Integration of the M6 Cyberknife in the Moderato Monte Carlo platform and prediction of beam parameters using machine learning |
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| Institution: | 1. Department of Medical Physics, Centre Oscar Lambret, Lille, France;2. Faculty of Biomedical Sciences, University of Brussels ULB, Belgium;3. University of Lille, CNRS, CRIStAL, Centrale Lille, France;4. Department of Radiation Therapy, Institut Jules Bordet, Brussels, Belgium;5. Department of Medical Physics, Institut Jules Bordet, Brussels, Belgium;1. Department of Radiation Oncology, Dong Nai General Hospital, Bien Hoa, Viet Nam;2. Chi Anh Medical Technology Co., Ltd., Ho Chi Minh, Viet Nam;3. Faculty of Physics & Engineering Physics, University of Science, Ho Chi Minh, Viet Nam;4. PTW-Asia Pacific Ltd, Hong Kong;1. Department of Radiation Physics, Unit 94, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA;2. Department of Radiation Oncology, Unit 97, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA;1. Tulane University School of Medicine, New Orleans, LA;2. Department of Internal Medicine, Baton Rouge General Hospital, Baton Rouge, LA;3. Department of Radiation Oncology, Pennington Cancer Center, Baton Rouge, LA;1. Medical Physics Department, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Torino, Italy;2. Institute of Radiological Sciences, University of Sassari, Italy;3. Radiotherapy Department, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Torino, Italy;4. Radiotherapy, Centro Aktis, Marano di Napoli, Italy;5. U.O. Unità Fegato, A.O. Moscati, Avellino, Italy;6. Radiation Oncology Department, University Hospital, Verona, Italy;7. Radiotherapy Department, S. Camillo-Forlanini, Roma, Italy;8. Radiotherapy Department, Oncologic Businco Hospital, Cagliari, Italy;9. Hyperthermia Service, Centro Medico Serena, Padova, Italy |
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| Abstract: | PurposeThis work describes the integration of the M6 Cyberknife in the Moderato Monte Carlo platform, and introduces a machine learning method to accelerate the modelling of a linac.MethodsThe MLC-equipped M6 Cyberknife was modelled and integrated in Moderato, our in-house platform offering independent verification of radiotherapy dose distributions. The model was validated by comparing TPS dose distributions with Moderato and by film measurements. Using this model, a machine learning algorithm was trained to find electron beam parameters for other M6 devices, by simulating dose curves with varying spot size and energy. The algorithm was optimized using cross-validation and tested with measurements from other institutions equipped with a M6 Cyberknife.ResultsOptimal agreement in the Monte Carlo model was reached for a monoenergetic electron beam of 6.75 MeV with Gaussian spatial distribution of 2.4 mm FWHM. Clinical plan dose distributions from Moderato agreed within 2% with the TPS, and film measurements confirmed the accuracy of the model. Cross-validation of the prediction algorithm produced mean absolute errors of 0.1 MeV and 0.3 mm for beam energy and spot size respectively. Prediction-based simulated dose curves for other centres agreed within 3% with measurements, except for one device where differences up to 6% were detected.ConclusionsThe M6 Cyberknife was integrated in Moderato and validated through dose re-calculations and film measurements. The prediction algorithm was successfully applied to obtain electron beam parameters for other M6 devices. This method would prove useful to speed up modelling of new machines in Monte Carlo systems. |
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| Keywords: | Cyberknife Treatment planning Monte Carlo Machine learning |
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