Machine learning automated treatment planning for online magnetic resonance guided adaptive radiotherapy of prostate cancer.

Autor:	Khalifa A; Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.; Toronto General Hospital Research Institute, University Health Network, Toronto, Ontario, Canada., Winter JD; Medical Physics, Princess Margaret Cancer Centre, Toronto, Ontario, Canada.; Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada.; Princess Margaret Cancer Research Institute, University Health Network, Toronto, Ontario, Canada., Tadic T; Medical Physics, Princess Margaret Cancer Centre, Toronto, Ontario, Canada.; Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada.; Princess Margaret Cancer Research Institute, University Health Network, Toronto, Ontario, Canada., Purdie TG; Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.; Medical Physics, Princess Margaret Cancer Centre, Toronto, Ontario, Canada.; Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada.; Princess Margaret Cancer Research Institute, University Health Network, Toronto, Ontario, Canada., McIntosh C; Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.; Toronto General Hospital Research Institute, University Health Network, Toronto, Ontario, Canada.; Peter Munk Cardiac Centre and Ted Rogers Centre for Heart Research, University Health Network, Toronto, Ontario, Canada.; Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.; Joint Department of Medical Imaging, University Health Network, Toronto, Ontario, Canada.; Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada.; Vector Institute, Toronto, Ontario, Canada.
Jazyk:	angličtina
Zdroj:	Physics and imaging in radiation oncology [Phys Imaging Radiat Oncol] 2024 Sep 14; Vol. 32, pp. 100649. Date of Electronic Publication: 2024 Sep 14 (Print Publication: 2024).
DOI:	10.1016/j.phro.2024.100649
Abstrakt:	Background and Purpose: No best practices currently exist for achieving high quality radiation therapy (RT) treatment plan adaptation during magnetic resonance (MR) guided RT of prostate cancer. This study validates the use of machine learning (ML) automated RT treatment plan adaptation and benchmarks it against current clinical RT plan adaptation methods. Materials and Methods: We trained an atlas-based ML automated treatment planning model using reference MR RT treatment plans (42.7 Gy in 7 fractions) from 46 patients with prostate cancer previously treated at our institution. For a held-out test set of 38 patients, retrospectively generated ML RT plans were compared to clinical human-generated adaptive RT plans for all 266 fractions. Differences in dose-volume metrics and clinical objective pass rates were evaluated using Wilcoxon tests (p < 0.05) and Exact McNemar tests (p < 0.05), respectively. Results: Compared to clinical RT plans, ML RT plans significantly increased sparing and objective pass rates of the rectum, bladder, and left femur. The mean ± standard deviation of rectum D20 and D50 in ML RT plans were 2.5 ± 2.2 Gy and 1.6 ± 1.3 Gy lower than clinical RT plans, respectively, with 14 % higher pass rates; bladder D40 was 4.6 ± 2.9 Gy lower with a 20 % higher pass rate; and the left femur D5 was 0.8 ± 1.8 Gy lower with a 7 % higher pass rate. Conclusions: ML automated RT treatment plan adaptation increases robustness to interfractional anatomical changes compared to current clinical adaptive RT practices by increasing compliance to treatment objectives. Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Thomas G Purdie and Chris McIntosh receive royalties from RaySearch Laboratories for machine-learning based treatment planning methods. (Crown Copyright © 2024 Published by Elsevier B.V. on behalf of European Society of Radiotherapy & Oncology.)
Databáze:	MEDLINE
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