Systematic method for a deep learning-based prediction model for gamma evaluation in patient-specific quality assurance of volumetric modulated arc therapy.

Journal: Medical physics
Published Date: Jan 28, 2021

Abstract

PURPOSE: This study aimed to develop and evaluate a novel strategy for establishing a deep learning-based gamma passing rate (GPR) prediction model for volumetric modulated arc therapy (VMAT) using dummy target plan data, one measurement process, and a multicriteria prediction method.

Authors

  • Seiji Tomori
    Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan.
  • Noriyuki Kadoya
    Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan. kadoya.n@rad.med.tohoku.ac.jp.
  • Tomohiro Kajikawa
    Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan.
  • Yuto Kimura
    Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan; Radiation Oncology Center, Ofuna Chuo Hospital, Kamakura, Japan.
  • Kakutarou Narazaki
    Department of Radiology, National Hospital Organization Sendai Medical Center, Sendai, Miyagi, 983-8520, Japan.
  • Takahiro Ochi
    Department of Radiology, National Hospital Organization Sendai Medical Center, Sendai, Miyagi, 983-8520, Japan.
  • Keiichi Jingu
    Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan.

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