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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.

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Publication history

Received: 29 December 2021
Revised: 15 February 2022
Accepted: 15 February 2022
Published: 10 March 2022
Issue date: March 2022

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© 2022 Published by Elsevier Ltd on behalf of Tsinghua University Press.

Acknowledgements

Acknowledgements

This study has received funding by the National Key Research and Development Program of China under Grant 2017YFA0700401 and 2021YFC2500402, Ministry of Science and Technology of China under Grant No. 2017YFA0205200, National Natural Science Foundation of China under Grant No. 82001917, 81930053, 82090052, 82090051, 82093219055, 81227901, 92159202 and 81527805, Beijing Natural Science Foundation under Grant No. L192061, the Project of High-Level Talents Team Introduction in Zhuhai City.

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This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

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