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Predictive gamma passing rate of 3D detector array-based volumetric modulated arc therapy quality assurance for prostate cancer via deep learning

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Abstract

To predict the gamma passing rate (GPR) of the three-dimensional (3D) detector array-based volumetric modulated arc therapy (VMAT) quality assurance (QA) for prostate cancer using a convolutional neural network (CNN) with the 3D dose distribution. One hundred thirty-five VMAT plans for prostate cancer were selected: 110 plans were used for training and validation, and 25 plans were used for testing. Verification plans were measured using a helical 3D diode array (ArcCHECK). The dose distribution on the detector element plane of these verification plans was used as input data for the CNN model. The measured GPR (mGPR) values were used as the training data. The CNN model comprises eighteen layers and predicted GPR (pGPR) values. The mGPR and pGPR values were compared, and a cumulative frequency histogram of the prediction error was created to clarify the prediction error tendency. The correlation coefficients of pGPR and mGPR were 0.67, 0.69, 0.66, and 0.73 for 3%/3-mm, 3%/2-mm, 2%/3-mm, and 2%/2-mm gamma criteria, respectively. The respective mean±standard deviations of pGPR-mGPR were -0.87±2.18%, -0.65±2.93%, -0.44±2.53%, and -0.71±3.33%. The probabilities of false positive error cases (pGPR < mGPR) were 72%, 60%, 68%, and 56% for each gamma criterion. We developed a deep learning-based prediction model of the 3D detector array-based VMAT QA for prostate cancer, and evaluated the accuracy and tendency of prediction GPR. This model can provide a proactive estimation for the results of the patient-specific QA before the verification measurement.

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Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Authors and Affiliations

  1. Hiroshima High-Precision Radiotherapy Cancer Center, 732-0057, Hiroshima, Japan

    Takaaki Matsuura, Hideharu Miura, Kiyoshi Yamada, Shuichi Ozawa & Yasushi Nagata

  2. Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, 734-8551, Hiroshima, Japan

    Takaaki Matsuura, Daisuke Kawahara, Hideharu Miura, Shuichi Ozawa & Yasushi Nagata

  3. Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University Hospital, 1-2-3 Kasumi, Minami, 734-8551, Hiroshima, Japan

    Akito Saito

Authors
  1. Takaaki Matsuura
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  2. Daisuke Kawahara
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  3. Akito Saito
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  4. Hideharu Miura
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  5. Kiyoshi Yamada
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  6. Shuichi Ozawa
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  7. Yasushi Nagata
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by TM. The first draft of the manuscript was written by TM and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Daisuke Kawahara.

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Institutional Review Board Statement: This study was approved by the Institutional Review Board of Hiroshima University Hospital (IRB number: E-1656-3).

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Matsuura, T., Kawahara, D., Saito, A. et al. Predictive gamma passing rate of 3D detector array-based volumetric modulated arc therapy quality assurance for prostate cancer via deep learning. Phys Eng Sci Med 45, 1073–1081 (2022). https://doi.org/10.1007/s13246-022-01172-w

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  • DOI: https://doi.org/10.1007/s13246-022-01172-w

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