Closing the gap in plan quality: Leveraging deep-learning dose prediction for adaptive radiotherapy.

Abstract

Purpose: Balancing quality and efficiency has been a challenge for online adaptive therapy. Most systems start the online re-optimization with the original planning goals. While some systems allow planners to modify the planning goals, achieving a high-quality plan within time constraints remains a common barrier. This study aims to bolster plan quality by leveraging a deep-learning dose prediction model to predict new planning goals that account for inter-fractional anatomical changes.

Methods: Fine-tuned patient-specific (FT-PS) models were clinically evaluated to accurately predict dose for 23 adaptive fractions of 15 head-and-neck (H&N) patients treated with Ethos ART. The original adapted plan from the adaptive treatment session was used as the quality baseline. Based on physician-approved adaptive treatment contours, the FT-PS model predicted subsequent planning goals for high-impact organs at risk (OARs). These goals were retrospectively re-optimized in Ethos to compare the original adapted plan (IOE-Auto Plan) with the newly re-optimized plan (AI-guided IOE Plan). A physician blindly selected the preferred plan.

Results: Dose savings were observed for nine high impact OAR's including the constrictor, ipsilateral/contralateral parotid, ipsilateral/contralateral submandibular gland, oral cavity, and esophagus, mandible and larynx with a maximum value of 5.47 Gy. Of the 23 plans reviewed in the blind observer study, 19 re-optimized plans were chosen over the original adapted session plan.

Conclusions: Our preliminary results demonstrate the feasibility of utilizing an AI dose predictor to predict optimal planning goals with anatomical changes, thereby improving adaptive plan quality. This method is feasible for both online and offline adaptive radiotherapy (ART) and has the potential to significantly enhance treatment outcomes for head-and-neck (H&N) cancer patients.