Construction of a predictive model for gemcitabine combined with cisplatin resistance in intrahepatic cholangiocarcinoma based on multidimensional inflammatory indices.

Background: Intrahepatic cholangiocarcinoma (iCCA) is a challenging malignancy, often diagnosed at an advanced stage, making it difficult to be treated effectively. The standard first-line chemotherapy regimen, gemcitabine combined with cisplatin (CisGem), shows limited efficacy due to primary drug resistance. Identifying patients likely to develop resistance early can help optimize treatment strategies. This study aims to develop a predictive model for the early identification of CisGem resistance in iCCA patients by integrating peripheral blood inflammatory markers and clinical characteristics, evaluate the differences in survival prognosis between the resistant and non-resistant groups, and validate the clinical relevance of the model's predictive results. Methods: A single-center retrospective cohort study included patients with unresectable iCCA who received first-line CisGem treatment from 2018 to 2022. Variables were screened using least absolute shrinkage and selection operator (LASSO) regression, and a multivariate logistic regression model was constructed based on pre-treatment peripheral blood tests for carbohydrate antigen 19-9 (CA19-9), albumin (ALB), and systemic immune-inflammation index (SII). The model's performance was evaluated using a receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). Results: In the training set (n=80), the CA19-9 level was significantly elevated in the drug-resistant group (79.00 Conclusions: The predictive model based on CA19-9, ALB, and SII can identify early the risk of CisGem resistance in iCCA patients, characterized by high statistical efficacy, low testing costs, and strong clinical applicability, suggesting potential for clinical application pending prospective validation. This model serves as a clinical risk stratification tool to assist in identifying high-risk iCCA patients requiring treatment adjustment, particularly suitable for primary and resource-limited healthcare settings with strong potential for widespread implementation. Multi-center external validation remains the necessary next step.