A support vector machine tool for adaptive tomotherapy treatments: Prediction of head and neck patients criticalities |
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| Institution: | 1. Medical Physics Department, Az. Ospedaliero-Universitaria di Modena, Italy;2. Radiation Oncology Department, Az. Ospedaliero-Universitaria di Modena, Italy;3. Physics Department, University of Bologna, Italy;4. Post-graduate School in Medical Physics, University of Bologna, Italy;1. Institute of Clinical Research, University of Southern Denmark, Denmark;2. Laboratory of Radiation Physics, Odense University Hospital, Denmark;3. Department of Oncology, Odense University Hospital, Denmark;4. Division of Biostatistics and Bioinformatics, University of Maryland Greenebaum Cancer Center, and Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD;1. Radiation Oncology Department, Princess Alexandra Hospital, Brisbane, Australia;2. Queensland University of Technology, Institute of Health and Biomedical Innovation, Brisbane, Australia;3. Radiation Oncology Department, Radiation Oncology Mater Centre, Brisbane, Australia;4. Radiation Oncology Department, Royal Brisbane and Women’s Hospital, Australia;5. School of Medicine, University of Queensland, Brisbane, Australia;1. Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California;2. Department of Oncology and Radiotherapy, University Hospital, Hradec Kralove, Czech Republic;3. Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas;4. Department of Radiation Oncology, Washington University in St Louis, St Louis, Missouri;1. Istituto di Bioimmagini e Fisiologia Molecolare (IBFM), CNR, Segrate, Italy;2. Dept of Medical Physics, San Raffaele Scientific Institute, Milano, Italy;3. Radiation Oncology, The Johns Hopkins University, Baltimore, USA;1. University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, The Netherlands;2. University of Groningen, University Medical Center Groningen, Department of Epidemiology, The Netherlands;1. Department of Radiation Oncology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands;2. Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, the Netherlands |
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| Abstract: | PurposeAdaptive radiation therapy (ART) is an advanced field of radiation oncology. Image-guided radiation therapy (IGRT) methods can support daily setup and assess anatomical variations during therapy, which could prevent incorrect dose distribution and unexpected toxicities. A re-planning to correct these anatomical variations should be done daily/weekly, but to be applicable to a large number of patients, still require time consumption and resources. Using unsupervised machine learning on retrospective data, we have developed a predictive network, to identify patients that would benefit of a re-planning.Methods1200 MVCT of 40 head and neck (H&N) cases were re-contoured, automatically, using deformable hybrid registration and structures mapping. Deformable algorithm and MATLAB® homemade machine learning process, developed, allow prediction of criticalities for Tomotherapy treatments.ResultsUsing retrospective analysis of H&N treatments, we have investigated and predicted tumor shrinkage and organ at risk (OAR) deformations. Support vector machine (SVM) and cluster analysis have identified cases or treatment sessions with potential criticalities, based on dose and volume discrepancies between fractions. During 1st weeks of treatment, 84% of patients shown an output comparable to average standard radiation treatment behavior. Starting from the 4th week, significant morpho-dosimetric changes affect 77% of patients, suggesting need for re-planning. The comparison of treatment delivered and ART simulation was carried out with receiver operating characteristic (ROC) curves, showing monotonous increase of ROC area.ConclusionsWarping methods, supported by daily image analysis and predictive tools, can improve personalization and monitoring of each treatment, thereby minimizing anatomic and dosimetric divergences from initial constraints. |
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| Keywords: | Adaptive radiation therapy Cluster analysis Support vector machines ROC curves |
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