To quantitatively predict the impact of cardiopulmonary dose on overall survival (OS) after radiotherapy for locally advanced non-small cell lung cancer.

We used the NRG Oncology/RTOG 0617 dataset. The model building procedure was preregistered on a public website. Patients were split between a training and a set-aside validation subset ( = 306/131). The 191 candidate variables covered disease, patient, treatment, and dose-volume characteristics from multiple cardiopulmonary substructures (atria, lung, pericardium, and ventricles), including the minimum dose to the hottest x% volume (Dx%[Gy]), mean dose of the hottest x% (MOHx%[Gy]), and minimum, mean (Mean[Gy]), and maximum dose. The model building was based on Cox regression and given 191 candidate variables; a Bonferroni-corrected value threshold of 0.0003 was used to identify predictors. To reduce overreliance on the most highly correlated variables, stepwise multivariable analysis (MVA) was repeated on 1000 bootstrapped replicates. Multivariate sets selected in ≥10% of replicates were fit to the training subset and then averaged to generate a final model. In the validation subset, discrimination was assessed using Harrell -index, and calibration was tested using risk group stratification.

Four MVA models were identified on bootstrap. The averaged model included atria D45%[Gy], lung Mean[Gy], pericardium MOH55%[Gy], and ventricles MOH5%[Gy]. This model had excellent performance predicting OS in the validation subset ( = 0.89).

The risk of death due to cardiopulmonary irradiation was accurately modeled, as demonstrated by predictions on the validation subset, and provides guidance on the delivery of safe thoracic radiotherapy.