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Radiomics in hepatocellular carcinoma: A state-of-the-art review

Hepatocellular carcinoma (HCC) is the most common cancer and the second major contributor to cancer-related mortality. Radiomics, a burgeoning technology that can provide invisible high-dimensional quantitative and mineable data derived from routine-acquired images, has enormous potential for HCC ma...

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Autores principales: Yao, Shan, Ye, Zheng, Wei, Yi, Jiang, Han-Yu, Song, Bin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Baishideng Publishing Group Inc 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8603458/
https://www.ncbi.nlm.nih.gov/pubmed/34853638
http://dx.doi.org/10.4251/wjgo.v13.i11.1599
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author Yao, Shan
Ye, Zheng
Wei, Yi
Jiang, Han-Yu
Song, Bin
author_facet Yao, Shan
Ye, Zheng
Wei, Yi
Jiang, Han-Yu
Song, Bin
author_sort Yao, Shan
collection PubMed
description Hepatocellular carcinoma (HCC) is the most common cancer and the second major contributor to cancer-related mortality. Radiomics, a burgeoning technology that can provide invisible high-dimensional quantitative and mineable data derived from routine-acquired images, has enormous potential for HCC management from diagnosis to prognosis as well as providing contributions to the rapidly developing deep learning methodology. This article aims to review the radiomics approach and its current state-of-the-art clinical application scenario in HCC. The limitations, challenges, and thoughts on future directions are also summarized.
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spelling pubmed-86034582021-11-30 Radiomics in hepatocellular carcinoma: A state-of-the-art review Yao, Shan Ye, Zheng Wei, Yi Jiang, Han-Yu Song, Bin World J Gastrointest Oncol Review Hepatocellular carcinoma (HCC) is the most common cancer and the second major contributor to cancer-related mortality. Radiomics, a burgeoning technology that can provide invisible high-dimensional quantitative and mineable data derived from routine-acquired images, has enormous potential for HCC management from diagnosis to prognosis as well as providing contributions to the rapidly developing deep learning methodology. This article aims to review the radiomics approach and its current state-of-the-art clinical application scenario in HCC. The limitations, challenges, and thoughts on future directions are also summarized. Baishideng Publishing Group Inc 2021-11-15 2021-11-15 /pmc/articles/PMC8603458/ /pubmed/34853638 http://dx.doi.org/10.4251/wjgo.v13.i11.1599 Text en ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved. https://creativecommons.org/licenses/by-nc/4.0/This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/
spellingShingle Review
Yao, Shan
Ye, Zheng
Wei, Yi
Jiang, Han-Yu
Song, Bin
Radiomics in hepatocellular carcinoma: A state-of-the-art review
title Radiomics in hepatocellular carcinoma: A state-of-the-art review
title_full Radiomics in hepatocellular carcinoma: A state-of-the-art review
title_fullStr Radiomics in hepatocellular carcinoma: A state-of-the-art review
title_full_unstemmed Radiomics in hepatocellular carcinoma: A state-of-the-art review
title_short Radiomics in hepatocellular carcinoma: A state-of-the-art review
title_sort radiomics in hepatocellular carcinoma: a state-of-the-art review
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8603458/
https://www.ncbi.nlm.nih.gov/pubmed/34853638
http://dx.doi.org/10.4251/wjgo.v13.i11.1599
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