades and established a nomogram incorporating both radiomics features and clinical risk factors to assist clinical decision-making for PNET patients.”
Radiomics in CT and MR imaging of the liver and pancreas: tools with potential for clinical application
M. lvaro Berbis et al.
Abdominal Radiology (in press) - “In the pancreas, radiomics can contribute to improving the accuracy of the diagnosis, prognosis, and prediction of PC (the fourth leading cause of cancer death in Europe) and its differentiation from healthy tissue and other pancreatic pathologies. Although still limited by the scarcity of studies with good methodologic quality and the methodological difficulties inherently associated with DL algorithms, we can expect a bright future for radiomics in these fields, presumably brought about by the fast development of high-performance computers and DL tools and theincreasing availability of higher volumes of clinical imaging datasets.”
Radiomics in CT and MR imaging of the liver and pancreas: tools with potential for clinical application
M. lvaro Berbis et al.
Abdominal Radiology (in press) - Objectives To develop and validate a contrast-enhanced computed tomography (CECT)–based radiomics nomogram for the preoperative evaluation of Ki-67 proliferation status in pancreatic ductal adenocarcinoma (PDAC).
Conclusions The radiomics-clinical nomogram is an effective and non-invasive computer-aided tool to predict the Ki-67 expression status in patients with PDAC.
Clinical relevance statement The radiomics-clinical nomogram is an effective and non-invasive computer-aided tool to predict the Ki-67 expression status in patients with pancreatic ductal adenocarcinoma.
Development of a CT radiomics nomogram for preoperative prediction of Ki‑67 index in pancreatic ductal adenocarcinoma:a two‑center retrospective study
Qian Li et al.
European Radiology 2023 https://doi.org/10.1007/s00330-023-10393-w - Ki-67, as a nuclear protein, is expressed in the proliferative stage except for the G0 phase of cells, which can accurately reflect proliferative status of tumor cells. Regarding PDAC, some previous studies have reported that higher Ki-67 expression may be indicative of higher tumor grade, which could reflect the aggressiveness of the tumor. Ki-67 expression > 50% is associated with poor disease-free survival, disease-specific survival, and overall survival. It is an independent indicator for postoperative early recurrence, which may prompt neoadjuvant therapy instead of upfront surgery.
Development of a CT radiomics nomogram for preoperative prediction of Ki‑67 index in pancreatic ductal adenocarcinoma:a two‑center retrospective study
Qian Li et al.
European Radiology 2023 https://doi.org/10.1007/s00330-023-10393-w - The increasedKi-67 index had bad impact on prognosis, so the high Ki-67 group was more prone to have elevated CA19-9 levels. Another important finding was that CTreportedpositive LN status was more prevalent in the high Ki-67 group. Some previous studies had found the PDAC patients with high Ki-67 index were more likely to present LN metastasis, and CT-reported LN status was significantly related to the pathological LN status. Therefore, we think it may explain why CT-reported LN status could predict Ki-67 expression status.
Development of a CT radiomics nomogram for preoperative prediction of Ki‑67 index in pancreatic ductal adenocarcinoma:a two‑center retrospective study
Qian Li et al.
European Radiology 2023 https://doi.org/10.1007/s00330-023-10393-w - “In conclusion, this study used CECT radiomics features to develop an effective and non-invasive radiomicsclinical nomogram that showed favorable performance in preoperative evaluation of the Ki-67 expression status for patients with PDA.”
Development of a CT radiomics nomogram for preoperative prediction of Ki‑67 index in pancreatic ductal adenocarcinoma:a two‑center retrospective study
Qian Li et al.
European Radiology 2023 https://doi.org/10.1007/s00330-023-10393-w
- Purpose: The purpose of this study was to develop a radiomics-signature using computed tomography (CT) data for the preoperative prediction of grade of nonfunctional pancreatic neuroendocrine tumors (NF-PNETs).
Conclusion: Non-invasive assessment of tumor grade in patients with PNETs using the proposed radiomic-signature demonstrated high accuracy. Prospective validation and optimization could overcome the commonly experienced diagnostic uncertainty in the assessment of tumor grade in patients with PNETs and could facilitate clinical decision-making.
Accurate non-invasive grading of nonfunctional pancreatic neuroendocrine tumors with a CT derived radiomics signature.
Javed AA, Zhu Z, Kinny-Köster B, Habib JR, Kawamoto S, Hruban RH, Fishman EK, Wolfgang CL, He J, Chu LC.
Diagn Interv Imaging. 2023 Aug 17:S2211-5684(23)00155-9. doi: 10.1016/j.diii.2023.08.002. Epub ahead of print. PMID: 37598013. - •A radiomic signature derived from CT data was developed to preoperatively predict tumor grade of pancreatic neuroendocrine tumors.
•This study demonstrates that non-invasive assessment of tumor grade using the proposed radiomic-signature is feasible and highly accurate.
•Accurate prediction of pancreatic neuroendocrine tumor grade via radiomic signature could allow timely tailoring of patient management and allow non-invasive serial assessment in patients on surveillance.
Accurate non-invasive grading of nonfunctional pancreatic neuroendocrine tumors with a CT derived radiomics signature.
Javed AA, Zhu Z, Kinny-Köster B, Habib JR, Kawamoto S, Hruban RH, Fishman EK, Wolfgang CL, He J, Chu LC.
Diagn Interv Imaging. 2023 Aug 17:S2211-5684(23)00155-9. doi: 10.1016/j.diii.2023.08.002. Epub ahead of print. PMID: 37598013. - “In conclusion, we developed a multianalyte model based on primary tumor radiomic features and clinicopathological characteristics that can preoperatively predict tumor grade in PNETs. The developed model offers a higher sensitivity of detecting G2/G3 PNETs as compared to EUS-FNA which is the current gold standard for preoperative assessment of PNETs. This non-invasive tool can help in assessment of grade at diagnosis to determine need for resection as well as serial assessment of patients on surveillance to identify potential changes in tumor biology.”
Accurate non-invasive grading of nonfunctional pancreatic neuroendocrine tumors with a CT derived radiomics signature.
Javed AA, Zhu Z, Kinny-Köster B, Habib JR, Kawamoto S, Hruban RH, Fishman EK, Wolfgang CL, He J, Chu LC.
Diagn Interv Imaging. 2023 Aug 17:S2211-5684(23)00155-9. doi: 10.1016/j.diii.2023.08.002. Epub ahead of print. PMID: 37598013.
- “Mukherjee et al. [1] explored the utility of quantitative CT radiomic features of the pancreas in identifying patients who would develop pancreatic cancer in the subsequent 3–36 months. They found that their radiomics-based model had good predictive capacity, achieving sensitivity of 95% and specificity of 90% in a validation sample. They found performance robustness across CT scanners and slice thicknesses, and the model outperformed radiologists in identifying cases of pancreatic cancer. These findings add to the growing body of evidence that the indirect effects of pancreatic cancer, including endocrine and exocrine dysfunction and, now, whole-organ radiomic changes, may precede the diagnosis of cancer and serve as early detection biomarkers.”
Beyond the AJR: CT Radiomic Features of the Pancreas Predict Development of Pancreatic Cancer
Michael H. Rosenthal,Khoschy Schawkat
AJR 2023; 220:763 - “In this study, the most predictive radiomic features were measures of local pancreatic gland heterogeneity. These features are not discernible in visual assessment and do not meet the threshold for radiologists to report but are biologically plausible given existing evidence that pancreatic cancer affects broader pancreatic function early in the disease process.”
Beyond the AJR: CT Radiomic Features of the Pancreas Predict Development of Pancreatic Cancer
Michael H. Rosenthal, Khoschy Schawkat
AJR 2023; 220:763 - “These automated CT biomarkers could be deployed as part of the radiologist’s clinical workflow, allowing prospective risk profiling in practice. In pancreatic cancer, opportunistic screening could identify individuals at sufficiently high risk to warrant active screening, as is currently performed for families at high risk. Such an approach, however, would generate a high rate of false-positives for every true-positive. To be clinically useful, the approach will likely have to be integrated with other risk markers.”
Beyond the AJR: CT Radiomic Features of the Pancreas Predict Development of Pancreatic Cancer
Michael H. Rosenthal, Khoschy Schawkat
AJR 2023; 220:763 - “Machine learning–based radiomic analyses may be a novel strategy to screen for pancreatic cancer by revealing changes in the pancreas that precede the development of pancreatic cancer and the emergence of a radiologically detectable mass.”
Beyond the AJR: CT Radiomic Features of the Pancreas Predict Development of Pancreatic Cancer
Michael H. Rosenthal, Khoschy Schawkat
AJR 2023; 220:763 - “In conclusion, we detected and quantified the imaging signature of early pancreatic carcinogenesis from volumetrically segmented normal pancreas on standard-of-care CTs. The radiomics-based ML classifiers had high discrimination accuracy for classification of pancreas into prediagnostic for PDAC vs normal. The high accuracy of the SVM model was validated on CTs from external institutions. Its high specificity was generalizable on an independent internal cohort and on an external public dataset. In contrast, radiologist readers had low interreader agreement, sensitivity, and discrimination accuracy, which shows that novel AI-based approaches can detect PDAC at a subclinical stage when it is beyond the scope of the human interrogation.”
Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis.
Mukherjee S, et al.
Gastroenterology. 2022 Nov;163(5):1435-1446.e3 - “Prospective validation of these ML models and their integration with complementary blood and other fluid-based biomarkers has the potential to further improve cancer prediction capabilities at the prediagnostic or symptom-free stage. Such models also have the potential to elucidate the longitudinal changes of carcinogenesis that precede the clinical diagnosis of PDAC. Finally, such models can be deployed to detect early cancer in ongoing clinical trials such as the Early Detection Initiative that seeks to evaluate outcomes of a screening strategy by using clinical risk-prediction models and CT in cohorts at high risk for PDAC.”
Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis.
Mukherjee S, et al.
Gastroenterology. 2022 Nov;163(5):1435-1446.e3 - “CONCLUSIONS: Radiomics-based ML models can detect PDAC from normal pancreas when it is beyond human interrogation capability at a substantial lead time before clinical diagnosis. Prospective validation and integration of such models with complementary fluid-based biomarkers has the potential for PDAC detection at a stage when surgical cure is a possibility.”
Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis.
Mukherjee S, et al.
Gastroenterology. 2022 Nov;163(5):1435-1446.e3 - The median (range) CT slice thickness was 3 (0.5–5) mm. CTs were acquired on CT systems from 4 different vendors (81 Siemens, Munich, Germany [52.3%]; 41 GE, Boston, MA [26.4%]; 25 Toshiba, Tokyo, Japan [16.1%]; and 8 Philips, Amsterdam, The Netherlands [5.2%]). All these CTs had been previously interpreted to be negative for PDAC during routine clinical interpretation at our institution. Each CT was rereviewed by 1 of 3 radiologists (R1, R2, R3 with 2–4 years of post-radiology residency experience) who confirmed optimal image quality (eg, lack of motion artifacts compromising visual interpretation), portal venous phase of enhancement, absence of pancreatitis, focal solid or cystic lesion, and biliary or pancreatic duct stent. In addition, radiologists in consensus evaluated each prediagnostic CT for any indirect or secondary imaging signs of early PDAC, such as focal contour abnormality or attenuation difference, biliary or pancreatic duct dilatation or cutoff, and focal parenchymal atrophy.
Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis.
Mukherjee S, et al.
Gastroenterology. 2022 Nov;163(5):1435-1446 - Conclusion The current twelve published IBSI-compliant PDAC radiomic studies show high variability and often incomplete methodology resulting in low robustness and reproducibility. Clinical relevance statement Radiomics research requires IBSI compliance, data harmonization, and reproducible feature extraction methods for non-invasive imaging biomarker discoveries to be valid. This will ensure a successful clinical implementation and ultimately an improvement of patient outcomes as part of precision and personalised medicine.
Current state of radiomic research in pancreatic cancer: focusing on study design and reproducibility of findings
James Alex Malcolm et al.
European Radiology 2023 (in press) - “Radiomics is a computational method of extracting features (RF) from medical images that has the potential to develop non-invasive imaging biomarkers aiding in improved PDAC delineation and treatment. To date, there is limited PDAC CT radiomics primary research data available.”
Current state of radiomic research in pancreatic cancer: focusing on study design and reproducibility of findings
James Alex Malcolm et al.
European Radiology 2023 (in press) - “Our analysis revealed several challenges associated with the use of retrospective data from heterogeneous small to moderately sized cohorts. Furthermore, there was a lack agreement of RFs deemed significant. Additionally, variations in CT techniques and lack of consistency in RF segmentation and selection further hindered reproducibility of findings. However, it is noteworthy that GLCM-associated RFs were observed in 6 of the 12 studies reviewed, albeit without any consistent subcategories. The median cohort size was 106 patients, with 7 out of the 12 studies having such small cohort sizes rendering a validation process impossible. Training-validation-ratio of cohorts is an important factor in ensuring a robust prognostic model.”
Current state of radiomic research in pancreatic cancer: focusing on study design and reproducibility of findings
James Alex Malcolm et al.
European Radiology 2023 (in press)
- “This study showed that a radiomics-based model can achieve equivalent performance as an experienced academic radiologist in the classification of a wide array of pancreatic cysts with variable malignant potential. This model has the potential to refine pancreatic cyst management by improving diagnostic accuracy of cystic lesions, which can minimize healthcare utilization while maximizing detection of malignant lesions. This study confirms the ability of a radiomic based model to accurately classify pancreatic cystic neoplasms. Further validation and clinical integration of this model could help optimize management of pancreatic cysts by maximizing the rate of detection of malignant lesions while reducing healthcare utilization.”
Classification of pancreatic cystic neoplasms using radiomic feature analysis is equivalent to an experienced academic radiologist: a step toward computer‑augmented diagnostics for radiologists
Linda C. Chu et al.
Abdominal Radiology (2022) 47:4139–4150 - “Among the whole 490 features (488 radiomics features plus age and gender), thirty features were found to reduce redundancy by the minimum-redundancy maximum-relevancy feature selection based on mutual information, which showed the best classification performance, with AUC of 0.940. Age and gender were included in the model due to the known gender and gender associations for pancreatic cysts. These demographic features would be available to the radiologist at the time of exam, and this would simulate the real-world application. Age, median and mean intensities of the original images and wavelets, and fractal dimension were highly ranked for the classifications. Gender was ranked as 29th feature for the classification."
Classification of pancreatic cystic neoplasms using radiomic feature analysis is equivalent to an experienced academic radiologist: a step toward computer‑augmented diagnostics for radiologists
Linda C. Chu et al.
Abdominal Radiology (2022) 47:4139–4150 - “In this study, the performance of the radiomics featurebased classification achieved AUC of 0.940 in distinguishing among five types of pancreatic cystic neoplasms. The performance was similar to previous studies with multi-class pancreatic cyst classifications that included three or four cyst types, with accuracy of 79.6–83.6%. Previous studies on radiomics-based pancreatic cyst classification did not include a direct comparison with a radiologist, therefore, it was difficult to assess if the radiomics-based classification reported provided any added value relative to the standard of care. The current study showed that the radiomics- based pancreatic cyst classification achieved equivalent performance as an academic radiologist with more than 25 years of experience.”
Classification of pancreatic cystic neoplasms using radiomic feature analysis is equivalent to an experienced academic radiologist: a step toward computer‑augmented diagnostics for radiologists
Linda C. Chu et al.
Abdominal Radiology (2022) 47:4139–4150 - "Secondly, the performance of the radiomics-based model was compared to the performance of a single-academic radiologist. The experienced academic radiologist in this study may be more accurate at pancreatic cyst classification than an average radiologist in the community, which may underestimate the incremental value of the radiomics-based model. Future reader studies should also recruit multiple readers with a wide range of experience to measure the real-world impact of these radiomics tools. Thirdly, the current radiomics model only used CT-based features plus patient age and demographics. Other important clinical features such as symptoms, family history, laboratory values, and cyst fluid molecular markers were not included in the current model, which should be incorporated into future models. Our prior experience has demonstrated that the predictive power offered by multiple features is often additive.”
Classification of pancreatic cystic neoplasms using radiomic feature analysis is equivalent to an experienced academic radiologist: a step toward computer‑augmented diagnostics for radiologists
Linda C. Chu et al.
Abdominal Radiology (2022) 47:4139–4150
- “Mukherjee et al. explored the ability of quantitative CT radiomic features of the pancreas to identity patients who would develop pancreatic cancer in the subsequent 3 to 36 months. They found that their radiomics-based model showed good predictive capacity, achieving sensitivity of 95% and specificity of 90% in a validation sample. Importantly, they showed performance robustness across CT scanners and slice thicknesses, and the model outperformed radiologists in identifying cases of pancreatic cancer. These findings add to the growing body of evidence that the indirect effects of pancreatic cancer, including endocrine and exocrine dysfunction and now whole-organ radiomic changes, may precede the diagnosis of cancer and could serve as early detection biomarkers.”
Beyond the AJR: CT Radiomic Features of the Pancreas Predict Development of Pancreatic Cancer.
Rosenthal MH, Schawkat K.
AJR 2022 Oct 5 [published online]. Accepted manuscript. doi:10.2214/AJR.22.28582 - “This work adds another potential tool to the radiologist’s arsenal for opportunistic screening from routine clinical imaging. Opportunistic screening takes advantage of features within imaging examinations that are not the subject of the examination but nonetheless convey important information about entities such as cardiovascular risk . Potential CT-based biomarkers for cancer include body composition analysis, CT based radiomic and texture analysis, and organ-based volumetry. These automated CT biomarkers could be deployed as part of the radiologist’s clinical workflow, allowing for prospective risk profiling in practice. In pancreatic cancer, opportunistic screening could identify individuals at sufficiently high risk to warrant active screening, as is currently performed for high-risk families. Such an approach, however, would generate a high rate of false positives for every true positive. To be clinically useful, it will likely need to be integrated with other risk markers.”
Beyond the AJR: CT Radiomic Features of the Pancreas Predict Development of Pancreatic Cancer.
Rosenthal MH, Schawkat K.
AJR 2022 Oct 5 [published online]. Accepted manuscript. doi:10.2214/AJR.22.28582 - “The use of ML-based radiomic analyses may offer a novel screening strategy for pancreatic cancer by detecting changes in the pancreas that precede the development of pancreatic cancer and the emergence of a radiologically detectable mass.”
Beyond the AJR: CT Radiomic Features of the Pancreas Predict Development of Pancreatic Cancer.
Rosenthal MH, Schawkat K.
AJR 2022 Oct 5 [published online]. Accepted manuscript. doi:10.2214/AJR.22.28582 - “Radiomics is a rapidly growing field that quantitatively extracts image features in a high-throughput manner from medical imaging. In this study, we analyzed the radiomics features of the whole pancreas between healthy individuals and pancreatic cancer patients, and we established a predictive model that can distinguish cancer patients from healthy individuals based on these radiomics features.”
Compute Tomography Radiomics Analysis on Whole Pancreas Between Healthy Individual and Pancreatic Ductal Adenocarcinoma Patients: Uncertainty Analysis and Predictive Modeling
Shuo Wang et al.
Technology in Cancer Research & Treatment 2022; Volume 21: 1-14 - Results: We identified that 91 radiomics features are stable against various uncertainty sources, including bin width, resampling, image transformation, image noise, and segmentation uncertainty. Eight of the 91 features are nonredundant. Our final predictive model, using these 8 features, has achieved a mean area under the receiver operating characteristic curve (AUC) of 0.99±0.01 on the training dataset (189 subjects) by cross-validation. The model achieved an AUC of 0.910 on the independent test set (77 subjects)and an accuracy of 0.935.
Conclusion: CT-based radiomics analysis based on the whole pancreas can distinguish cancer patients from healthy individuals, and it could potentially become an early detection tool for pancreatic cancer.
Compute Tomography Radiomics Analysis on Whole Pancreas Between Healthy Individual and Pancreatic Ductal Adenocarcinoma Patients: Uncertainty Analysis and Predictive Modeling
Shuo Wang et al.
Technology in Cancer Research & Treatment 2022; Volume 21: 1-14 - “Our study proved that CT-based radiomics analysis and modeling can distinguish healthy individuals from pancreatic cancer patients, and potentially can become an effective tool to detect cancerous pancreatic tissue at an early stage.”
Compute Tomography Radiomics Analysis on Whole Pancreas Between Healthy Individual and Pancreatic Ductal Adenocarcinoma Patients: Uncertainty Analysis and Predictive Modeling
Shuo Wang et al.
Technology in Cancer Research & Treatment 2022; Volume 21: 1-14
- BACKGROUND & AIMS: Our purpose was to detect pancreatic ductal adenocarcinoma(PDAC) at the prediagnostic stage (3-36 months prior to clinical diagnosis) using radiomics based machine learning (ML) models, and to compare performance against radiologists in a case control study.
METHODS: Volumetric pancreas segmentation was performed on prediagnostic CTs (median interval between CT and PDAC diagnosis: 398 days) of 155 patients and an age-matched cohort of 265 subjects with normal pancreas. Total 88 first order and gray level radiomic features were extracted and 34 features were selected through LASSO-based feature selection method. Dataset was randomly divided into training (292 CTs: 110 prediagnostic and 182 controls) and test subsets (128 CTs: 45 prediagnostic and 83 controls). Four ML classifiers - K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF) and XGBoost (XGB) - were evaluated. Specificity of model with highest accuracy was further validated on an independent internal dataset (n=176) and the public NIH dataset (n=80). Two radiologists (R4 and R5) independently evaluated the pancreas on a five-point diagnostic scale.
Radiomics-Based Machine Learning Models Can Detect Pancreatic Cancer on Prediagnostic CTs at a Substantial Lead Time Prior to Clinical Diagnosis
Sovanlal Mukherjee,et al.
Gastroenterology (2022),doi:https://doi.org/10.1053/j.gastro.2022.06.066. - “Our study has limitations. The retrospective nature of the study is generally prone to selection bias. As with other radiomics studies, the precise pathologic correlates of the radiomic features that constitute the ML classifiers are not entirely known. We did not investigate the impact of differences in all the acquisition or post-processing parameters (e.g., voxel width, bin width, etc.) on the classifiers, which will be subject of the next phase of our ongoing investigation. Although we validated the high specificity of the SVM classifier on an independent internal cohort of control CTs as well as on the public NIH-PCT dataset, the sample size of these cohorts was small and the subjects in these cohorts were relatively younger. Thus, prospective larger cohorts with both cases and controls are warranted for further validation. Such prospective studies would also help determine the optimal operating point for the models to avoid a high false positive rate in context of a screening paradigm.”
Radiomics-Based Machine Learning Models Can Detect Pancreatic Cancer on Prediagnostic CTs at a Substantial Lead Time Prior to Clinical Diagnosis
Sovanlal Mukherjee,et al.
Gastroenterology (2022),doi:https://doi.org/10.1053/j.gastro.2022.06.066. - “In conclusion, we detected and quantified the imaging signature of early pancreatic carcinogenesis from volumetrically segmented normal pancreas on standard-of-care CTs. The radiomics-based ML classifiers had high discrimination accuracy for classification of pancreas into prediagnostic for PDAC versus normal. The high accuracy of the SVM model was validated on CTs from external institutions. Its high specificity was generalizable on an independent internal cohort and on an external public dataset. In contrast, radiologist readers had low interreader agreement, sensitivity, and discrimination accuracy, which shows that novel AI-based approaches can detect PDAC at a subclinical stage when it is beyond the scope of the human interrogation. Prospective validation of these ML models and their integration with complementary blood and other fluid-based biomarkers has the potential to further improve cancer prediction capabilities at the prediagnostic or symptom-free stage. Such models also have the potential to elucidate the longitudinal changes of carcinogenesis that precede the clinical diagnosis of PDAC. Finally, such models can be deployed to detect early cancer in ongoing clinical trials such as the Early Detection Initiative that seeks to evaluate outcomes of a screening strategy utilizing clinical risk-prediction models and CT in cohorts at high-risk for PDAC.”
Radiomics-Based Machine Learning Models Can Detect Pancreatic Cancer on Prediagnostic CTs at a Substantial Lead Time Prior to Clinical Diagnosis
Sovanlal Mukherjee,et al.
Gastroenterology (2022),doi:https://doi.org/10.1053/j.gastro.2022.06.066. - Objective: Quality gaps in medical imaging datasets lead to profound errors in experiments. Our objective was to characterize such quality gaps in public pancreas imaging datasets (PPIDs), to evaluate their impact on previously published studies, and to provide post-hoc labels and segmentations as a value-add for these PPIDs.
Conclusion: Substantial quality gaps, sources of bias, and high proportion of CTs unsuitable for AI characterize the available limited PPIDs. Published studies on these PPIDs do not account for these quality gaps. We complement these PPIDs through post-hoc labels and segmentations for public release on the TCIA portal. Collaborative efforts leading to large, well-curated PPIDs supported by adequate documentation are critically needed to translate the promise of AI to clinical practice.
Quality gaps in public pancreas imaging datasets: Implications & challenges for AI applications
Garima Suman et al.
Pancreatology 21 (2021) 1001-1008 - "Public medical imaging datasets have stimulated widespread interest to explore AI to address unmet healthcare needs. In order to fully leverage these public datasets, there is a critical need to understand their strengths and limitations. Our study of public datasets in the pancreas imaging domain identified only three public datasets. The MSD dataset is the largest one with 420 CTs. Both the NIH-PCT and the TCIA PDA datasets have less than 100 CTs each. These datasets are insufficient for deep learning applications, which require very large volumes of data. There is a general hesitation to share digital assets due to concerns related to data ownership and patient privacy. Ongoing developments in federated learning architecture and privacy-preserving AI could promote wider sharing of such datasets.”
Quality gaps in public pancreas imaging datasets: Implications & challenges for AI applications
Garima Suman et al.
Pancreatology 21 (2021) 1001-1008 - “Finally, presence of medical devices such as stents is another critical confounding factor. In the context of PDA, a tumor classification, or detection model can learn to associate the presence of a biliary stent with the diagnosis of PDA, which can lead to inadvertent overestimation of the model’s performance. Secondly, the course of such stents through the pancreatic head results in streak, artifacts and can obscure delineation of tumors in the pancreatic head. These challenges can increase the variability in tumor segmentation or result in the stent being included in segmentation mask with consequent errors in AI models. Therefore, if CTs with stents form a part of PPIDs, these should be explicitly specified in the metadata to ensure that users can make an Informed decision regarding their potential use for AI experiments.”
Quality gaps in public pancreas imaging datasets: Implications & challenges for AI applications
Garima Suman et al.
Pancreatology 21 (2021) 1001-1008 - "In summary, there is a need for carefully curated public imaging datasets supported by adequate documentation in the pancreas imaging domain. The available datasets for pancreatic pathologies have substantial quality gaps, sources of bias, and high proportion of CTs unsuitable for AI experiments. In our assessment, the factors responsible for such quality gaps include general hesitation to share highly curated digital assets due to concerns related to data ownership and patient privacy, absence of tangible incentives fordata sharing, limited guidance on the dataset preparation process, inadequate involvement of domain experts in dataset curation process, and lack of awareness of the impact of insufficient documentation on the AI development pipeline.”
Quality gaps in public pancreas imaging datasets: Implications & challenges for AI applications
Garima Suman et al.
Pancreatology 21 (2021) 1001-1008 
- BACKGROUND & AIMS: Our purpose was to detect pancreatic ductal adenocarcinoma (PDAC) at the prediagnostic stage (3-36 months prior to clinical diagnosis) using radiomics based machine learning (ML) models, and to compare performance against radiologists in a case control study.
CONCLUSIONS: Radiomics-based ML models can detect PDAC from normal pancreas when it is beyond human interrogation capability at a substantial lead time prior to clinical diagnosis. Journal Pre-proof 6 Prospective validation and integration of such models with complementary fluid-based biomarkers has the potential for PDAC detection at a stage when surgical cure is a possibility
Radiomics-Based Machine Learning Models Can Detect Pancreatic Cancer on Prediagnostic CTs at a Substantial Lead Time Prior to Clinical Diagnosis,
Sovanlal Mukherjee et al.
Gastroenterology 2022 (in press) - METHODS
Volumetric pancreas segmentation was performed on prediagnostic CTs (median interval between CT and PDAC diagnosis: 398 days) of 155 patients and an age-matched cohort of 265 subjects with normal pancreas. Total 88 first order and gray level radiomic features were extracted and 34 features were selected through LASSO-based feature selection method. Dataset was randomly divided into training (292 CTs: 110 prediagnostic and 182 controls) and test subsets (128 CTs: 45 prediagnostic and 83 controls). Four ML classifiers - K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF) and XGBoost (XGB) - were evaluated. Specificity of model with highest accuracy was further validated on an independent internal dataset (n=176) and the public NIH dataset (n=80). Two radiologists (R4 and R5) independently evaluated the pancreas on a five-point diagnostic scale. - RESULTS
Median (range) time between prediagnostic CTs of the test subset and PDAC diagnosis was 386 (97-1092) days. SVM had the highest sensitivity (mean; 95% CI) (95.5; 85.5-100.0), specificity (90.3; 84.3-91.5), F1-score (89.5; 82.3-91.7), AUC (0.98; 0.94-0.98) and accuracy (92.2%; 86.7-93.7) for classification of CTs into prediagnostic versus normal. All three other ML models KNN, RF, and XGB had comparable AUCs (0.95, 0.95, and 0.96, respectively). The high specificity of SVM was generalizable to both the independent internal (92.6%) and the NIH dataset (96.2%). In contrast, inter-reader radiologist agreement was only fair (Cohen’s kappa 0.3) and their mean AUC (0.66; 0.46-0.86) was lower than each of the four ML models (AUCs: 0.95-0.98) (p < 0.001). Radiologists also recorded false positive indirect findings of PDAC in control subjects (n=83) (7% R4, 18% R5). - CONCLUSIONS
Radiomics-based ML models can detect PDAC from normal pancreas when it is beyond human interrogation capability at a substantial lead time prior to clinical diagnosis. Prospective validation and integration of such models with complementary fluid-based biomarkers has the potential for PDAC detection at a stage when surgical cure is a possibility. - “These observations support the biologic insights from prior studies that the prediagnostic stage of PDAC is marked by substantial cellular activity and infiltration, which results in marked tissue heterogeneity . Our study suggests that this tissue heterogeneity is beyond the human perceptive ability but can be captured and leveraged for actionable insights through computational postprocessing techniques such as radiomics.”
Radiomics-Based Machine Learning Models Can Detect Pancreatic Cancer on Prediagnostic CTs at a Substantial Lead Time Prior to Clinical Diagnosis,
Sovanlal Mukherjee et al.
Gastroenterology 2022 (in press) - “The radiomics-based ML classifiers had high discrimination accuracy for classification of pancreas into prediagnostic for PDAC versus normal. The high accuracy of the SVM model was validated on CTs from external institutions. Its high specificity was generalizable on an independent internal cohort and on an external public dataset. In contrast, radiologist readers had low interreader agreement, sensitivity, and discrimination accuracy, which shows that novel AI-based approaches can detect PDAC at a subclinical stage when it is beyond the scope of the human interrogation. Prospective validation of these ML models and their integration with complementary blood and other fluid-based biomarkers has the potential to further improve cancer prediction capabilities at the prediagnostic or symptom-free stage. Such models also have the potential to elucidate the longitudinal changes of carcinogenesis that precede the clinical diagnosis of PDAC.”
Radiomics-Based Machine Learning Models Can Detect Pancreatic Cancer on Prediagnostic CTs at a Substantial Lead Time Prior to Clinical Diagnosis,
Sovanlal Mukherjee et al.
Gastroenterology 2022 (in press) - Background The diagnostic performance of CT for pancreatic cancer is interpreter-dependent, and approximately 40% of tumours smaller than 2 cm evade detection. Convolutional neural networks (CNNs) have shown promise in image analysis, but the networks’ potential for pancreatic cancer detection and diagnosis is unclear. We aimed to investigate whether CNN could distinguish individuals with and without pancreatic cancer on CT, compared with radiologist interpretation.
Interpretation CNN could accurately distinguish pancreatic cancer on CT, with acceptable generalisability to images of patients from various races and ethnicities. CNN could supplement radiologist interpretation.
Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation
Kao-Lang Liu et al.
Lancet Digital Health 2020; 2: e303–13
- Purpose: A deep neural network (DNN) model termed Multi-channel-Multiclassifier-Random Forest-ResNet (MMRF- ResNet) was constructed to provide an objective CT imaging basis for differential diagnosis between pancreatic serous cystic neoplasms (SCNs) and mucinous cystic neoplasms (MCNs).
Results: Multi-channel-ResNet (AUC 0.98) was superior to Manual-ResNet (AUC 0.91).CT image characteristics of lesions extracted by ResNet are more representative than wavelet, LBP, HOG, GLCM, Gabor, and AlexNet. Compared to the use of three classifiers alone and Majority Voting rule method, the use of the MMRF-ResNet model exhibits a better evaluation effect (AUC 0.96) for the classification of the pancreatic SCNs and MCNs.
Conclusion: The CT image classification model MMRF-ResNet is an effective method to distinguish between pancreatic SCNs and MCNs.
CT classification model of pancreatic serous cystic neoplasms and mucinous cystic neoplasms based on a deep neural network
Rong Yang et al.
Abdominal Radiology (2022) 47:232–241 - “we only analyzed the region of interest in images and did not analyze location information of the lesions (such as the head, body, and tail of the pancreas) and patient clinical information, such as gender, age, family history, and clinical symptoms, and the characteristics of the tumor have not been considered: size, grading, vascularization etc., for example are informations that can complete the clinical situation and they could be very useful notions.”
CT classification model of pancreatic serous cystic neoplasms and mucinous cystic neoplasms based on a deep neural network
Rong Yang et al.
Abdominal Radiology (2022) 47:232–241 
- “In conclusion, in this study, Multi-channel CT images were obtained through preprocessing based on single-channel manual outline ROI images, and ResNet was used to extract CT image features of pancreatic SCNs and MCNs. The random forest classifier is used to integrate the classification probabilities of the KNN, Bayesian, and Softmax classifiers to determine the CT image properties of pancreatic SCNs and MCNs. Finally, a better classification result was obtained relative to the commonly used radiomics methods, suggesting that MMRF-ResNet is an ideal CT classification model for distinguishing between pancreatic SCNs and MCNs.”
CT classification model of pancreatic serous cystic neoplasms and mucinous cystic neoplasms based on a deep neural network
Rong Yang et al.
Abdominal Radiology (2022) 47:232–241
- OBJECTIVE. Pancreatic ductal adenocarcinoma (PDAC) is often a lethal malignancy with limited preoperative predictors of long-term survival. The purpose of this study was to evaluate the prognostic utility of preoperative CT radiomics features in predict- ing postoperative survival of patients with PDAC.
RESULTS. The mean age of patients with PDAC was 67 ± 11 (SD) years. The mean tumor size was 3.31 ± 2.55 cm. The 10 most relevant radiomics features showed 82.2% ac- curacy in the classification of high-risk versus low-risk groups. The C-index of survival prediction with clinical parameters alone was 0.6785. The addition of CT radiomics features improved the C-index to 0.7414.
CONCLUSION. Addition of CT radiomics features to standard clinical factors improves survival prediction in patients with PDAC.
CT Radiomics–Based Preoperative Survival Prediction in Patients With Pancreatic Ductal Adenocarcinoma
Seyoun Park et al.
AJR 2021; 217:1104–1112 - “Tumors are spatially heterogeneous structures that can be characterized at a macro scale, and normal parenchyma can also be affected by the growth of the tumor. Texture analysis using medical images, especially radiomics approaches, is an established technique that describes spatial variations in pixel intensities in images for quantitative assess- ment. Whereas radiologists may qualitatively describe PDAC enhancement patterns as, for example, homogeneously isoattenuating or heterogeneously hypoattenuating, tex- ture analysis can capture more subtle underlying differences that may reflect important pathologic differences and thereby help predict patient survival.”
CT Radiomics–Based Preoperative Survival Prediction in Patients With Pancreatic Ductal Adenocarcinoma
Seyoun Park et al.
AJR 2021; 217:1104–1112 - “A total 489 radiomics features from the whole seg- mented 3D tumor and from the remaining pancreatic parenchyma were extracted to express pancreas and tumor phenotypes of patients with PDAC. Radiomics features used in this study included 478 features from the whole 3D tumor and 11 features from the pancreatic parenchyma not involved by the tumor. The tumor features include 14 first-order statistics of the volumet- ric CT intensities, eight shape features of the target structure, 33 texture features from a gray-level co-occurrence matrix (GLCM) and a gray-level run-length matrix, and 368 texture features from the eight volumes filtered by wavelets.”
CT Radiomics–Based Preoperative Survival Prediction in Patients With Pancreatic Ductal Adenocarcinoma
Seyoun Park et al.
AJR 2021; 217:1104–1112 - “Based on the selected radiomics features, a random survival forest was applied for survival prediction in a multivariate dataset with missing variables. Each decision node was divided until three unique deaths (d = 3) remained in the leaf node. Ten thousand trees were built by the training set using the AUC for the split of internal nodes. Each end node stored the survival sta- tus (dead or alive), survival time, and a Cox proportional hazard function of the assigned cases. The survival time and survival status predictions in the validation cohort were determined by majority voting based on the trained trees.”
CT Radiomics–Based Preoperative Survival Prediction in Patients With Pancreatic Ductal Adenocarcinoma
Seyoun Park et al.
AJR 2021; 217:1104–1112 
CT Radiomics–Based Preoperative Survival Prediction in Patients With Pancreatic Ductal Adenocarcinoma
Seyoun Park et al.
AJR 2021; 217:1104–1112- "The accuracy of survival prediction using only preoperative clinical variables was 0.6785 in terms of the C-index. This value improved to 0.7321 when the 10 selected image features from the tumor phenotype were added and further improved to 0.7414 with additional pancreatic image features. Figure 6 shows the Kaplan-Meier curves for the overall survival prediction of the validation cohort and for the ground-truth prediction.”
CT Radiomics–Based Preoperative Survival Prediction in Patients With Pancreatic Ductal Adenocarcinoma
Seyoun Park et al.
AJR 2021; 217:1104–1112 - "Preoperative CT scans show the global status of pancreatic parenchyma and tumor texture. The overall survival of patients who underwent surgical resection was explored in this study using preoperative CT radiomics features. However, information on different types of therapies (e.g., adjuvant chemotherapy) that each patient received can be combined to analyze the response of each therapy. Even though we found no clear evidence of differences in adjuvant therapy between high-risk and low-risk groups in our dataset, the correlation with detailed types of adjuvant therapy could be further studied with a proper study design. In addition, postoperative clinical parameters can be also considered for better prediction of overall survival.”
CT Radiomics–Based Preoperative Survival Prediction in Patients With Pancreatic Ductal Adenocarcinoma
Seyoun Park et al.
AJR 2021; 217:1104–1112 - "We found that radiomics features extracted from tumors and from the nonneoplastic pancreas can be used to improve survival prediction models of patients who underwent surgery for PDAC. This algorithm could be combined with other pathologic and genetic biomarkers.”
CT Radiomics–Based Preoperative Survival Prediction in Patients With Pancreatic Ductal Adenocarcinoma
Seyoun Park et al.
AJR 2021; 217:1104–1112 
CT Radiomics–Based Preoperative Survival Prediction in Patients With Pancreatic Ductal Adenocarcinoma
Seyoun Park et al.
AJR 2021; 217:1104–1112
CT Radiomics–Based Preoperative Survival Prediction in Patients With Pancreatic Ductal Adenocarcinoma
Seyoun Park et al.
AJR 2021; 217:1104–1112
CT Radiomics–Based Preoperative Survival Prediction in Patients With Pancreatic Ductal Adenocarcinoma
Seyoun Park et al.
AJR 2021; 217:1104–1112- “Radiomics is the high throughput extraction of large sets of quantitative data from imaging studies that can be used to characterize healthy and pathological tissues to inform diagnosis and prognosis. Texture analysis, a subtype of radiomics, quantifies gray-level pixels and voxels in a frequency histogram and their spatial relationships to describe lesion heterogeneity within a 2-dimensional region of interest (ROI) or 3-dimensional volume of interest (VOI). Computed tomography (CT) texture analysis has dem- onstrated promise in diagnosing and risk-stratifying patients with PCs. Predictive ability of radiomics models can be enhanced by integrating clinical features in pancreas and non-pancreas tissues.”
Machine learning principles applied to CT radiomics to predict mucinous pancreatic cysts
Adam M. Awe et al.
Abdominal Radiology https://doi.org/10.1007/s00261-021-03289-0 - "This retrospective analysis demonstrated that machine learn- ing principles applied to radiomics, clinical parameters, and surgical pathology can be used to create a mucinous classifier of PCs. The machine learning mucinous classifiers out- performed the baseline mucinous classifiers on G-mean and AUC scoring metrics, which we believe are the metrics best suited to assess the model quality and potential for useful predictions. Performance was comparable between XGBoost texture feature only and combined models. Shapely additive explanation analysis demonstrated that trends in important model-building variables can be identified. However, overall this remains a challenging task with only moderate performance of the best model.”
Machine learning principles applied to CT radiomics to predict mucinous pancreatic cysts
Adam M. Awe et al.
Abdominal Radiology https://doi.org/10.1007/s00261-021-03289-0 - “In conclusion, our study demonstrates that machine learning principles can be applied to radiomics data of PCs to help detect mucinous phenotypes. While this information does not obviate the need for other diagnostic testing, it may help risk stratify patients with PCs. We also demonstrate that integration of radiologic and clinical fea- tures with texture feature radiomics data does not improve performance of our mucinous classifier. However, unique radiomic, radiologic, and clinical features were important in building our machine learning mucinous classifiers. These results highlight the potential of machine learning algorithms applied to high-throughput PC radiomics features in helping to detect mucinous cyst phenotype in patients and deserves further study to improve and validate such models.”
Machine learning principles applied to CT radiomics to predict mucinous pancreatic cysts
Adam M. Awe et al.
Abdominal Radiology https://doi.org/10.1007/s00261-021-03289-0
- “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