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      Clinical implementation of artificial intelligence-driven cone-beam computed tomography-guided online adaptive radiotherapy in the pelvic region

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          Abstract

          Background and purpose

          Studies have demonstrated the potential of online adaptive radiotherapy (oART). However, routine use has been limited due to resource demanding solutions. This study reports on experiences with oART in the pelvic region using a novel cone-beam computed tomography (CBCT)-based, artificial intelligence (AI)-driven solution.

          Material and methods

          Automated pre-treatment planning for thirty-nine pelvic cases (bladder, rectum, anal, and prostate), and one hundred oART simulations were conducted in a pre-clinical release of Ethos (Varian Medical Systems, Palo Alto, CA). Plan quality, AI-segmentation accuracy, oART feasibility and an integrated calculation-based quality assurance solution were evaluated. Experiences from the first five clinical oART patients (three bladder, one rectum and one sarcoma) are reported.

          Results

          Auto-generated pre-treatment plans demonstrated similar planning target volume (PTV) coverage and organs at risk doses, compared to institution reference. More than 75% of AI-segmentations during simulated oART required none or minor editing and the adapted plan was superior in 88% of cases. Limitations in AI-segmentation correlated to cases where AI model training was lacking. The five first treated patients complied well with the median adaptive procedure duration of 17.6 min (from CBCT acceptance to treatment delivery start). The treated bladder patients demonstrated a 42% median primary PTV reduction, indicating a 24%-30% reduction in V 45Gy to the bowel cavity, compared to non-ART.

          Conclusions

          A novel commercial oART solution was demonstrated feasible for various pelvic sites. Clinically acceptable AI-segmentation and auto-planning enabled adaptation within reasonable timeslots. Possibilities for reduced PTVs observed for bladder cancer indicated potential for toxicity reductions.

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          Most cited references26

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          Is Open Access

          Phase I trial of stereotactic MR-guided online adaptive radiation therapy (SMART) for the treatment of oligometastatic or unresectable primary malignancies of the abdomen.

          SBRT is used to treat oligometastatic or unresectable primary abdominal malignancies, although ablative dose delivery is limited by proximity of organs-at-risk (OAR). Stereotactic, magnetic resonance (MR)-guided online-adaptive radiotherapy (SMART) may improve SBRT's therapeutic ratio. This prospective Phase I trial assessed feasibility and potential advantages of SMART to treat abdominal malignancies.
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            Automatic treatment planning based on three-dimensional dose distribution predicted from deep learning technique

            To develop an automated treatment planning strategy for external beam intensity-modulated radiation therapy (IMRT), including a deep learning-based three-dimensional (3D) dose prediction and a dose distribution-based plan generation algorithm.
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              • Article: not found

              Online Magnetic Resonance Image Guided Adaptive Radiation Therapy: First Clinical Applications.

              To demonstrate the feasibility of online adaptive magnetic resonance (MR) image guided radiation therapy (MR-IGRT) through reporting of our initial clinical experience and workflow considerations.
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                Author and article information

                Contributors
                Journal
                Phys Imaging Radiat Oncol
                Phys Imaging Radiat Oncol
                Physics and Imaging in Radiation Oncology
                Elsevier
                2405-6316
                18 December 2020
                January 2021
                18 December 2020
                : 17
                : 1-7
                Affiliations
                Department of Oncology, Herlev & Gentofte Hospital, Herlev, Denmark
                Author notes
                [* ]Corresponding author at: Department of Oncology, Herlev & Gentofte Hospital, University of Copenhagen, Radiotherapy Research Unit (52AA), Borgmester Ib Juuls vej 7, DK-2730 Herlev, Denmark. patrik.sibolt@ 123456regionh.dk
                Article
                S2405-6316(20)30081-6
                10.1016/j.phro.2020.12.004
                8057957
                33898770
                8e295591-e1ef-4d36-a189-f6b9eeceb058
                © 2020 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 15 May 2020
                : 3 December 2020
                : 14 December 2020
                Categories
                Original Research Article

                online adaptive radiotherapy (oart),cbct image-guided radiotherapy,artificial intelligence,automated treatment planning,bladder cancer,workflow

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