Predictive model for very early recurrence of patients with perihilar cholangiocarcinoma: a machine learning approach.

ABSTRACT:

Background

Although offering the best chance of potential cure for patients with localized perihilar cholangiocarcinoma (pCCA), resection has been associated with high morbidity and sometimes poor long-term outcomes due to recurrence. We sought to develop a predictive model to identify individuals at high risk for very early recurrence (VER) after curative-intent surgery for pCCA.

Methods

Patients who underwent curative-intent surgery for pCCA between 2000-2023 were identified from a multi-institutional database. An eXtreme Gradient Boosting (XGBoost) model was developed to estimate the risk of VER, defined as recurrence within 6 months after resection. The relative importance of clinicopathologic factors was determined using SHapley Additive exPlanations (SHAP) values.

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SUBMITTER: Kawashima J 

PROVIDER: S-EPMC11806131 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature