When your MR linac is down: Can an automated pipeline bail you out of trouble? |
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| Institution: | 1. Fondazione Policlinico Universitario ‘‘A. Gemelli’’ IRCCS, Rome, Italy;2. Università Cattolica del Sacro Cuore, Rome, Italy;3. Tecnologie Avanzate, Torino, Italy;1. Radiation Application Research School, Nuclear Science and Technology Research Institute (NSTRI), Tehran, Iran;2. Department of Medical Radiation Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran;3. Medical Physics Department, School of Medicine, Iran University of Medical Sciences, P.O. Box: 14155-6183 Tehran, Iran;1. Cancer Center of Southeastern Ontario, 25 King Street West, Kingston, Ontario K7L 5P9, Canada;2. Simcoe Muskoka Regional Cancer Centre, Royal Victoria Regional Health Centre, 201 Georgian Drive, Barrie, Ontario L4M 6M2, Canada;3. R.S. McLaughlin Durham Regional Cancer Centre, Lakeridge Health Oshawa, 1 Hospital Court, Oshawa, Ontario, L1G 2B9, Canada;4. Odette Cancer Centre, Sunnybrook Health Sciences Centre, T-wing 2075 Bayview Avenue TG 260, Toronto, Ontario M4N 3M5, Canada;5. Princess Margaret Cancer Centre, Princess Margaret Hospital, 610 University Avenue, Toronto, Ontario M5G 2C1, Canada;6. Juravinski Cancer Centre – Hamilton Health Sciences, 699 Concession Street, Hamilton, Ontario L8V 5C2, Canada;1. Department of Medical Physics and Radiation Protection, Hospital Universitario La Paz, Madrid, Spain;2. Servicio de Radiofísica y Protección Radiológica, ESI/OSI Donostialdea, Donostia, Spain;3. Department of Nuclear Medicine, Hospital Universitario La Paz, Madrid, Spain;4. Servicio de Radiofísica y Protección Radiológica. Hospital Universitario Rey Juan Carlos, Madrid, Spain;5. Independent researcher, Madrid, Spain;1. Trillium Health Partners, Peel Regional Cancer Centre, Mississauga, ON, Canada;2. University of Toronto, Department of Radiation Oncology, Toronto, ON, Canada;3. University of Ryerson, Department of Physics, Toronto, Canada;4. Sunnybrook Health Sciences Centre, Toronto, Canada;5. York University, Department of Physics and Astronomy, Toronto, ON, Canada;1. Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Aichi 461-8673, Japan;2. Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi 466-8550, Japan;3. Department of Radiological Technology, Nagoya University Hospital, Nagoya, Aichi 466-8560, Japan |
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| Abstract: | PurposeThe unique treatment delivery technique provided by magnetic resonance guided radiotherapy (MRgRT) can represent a significant drawback when system fail occurs. This retrospective study proposes and evaluates a pipeline to completely automate the workflow necessary to shift a MRgRT treatment to a traditional radiotherapy linac.Material and methodsPatients undergoing treatment during the last MRgRT system failure were retrospectively included in this study. The core of the proposed pipeline was based on a tool able to mimic the original MR linac dose distribution. The so obtained dose distribution (AUTO) has been compared with the distribution obtained in the conventional radiotherapy linac (MAN). Plan comparison has been performed in terms of time required to obtain the final dose distribution, DVH parameters, dosimetric indices and visual analogue scales scoring by radiation oncologists.ResultsAUTO plans generation has been obtained within 10 min for all the considered cases. All AUTO plans were found to be within clinical tolerance, showing a mean target coverage variation of 1.7% with a maximum value of 4.3% and a minimum of 0.6% when compared with MAN plans. The highest OARs mean variation has been found for rectum V60 (6.7%). Dosimetric indices showed no relevant differences, with smaller gradient measure in favour of AUTO plans. Visual analogue scales scoring has confirmed comparable plan quality for AUTO plans.ConclusionThe proposed workflow allows a fast and accurate generation of automatic treatment plans. AUTO plans can be considered equivalent to MAN ones, with limited clinical impact in the worst-case scenario. |
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| Keywords: | Autoplanning MRgRT Dose mimicking System downtime Treatment interruption |
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