Evaluation of AI-derived LAD artery dose metrics for survival stratification in stage III NSCLC: A secondary analysis of RTOG 0617 trial.

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Abstract

While the radiation dose to left anterior descending (LAD) artery is associated with overall survival (OS) in NSCLC patients, manual segmentation is labor-intensive. Several auto-segmentation models were developed on average 4DCT with promising geometric agreement, yet their feasibility in clinical outcome analysis on lung CT remains unclear. This study evaluates whether automated LAD segmentation can reproduce, at the group level, the established dose-survival association in a multi-institutional clinical trial dataset. Three deep learning-based auto-segmentation (DLAS) models for automatic segmentation of the LAD coronary artery were applied to 460 patients from the NRG Oncology/RTOG 0617 dataset. Kaplan-Meier curves and Cox proportional hazard ratio (HR) were calculated using each model's segmentation. The LAD V15 Gy was used to stratify patients into high-and low-risk groups, and the cut-off values were determined by minimizing mean absolute error (MAE) between DLAS-derived and manual OS curves. All three models demonstrated significantly different OS curves between the high risk and low risk groups (p < 0.05). The HR was 1.40 (95% CI: 1.02-1.93; p = 0.0381; MAE = 7.1%), 1.32 (95% CI: 1.03-1.68; p = 0.0258; MAE = 9.6%), and 1.37 (95% CI: 1.03-1.84; p = 0.0331; MAE = 7.9%) for the three models using a common V15 Gy ≥ 10% cut-off. After optimizing the cut-off, one model aligned well with what was derived from manual segmentation (MAE = 4.3%). Among the evaluated models, one DL model showed the strongest performance of survival groups separation and achieved group-level overall survival stratification comparable to that previously reported using manual contours in the multi-institutional cohort. Further validation with paired manual contours and pre-specified cutoffs is warranted.

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