{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T01:36:34Z","timestamp":1793410594927,"version":"4.5.0"},"reference-count":52,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,6,30]],"date-time":"2025-06-30T00:00:00Z","timestamp":1751241600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Existing research on the development of prognostic models for renal cell carcinoma (RCC) patients with brain metastases (BM) remains limited. This study aimed to develop a prognostic prediction model for RCC patients with BM and to identify critical factors influencing clinical outcomes.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Patients diagnosed with BM between 2010 and 2019 were identified and extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Potential risk factors were initially screened applying the eXtreme Gradient Boosting (XGBoost) and Random Forest (RF) machine learning algorithms. Subsequently, multivariate COX regression analysis was performed to identify independent risk factors for constructing the predictive nomogram. Nomogram performance was comprehensively evaluated based on Harrell\u2019s concordance index (C-index), receiver operating characteristic (ROC) curve analysis, calibration plots, and decision curve analysis (DCA). The SHapley Additive exPlanations (SHAP) method was employed to demonstrate the ranking of feature importance affecting patient prognosis at different time points. Moreover, we conducted propensity score matching (PSM) and Kaplan-Meier (K-M) survival analysis to compare clinical outcomes between surgical and non-surgical treatment subgroups.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In total, 982 patients were assigned to the training cohort and 420 to the validation cohort. The constructed nomogram included four clinical variables: histologic type, T stage, N stage, surgery and chemotherapy. The AUC, C-index, calibration curves, and DCA curves showed excellent performance of the nomogram. In addition, the SHAP values indicated that surgical treatment was the most important prognostic risk factor for OS at 6-months, 1-year, 2-years, and 3-years. After further balancing the baseline characteristics between the surgical and non-surgical groups using PSM, we observed that patients with BM who underwent surgical intervention showed significantly better survival outcomes across all subgroups compared to non-surgical patients, though unmeasured confounders may contribute to this association.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>We developed a novel nomogram for predicting prognostic factors in RCC patients with BM, offering a valuable tool to support accurate clinical decision-making. Our research also confirmed that surgical intervention was significantly associated with improved survival outcomes for patients with BM.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fimmu.2025.1572580","type":"journal-article","created":{"date-parts":[[2025,6,30]],"date-time":"2025-06-30T05:26:17Z","timestamp":1751261177000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["A novel nomogram for survival prediction in renal cell carcinoma patients with brain metastases: an analysis of the SEER 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