Individualized Prediction of Postoperative Survival in Gallbladder Cancer: A Nomogram Based on SEER Data and External Validation

TL;DR

This study created a tool to predict survival after gallbladder cancer surgery, validated in multiple groups, and found that chemotherapy helps only advanced-stage patients.

Contribution

A novel nomogram for predicting gallbladder cancer survival, validated externally and showing chemotherapy benefits in advanced stages.

Findings

The nomogram showed strong predictive accuracy with C-indices of 0.767 to 0.798 in training and validation.

Adjuvant chemotherapy improved survival in patients with TNM stage >IIB but not in early-stage patients.

The model performed consistently across U.S. and Chinese patient cohorts.

Abstract

Gallbladder cancer (GBC) is a rare but aggressive malignancy with poor prognosis. Accurate prognostic tools are essential for guiding postoperative treatment strategies. In this study, we developed and externally validated a prognostic nomogram, demonstrating strong generalizability across both population-based and independent real-world cohorts, aiming to estimate the 1-, 3-, and 5-year overall survival of patients after gallbladder cancer surgery. The model demonstrated high accuracy and clinical utility. Furthermore, subgroup analyses indicated that adjuvant chemotherapy may improve survival in patients with advanced-stage disease. This tool can assist clinicians in making individualized treatment decisions and follow-up plans.

Background: Gallbladder cancer (GBC) is a rare but aggressive malignancy. Prognostic tools are essential for optimizing postoperative treatment strategies. We aim to develop and validate a prognostic nomogram to estimate 1-, 3-, and 5-year overall survival (OS) in GBC patients and explore the role of adjuvant chemotherapy across different subgroups. Methods: A total of 1848 postoperative GBC patients from the SEER database (2000–2020 17 regions) were analyzed, with an additional external validation cohort of 108 patients from China (2010–2020). Prognostic factors were identified using LASSO regression and multivariable Cox analysis. A nomogram was constructed and validated using the concordance index (C-index), time-dependent ROC curves, calibration curves, and decision curve analysis (DCA). Subgroup analyses were performed to evaluate the impact of adjuvant chemotherapy. Results: The nomogram demonstrated strong predictive performance, with C-indices of 0.767 (training), 0.798 (internal validation), and 0.750 (external validation). Time-dependent ROC curves in the training cohort showed AUCs of 0.777, 0.769, and 0.800 for 1-, 3-, and 5-year OS, respectively. In the internal validation cohort, the corresponding AUCs were 0.763, 0.743, and 0.803. External validation using the independent Chinese cohort of 108 patients showed consistent results, with AUCs of 0.771, 0.835, and 0.810 for 1-, 3-, and 5-year OS. Subgroup analysis revealed that adjuvant chemotherapy significantly improved survival in patients with TNM stage >IIB. In contrast, patients with early-stage disease (TNM ≤ IIB) showed no significant survival benefit from chemotherapy. Conclusions: This study developed a validated prognostic nomogram for postoperative GBC patients, demonstrating strong discrimination and calibration. Subgroup analysis suggests that adjuvant chemotherapy benefits select high-risk patients, aiding personalized decision-making in clinical practice.

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