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Noblet C, Maunet M, Duthy M, Coste F, Moreau M. A TPS integrated machine learning tool for predicting patient-specific quality assurance outcomes in volumetric-modulated arc therapy. Phys Med 2024; 118:103208. [PMID: 38211462 DOI: 10.1016/j.ejmp.2024.103208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 11/28/2023] [Accepted: 01/04/2024] [Indexed: 01/13/2024] Open
Abstract
PURPOSE Machine learning (ML) models have been demonstrated to be beneficial for optimizing the workload of patient-specific quality assurance (PSQA). Implementing them in clinical routine frequently requires third-party applications beyond the treatment planning system (TPS), slowing down the workflow. To address this issue, a PSQA outcomes predictive model was carefully selected and validated before being fully integrated into the TPS. MATERIALS AND METHODS Nine ML algorithms were evaluated using cross-validation. The learning database was built by calculating complexity metrics (CM) and binarizing PSQA results into "pass"/"fail" classes for 1767 VMAT arcs. The predictive performance was evaluated using area under the ROC curve (AUROC), sensitivity, and specificity. The ML model was integrated into the TPS via a C# script. Script-guided reoptimization impact on PSQA and dosimetric results was evaluated on ten VMAT plans with "fail"-predicted arcs. Workload reduction potential was also assessed. RESULTS The selected model exhibited an AUROC of 0.88, with a sensitivity and specificity exceeding 50 % and 90 %, respectively. The script-guided reoptimization of the ten evaluated plans led to an average improvement of 1.4 ± 0.9 percentage points in PSQA results, while preserving the quality of the dose distribution. A yearly savings of about 140 h with the use of the script was estimated. CONCLUSIONS The proposed script is a valuable complementary tool for PSQA measurement. It was efficiently integrated into the clinical workflow to enhance PSQA outcomes and reduce PSQA workload by decreasing the risk of failing QA and thereby, the need for repeated replanning and measurements.
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Affiliation(s)
- Caroline Noblet
- Department of Medical Physics, Clinique Mutualiste de l'Estuaire, Cité Sanitaire, Saint-Nazaire, France.
| | - Mathis Maunet
- Department of Medical Physics, Clinique Mutualiste de l'Estuaire, Cité Sanitaire, Saint-Nazaire, France
| | - Marie Duthy
- Department of Medical Physics, Clinique Mutualiste de l'Estuaire, Cité Sanitaire, Saint-Nazaire, France
| | - Frédéric Coste
- Department of Medical Physics, Clinique Mutualiste de l'Estuaire, Cité Sanitaire, Saint-Nazaire, France
| | - Matthieu Moreau
- Department of Medical Physics, Clinique Mutualiste de l'Estuaire, Cité Sanitaire, Saint-Nazaire, France
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Bertholet J, Mackeprang PH, Mueller S, Guyer G, Loebner HA, Wyss Y, Frei D, Volken W, Elicin O, Aebersold DM, Fix MK, Manser P. Organ-at-risk sparing with dynamic trajectory radiotherapy for head and neck cancer: comparison with volumetric arc therapy on a publicly available library of cases. Radiat Oncol 2022; 17:122. [PMID: 35841098 PMCID: PMC9284789 DOI: 10.1186/s13014-022-02092-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Accepted: 06/30/2022] [Indexed: 11/10/2022] Open
Abstract
Background Dynamic trajectory radiotherapy (DTRT) extends volumetric modulated arc therapy (VMAT) with dynamic table and collimator rotation during beam-on. The aim of the study is to establish DTRT path-finding strategies, demonstrate deliverability and dosimetric accuracy and compare DTRT to state-of-the-art VMAT for common head and neck (HN) cancer cases. Methods A publicly available library of seven HN cases was created on an anthropomorphic phantom with all relevant organs-at-risk (OARs) delineated. DTRT plans were generated with beam incidences minimizing fractional target/OAR volume overlap and compared to VMAT. Deliverability and dosimetric validation was carried out on the phantom. Results DTRT and VMAT had similar target coverage. For three locoregionally advanced oropharyngeal carcinomas and one adenoid cystic carcinoma, mean dose to the contralateral salivary glands, pharynx and oral cavity was reduced by 2.5, 1.7 and 3.1 Gy respectively on average with DTRT compared to VMAT. For a locally recurrent nasopharyngeal carcinoma, D0.03 cc to the ipsilateral optic nerve was above tolerance (54.0 Gy) for VMAT (54.8 Gy) but within tolerance for DTRT (53.3 Gy). For a laryngeal carcinoma, DTRT resulted in higher dose than VMAT to the pharynx and brachial plexus but lower dose to the upper oesophagus, thyroid gland and contralateral carotid artery. For a single vocal cord irradiation case, DTRT spared most OARs better than VMAT. All plans were delivered successfully on the phantom and dosimetric validation resulted in gamma passing rates of 93.9% and 95.8% (2%/2 mm criteria, 10% dose threshold). Conclusions This study provides a proof of principle of DTRT for common HN cases with plans that were deliverable on a C-arm linac with high accuracy. The comparison with VMAT indicates substantial OAR sparing could be achieved. Supplementary Information The online version contains supplementary material available at 10.1186/s13014-022-02092-5.
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Affiliation(s)
- Jenny Bertholet
- Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital and University of Bern, Friedbühlschulhaus, 3010, Bern, Switzerland.
| | - Paul-Henry Mackeprang
- Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital and University of Bern, Friedbühlschulhaus, 3010, Bern, Switzerland
| | - Silvan Mueller
- Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital and University of Bern, Friedbühlschulhaus, 3010, Bern, Switzerland
| | - Gian Guyer
- Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital and University of Bern, Friedbühlschulhaus, 3010, Bern, Switzerland
| | - Hannes A Loebner
- Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital and University of Bern, Friedbühlschulhaus, 3010, Bern, Switzerland
| | - Yanick Wyss
- Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital and University of Bern, Friedbühlschulhaus, 3010, Bern, Switzerland.,Department of Physics, ETH Zurich, Zurich, Switzerland
| | - Daniel Frei
- Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital and University of Bern, Friedbühlschulhaus, 3010, Bern, Switzerland
| | - Werner Volken
- Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital and University of Bern, Friedbühlschulhaus, 3010, Bern, Switzerland
| | - Olgun Elicin
- Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital and University of Bern, Friedbühlschulhaus, 3010, Bern, Switzerland
| | - Daniel M Aebersold
- Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital and University of Bern, Friedbühlschulhaus, 3010, Bern, Switzerland
| | - Michael K Fix
- Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital and University of Bern, Friedbühlschulhaus, 3010, Bern, Switzerland
| | - Peter Manser
- Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital and University of Bern, Friedbühlschulhaus, 3010, Bern, Switzerland
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