A novel skeletal muscle quantitative method and deep learning-based sarcopenia diagnosis for cervical cancer patients treated with radiotherapy.

Journal: Medical physics
PMID: 40170435

Abstract

BACKGROUND: Sarcopenia is associated with decreased survival in cervical cancer patients treated with radiotherapy. Cone-beam computed tomography (CBCT) was widely used in image-guided radiotherapy. Sarcopenia is assessed by the skeletal muscle index (SMI) of third lumbar vertebra (L3). Whereas, L3 is usually not included on the cervical cancer radiotherapy CBCT images.

Authors

  • Zhe Wu
    School of Automation, Central South University, Changsha, China.
  • Lihua Deng
    Department of Radiology, The First Affiliated Hospital of the Army Medical University, Chongqing, China.
  • Wanyang Wu
    School of Computer and Communication Engineering, Northeastern University at Qinhuangdao, Qinhuangdao, China.
  • Bin Zeng
    Jiangxi Key Laboratory of Bioprocess Engineering, Jiangxi Science and Technology Normal University, Nanchang, 330013 People's Republic of China.
  • Cheng Xu
    School of Photovoltaic and Renewable Energy Engineering, University of New South Wales, 2052 Sydney, Australia.
  • Li Liu
    Metanotitia Inc., Shenzhen, China.
  • Mujun Liu
    Department of Digital Medicine, School of Biomedical Engineering and Medical Imaging, Army Medical University (Third Military Medical University), Chongqing, China.
  • Yi Wu
    School of International Communication and Arts, Hainan University, Haikou, China.

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