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

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.

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 [without-organs of interest (OOI)]. The other workflow additionally used predicted OOI segmentations (with-OOI). Optimisation objectives were extracted from the predicted dose by the treatment planning system (TPS). Generated plans were compared with clinical plans with the number of achieved goals, conformity index CI₁₀₀, and control point modulation complexity score (cpMCS). Plan complexity increases with decreasing cpMCS. Processing times were recorded.

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 (CI₁₀₀ = -5.3% / -3.9% relative to the clinical value, p-values <0.001) at the cost of increased complexity (cpMCS = -19.4% / -19.0%, p-values <0.001).

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.