Efficient deep learning-driven workflows for accelerated single-fraction radiation therapy planning of a solitary tumour in the lung.

Background and Purpose: Same-day, single-fraction radiotherapy requires efficient planning. We introduce two deep learning (DL) dose prediction-driven workflows to generate clinically acceptable plans for treating metastases in the lung. Materials and methods: Consecutive patients treated for a tumour in the lung (11/2018-11/2024) were considered. One workflow used the computed tomography density map and a synthetic dose built from the tumour segmentation as inputs for the DL model [ Results: Training and testing datasets contained 200 and 56 treatments, respectively. Without- / with-OOI workflows generated plans in 8.8 and 9.6 min. The 3%/3 mm gamma passing rates between predicted and clinical doses were median ± interquartile range 99.4 ± 0.7% / 99.3 ± 0.8%. After dose optimisation in the TPS, 25% of plans had a higher number of achieved clinical goals relative to clinical plans in both workflows. The conformity was improved (CI Conclusions: Workflows driven by DL dose prediction could generate a clinically acceptable plan in less than ten minutes for same-day, single-fraction treatment of a tumour in the lung.