A deep learning-based clinical-radiomics model predicting the treatment response of immune checkpoint inhibitors (ICIs)-based conversion therapy in potentially convertible hepatocelluar carcinoma patients: a tumor marker prognostic study.

Journal: International journal of surgery (London, England)
Published Date:

Abstract

BACKGROUND: The majority of patients with hepatocellular carcinoma (HCC) miss the opportunity of radical resection, making immune check-point inhibitors (ICIs)-based conversion therapy a primary option. However, challenges persist in predicting response and identifying the optimal patient subset. The objective is to develop a CT-based clinical-radiomics model to predict durable clinical benefit (DCB) of ICIs-based treatment in potentially convertible HCC patients.

Authors

  • Zijian Lin
    Department of Hepatobiliary Surgery, Sun Yat-Sen Memorial Hospital, Guangzhou, Guangdong, China.
  • Weidong Wang
    Zhejiang Huade New Materials Co., Ltd., Zhejiang Province, Hangzhou, China.
  • Yongcong Yan
    Department of Hepatobiliary Surgery, Sun Yat-Sen Memorial Hospital, Guangzhou, Guangdong, China.
  • Zifeng Ma
    Shanghai Public Health Clinical Center, Shanghai, China.
  • Zhiyu Xiao
    Department of Hepatobiliary Surgery, Sun Yat-Sen Memorial Hospital, Guangzhou, Guangdong, China.
  • Kai Mao
    Department of Hepatobiliary Surgery, Sun Yat-Sen Memorial Hospital, Guangzhou, Guangdong, China.