DSAI-10 ERA-SPECIFIC PROGNOSTIC FACTORS AND AN INTERPRETABLE MACHINE-LEARNING SURVIVAL MODEL FOR RENAL CELL CARCINOMA BRAIN METASTASES
Semiha Ozgul, Zeynep Feyza Akpinar, Dima Suki, Peng Li, Zuhair Majeed, Yusuf Acikgoz, Sherise Desiree Ferguson, Eric Jonasch, Merve Hasanov, Elshad HasanovAbstract
Background
Brain metastases (BM) in renal cell carcinoma (RCC) are associated with poor prognosis and limited clinical guidance. We aimed to identify prognostic factors for overall survival (OS) in RCC BM and develop an interpretable machine-learning (ML) model for individualized risk prediction.
Methods
We retrospectively analyzed 1,149 patients with RCC BM treated with stereotactic radiosurgery or surgical resection and stratified them by treatment era (interferons-IFN, tyrosine kinase inhibitors-TKI, and immune checkpoint inhibitors-IO). Univariate Cox regression analyses were performed, and a prognostic classification tree was constructed for the IO era. ML analyses used a comprehensive survival modeling framework integrating multiple algorithms and ensemble strategies, with final model selection based on independent test performance and interpretation using SHAP. Model performance was evaluated using the concordance index (C-index) and time-dependent area under the ROC curve (AUC), and an interactive web-based calculator was developed.
Results
Median OS improved from 1.2 years in the IFN to 1.8 years in the TKI and 2.9 years in the IO era. The gradient boosted survival model achieved a C-index of 0.71 and a 1-year AUC of 0.73. Model interpretation identified functional status and disease burden as key prognostic factors. Limitations include lack of patient-level systemic therapy details, tumor volume measurements, and laboratory variables used in prior prognostic models.
Conclusions
This study reports the largest cohort of patients with RCC BM and presents an interpretable ML model for individualized survival prediction to support clinical decision-making.