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Deep learning in hepatocellular carcinoma: Current status and future perspectives
Hepatocellular carcinoma (HCC) is among the leading causes of cancer incidence and death. Despite decades of research and development of new treatment options, the overall outcomes of patients with HCC continue to remain poor. There are areas of unmet need in risk prediction, early diagnosis, accura...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Baishideng Publishing Group Inc
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8727204/ https://www.ncbi.nlm.nih.gov/pubmed/35070007 http://dx.doi.org/10.4254/wjh.v13.i12.2039 |
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author | Ahn, Joseph C Qureshi, Touseef Ahmad Singal, Amit G Li, Debiao Yang, Ju-Dong |
author_facet | Ahn, Joseph C Qureshi, Touseef Ahmad Singal, Amit G Li, Debiao Yang, Ju-Dong |
author_sort | Ahn, Joseph C |
collection | PubMed |
description | Hepatocellular carcinoma (HCC) is among the leading causes of cancer incidence and death. Despite decades of research and development of new treatment options, the overall outcomes of patients with HCC continue to remain poor. There are areas of unmet need in risk prediction, early diagnosis, accurate prognostication, and individualized treatments for patients with HCC. Recent years have seen an explosive growth in the application of artificial intelligence (AI) technology in medical research, with the field of HCC being no exception. Among the various AI-based machine learning algorithms, deep learning algorithms are considered state-of-the-art techniques for handling and processing complex multimodal data ranging from routine clinical variables to high-resolution medical images. This article will provide a comprehensive review of the recently published studies that have applied deep learning for risk prediction, diagnosis, prognostication, and treatment planning for patients with HCC. |
format | Online Article Text |
id | pubmed-8727204 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Baishideng Publishing Group Inc |
record_format | MEDLINE/PubMed |
spelling | pubmed-87272042022-01-20 Deep learning in hepatocellular carcinoma: Current status and future perspectives Ahn, Joseph C Qureshi, Touseef Ahmad Singal, Amit G Li, Debiao Yang, Ju-Dong World J Hepatol Minireviews Hepatocellular carcinoma (HCC) is among the leading causes of cancer incidence and death. Despite decades of research and development of new treatment options, the overall outcomes of patients with HCC continue to remain poor. There are areas of unmet need in risk prediction, early diagnosis, accurate prognostication, and individualized treatments for patients with HCC. Recent years have seen an explosive growth in the application of artificial intelligence (AI) technology in medical research, with the field of HCC being no exception. Among the various AI-based machine learning algorithms, deep learning algorithms are considered state-of-the-art techniques for handling and processing complex multimodal data ranging from routine clinical variables to high-resolution medical images. This article will provide a comprehensive review of the recently published studies that have applied deep learning for risk prediction, diagnosis, prognostication, and treatment planning for patients with HCC. Baishideng Publishing Group Inc 2021-12-27 2021-12-27 /pmc/articles/PMC8727204/ /pubmed/35070007 http://dx.doi.org/10.4254/wjh.v13.i12.2039 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 | Minireviews Ahn, Joseph C Qureshi, Touseef Ahmad Singal, Amit G Li, Debiao Yang, Ju-Dong Deep learning in hepatocellular carcinoma: Current status and future perspectives |
title | Deep learning in hepatocellular carcinoma: Current status and future perspectives |
title_full | Deep learning in hepatocellular carcinoma: Current status and future perspectives |
title_fullStr | Deep learning in hepatocellular carcinoma: Current status and future perspectives |
title_full_unstemmed | Deep learning in hepatocellular carcinoma: Current status and future perspectives |
title_short | Deep learning in hepatocellular carcinoma: Current status and future perspectives |
title_sort | deep learning in hepatocellular carcinoma: current status and future perspectives |
topic | Minireviews |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8727204/ https://www.ncbi.nlm.nih.gov/pubmed/35070007 http://dx.doi.org/10.4254/wjh.v13.i12.2039 |
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