Outcome modeling techniques for prostate cancer radiotherapy: Data,models, and validation |
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| Affiliation: | 1. Department of Oncology, University of Oxford, Oxford, UK;2. Department of Radiation Oncology, University of Michigan, Ann Arbor, USA;1. Crown Princess Mary Cancer Centre, Westmead, NSW, Australia;2. Institute of Medical Physics, School of Physics, University of Sydney, Camperdown, NSW, Australia;3. Sydney Medical School Nepean, University of Sydney, Kingswood, NSW, Australia;4. Blacktown Cancer & Haematology Centre, Blacktown, NSW, Australia;5. Department of Radiation Oncology, University of Washington, WA;6. Department of Radiation Oncology, Calvary Mater Newcastle Hospital, Newcastle, NSW, Australia;7. School of Health Sciences, University of Newcastle, Newcastle, NSW, Australia;8. Ingham Institute and Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW, Australia;9. Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW, Australia;10. South Western Sydney Clinical School, University of New South Wales, Liverpool, NSW, Australia;1. Cancer Therapy Centre, Liverpool Hospital, Australia;2. South Western Sydney Clinical School, University of New South Wales, Australia;3. Western Sydney University, Australia;4. Ingham Institute of Applied Medical Research, Liverpool Hospital, Australia;5. Centre for Medical Radiation Physics, University of Wollongong, Australia;1. South West Sydney Cancer Services, Liverpool, Australia;2. Ingham Institute for Applied Medical Research, Liverpool, Australia;3. South Western Sydney Clinical School, University of New South Australia, Sydney, Australia;4. Institute of Medical Physics, School of Physics, University of Sydney, Sydney, Australia;5. School of Mathematical and Physical Sciences, University of Newcastle, Newcastle, Australia;6. Centre for Medical Radiation Physics, University of Wollongong, Wollongong, Australia;7. ICON Cancer Centre, Concord, Australia;8. School of Medicine, Western Sydney University, Campbelltown, Australia;9. Sydney Medical School, Public Health, University of Sydney, Sydney, Australia;10. Prince of Wales Hospital, Sydney, Australia;11. School of Medical Sciences, Faculty of Medicine, The University of New South Wales, Sydney, Australia;1. State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Department of Radiation Oncology, Sun Yat-sen University Cancer Center, Guangzhou, People’s Republic of China;1. Department of Radiation Oncology, Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, Australia;2. Australian Institute of Health Innovation, Macquarie University, Sydney, Australia;3. Northern Clinical School, University of Sydney, Sydney, Australia;1. Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, The Netherlands;2. Helmholtz-Zentrum Dresden – Rossendorf, Dresden, Institute of Radiooncology – OncoRay, Dresden, Germany;3. Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany;4. OncoRay – National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden – Rossendorf, Dresden, Germany;5. German Cancer Consortium (DKTK), Partner Site Dresden, and German Cancer Research Center (DKFZ), Heidelberg, Germany;6. Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands;7. Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands;8. Department of Radiation Oncology, Radiotherapiegroep, Arnhem, The Netherlands;9. Department of Radiation Oncology, The Netherlands Cancer Institute – Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands;10. Department of Pulmonary Diseases, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands;11. Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands;12. Biomedical Photonic Imaging Group, MIRA Institute, University of Twente, Enschede, The Netherlands;13. Department for Health Evidence, Radboud University Medical Center, Nijmegen, The Netherlands |
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| Abstract: | Prostate cancer is a frequently diagnosed malignancy worldwide and radiation therapy is a first-line approach in treating localized as well as locally advanced cases. The limiting factor in modern radiotherapy regimens is dose to normal structures, an excess of which can lead to aberrant radiation-induced toxicities. Conversely, dose reduction to spare adjacent normal structures risks underdosing target volumes and compromising local control. As a result, efforts aimed at predicting the effects of radiotherapy could invaluably optimize patient treatments by mitigating such toxicities and simultaneously maximizing biochemical control. In this work, we review the types of data, frameworks and techniques used for prostate radiotherapy outcome modeling. Consideration is given to clinical and dose-volume metrics, such as those amassed by the QUANTEC initiative, and also to newer methods for the integration of biological and genetic factors to improve prediction performance. We furthermore highlight trends in machine learning that may help to elucidate the complex pathophysiological mechanisms of tumor control and radiation-induced normal tissue side effects. |
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| Keywords: | Prostate cancer Radiotherapy Outcomes modeling Machine learning |
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