Quality assurance-based optimization (QAO): Towards improving patient-specific quality assurance in volumetric modulated arc therapy plans using machine learning |
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| Affiliation: | 1. Department of Physics and Astronomy, Louisiana State University and Agricultural and Mechanical College, 202 Tower Drive, Baton Rouge, LA 70803-4001, USA;2. Department of Physics, Mary Bird Perkins Cancer Center, 4950 Essen Lane, Baton Rouge, LA 70809, USA;1. Princess Margaret Cancer Centre, University Health Network, Department of Radiation Oncology, University of Toronto, 610 University Avenue, Toronto, Ontario M5G 2M9, Canada;2. Modus QA, London, Ontario N6H 5L6, Canada;3. Cross Cancer Institute, Alberta Health Services, Department of Radiation Oncology, University of Alberta, 11560 University Avenue, Edmonton, AB T6G 1Z2, Canada;1. Department of Medical Physics, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162 Milan, Italy;2. Department of Radiology, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162 Milan, Italy;3. Postgraduate School of Diagnostic and Interventional Radiology, Università degli Studi di Milano, via Festa del Perdono 7, 20122, Milan, Italy;4. Department of Physics, Università degli Studi di Milano, via Giovanni Celoria 16, 20133 Milan, Italy;5. Department of Physics, INFN Sezione di Milano, via Giovanni Celoria 16, 20133 Milan, Italy;6. Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, via Festa del Perdono 7, 20122, Milan, Italy;1. Instituto de Fı́sica, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil;2. Instituto de Radioproteção e Dosimetria, IRD/CNEN, Rio de Janeiro, Brazil;3. Oncologia D’Or São Cristóvão, Rede D’Or São Luiz, Rio de Janeiro, Brazil;1. Institute of Medical Physics, School of Physics, The University of Sydney, Sydney, New South Wales, Australia;2. School of Medicine, Taif University, Taif, Saudi Arabia;3. Liverpool and Macarthur Cancer Therapy Centers, Liverpool, NSW, Australia;4. Ingham Institute for Applied Medical Research, Sydney, NSW, Australia;5. Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW, Australia;6. South Western Sydney Clinical School, University of New South Wales, Sydney, NSW, Australia;1. Laboratory of Experimental Radiotherapy, Department of Oncology, KU Leuven – University of Leuven, Belgium;2. Department of Radiation Oncology, University Hospitals Leuven, Belgium |
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| Abstract: | IntroductionPrevious literature has shown general trade-offs between plan complexity and resulting quality assurance (QA) outcomes. However, existing solutions for controlling this trade-off do not guarantee corresponding improvements in deliverability. Therefore, this work explored the feasibility of an optimization framework for directly maximizing predicted QA outcomes of plans without compromising the dosimetric quality of plans designed with an established knowledge-based planning (KBP) technique.Materials and MethodsA support vector machine (SVM) was developed – using a database of 500 previous VMAT plans – to predict gamma passing rates (GPRs; 3%/3mm percent dose-difference/distance-to-agreement with local normalization) based on selected complexity features. A heuristic, QA-based optimization (QAO) framework was devised by utilizing the SVM model to iteratively modify mechanical treatment features most commonly associated with suboptimal GPRs. Specifically, leaf gaps (LGs) <50 mm were widened by random amounts, which impacts all aperture-based complexity features. 13 prostate KBP-guided VMAT plans were optimized via QAO using user-specified maximum LG displacements before corresponding changes in predicted GPRs and dose were assessed.ResultsPredicted GPRs increased by an average of 1.14 ± 1.25% (p = 0.006) with QAO using a 3 mm maximum random LG displacement. There were small differences in dose, resulting in similarly small changes in tumor control probability (maximum increase = 0.05%) and normal tissue complication probabilities in the bladder, rectum, and femoral heads (maximum decrease = 0.2% in the rectum).ConclusionThis study explored the feasibility of QAO and warrants future investigations of further incorporating QA endpoints into plan optimization. |
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| Keywords: | Radiation therapy treatment planning Volumetric modulated arc therapy Patient-specific quality assurance Machine learning Knowledge-based planning |
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