Novel multifactor predictive model for postoperative survival in gallbladder cancer: a multi-center study

Area of Science:

• Oncology
• Surgical Oncology
• Biostatistics

Background:

• Gallbladder cancer (GBC) is an aggressive malignancy with poor survival rates post-surgery.
• Accurate prediction of postoperative overall survival (OS) is crucial for GBC patient management.

Purpose of the Study:

• To develop and validate a practical nomogram model for predicting postoperative OS in GBC patients.
• To identify key prognostic factors for GBC survival.

Main Methods:

• Multivariate Cox regression analysis was used for feature selection from data of 202 patients (development/internal validation) and 85 patients (external testing).
• A nomogram was constructed to predict 1-, 3-, and 5-year survival rates.
• Model performance was assessed using C-index, ROC curves, calibration, Kaplan-Meier, and decision curves.

Main Results:

• Eight factors including TNM stage, age-adjusted Charlson Comorbidity Index (aCCI), BMI, R0 resection, platelet count, albumin, CA125, and CA199 were identified.
• The nomogram demonstrated strong predictive performance with a time-dependent C-index >0.70 and AUC >75% for 1-, 3-, and 5-year survival.
• The model showed good generalizability in an external validation set and outperformed TNM staging and CA199 alone.

Conclusions:

• The developed nomogram is a promising tool for predicting GBC patient survival.
• It offers robust prognostic efficacy and generalizability, aiding clinical decision-making for GBC.
• The nomogram provides a practical approach to enhance prognostic accuracy in gallbladder cancer.