Qiao Lin1,2, Yi-Fan Ji1, Yong Chen1, Huan Sun1, Dan-Dan Yang1, Ai-Li Chen1, Tian-Wu Chen1, Xiao Ming Zhang1. 1. Sichuan Key Laboratory of Medical Imaging and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, China. 2. Medical Imaging and Department of Radiology, Gaoping District People's Hospital of Nanchong, Nanchong, Sichuan, China.
Abstract
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, T1 -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.
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, T1 -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.
Authors: Maria Elena Laino; Angela Ammirabile; Ludovica Lofino; Lorenzo Mannelli; Francesco Fiz; Marco Francone; Arturo Chiti; Luca Saba; Matteo Agostino Orlandi; Victor Savevski Journal: Healthcare (Basel) Date: 2022-08-11