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

作者：Sean Domal, Austen Maniscalco, Justin Visak, Michael Dohopolski, Dominic H. Moon, Vladimir Avkshtol, Dan Nguyen, Steve Jiang, David J. Sher, M.H. Lin · 发表于：Journal of Applied Clinical Medical Physics · 年份：2025 · DOI：10.1002/acm2.70045 · 被引用次数：4 · 研究领域：Advanced Radiotherapy Techniques、Radiomics and Machine Learning in Medical Imaging、Prostate Cancer Diagnosis and Treatment

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 ada...