The Integrated Prediction of Clinical and Pathological Factors on the Prognosis of Intrahepatic Cholangiocarcinoma.

ABSTRACT

OBJECTIVE: The primary objective was to construct a high-performing prognostic risk model to accurately forecast the prognosis of patients diagnosed with intrahepatic cholangiocarcinoma (iCCA). METHODS: We retrospectively collected clinical data from the MSK database on 125 patients diagnosed with iCCA. Random sampling was utilized to divide patients into a training set and a validation set, maintaining a ratio of 73. Univariate and multivariate Cox proportional hazards regression models were utilized to identify independent prognostic factors influencing OS. Based on these independent factors, a model nomogram was established. The performance of the prognostic prediction models was assessed through calibration curves and C-index calculations. The Kaplan-Meier method was used to plot survival curves. Time-dependent ROC curve was used to evaluate the accuracy of the model. RESULTS: A nomogram was developed, incorporating hepatitis C, CA19, tumor extent, tumor size, LVI, positive lymph nodes, and TMB as predictive factors. The C-index for the training set was .78 and the validation set was .68. Using the riskscore derived from the nomogram, patients were stratified into high- and low-risk groups. The high-risk group exhibited considerably lower OS and RFS compared to the low-risk group in the training set (P < .05). However, no significant difference was detected in RFS among different risk groups in the validation set (P > .05). The AUC for 1-year, 3-year, and 5-year survival was .89, .69, and .69, respectively. CONCLUSION: We successfully developed and validated a prognostic nomogram for iCCA, demonstrating its excellent accuracy in predicting patient outcomes and providing clinicians with a potential prognostic tool.