Artificial intelligence in tumor subregion analysis based on medical imaging: A review

Medical imaging is widely used in the diagnosis and treatment of cancer, and artificial intelligence (AI) has achieved tremendous success in medical image analysis. This paper reviews AI‐based tumor subregion analysis in medical imaging. We summarize the latest AI‐based methods for tumor subregion a...

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Detalles Bibliográficos
Autores principales: Lin, Mingquan, Wynne, Jacob F., Zhou, Boran, Wang, Tonghe, Lei, Yang, Curran, Walter J., Liu, Tian, Yang, Xiaofeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Review Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8292694/
https://www.ncbi.nlm.nih.gov/pubmed/34164913
http://dx.doi.org/10.1002/acm2.13321
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author Lin, Mingquan
Wynne, Jacob F.
Zhou, Boran
Wang, Tonghe
Lei, Yang
Curran, Walter J.
Liu, Tian
Yang, Xiaofeng
author_facet Lin, Mingquan
Wynne, Jacob F.
Zhou, Boran
Wang, Tonghe
Lei, Yang
Curran, Walter J.
Liu, Tian
Yang, Xiaofeng
author_sort Lin, Mingquan
collection PubMed
description Medical imaging is widely used in the diagnosis and treatment of cancer, and artificial intelligence (AI) has achieved tremendous success in medical image analysis. This paper reviews AI‐based tumor subregion analysis in medical imaging. We summarize the latest AI‐based methods for tumor subregion analysis and their applications. Specifically, we categorize the AI‐based methods by training strategy: supervised and unsupervised. A detailed review of each category is presented, highlighting important contributions and achievements. Specific challenges and potential applications of AI in tumor subregion analysis are discussed.
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spelling pubmed-82926942021-07-22 Artificial intelligence in tumor subregion analysis based on medical imaging: A review Lin, Mingquan Wynne, Jacob F. Zhou, Boran Wang, Tonghe Lei, Yang Curran, Walter J. Liu, Tian Yang, Xiaofeng J Appl Clin Med Phys Review Article Medical imaging is widely used in the diagnosis and treatment of cancer, and artificial intelligence (AI) has achieved tremendous success in medical image analysis. This paper reviews AI‐based tumor subregion analysis in medical imaging. We summarize the latest AI‐based methods for tumor subregion analysis and their applications. Specifically, we categorize the AI‐based methods by training strategy: supervised and unsupervised. A detailed review of each category is presented, highlighting important contributions and achievements. Specific challenges and potential applications of AI in tumor subregion analysis are discussed. John Wiley and Sons Inc. 2021-06-24 /pmc/articles/PMC8292694/ /pubmed/34164913 http://dx.doi.org/10.1002/acm2.13321 Text en © 2021 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Review Article
Lin, Mingquan
Wynne, Jacob F.
Zhou, Boran
Wang, Tonghe
Lei, Yang
Curran, Walter J.
Liu, Tian
Yang, Xiaofeng
Artificial intelligence in tumor subregion analysis based on medical imaging: A review
title Artificial intelligence in tumor subregion analysis based on medical imaging: A review
title_full Artificial intelligence in tumor subregion analysis based on medical imaging: A review
title_fullStr Artificial intelligence in tumor subregion analysis based on medical imaging: A review
title_full_unstemmed Artificial intelligence in tumor subregion analysis based on medical imaging: A review
title_short Artificial intelligence in tumor subregion analysis based on medical imaging: A review
title_sort artificial intelligence in tumor subregion analysis based on medical imaging: a review
topic Review Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8292694/
https://www.ncbi.nlm.nih.gov/pubmed/34164913
http://dx.doi.org/10.1002/acm2.13321
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