Development and validation of a nomogram for predicting disease outcomes in elderly females with lung cancer metastatic to the bone

The incidence of lung cancer in elderly women is gradually increasing and the prognosis is even more worrying. Notably, approximately one-third of these patients develop bone metastases. The main objective of this study is to develop and validate a tool to achieve accurate prognostic prediction of elderly female lung cancer patients with bone metastases (EFLCBM) and to further identify their optimal beneficiary population for radiotherapy and chemotherapy. EFLCBM from the Surveillance, Epidemiology, and End Results (SEER) database were included in this study. In addition, patients from our medical institution were collected as the external validation cohort. Univariate and multivariable Cox regression analyses were used to screen independent prognostic factors, and a nomogram model was established. Calibration curves, area under the curve (AUC), and decision curve analysis (DCA) were used to assess the performance of the nomogram. A risk classification system was developed based on the model, depicting Kaplan-Meier curves for subgroup analysis. A total of 3,194 EFLCBM were identified from the SEER database, and 71 patients from our institution were admitted as an external validation cohort. A nomogram model was successfully constructed based on the results of multivariable Cox regression analyses. The AUC values of the model for predicting 12-, 18-, and 24-month overall survival were 0.752, 0.766, and 0.786 in the training cohort; 0.743, 0.727, and 0.716 in the internal validation cohort; and 0.679, 0.633, and 0.690 in the external validation cohort, respectively. The calibration curves showed that the predictions of the model were highly consistent with the actual results. DCA showed that the model could obtain more clinical net benefits within a wide range of threshold probabilities. The results of the subgroup analysis based on the risk classification system showed that chemotherapy was recommended as the mainstay for low-risk patients, and early radiotherapy was more recommended for high-risk patients. Our study constructed a new prognostic model that demonstrated the clinical pathological features and survival analysis of EFLCBM. The results showed that the nomogram had good differentiation and clinical benefits. In addition, based on the risk classification system constructed by the model, we successfully identified the best beneficiary population for radiotherapy and chemotherapy in EFLCBM.