Duodenal papilla radiomics-based prediction model for post-endoscopic retrograde cholangiopancreatography 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 aimed to investigate the association between papilla morphology and post-ERCP pancreatitis (PEP), and to construct a robust model for PEP prediction. METHODS: We 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. RESULTS: A 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). CONCLUSIONS: Radiomic signature of 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.