Automated evaluation of masseter muscle volume: deep learning prognostic approach in oral cancer.

Journal: BMC cancer
PMID: 38267924

Abstract

BACKGROUND: Sarcopenia has been identified as a potential negative prognostic factor in cancer patients. In this study, our objective was to investigate the relationship between the assessment method for sarcopenia using the masseter muscle volume measured on computed tomography (CT) images and the life expectancy of patients with oral cancer. We also developed a learning model using deep learning to automatically extract the masseter muscle volume and investigated its association with the life expectancy of oral cancer patients.

Authors

  • Katsuya Sakamoto
    Department of Oral and Maxillofacial Surgery, Graduate School of Dentistry, Osaka University, 1-8 Yamada-Oka, 565-0871, Suita, Osaka, Japan.
  • Shin-Ichiro Hiraoka
    Department of Oral and Maxillofacial Surgery, Graduate School of Dentistry, Osaka University, 1-8 Yamada-Oka, 565-0871, Suita, Osaka, Japan. hirashins2@gmail.com.
  • Kohei Kawamura
    The 1st Dept. of Oral and Maxillofacial Surgery, Graduate School of Dentistry, Osaka University.
  • Peiying Ruan
    NVIDIA, Santa Clara, CA, USA.
  • Shuji Uchida
    Department of Oral and Maxillofacial Surgery, Graduate School of Dentistry, Osaka University, 1-8 Yamada-Oka, 565-0871, Suita, Osaka, Japan.
  • Ryo Akiyama
    Department of Oral and Maxillofacial Surgery, Graduate School of Dentistry, Osaka University, 1-8 Yamada-Oka, 565-0871, Suita, Osaka, Japan.
  • Chonho Lee
    Cybermedia Center, Osaka University, Suita, Japan.
  • Kazuki Ide
    Division of Scientific Information and Public Policy, Center for Infectious Disease Education and Research (CiDER), Osaka University.
  • Susumu Tanaka
    Department of Oral and Maxillofacial Surgery, Graduate School of Dentistry, Osaka University, 1-8 Yamada-Oka, 565-0871, Suita, Osaka, Japan.

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