• Tian-Yu Tang, Xiang Li, Qi Zhang, Cheng-Xiang Guo, Xiao-Zhen Zhang, Meng-Yi Lao, Yi-Nan Shen, Wen-Bo Xiao, Shi-Hong Ying, Ke Sun, Ri-Sheng Yu, Shun-Liang Gao, Ri-Sheng Que, Wei Chen, Da-Bing Huang, Pei-Pei Pang, Xue-Li Bai, and Ting-Bo Liang.
• Department of Hepatobiliary and Pancreatic Surgery, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
• J Magn Reson Imaging. 2020 Jul 1; 52 (1): 231-245.

BackgroundIn pancreatic cancer, methods to predict early recurrence (ER) and identify patients at increased risk of relapse are urgently required.PurposeTo develop a radiomic nomogram based on MR radiomics to stratify patients preoperatively and potentially improve clinical practice.Study TypeRetrospective.PopulationWe enrolled 303 patients from two medical centers. Patients with a disease-free survival ≤12 months were assigned as the ER group (n = 130). Patients from the first medical center were divided into a training cohort (n = 123) and an internal validation cohort (n = 54). Patients from the second medical center were used as the external independent validation cohort (n = 126).Field Strength/Sequence3.0T axial T1 -weighted (T1 -w), T2 -weighted (T2 -w), contrast-enhanced T1 -weighted (CET1 -w).AssessmentER was confirmed via imaging studies as MRI or CT. Risk factors, including clinical stage, CA19-9, and radiomic-related features of ER were assessed. In addition, to determine the intra- and interobserver reproducibility of radiomic features extraction, the intra- and interclass correlation coefficients (ICC) were calculated.Statistical TestsThe area under the receiver-operator characteristic (ROC) curve (AUC) was used to evaluate the predictive accuracy of the radiomic signature in both the training and test groups. The results of decision curve analysis (DCA) indicated that the radiomic nomogram achieved the most net benefit.ResultsThe AUC values of ER evaluation for the radiomics signature were 0.80 (training cohort), 0.81 (internal validation cohort), and 0.78 (external validation cohort). Multivariate logistic analysis identified the radiomic signature, CA19-9 level, and clinical stage as independent parameters of ER. A radiomic nomogram was then developed incorporating the CA19-9 level and clinical stage. The AUC values for ER risk evaluation using the radiomic nomogram were 0.87 (training cohort), 0.88 (internal validation cohort), and 0.85 (external validation cohort).Data ConclusionThe radiomic nomogram can effectively evaluate ER risks in patients with resectable pancreatic cancer preoperatively, which could potentially improve treatment strategies and facilitate personalized therapy in pancreatic cancer.Level Of Evidence4 Technical Efficacy: Stage 4 J. Magn. Reson. Imaging 2020;52:231-245.© 2019 The Authors. Journal of Magnetic Resonance Imaging published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine.

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