Integrating Etiological Insights With Machine Learning for Precision Diagnosis of Obstructive Jaundice: Findings From...

Large-scale cohort studies exploring the etiology of obstructive jaundice (OJ) are scarce, with current serum-based diagnostic markers offering suboptimal performance. This study leverages the largest retrospective cohort of patients with OJ to date to investigate its disease spectrum and to develop a novel diagnostic system.

This study involves 2 retrospective observational cohorts. The biliary surgery cohort (BS cohort, n = 349) served for initial data exploration and external validation of machine learning (ML) models. The large general cohort (LG cohort, n = 5,726) enabled an in-depth analysis of etiologies and the determination of relevant diagnostic indicators, in addition to supporting ML model development. Interpretable ML techniques were used to derive insights from the models.

The LG cohort highlighted a diverse disease spectrum of OJ, including cholangiocarcinoma (10.39% distal, 10.01% perihilar, and 5.59% intrahepatic), pancreatic adenocarcinoma (19.11%), and common bile duct stones (18.27%) as leading causes. Traditional serum markers such as carbohydrate antigen 19-9 and carcinoembryonic antigen lacked stand-alone diagnostic accuracy. Two ML-based models (collectively termed the ML of OJ based on common laboratory tests model) were developed: a classifier to differentiate benign from malignant causes (AUROC = 0.862) and a multiclass model to further stratify malignant and benign diseases (ACC = 0.777). Interpretable ML tools provided clarity on critical features, offering actionable insights and enhancing transparency in the decision-making process.

This study elucidates the etiological spectrum of OJ, meanwhile providing a practical and interpretable ML-based diagnostic tool. By leveraging large-scale clinical data, our model provides a rapid and reliable primary assessment for patients with OJ, enabling clinicians to identify potential etiologies and guide further diagnostic workup.