Explainable ML/AI Model for Estimating Postoperative Survival in Renal Cell Carcinoma Brain Metastasis Patients
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ID: 315312
2026
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Abstract
Abstract Background Brain metastases (BM) from renal cell carcinoma (RCC) are associated with poor prognosis and limited survival. Prognostic tools specific to patients with RCC undergoing surgical resection of BM are lacking, and current models do not incorporate advanced machine learning (ML) approaches. This study aimed to develop and validate an ML-based model to predict overall survival (OS) after BM resection in RCC. Methods We retrospectively analyzed 253 patients with histologically confirmed RCC and radiographically or pathologically confirmed BM who underwent neurosurgical resection at a tertiary referral center (1993–2021). Clinical and radiologic features were used to train and internally validate multiple ML models for OS prediction. Model performance was assessed using the concordance index (C-index) and time-dependent Area Under the Curve (AUC) at 1, 2, and 5 years. Feature importance and interpretability were evaluated using SHapley Additive exPlanations (SHAP). Results The XGBoostCox + plsRcox model outperformed other algorithms, achieving a test C-Index of 0.59. AUCs at 1, 2, and 5 years were 0.61, 0.64, and 0.69 in the test cohort. SHAP analysis revealed extracranial disease status, number of BM, pre-operative symptoms and age at surgical resection as the most influential predictors. Kaplan-Meier analysis using optimal cutoff based on training cohort demonstrated significant survival differences between high- and low-risk groups in the test cohort (HR: 2.06 (1.26–3.35), P = .004). Conclusions and Relevance An explainable XGBoostCox + plsRcox model accurately predicts OS after BM resection in RCC and enables personalized risk assessment via an online calculator (https://hasanovlab.shinyapps.io/rcc_brainmetresection_prognosticml/).
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| Authors | Zuhair Majeed, Semiha Özgül, Peng Li, Dima Suki, Yusuf Açıkgöz, Eric Jonasch, Sherise Desiree Ferguson, Merve Hasanov, Elshad Hasanov |
| Journal | Neuro-Oncology Advances |
| Year | 2026 |
| DOI |
10.1093/noajnl/vdag140
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| URL | |
| Keywords | Keywords not found |
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