Novel Dose Criteria for Lung Cancer SBRT to Improve Local Control in Patients Treated to 50 Gy/5 Fractions Using Deep Learning Methods and Explainability Techniques

A novel deep learning model for prediction of LR, incorporating 3D dose data, CT images and patient demographics, was developed and tested. Grad-CAM demonstrated superior significance of peripheral (PTV-GTV) dose features. Subsequently determined optimal cut-points have significant prognostic power (log rank, p<0.001) and could be used as additional criteria in treatment planning. While these data have repercussions in treatment planning, they do not suggest that a significantly higher BED for the prescription dose is necessary for tumor control in NSCLC. Nevertheless, it might be effective to slightly elevate the prescribed dose, i.e., from 100 Gy BED to 104 Gy BED.