The Rboost causal model can identify patients who are most likely to benefit among HCC-BDTT patients. Adherence to the individualized recommendation improves survival. This data-driven tool offers vital support for precision surgical decision-making in this high-risk disease.
This dual-center study recruited 151 HCC-BDTT patients who underwent radical hepatectomy (training cohort: 92; validation cohort: 59). An XGBoost-based R-learner (Rboost) algorithm was developed to estimate the individualized treatment effect (ITE) on 5-year overall survival (OS) between BDR and NBDR. Patients were stratified into ITE tertiles: BDR-recommended, ambiguous benefit, and NBDR-recommended. Model performance assessed with heterogeneity metrics (C-for-benefit, adjusted Qini index). Survival outcomes were compared within ITE strata between surgeries concordant and discordant with model recommendations (Kaplan-Meier/log-rank). Two-year disease-free survival (DFS) served as a treatment-proximal validity check.
Study Type
OBSERVATIONAL
Enrollment
151
This group includes biliary/enteric reconstruction, bile duct resection with thrombectomy, and biliary tract interventions
China, Guangdong
Guangzhou, Guangdong, China
Treatment Effects
5-year overall survival (OS)
Time frame: 2025.09.01
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