Development and Validation of a Diagnostic Model for Biliary Tract Cancer Detection in Patients with Benign Biliary Disease: A Multicenter, Retrospective Case-Control Study

Background/Aims Biliary tract cancer (BTC) is an aggressive malignancy often diagnosed late due to its frequently asymptomatic presentation. Given substantial clinical overlap between BTC and benign biliary tract disease (BTD), early and accurate differentiation remains challenging. This study represents the first effort to develop and validate a non-invasive clinical model capable of identifying patients at especially high risk of BTC within the BTD population, thereby facilitating earlier diagnosis and intervention. Methods In this multicenter observational study, we prospectively collected patient-reported survey data and retrospectively extracted laboratory, diagnostic data from electronic medical records at four tertiary centers in South Korea. A machine learning model was trained to differentiate biliary tract cancer (BTC) from benign biliary tract disease (BTD) using 1,439 patients from three centers and externally validated the model in 245 patients from an independent center. Explainable machine learning quantified biomarker contributions to model predictions. Results Our model demonstrated robust diagnostic performance, achieving an AUROC of 0.893 (95% CI, 0.863–0.920) and sensitivity of 0.784 (0.727–0.827). External validation from an independent center dataset produced consistent results. Notably, the model substantially outperformed CA19-9 and was developed without reliance on this biomarker, enabling applicability in Lewis antigen–negative patients. It also accurately identified BTC cases even among patients with normal CA19-9 levels, supporting its utility in broader clinical populations. Conclusions These results suggest our model’s potential to serve as a non-invasive screening tool, identifying patients at high risk of BTC among those presenting with suspicious biliary pathology, particularly when CA19-9 is uninformative.