Recurrence patterns and prediction of survival after recurrence for gallbladder cancer
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View abstract on PubMed
Summary
This summary is machine-generated.Gallbladder cancer recurrence patterns significantly impact survival. Machine learning models can predict survival after recurrence (SAR), aiding in identifying patients for potential re-treatment.
Area Of Science
- Oncology
- Machine Learning in Medicine
- Cancer Prognostics
Background
- Gallbladder cancer (GBC) presents a poor prognosis, with recurrence patterns and their impact on survival poorly understood.
- Understanding these patterns is crucial for improving patient outcomes.
Purpose Of The Study
- To analyze recurrence patterns in gallbladder cancer post-curative resection.
- To develop and validate a machine learning model for predicting survival after recurrence (SAR) in GBC patients.
Main Methods
- Utilized an international database of 348 patients who underwent curative-intent GBC resection (1999-2022).
- Developed and validated an Extreme Gradient Boosting machine learning model to predict SAR.
Main Results
- 31.6% of patients experienced recurrence, with local recurrence being most common (29.1%).
- Survival after recurrence varied significantly by site: longest for lung (36.0 months) and shortest for peritoneal (8.9 months) and liver (8.5 months) recurrence.
- The ML model showed good predictive performance (AUC 71.4%), with key predictors including ASA classification, local recurrence, adjuvant chemotherapy, AJCC stage, and early recurrence (<12 months).
Conclusions
- Survival after GBC recurrence is generally poor, except for lung recurrence.
- A subset of patients may exhibit less aggressive disease biology, leading to favorable SAR.
- Machine learning-based SAR prediction can assist in identifying candidates for curative re-resection.
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