Deep Learning Algorithm for Fully Automated Detection of Small (≤4 cm) Renal Cell Carcinoma in Contrast-Enhanced Computed Tomography Using a Multicenter Database.

Journal: Investigative radiology
Published Date: May 1, 2022

Abstract

OBJECTIVES: Renal cell carcinoma (RCC) is often found incidentally in asymptomatic individuals undergoing abdominal computed tomography (CT) examinations. The purpose of our study is to develop a deep learning-based algorithm for fully automated detection of small (≤4 cm) RCCs in contrast-enhanced CT images using a multicenter database and to evaluate its performance.

Authors

  • Naoki Toda
    Department of Radiology, Keio University School of Medicine, Tokyo, Japan.
  • Masahiro Hashimoto
    Department of Radiology, Keio University School of Medicine, Tokyo, Japan. m.hashimoto@rad.med.keio.ac.jp.
  • Yuki Arita
    From the Department of Radiology, Keio University School of Medicine, Tokyo.
  • Hasnine Haque
    GE Healthcare, Tokyo, Japan.
  • Hirotaka Akita
    Preferred Networks, Tokyo, 1000004, Japan.
  • Toshiaki Akashi
  • Hideo Gobara
    Department of Radiology, Okayama University Hospital, 2-5-1 Shikata-cho kita-ku, Okayama, 700-8558, Japan.
  • Akihiro Nishie
    Department of Radiology, Graduate School of Medical Science, University of the Ryukyus, 1076 Kiyuna, Ginowan-shi, Okinawa, 901-2720, Japan.
  • Masahiro Yakami
    Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan.
  • Atsushi Nakamoto
    From the Department of Radiology, Osaka University Graduate School of Medicine.
  • Takeyuki Watadani
    Department of Radiology, Faculty of Medicine, The University of Tokyo.
  • Mototsugu Oya
    Department of Urology Keio University School of Medicine Tokyo Japan.
  • Masahiro Jinzaki
    Department of Radiology, Keio University School of Medicine, Tokyo, Japan.

Keywords

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