Construction of a survival prediction model and analysis of influencing factors for brain metastasis in non-small cell lung cancer based on multi‑parameter clinical data.

Abstract

ObjectiveTo identify real-world prognostic indicators affecting patients with non-small cell lung cancer brain metastasis (NSCLC BM) and to develop a pragmatic nomogram for individualized survival prediction.MethodsWe collected clinical data from 590 patients with NSCLC BM treated in the department of oncology at our hospital from January 2020 to December 2023, and randomly divided them into a training set (n = 413) and a validation set (n = 177) at a 7:3 ratio. Cox regression analysis was used to screen independent risk factors, and a nomogram prediction model was constructed. Model performance was evaluated using receiver operating characteristic(ROC) curves, C-index, calibration curves, decision curve analysis (DCA) and Kaplan-Meier (KM) survival analysis.ResultsMultivariate Cox analysis revealed that Karnofsky Performance Score (KPS) score ConclusionThe nomogram incorporating KPS score, number of BMs, radiotherapy modality, and epidermal growth factor receptor (EGFR) mutation status demonstrated good discrimination, calibration and clinical utility. Our model distinctively integrates real-world treatment triage (WBRT versus SRS/SRT) as a comprehensive prognostic dimension. This tool enables individualized risk stratification and may aid in early identification of high-risk patients, thereby guiding precise clinical treatment.