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Magnetic resonance imaging-based artificial intelligence model in rectal cancer

Rectal magnetic resonance imaging (MRI) is the preferred method for the diagnosis of rectal cancer as recommended by the guidelines. Rectal MRI can accurately evaluate the tumor location, tumor stage, invasion depth, extramural vascular invasion, and circumferential resection margin. We summarize th...

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Detalles Bibliográficos
Autores principales: Wang, Pei-Pei, Deng, Chao-Lin, Wu, 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/PMC8117733/
https://www.ncbi.nlm.nih.gov/pubmed/34025068
http://dx.doi.org/10.3748/wjg.v27.i18.2122
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author Wang, Pei-Pei
Deng, Chao-Lin
Wu, Bin
author_facet Wang, Pei-Pei
Deng, Chao-Lin
Wu, Bin
author_sort Wang, Pei-Pei
collection PubMed
description Rectal magnetic resonance imaging (MRI) is the preferred method for the diagnosis of rectal cancer as recommended by the guidelines. Rectal MRI can accurately evaluate the tumor location, tumor stage, invasion depth, extramural vascular invasion, and circumferential resection margin. We summarize the progress of research on the use of artificial intelligence (AI) in rectal cancer in recent years. AI, represented by machine learning, is being increasingly used in the medical field. The application of AI models based on high-resolution MRI in rectal cancer has been increasingly reported. In addition to staging the diagnosis and localizing radiotherapy, an increasing number of studies have reported that AI models based on high-resolution MRI can be used to predict the response to chemotherapy and prognosis of patients.
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spelling pubmed-81177332021-05-20 Magnetic resonance imaging-based artificial intelligence model in rectal cancer Wang, Pei-Pei Deng, Chao-Lin Wu, Bin World J Gastroenterol Minireviews Rectal magnetic resonance imaging (MRI) is the preferred method for the diagnosis of rectal cancer as recommended by the guidelines. Rectal MRI can accurately evaluate the tumor location, tumor stage, invasion depth, extramural vascular invasion, and circumferential resection margin. We summarize the progress of research on the use of artificial intelligence (AI) in rectal cancer in recent years. AI, represented by machine learning, is being increasingly used in the medical field. The application of AI models based on high-resolution MRI in rectal cancer has been increasingly reported. In addition to staging the diagnosis and localizing radiotherapy, an increasing number of studies have reported that AI models based on high-resolution MRI can be used to predict the response to chemotherapy and prognosis of patients. Baishideng Publishing Group Inc 2021-05-14 2021-05-14 /pmc/articles/PMC8117733/ /pubmed/34025068 http://dx.doi.org/10.3748/wjg.v27.i18.2122 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 which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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.
spellingShingle Minireviews
Wang, Pei-Pei
Deng, Chao-Lin
Wu, Bin
Magnetic resonance imaging-based artificial intelligence model in rectal cancer
title Magnetic resonance imaging-based artificial intelligence model in rectal cancer
title_full Magnetic resonance imaging-based artificial intelligence model in rectal cancer
title_fullStr Magnetic resonance imaging-based artificial intelligence model in rectal cancer
title_full_unstemmed Magnetic resonance imaging-based artificial intelligence model in rectal cancer
title_short Magnetic resonance imaging-based artificial intelligence model in rectal cancer
title_sort magnetic resonance imaging-based artificial intelligence model in rectal cancer
topic Minireviews
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8117733/
https://www.ncbi.nlm.nih.gov/pubmed/34025068
http://dx.doi.org/10.3748/wjg.v27.i18.2122
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