Machine Learning-Driven Prognostic Model Integrating Lymphocyte-to-C-Reactive Protein Ratio and TNM Staging in Gallbladder Cancer.

ABSTRACT:

Background

A comprehensive preoperative assessment of the patient's physical condition is crucial for predicting the prognosis of patients undergoing radical cholecystectomy for gallbladder cancer (GBC). This study aimed to develop a prognostic model integrating preoperative hematological parameters and clinical information to predict postoperative survival in patients with GBC.

Methods

Patients who underwent radical cholecystectomy for GBC between 2000 and 2024 at Xinhua Hospital, affiliated with Shanghai Jiao Tong University School of Medicine, and Shigatse People's Hospital were included in this study. Data on demographic features, clinical parameters, laboratory results, and clinical outcomes were collected. Univariate and multivariate Cox regression analyses, time-depen

SUBMITTER: Wang M 

PROVIDER: S-EPMC12928047 | biostudies-literature | 2026 Mar

REPOSITORIES: biostudies-literature