Abstract

Purpose: NSCLC SBRT with 50 Gy/5 fractions is sometimes considered controversial, as the nominal BED of 100 Gy is felt by some to be insufficient for long-term local control of some lesions. In this study, we analyzed such patients using explainable deep learning techniques, and consequently proposed appropriate treatment planning criteria. These novel criteria could help planners achieve optimized treatment plans, for maximal local control. Methods and MaterialsA total of 535 Patients treated with 50 Gy/ 5 fractions were used to develop a novel deep learning local response model. A multi-modality approach, incorporating CT images, 3D dose distribution and patient demographics, combined with a discrete-time survival model was applied to predict time to failure and the probability of local control. Subsequently, an integrated Grad-CAM method was used to identify the most significant dose-volume metrics predictive of local failure and their optimal cut-points. ResultsThe model was cross-validated showing an acceptable performance (c-index: 0.72, 95% CI: 0.68-0.75); the testing c-index was 0.69. The model's spatial attention was concentrated mostly in the tumors’ periphery (PTV-IGTV) region. Statistically significant dose-volume metrics in improved local control were BED Dnear-min≥103.8 Gy in IGTV (HR, 0.31; 95% CI, 015-0.63), V104≥98% in IGTV (HR, 0.30; 95% CI, 0.15-0.60), gEUD≥103.8 Gy in PTV-IGTV (HR, 0.25; 95% CI, 0.12-0.50) and Dmean≥104.5 Gy in PTV-IGTV (HR, 0.25; 95% CI, 0.12-0.51). ConclusionsDeep learning identified dose-volume metrics have shown significant prognostic power (Logrank, p=0.003) and could be used as additional actionable criteria for treatment planning in NSCLC SBRT patients receiving 50 Gy in 5 fractions. While our data do not confirm or refute that a significantly higher BED for the prescription dose is necessary for tumor control in NSCLC, it might be clinically effective to escalate the nominal prescribed dose from BED 100 Gy to 105 Gy.

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