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Wada T, Kawahara D, Murakami Y, Nakashima T, Nagata Y. Robust optimization of VMAT for prostate cancer accounting for geometric uncertainty. J Appl Clin Med Phys 2022; 23:e13738. [PMID: 35920105 PMCID: PMC9512334 DOI: 10.1002/acm2.13738] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 06/28/2022] [Accepted: 07/11/2022] [Indexed: 11/07/2022] Open
Abstract
The aim of this study was to propose optimal robust planning by comparing the robustness with setup error with the robustness of a conventional planning target volume (PTV)‐based plan and to compare the robust plan to the PTV‐based plan for the target and organ at risk (OAR). Data from 13 patients with intermediate‐to‐high‐risk localized prostate cancer who did not have T3b disease were analyzed. The dose distribution under multiple setup error scenarios was assessed using a conventional PTV‐based plan. The clinical target volume (CTV) and OAR dose in moving coordinates were used for the dose constraint with the robust plan. The hybrid robust plan added the dose constraint of the PTV‐rectum to the static coordinate system. When the isocenter was shifted by 10 mm in the superior–inferior direction and 8 mm in the right‐left and anterior directions, the doses to the CTV, bladder, and rectum of the PTV‐based plan, robust plan, and hybrid robust plan were compared. For the CTV D99% in the PTV‐based plan and hybrid robust plan, over 95% of the prescribed dose was secured in all directions, except in the inferior direction. There was no significant difference between the PTV‐based plan and the hybrid robust plan for rectum V70Gy, V60Gy, and V40Gy. This study proposed an optimization method for patients with prostate cancer. When the setup error occurred within the PTV margin, the dose robustness of the CTV for the hybrid robust plan was higher than that of the PTV‐based plan, while maintaining the equivalent OAR dose.
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Affiliation(s)
- Takuya Wada
- Section of Radiation Therapy, Department of Clinical Practice and Support, Hiroshima University Hospital, Minami-ku, Japan
| | - Daisuke Kawahara
- Department of Radiation Oncology, Institute of Biomedical and Health Sciences, Hiroshima University Hospital, Minami-ku, Japan
| | - Yuji Murakami
- Department of Radiation Oncology, Institute of Biomedical and Health Sciences, Hiroshima University Hospital, Minami-ku, Japan
| | - Takeo Nakashima
- Section of Radiation Therapy, Department of Clinical Practice and Support, Hiroshima University Hospital, Minami-ku, Japan
| | - Yasushi Nagata
- Department of Radiation Oncology, Institute of Biomedical and Health Sciences, Hiroshima University Hospital, Minami-ku, Japan
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Abstract
The delineation of organs at risk is the basis of radiotherapy oncologists' work. Indeed, the knowledge of this delineation enables to better identify the target volumes and to optimize dose distribution, involving the prognosis of the patients but also their future. The learning of this delineation must continue throughout the clinician's career. Some contour changes have appeared with better imaging, some volumes are now required due to development of knowledge of side effects. In addition, the increasing survival time of patients requires to be more systematic and precise in the delineations, both to avoid complications until now exceptional but also because re-irradiations are becoming more and more frequent. We present the update of the recommendations of the French Society for Radiation Oncology (SFRO) on new findings or adaptations to volumes at risk.
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Affiliation(s)
- G Noël
- Department of Radiation Oncology, Institut de Cancérologie Strasbourg Europe (ICANS), 17, rue Albert-Calmette, BP 23025, 67033 Strasbourg, France.
| | - C Le Fèvre
- Department of Radiation Oncology, Institut de Cancérologie Strasbourg Europe (ICANS), 17, rue Albert-Calmette, BP 23025, 67033 Strasbourg, France
| | - D Antoni
- Department of Radiation Oncology, Institut de Cancérologie Strasbourg Europe (ICANS), 17, rue Albert-Calmette, BP 23025, 67033 Strasbourg, France
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Conroy L, Khalifa A, Berlin A, McIntosh C, Purdie TG. Performance stability evaluation of atlas-based machine learning radiation therapy treatment planning in prostate cancer. Phys Med Biol 2021; 66. [PMID: 34156354 DOI: 10.1088/1361-6560/abfff0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Accepted: 05/11/2021] [Indexed: 11/12/2022]
Abstract
Atlas-based machine learning (ML) for radiation therapy (RT) treatment planning is effective at tailoring dose distributions to account for unique patient anatomies by selecting the most appropriate patients from the training database (atlases) to inform dose prediction for new patients. However, variations in clinical practice between the training dataset and a new patient to be planned may impact ML performance by confounding atlas selection. In this study, we simulated various contouring practices in prostate cancer RT to investigate the impact of changing input data on atlas-based ML treatment planning. We generated 225 ML plans for nine bespoke contouring protocol scenarios (reduced target margins, modified organ-at-risk (OAR) definitions, and inclusion of optional OARs less represented in the training database) on 25 patient datasets by applying a single, previously trained and validated ML model for prostate cancer followed by dose mimicking to create a final deliverable plan. ML treatment plans for each scenario were compared to base ML treatment plans that followed a contouring protocol consistent with the model training data. ML performance was evaluated based on atlas distance metrics that are calculated during ML dose prediction. There were significant changes between atlases selected for the base ML treatment plans and treatment plans when planning target volume margins were reduced and/or optional OARs were included. The deliverability of ML predicted dose distributions based on gamma analysis between predicted and mimicked final deliverable dose showed significant differences for seven out of eight scenarios compared with the base ML treatment plans. Overall, there were small but statistically significant dosimetric changes in predicted and mimicked dose with addition of optional OAR contours. This work presents a framework for benchmarking and performance monitoring of ML treatment planning algorithms in the context of evolving clinical practices.
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Affiliation(s)
- Leigh Conroy
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada.,Techna Institute, University Health Network, Toronto, Canada
| | - Aly Khalifa
- Department of Medical Biophysics, Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Alejandro Berlin
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada.,Techna Institute, University Health Network, Toronto, Canada
| | - Chris McIntosh
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Canada.,Department of Medical Biophysics, Faculty of Medicine, University of Toronto, Toronto, Canada.,Techna Institute, University Health Network, Toronto, Canada.,Peter Munk Cardiac Centre, University Health Network, Toronto, Canada.,Joint Department of Medical Imaging, University Health Network, Toronto, Canada.,Vector Institute, Toronto, Canada
| | - Thomas G Purdie
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada.,Department of Medical Biophysics, Faculty of Medicine, University of Toronto, Toronto, Canada.,Techna Institute, University Health Network, Toronto, Canada
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