ArticleScientific reports2026
A novel prognostic nomogram for elderly renal cell carcinoma patients with lung metastases.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Research on the prognosis of elderly renal cell carcinoma (RCC) patients with lung metastases (LM) remains limited. This study aimed to develop and validate a prognostic model for this population and to identify key determinants of overall survival (OS). We extracted data on RCC patients aged ≥ 65 years diagnosed with LM between 2010 and 2019 from the SEER database. Independent risk factors for OS, identified through univariate and multivariate Cox regression, were incorporated into a prognostic nomogram. The model's performance was assessed using the C-index, ROC curves, calibration plots, and decision curve analysis (DCA). The XGBoost algorithm was employed to rank feature importance for OS at different time points. Subsequent propensity score matching (PSM) was used to balance baseline characteristics, enabling a comparison of OS between surgical and non-surgical groups via Kaplan-Meier analysis. Among 1,835 patients (2010-2017), a 7:3 split created training and internal validation cohorts; an additional 548 patients (2018-2019) served as an external validation cohort. The final nomogram integrated 11 variables: age, marital status, histologic type, grade, T stage, N stage, surgery, chemotherapy, and metastases to bone, brain, and liver. Internal and external validation confirmed the model's robust predictive accuracy and clinical utility. XGBoost analysis consistently identified surgery as the most critical prognostic factor. After PSM, the surgical group demonstrated significantly superior OS across all cohorts. In conclusion, we developed a practical nomogram for predicting OS in elderly RCC patients with LM. Our findings strongly suggest that surgical intervention is independently associated with markedly improved survival in this patient population.
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