Opportunistic Detection of Hepatocellular Carcinoma Using Noncontrast CT and Deep Learning Artificial Intelligence.

Journal: Journal of the American College of Radiology : JACR
PMID: 40044303

Abstract

OBJECTIVE: Hepatocellular carcinoma (HCC) poses a heavy global disease burden; early diagnosis is critical to improve outcomes. Opportunistic screening-the use of imaging data acquired for other clinical indications for disease detection-as well as the role of noncontrast CT have been poorly investigated in the context of HCC. We aimed to develop an artificial intelligence algorithm for efficient and accurate HCC detection using solely noncontrast CTs.

Authors

  • Chengzhi Peng
    Department of Medicine, School of Clinical Medicine, The University of Hong Kong, Hong Kong.
  • Philip Leung Ho Yu
    Department of Statistics and Actuarial Sciences.
  • Jianliang Lu
    Department of Medicine, School of Clinical Medicine, The University of Hong Kong, Hong Kong.
  • Ho Ming Cheng
    Department of Medicine, School of Clinical Medicine, The University of Hong Kong, Hong Kong.
  • Xin-Ping Shen
    Department of Medical Imaging, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China.
  • Keith Wan-Hang Chiu
    Department of Medical Imaging, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China; Consultant in Radiology, Department of Diagnostic and Interventional Radiology, Queen Elizabeth Hospital, Hong Kong; Honorary Associate Professor of the Department of Diagnostic Radiology, Department of Diagnostic Radiology, School of Clinical Medicine, The University of Hong Kong, Hong Kong. Electronic address: kwhchiu@hku.hk.
  • Wai-Kay Seto
    Department of Medicine, School of Clinical Medicine, The University of Hong Kong, Hong Kong; Department of Medicine, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China; Clinical Professor in Gastroenterology and Hepatology, State Key Laboratory of Liver Research, The University of Hong Kong, Hong Kong; Assistant Dean (Research), LKS Faculty of Medicine, The University of Hong Kong. Electronic address: wkseto@hku.hk.

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