Impact of contrast-enhanced agent on segmentation using a deep learning-based software "Ai-Seg" for head and neck cancer.

Journal: The British journal of radiology
Published Date: May 26, 2025

Abstract

OBJECTIVES: In radiotherapy, auto-segmentation tools using deep learning assist in contouring organs-at-risk (OARs). We developed a segmentation model for head and neck (HN) OARs dedicated to contrast-enhanced (CE) computed tomography (CT) using the segmentation software, Ai-Seg, and compared the performance between CE and non-CE (nCE) CT.

Authors

  • Sayaka Kihara
    Department of Medical Physics and Engineering, Osaka University Graduate School of Medicine, 1-7 Yamadaoka, Suita, Osaka, 565-0871, Japan.
  • Yoshihiro Ueda
    Department of Radiation Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka 541-8567, Japan; Department of Radiation Oncology, Osaka University Graduate School of Medicine, Suita, Japan.
  • Shuichi Harada
    Hyogo Ion Beam Medical Support, 1-2-1 Kouto Shingu-cho, Tatsuno City, Hyogo, 679-5165, Japan.
  • Akira Masaoka
    Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan.
  • Naoyuki Kanayama
    Department of Radiation Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka 541-8567, Japan.
  • Toshiki Ikawa
    Department of Radiation Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka, 541-8567, Japan.
  • Shoki Inui
    Department of Radiation Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka, 541-8567, Japan.
  • Takashi Akagi
    Hyogo Ion Beam Medical Center, Tatsuno, Japan.
  • Teiji Nishio
    Medical Physics Laboratory, Division of Health Science, Graduate School of Medicine, Osaka University, Osaka, Japan.
  • Koji Konishi
    Department of Radiation Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka, 537-8567, Japan.

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