AI End-to-End Radiation Treatment Planning Under One Second

TL;DR

A AIRT (Artificial Intelligence-based Radiotherapy), an end-to-end deep-learning framework that directly infers deliverable treatment plans from CT images and structure contours, is introduced, representing a significant step toward ultra-fast standardized RT planning and a streamlined clinical workflow.

Abstract

Artificial intelligence-based radiation therapy (RT) planning has the potential to reduce planning time and inter-planner variability, improving efficiency and consistency in clinical workflows. Most existing automated approaches rely on multiple dose evaluations and corrections, resulting in plan generation times of several minutes. We introduce AIRT (Artificial Intelligence-based Radiotherapy), an end-to-end deep-learning framework that directly infers deliverable treatment plans from CT images and structure contours. AIRT generates single-arc VMAT prostate plans, from imaging and anatomical inputs to leaf sequencing, in under one second on a single Nvidia A100 GPU. The framework includes a differentiable dose feedback, an adversarial fluence map shaping, and a plan generation augmentation to improve plan quality and robustness. The model was trained on more than 10,000 intact prostate cases. Non-inferiority to RapidPlan Eclipse was demonstrated across target coverage and OAR sparing metrics. Target homogeneity (HI = 0.10 $\pm$ 0.01) and OAR sparing were similar to reference plans when evaluated using AcurosXB. These results represent a significant step toward ultra-fast standardized RT planning and a streamlined clinical workflow.

Figures (23)

• Figure 1: AIRT end-to-end pipeline for AI VMAT plan generation.
• Figure 2: Dose–volume histograms (DVHs) for six cases corresponding to the 0th (minimum), 20th, 40th, 50th (median), 80th, and 100th (maximum) percentiles of PTV size in the validation dataset using Eclipse AcurosXB dose engine.
• Figure 3: Averaged DVHs (over the 62 cases of the validation dataset) of the AIRT method for various OAR sparing controls. The "(baseline)" planning, in the legend, means that no input OAR sparing control was used (equivalent to $s_r=s_b=0$). $s_b=2\%$ in the legend means that the dose feedback mechanism tried to decrease the dose in the bladder by $2\%$ voxel-wise (likewise for the rectum when $s_r=2\%$) compared to its input dose distribution.
• Figure 4: Two-stage training of the AIRT end-to-end VMAT planning pipeline. Top: Stage 1 Full pipeline training without adversarial loss. Bottom: Stage 2: Fluence correction network training with adversarial loss.
• Figure S1: Architecture of the DoseProposer network.