A convolutional neural network-based system to classify patients using FDG PET/CT examinations.

Journal: BMC cancer
PMID: 32183748

Abstract

BACKGROUND: As the number of PET/CT scanners increases and FDG PET/CT becomes a common imaging modality for oncology, the demands for automated detection systems on artificial intelligence (AI) to prevent human oversight and misdiagnosis are rapidly growing. We aimed to develop a convolutional neural network (CNN)-based system that can classify whole-body FDG PET as 1) benign, 2) malignant or 3) equivocal.

Authors

  • Keisuke Kawauchi
    Graduate School of Biomedical Science and Engineering, School of Medicine, Hokkaido University, N15 W7, Kita-ku, Sapporo, 0608638, Japan.
  • Sho Furuya
    Department of Diagnostic Imaging, Hokkaido University Graduate School of Medicine, N15 W7, Kita-ku, Sapporo, 0608638, Japan.
  • Kenji Hirata
    Department of Diagnostic Imaging, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Japan.
  • Chietsugu Katoh
    Graduate School of Biomedical Science and Engineering, School of Medicine, Hokkaido University, N15 W7, Kita-ku, Sapporo, 0608638, Japan.
  • Osamu Manabe
    Department of Nuclear Medicine, Hokkaido University Graduate School of Medicine, Hokkaido, 060-8638, Japan. Electronic address: osamumanabe817@med.hokudai.ac.jp.
  • Kentaro Kobayashi
    Department of Diagnostic Imaging, Hokkaido University Graduate School of Medicine, N15 W7, Kita-ku, Sapporo, 0608638, Japan.
  • Shiro Watanabe
    Department of Diagnostic Imaging, Hokkaido University Graduate School of Medicine, N15 W7, Kita-ku, Sapporo, 0608638, Japan.
  • Tohru Shiga
    Department of Nuclear Medicine, Hokkaido University Graduate School of Medicine, Hokkaido, 060-8638, Japan.

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