ArticleHepatobiliary surgery and nutrition2025
Predictive model for very early recurrence of patients with perihilar cholangiocarcinoma: a machine learning approach.
Article in Hepatobiliary surgery and nutrition, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Reconsidering predictive models in perihilar cholangiocarcinoma.Hepatobiliary surgery and nutrition · 2026Article
- Considerations for improving generalizability and robustness of predictive models in perihilar cholangiocarcinoma.Hepatobiliary surgery and nutrition · 2026Article
- Preoperative Systemic Immune-Inflammatory Index Predicts Occult Nodal Disease in Clinically Node-Negative Intrahepatic Cholangiocarcinoma.Annals of surgical oncology · 2025Article
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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. Results: Among 434 patients undergoing curative-intent resection for pCCA, 65 (15.0%) patients developed VER. Median overall survival (OS) among patients with and without VER was 8.4 [interquartile range (IQR) 6.6-11.3] versus 38.5 (IQR 31.9-45.7) months (P<0.001). An XGBoost model was able to stratify patients relative to the risk of VER [low-risk: 6-month recurrence-free survival (RFS) 94.6% Conclusions: A novel, machine learning based model was able to predict accurately the chance of VER after curative-intent resection of pCCA. In turn, the tool may help surgeons in the selection of patients likely to benefit the most from resection, as well as counsel individuals about the anticipated risk of recurrence in the early post-operative period.
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