Advances in artificial intelligence techniques drive the application of radiomics in the clinical research of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) remains the most common malignancy to threaten public health globally. With advances in artificial intelligence techniques, radiomics for HCC management provides a novel perspective to solve unmet needs in clinical settings, and reveals pixel-level radiological informa...

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Bibliographic Details
Published iniLIVER Vol. 1; no. 1; pp. 49 - 54
Main Authors Wei, Jingwei, Niu, Meng, Yabo, Ouyang, Zhou, Yu, Ma, Xiaoke, Yang, Xue, Jiang, Hanyu, Hui, Hui, Cao, Hongyi, Duan, Binwei, Li, Hongjun, Ding, Dawei, Tian, Jie
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.03.2022
Subjects
Artificial intelligence
Diagnosis
Hepatocellular carcinoma
Prognosis
Radiomics
Hepatocellular carcinoma
Prognosis
Diagnosis
Artificial intelligence
Radiomics
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Summary:Hepatocellular carcinoma (HCC) remains the most common malignancy to threaten public health globally. With advances in artificial intelligence techniques, radiomics for HCC management provides a novel perspective to solve unmet needs in clinical settings, and reveals pixel-level radiological information for medical imaging big data, correlating the radiological phenotype with targeted clinical issues. Conventional radiomics pipelines depend on handcrafted engineering features, and further deep learning-based radiomics pipelines are supplemented with deep features calculated via self-learning strategies. During the past decade, radiomics has been widely applied in accurate diagnoses and pathological or biological behavior evaluation, as well as in prognosis prediction. In this review, we systematically introduce the main pipelines of artificial intelligence-based radiomics and their efficacy in the clinical studies of HCC.
ISSN:2772-9478
DOI:10.1016/j.iliver.2022.02.005