Recurrence patterns and prediction of survival after recurrence for gallbladder cancer

Abstract

Background

Gallbladder cancer (GBC) is associated with a poor prognosis. Recurrence patterns and their effect on survival remain ill-defined. This study aimed to analyze recurrence patterns and develop a machine learning (ML) model to predict survival after recurrence (SAR) of GBC.

Methods

Patients who underwent curative-intent resection of GBC between 1999 and 2022 were identified using an international database. An Extreme Gradient Boosting ML model to predict SAR was developed and validated.

Results

Among 348 patients, 110 (31.6%) developed disease recurrence during follow-up. The most common recurrence site was local (29.1%), followed by multiple site (26.4%), liver (21.8%), peritoneal (18.2%), and lung (0.05%). The median SAR was the longest in patients with lung recurrence (36.0 months), followed by those with local recurrence (15.7 months). In contrast, patients with peritoneal (8.9 months), liver (8.5 months), or multiple-site (6.4 months) recurrence had a considerably shorter SAR. Patients with multiple-site recurrence had a worse SAR than individuals with single-site recurrence (6.4 vs 11.10 months, respectively; P =.014). The model demonstrated good performance in the evaluation and bootstrapping cohorts (area under the receiver operating characteristic curve: 71.4 and 71.0, respectively). The most influential variables were American Society of Anesthesiologists classification, local recurrence, receipt of adjuvant chemotherapy, American Joint Committee on Cancer T and N categories, and developing early disease recurrence (<12 months). To enable clinical applicability, an easy-to-use calculator was made available (https://catalano-giovanni.shinyapps.io/SARGB).

Conclusion

Except for lung recurrence, SAR for GBC was poor. A subset of patients with less aggressive disease biology may have favorable SAR. ML-based SAR prediction may help individuate candidates for curative re-resection when feasible.