Random forests to predict survival of octogenarians with brain metastases from nonsmall‐cell lung cancer

Background: To create and validate nomograms for the personalized prediction of survival in octogenarians with newly diagnosed nonsmall‐cell lung cancer (NSCLC) with sole brain metastases (BMs). Methods: Random forests (RF) were applied to identify independent prognostic factors for building nomogram models. The predictive accuracy of the model was evaluated based on the receiver operating characteristic (ROC) curve, C‐index, and calibration plots. Results: The area under the curve (AUC) values for overall survival at 6, 12, and 18 months in the validation cohort were 0.837, 0.867, and 0.849, respectively; the AUC values for cancer‐specific survival prediction were 0.819, 0.835, and 0.818, respectively. The calibration curves visualized the accuracy of the model. Conclusion: The new nomograms have good predictive power for survival among octogenarians with sole BMs related to NSCLC.