Development and internal validation of a prognostic model for cancer-specific survival in elderly patients with metastatic non-small cell lung cancer: a SEER-based study

Background The prognosis of elderly patients with distant metastasis (stage M1) of non-small cell lung cancer (NSCLC) is poor. This study aimed to develop and internally validate a prognostic model for estimating cancer-specific survival (CSS) in elderly patients with metastatic NSCLC.

Methods Clinical data of stage M1 NSCLC patients (2010-2022) were extracted from the Surveillance, Epidemiology, and End Results database and randomly split into a training cohort and an internal validation cohort at a ratio of 7:3. The primary outcome was CSS, defined as death from NSCLC. Prespecified demographic, tumor, metastatic, and treatment-related variables were evaluated using univariate and multivariable Cox proportional hazards regression analyses, followed by bidirectional stepwise selection based on the Akaike information criterion. The cohort was randomly divided into training and internal validation cohorts at a ratio of 7:3. Model discrimination was assessed using the C-index and time-dependent area under the receiver operating characteristic curve (AUC) at 1, 3, and 5 years, while calibration and decision curve analysis were used to evaluate model performance. A nomogram was developed based on the final model.

Results A total of 69,516 patients were randomly allocated in the training (n = 48,661) and validation (n = 20,855) cohorts. In the training cohort, Cox regression analysis identified independent prognostic factors for CSS (all P < 0.05), including age, gender, race, marital status, residential area, tumor size, primary tumor site, T stage, N stage, surgery, etc. The model showed moderate discrimination, with C-indices of 0.694 and 0.698 in the training and validation cohorts, respectively. The time-dependent AUCs at 1, 3, and 5 years were 0.740, 0.739, and 0.762 in the training cohort and 0.742, 0.739, and 0.766 in the validation cohort, respectively. The nomogram constructed based on the model could predict survival probability, and risk stratification showed significant differences in CSS among the three groups.

Conclusion This study developed and internally validated a prognostic model and corresponding nomogram for estimating 1-, 3-, and 5-year CSS in elderly patients with stage M1 NSCLC. The model showed moderate and relatively consistent discriminative ability and may assist prognostic risk stratification.