Duodenal papilla radiomics-based prediction model for post–endoscopic retrograde cholangiopancreatography pancreatitis using machine learning: a retrospective multicohort study

Abstract

Background and AimsThe duodenal papillae are the primary and essential pathway for ERCP, greatly determining its complexity and outcome. We aimed to investigate the association between papilla morphology and post-ERCP pancreatitis (PEP), and to construct a robust model for PEP prediction. MethodsWe enrolled retrospectively patients underwent ERCP in 2 centers from January 2019 and June 2022. Radiomic features of papilla were extracted from endoscopic images with deep learning. Potential predictors and their importance were evaluated with three machine learning algorithms. A predictive model was developed using best subset selection by logistic regression, and its performance was evaluated in terms of discrimination, calibration, and clinical utility based on area under curve (AUC) of receiver operation characteristics (ROC), calibration and clinical decision curve, respectively. ResultsA total of 2038 and 334 ERCP patients from 2 centers were enrolled in this study with PEP rates of 7.9% and 9.6%, respectively. The R-score was significantly associated with PEP and showed great diagnostic value (AUC, 0.755-0.821). Six hub predictors were selected to conduct a predictive model. The radiomics-based model demonstrated excellent discrimination (AUC, 0.825-0.857) and therapeutic benefits in the training, testing, and validation cohorts. The addition of the R-score significantly improved diagnostic accuracy of the predictive model (NRI, 0.151-0.583, p<0.05; IDI, 0.097-0.235, p<0.001). ConclusionsRadiomic signature of papilla is a crucial independent predictor of PEP. The papilla-radiomics-based model performs well for the clinical prediction of PEP.