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Texture Analysis in Brain Tumor MR Imaging
Texture analysis, as well as its broader category radiomics, describes a variety of techniques for image analysis that quantify the variation in surface intensity or patterns, including some that are imperceptible to the human visual system. Cerebral gliomas have been most rigorously studied in brai...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Japanese Society for Magnetic Resonance in Medicine
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9199980/ https://www.ncbi.nlm.nih.gov/pubmed/33692222 http://dx.doi.org/10.2463/mrms.rev.2020-0159 |
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author | Kunimatsu, Akira Yasaka, Koichiro Akai, Hiroyuki Sugawara, Haruto Kunimatsu, Natsuko Abe, Osamu |
author_facet | Kunimatsu, Akira Yasaka, Koichiro Akai, Hiroyuki Sugawara, Haruto Kunimatsu, Natsuko Abe, Osamu |
author_sort | Kunimatsu, Akira |
collection | PubMed |
description | Texture analysis, as well as its broader category radiomics, describes a variety of techniques for image analysis that quantify the variation in surface intensity or patterns, including some that are imperceptible to the human visual system. Cerebral gliomas have been most rigorously studied in brain tumors using MR-based texture analysis (MRTA) to determine the correlation of various clinical measures with MRTA features. Promising results in cerebral gliomas have been shown in the previous MRTA studies in terms of the correlation with the World Health Organization grades, risk stratification in gliomas, and the differentiation of gliomas from other brain tumors. Multiple MRTA studies in gliomas have repeatedly shown high performance of entropy, a measure of the randomness in image intensity values, of either histogram- or gray-level co-occurrence matrix parameters. Similarly, researchers have applied MRTA to other brain tumors, including meningiomas and pediatric posterior fossa tumors. However, the value of MRTA in the clinical use remains undetermined, probably because previous studies have shown only limited reproducibility of the result in the real world. The low-to-modest generalizability may be attributed to variations in MRTA methods, sampling bias that originates from single-institution studies, and overfitting problems to a limited number of samples. To enhance the reliability and reproducibility of MRTA studies, researchers have realized the importance of standardizing methods in the field of radiomics. Another advancement is the recent development of a comprehensive assessment system to ensure the quality of a radiomics study. These two-way approaches will secure the validity of upcoming MRTA studies. The clinical use of texture analysis in brain MRI will be accelerated by these continuous efforts. |
format | Online Article Text |
id | pubmed-9199980 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Japanese Society for Magnetic Resonance in Medicine |
record_format | MEDLINE/PubMed |
spelling | pubmed-91999802022-07-06 Texture Analysis in Brain Tumor MR Imaging Kunimatsu, Akira Yasaka, Koichiro Akai, Hiroyuki Sugawara, Haruto Kunimatsu, Natsuko Abe, Osamu Magn Reson Med Sci Review Texture analysis, as well as its broader category radiomics, describes a variety of techniques for image analysis that quantify the variation in surface intensity or patterns, including some that are imperceptible to the human visual system. Cerebral gliomas have been most rigorously studied in brain tumors using MR-based texture analysis (MRTA) to determine the correlation of various clinical measures with MRTA features. Promising results in cerebral gliomas have been shown in the previous MRTA studies in terms of the correlation with the World Health Organization grades, risk stratification in gliomas, and the differentiation of gliomas from other brain tumors. Multiple MRTA studies in gliomas have repeatedly shown high performance of entropy, a measure of the randomness in image intensity values, of either histogram- or gray-level co-occurrence matrix parameters. Similarly, researchers have applied MRTA to other brain tumors, including meningiomas and pediatric posterior fossa tumors. However, the value of MRTA in the clinical use remains undetermined, probably because previous studies have shown only limited reproducibility of the result in the real world. The low-to-modest generalizability may be attributed to variations in MRTA methods, sampling bias that originates from single-institution studies, and overfitting problems to a limited number of samples. To enhance the reliability and reproducibility of MRTA studies, researchers have realized the importance of standardizing methods in the field of radiomics. Another advancement is the recent development of a comprehensive assessment system to ensure the quality of a radiomics study. These two-way approaches will secure the validity of upcoming MRTA studies. The clinical use of texture analysis in brain MRI will be accelerated by these continuous efforts. Japanese Society for Magnetic Resonance in Medicine 2021-03-10 /pmc/articles/PMC9199980/ /pubmed/33692222 http://dx.doi.org/10.2463/mrms.rev.2020-0159 Text en ©2021 Japanese Society for Magnetic Resonance in Medicine https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) |
spellingShingle | Review Kunimatsu, Akira Yasaka, Koichiro Akai, Hiroyuki Sugawara, Haruto Kunimatsu, Natsuko Abe, Osamu Texture Analysis in Brain Tumor MR Imaging |
title | Texture Analysis in Brain Tumor MR Imaging |
title_full | Texture Analysis in Brain Tumor MR Imaging |
title_fullStr | Texture Analysis in Brain Tumor MR Imaging |
title_full_unstemmed | Texture Analysis in Brain Tumor MR Imaging |
title_short | Texture Analysis in Brain Tumor MR Imaging |
title_sort | texture analysis in brain tumor mr imaging |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9199980/ https://www.ncbi.nlm.nih.gov/pubmed/33692222 http://dx.doi.org/10.2463/mrms.rev.2020-0159 |
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