Denoised recurrence label-based deep learning for prediction of postoperative recurrence risk and sorafenib response in HCC.

Journal: BMC medicine
PMID: 40102873

Abstract

BACKGROUND: Pathological images of hepatocellular carcinoma (HCC) contain abundant tumor information that can be used to stratify patients. However, the links between histology images and the treatment response have not been fully unveiled.

Authors

  • Yixin Li
    Digestive System Department, Affiliated Hospital of North Sichuan Medical College, Nanchong, China.
  • Ji Xiong
    Department of Pathology, Huashan Hospital, Fudan University, 12 Wulumuqi Rd. Middle, Shanghai, 200040, China.
  • Zhiqiu Hu
    School of Automation, Qingdao University, Qingdao 266071, China.
  • Qimeng Chang
    Department of Hepatobiliary and Pancreatic Surgery, Minhang Hospital, Fudan University, Shanghai, 201199, China.
  • Ning Ren
    Key Laboratory of Whole-Period Monitoring and Precise Intervention of Digestive Cancer, Shanghai Municipal Health Commission, Minhang Hospital, Fudan University, No. 170 XinSong Road, Minhang, Shanghai, 201199, China. ren.ning@zs-hospital.sh.cn.
  • Fan Zhong
    College of Electrical Engineering, Sichuan University, Chengdu, China.
  • Qiongzhu Dong
    Key Laboratory of Whole-Period Monitoring and Precise Intervention of Digestive Cancer, Shanghai Municipal Health Commission, Minhang Hospital, Fudan University, No. 170 XinSong Road, Minhang, Shanghai, 201199, China. qzhdong@fudan.edu.cn.
  • Lei Liu
    Department of Science and Technology, Beijing Shijitan Hospital, Capital Medical University, Beijing, China.

Keywords

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