Radiomics model of contrast‐enhanced MRI for early prediction of acute pancreatitis severity

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.

Three-Dimensional Radiomics Features of Magnetic Resonance T2-Weighted Imaging Combined With Clinical Characteristics to Predict the Recurrence of Acute Pancreatitis

“…Two radiologists with 5 years of experience in abdominal imaging each used imaging biomarker explorer (IBEX, β1.0, http://bit.ly/IBEX_MDAnderson ) software to delineate the three dimensional area of interest around the pancreatic margin including the necrotic area of the pancreas, and to avoid the common bile duct and blood vessels ( Figure 2 ). IBEX software is an open infrastructure software platform that flexibly supports common radiomics workflow tasks and is widely used in radiomics analysis ( 17 , 18 ). Five common feature groups were extracted from IBEX, including the gray cooccurrence matrix, gray length matrix, histogram and shape.…”

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“…Although the methylomic features were numerical, Chi2 seemed to have outperformed Ttest on selecting the numerical features with an improvement 0.0814 in Acc. The literature also showed that Chi2 achieved similar performances in evaluating the numerical features as Ttest in both OMIC and imaging data [100][101][102][103].…”

Radiomics and Its Applications and Progress in Pancreatitis: A Current State of the Art Review

“…Lin et al (19) first reported a contrast-enhanced MRI (CEMRI) based radiomics model to predict the clinical severity of AP (MAP vs. MSAP and SAP). In their study, they included 259 AP patients into the training (n = 180, with 99 MAP and 81 MSAP and SAP patients) and validation cohorts (n = 79, with 43 MAP and 36 MSAP and SAP patients).…”