Duodenal papilla radiomics-based prediction model for post-ERCP pancreatitis using machine learning: a retrospective multicohort study.

BACKGROUND AND AIMS: The duodenal papillae are the primary and essential pathway for ERCP, greatly determining its complexity and outcome. We investigated the association between papilla morphology and post-ERCP pancreatitis (PEP) and constructed a robust model for PEP prediction. METHODS: We retrospectively enrolled patients who underwent ERCP in 2 centers from January 2019 to June 2022. Radiomic features of the papilla were extracted from endoscopic images with deep learning. Potential predictors and their importance were evaluated with 3 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 the area under curve (AUC) of the receiver-operating characteristic curve, calibration curve, and clinical decision curve, respectively. RESULTS: From 2 centers, 2038 and 334 ERCP patients were enrolled in this study with PEP rates of 7.9% and 9.6%, respectively. The radiomic score was significantly associated with PEP and showed great diagnostic value (AUC, .755-.821). Six hub predictors were selected to conduct a predictive model. The radiomics-based model demonstrated excellent discrimination (AUC, .825-.857) and therapeutic benefits in the training, testing, and validation cohorts. The addition of the radiomic score significantly improved the diagnostic accuracy of the predictive model (net reclassification improvement, .151-.583 [P < .05]; integrated discrimination improvement, .097-.235 [P < .001]). CONCLUSIONS: The radiomic signature of the papilla is a crucial independent predictor of PEP. The papilla radiomics-based model performs well for the clinical prediction of PEP.

Deep learning nomogram for preoperative distinction between Xanthogranulomatous cholecystitis and gallbladder carcinoma: A novel approach for surgical decision.

The distinction between Xanthogranulomatous Cholecystitis (XGC) and Gallbladder Carcinoma (GBC) is challenging due to their similar imaging features. This study aimed to differentiate between XGC and GBC using a deep learning nomogram model built from contrast enhanced computed tomography (CT) scans. 297 patients were included with confirmed XGC (94) and GBC (203) as the training and internal validation cohort from 2017 to 2021. The deep learning model Resnet-18 with Fourier transformation named FCovResnet18, shows most impressive potential in distinguishing XGC from GBC using 3-phase merged images. The accuracy, precision and area under the curve (AUC) of the model were then calculated. An additional cohort of 74 patients consisting of 22 XGC and 52 GBC patients was enrolled from two subsidiary hospitals as the external validation cohort. The accuracy, precision and AUC achieve 0.98, 0.99, 1.00 in the internal validation cohort and 0.89, 0.92, 0.92 in external validation cohort. A nomogram model combining clinical characteristics and deep learning prediction score showed improved predicting value. Altogether, FCovResnet18 nomogram has demonstrated its ability to effectively differentiate XGC from GBC preoperatively, which significantly aid surgeons in making informed and accurate surgical decisions for XGC and GBC patients.