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.