Evaluating machine learning enhanced intelligent‐optimization‐engine (IOE) performance for ethos head‐and‐neck (HN) plan generation

作者：Justin Visak, Enobong Inam, Boyu Meng, Siqiu Wang, David Parsons, Dan Nyugen, Tingliang Zhang, Dominic H. Moon, Vladimir Avkshtol, Steve Jiang, David J. Sher, Mu‐Han Lin · 发表于：Journal of Applied Clinical Medical Physics · 年份：2023 · DOI：10.1002/acm2.13950 · 被引用次数：24 · 研究领域：Advanced Radiotherapy Techniques、Advances in Oncology and Radiotherapy、Radiomics and Machine Learning in Medical Imaging

PURPOSE: Varian Ethos utilizes novel intelligent-optimization-engine (IOE) designed to automate the planning. However, this introduced a black box approach to plan optimization and challenge for planners to improve plan quality. This study aims to evaluate machine-learning-guided initial reference plan generation approaches for head & neck (H&N) adaptive radiotherapy (ART). METHODS: Twenty previously treated patients treated on C-arm/Ring-mounted were retroactively re-planned in the Ethos planning system using a fixed 18-beam intensity-modulated radiotherapy (IMRT) template. Clinical goals for IOE input were generated using (1) in-house deep-learning 3D-dose predictor (AI-Guided) (2) commercial knowledge-based planning (KBP) model with universal RTOG-based population criteria (KBP-RTOG) and (3) an RTOG-based constraint template only (RTOG) for in-depth analysis of IOE sensitivity. Similar training data was utilized for both models. Plans were optimized until their respective criteria were achieved or DVH-estimation band was satisfied. Plans were normalized such that the highest PTV dose level received 95% coverage. Target coverage, high-impact organs-at-risk (OAR) and plan deliverability was assessed in comparison to clinical (benchmark) plans. Statistical significance was evaluated using a paired two-tailed student t-test. RESULTS: AI-guided plans were superior to both KBP-RTOG and RTOG-only plans with respect to clinical benchmark cases. Overall, OAR doses were comparable o...