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Radiomics: A radiological evidence-based artificial intelligence technique to facilitate personalized precision medicine in hepatocellular carcinoma

  • Jingwei Wei
    Affiliations
    Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, PR. China

    Beijing Key Laboratory of Molecular Imaging, Beijing 100190, PR. China
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  • Hanyu Jiang
    Affiliations
    Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, PR. China
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  • Yu Zhou
    Affiliations
    Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, PR. China

    Beijing Key Laboratory of Molecular Imaging, Beijing 100190, PR. China

    School of Life Science and Technology, Xidian University, Xi'an, PR. China
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  • Jie Tian
    Correspondence
    Co-corresponding author.
    Affiliations
    Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, PR. China

    Beijing Key Laboratory of Molecular Imaging, Beijing 100190, PR. China

    Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, 100191, PR. China

    Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, PR. China
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  • Felipe S. Furtado
    Affiliations
    Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, United States

    Harvard Medical School, 25 Shattuck St, Boston, MA 02115, United States
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  • Onofrio A. Catalano
    Correspondence
    Corresponding author. Onofrio A. Catalano, Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, White 270, Boston, Massachusetts, USA.
    Affiliations
    Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, United States

    Harvard Medical School, 25 Shattuck St, Boston, MA 02115, United States
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Published:January 13, 2023DOI:https://doi.org/10.1016/j.dld.2022.12.015

      Abstract

      The high postoperative recurrence rates in hepatocellular carcinoma (HCC) remain a major hurdle in its management. Appropriate staging and treatment selection may alleviate the extent of fatal recurrence. However, effective methods to preoperatively evaluate pathophysiologic and molecular characteristics of HCC are lacking. Imaging plays a central role in HCC diagnosis and stratification due to the non-invasive diagnostic criteria. Vast and crucial information is hidden within image data. Other than providing a morphological sketch for lesion diagnosis, imaging could provide new insights to describe the pathophysiological and genetic landscape of HCC. Radiomics aims to facilitate diagnosis and prognosis of HCC using artificial intelligence techniques to harness the immense information contained in medical images. Radiomics produces a set of archetypal and robust imaging features that are correlated to key pathological or molecular biomarkers to preoperatively risk-stratify HCC patients. Inferred with outcome data, comprehensive combination of radiomic, clinical and/or multi-omics data could also improve direct prediction of response to treatment and prognosis. The evolution of radiomics is changing our understanding of personalized precision medicine in HCC management. Herein, we review the key techniques and clinical applications in HCC radiomics and discuss current limitations and future opportunities to improve clinical decision making.

      Keywords

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