Tian‐wu Chen

Background Computed tomography (CT) or MR images may cause the severity of early acute pancreatitis (AP) to be underestimated. As an innovative image analysis method, radiomics may have potential clinical value in early prediction of AP severity. Purpose To develop a contrast‐enhanced (CE) MRI‐based radiomics model for the early prediction of AP severity. Study Type Retrospective. Subjects A total of 259 early AP patients were divided into two cohorts, a training cohort (99 nonsevere, 81 severe), and a validation cohort (43 nonsevere, 36 severe). Field Strength/Sequence 3.0T, T 1 ‐weighted CE‐MRI. Assessment Radiomics features were extracted from the portal venous‐phase images. The "Boruta" algorithm was used for feature selection and a support vector machine model was established with optimal features. The MR severity index (MRSI), the Acute Physiology and Chronic Health Evaluation (APACHE) II, and the bedside index for severity in acute pancreatitis (BISAP) were calculated to predict the severity of AP. Statistical Tests Independent t ‐test, Mann–Whitney U ‐test, chi‐square test, Fisher's exact tests, Boruta algorithm, receiver operating characteristic analysis, DeLong test. Results Eleven potential features were chosen to develop the radiomics model. In the training cohort, the area under the curve (AUC) of the radiomics model, APACHE II, BISAP, and MRSI were 0.917, 0.750, 0.744, and 0.749, and the P value of AUC comparisons between the radiomics model and scoring systems were all less than 0.001. In the validation cohort, the AUC of the radiomics model, APACHE II, BISAP, and MRSI were 0.848, 0.725, 0.708, and 0.719, respectively, and the P value of AUC comparisons were 0.96 (radiomics vs. APACHE II), 0.40 (radiomics vs. BISAP), and 0.46 (radiomics vs. MRSI). Data Conclusion The radiomics model had good performance in the early prediction of AP severity. Level of Evidence: 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:397–406.

PURPOSE: To determine whether the volume of resectable adenocarcinoma of the esophagogastric junction (AEG) measured at multidetector computed tomography (CT) is associated with regional lymph node metastasis and N stage. MATERIALS AND METHODS: The study was approved by the institutional ethics committee, and written informed consent was obtained from each participant. Two hundred sixteen patients with resectable AEG prospectively underwent contrast material-enhanced thoracoabdominal multidetector CT less than 2 weeks before curative resection. Gross tumor volume was retrospectively measured on CT scans. Univariate and multivariate analyses were performed to identify whether gross tumor volume is associated with regional lymph node metastasis. The Mann-Whitney U test was performed to compare gross tumor volume among N stages, with Bonferroni correction for multigroup comparisons. Receiver operating characteristic analysis was performed to determine if gross tumor volume could help classify N stage. RESULTS: Univariate analysis showed that gross tumor volume is associated with regional lymph node metastasis (P < .0001). Multivariate analysis revealed that gross tumor volume is an independent risk factor of lymph node metastasis (P = .023, odds ratio = 2.791). The Mann-Whitney U test showed that gross tumor volume could help differentiate between stage N0 and stages N1-N2 or N1-N3 disease and between stages N1-N2 and stage N3 disease (P < .0001 for all). In patients with stage T1-T3 AEG, gross tumor volume could help differentiate between stage N0 and stages N1-N2 (cutoff, 15.23 cm(3)) or N1-N3 (cutoff, 17.16 cm(3)) disease and between stages N1-N2 and stage N3 disease (cutoff, 33.96 cm(3)). In patients with stage T3 AEG, gross tumor volume could help differentiate stage N0 from stages N1-N2 (cutoff, 18.41 cm(3)) or N1-N3 (cutoff, 19.30 cm(3)) disease and stages N1-N2 from stage N3 disease (cutoff, 33.96 cm(3)). CONCLUSION: Gross tumor volume of AEG measured with multidetector CT is associated with regional lymph node metastasis and N stage.