Deep learning-based CT radiomics predicts prognosis of unresectable hepatocellular carcinoma treated with TACE-HAIC combined with PD-1 inhibitors and tyrosine kinase inhibitors.

Journal: BMC gastroenterology
PMID: 39838292

Abstract

OBJECTIVE: To develop and validate a computed tomography (CT)-based deep learning radiomics model to predict treatment response and progression-free survival (PFS) in patients with unresectable hepatocellular carcinoma (uHCC) treated with transarterial chemoembolization (TACE)-hepatic arterial infusion chemotherapy (HAIC) combined with PD-1 inhibitors and tyrosine kinase inhibitors (TKIs).

Authors

  • Linan Yin
    Department of Interventional Radiology, Harbin Medical University Cancer Hospital, No. 150 Haping Road, Nangang District, Harbin, Heilongjiang Province, 150081, China.
  • Ruibao Liu
    Department of Interventional Radiology, Harbin Medical University Cancer Hospital, No. 150 Haping Road, Nangang District, Harbin, Heilongjiang Province, 150081, China. Liu_ruibao@sina.com.
  • Wei Li
    Department of Nephrology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
  • Shijie Li
    National Laboratory for Parallel and Distributed Processing, National University of Defense Technology, Changsha, China.
  • Xunbo Hou
    Department of Interventional Radiology, Harbin Medical University Cancer Hospital, No. 150 Haping Road, Nangang District, Harbin, Heilongjiang Province, 150081, China.

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