Development and Validation of a Nomogram for Predicting Overall and Cancer-Specific Survival in Elderly NSCLC Patients with Brain Metastases

Background Brain metastases significantly compromise survival in elderly patients with non-small cell lung cancer; however, prognostic tools specifically designed for this vulnerable population are still lacking. Traditional TNM staging does not adequately address the intricate interplay of age, comorbidities, and metastasis patterns that are essential for guiding therapeutic decisions. Methods We analyzed 18,209 elderly patients (≥ 60 years) with non-small cell lung cancer and from the SEER database spanning the years 2000 to 2020. The X-tile software was employed to optimize the stratification of continuous variables, specifically age and tumor size. Independent predictors of overall survival and cancer-specific survival were identified using Cox regression and subsequently integrated into nomograms. The performance of the model was validated through the use of the C-index, area under the curve, calibration curves and decision curve analysis, with comparisons made to TNM staging. Results The cohort consisted of 9,514 patients with bone metastases. Multivariate analysis identified ten independent prognostic factors: age, race, grade, tumor size, N stage, surgical intervention, chemotherapy, and metastases to bone, brain, liver, and lung. Nomograms accurately predicted overall survival and cancer-specific survival at 6, 12, and 24 months. The area under the curve values ranged from 0.772 to 0.803, and decision curve analysis confirmed a superior discriminative ability and clinical net benefit compared to TNM staging (AUCs: 0.541–0.580). Surgical intervention and chemotherapy were associated with significantly improved survival outcomes, whereas radiotherapy demonstrated limited benefits in terms of overall survival. Conclusion This novel nomogram offers individualized survival predictions for elderly patients with non-small cell lung cancer and brain metastases, surpassing conventional staging methods. It improves surgical decision-making by incorporating metastasis burden and treatment modalities, thereby supporting personalized management in high-risk geriatric oncology.