A combined Cox and logistic model provides accurate predictive performance in estimation of time-dependent probabilities for recurrence of intrahepatic cholangiocarcinoma after resection

Background: Intrahepatic cholangiocarcinoma has heterogeneous outcomes after resection. There remains a need for broadly applicable recurrence-specific tool offering precise evaluation on curativeness of resection. Methods: A four hospital-based clinical cohort involving 1,655 patients with intrahepatic cholangiocarcinoma who received surgical resection were studied. Cox and logistic models were networked into one system containing risk categories with distinctive probabilities of recurrence. Prediction of time to-recurrence was performed by formulizing time-dependent risk probabilities. The model was validated in three clinical cohorts (n=332). Results: From the training cohort, 10 and 11 covariates, including diabetes, cholelithiasis, albumin, platelet count, alpha fetoprotein, carbohydrate antigen 19-9, carcinoembryonic antigen, hepatitis B virus infection, tumor size and number, resection type, and lymph node metastasis, from Cox and logistic models were identified significant for recurrence-free survival (RFS). The combined Cox & logistic ranking system (CCLRS)-adjusted time-dependent probabilities were categorized into seven ranks (5-yr RFS for lowest and highest ranks were 75% vs. 0%; hazard ratio 18.5, 95% CI: 14.7-24.9, P<0.0001). The CCLRS was validated with a minimum area under curve value of 0.8086. Prediction of time-to-recurrence was validated to be excellent (Pearson r, 0.8204; P<0.0001). Conclusions: The CCLRS allows precise estimation on risk of recurrence for intrahepatic cholangiocarcinoma after resection. It could be applicative when estimating time-dependent disease status and stratifying individuals who sole resection of the tumor would not be curative.