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Added value of histogram analysis of apparent diffusion coefficient maps for differentiating triple-negative breast cancer from other subtypes of breast cancer on standard MRI

BACKGROUND: Triple-negative breast cancers generally occur in young women with remarkable potential to be aggressive. It will be of great help to detect this subtype of tumor early. To retrospectively evaluate the performance of histogram analysis of apparent diffusion coefficient (ADC) maps in dist...

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Autores principales: Liu, Hong-Li, Zong, Min, Wei, Han, Wang, Cong, Lou, Jian-Juan, Wang, Si-Qi, Zou, Qi-Gui, Jiang, Yan-Ni
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6735623/
https://www.ncbi.nlm.nih.gov/pubmed/31564982
http://dx.doi.org/10.2147/CMAR.S210583
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author Liu, Hong-Li
Zong, Min
Wei, Han
Wang, Cong
Lou, Jian-Juan
Wang, Si-Qi
Zou, Qi-Gui
Jiang, Yan-Ni
author_facet Liu, Hong-Li
Zong, Min
Wei, Han
Wang, Cong
Lou, Jian-Juan
Wang, Si-Qi
Zou, Qi-Gui
Jiang, Yan-Ni
author_sort Liu, Hong-Li
collection PubMed
description BACKGROUND: Triple-negative breast cancers generally occur in young women with remarkable potential to be aggressive. It will be of great help to detect this subtype of tumor early. To retrospectively evaluate the performance of histogram analysis of apparent diffusion coefficient (ADC) maps in distinguishing triple-negative breast cancer (TNBC) from other subtypes of breast cancer (non-TNBC), when combined with magnetic resonance imaging (MRI) features. MATERIALS AND METHODS: From February 2014 to December 2018, 192 patients were included in this study taking preoperative standard MRI (s-MRI) and DWI. Seventy-six of them were pathologically confirmed with TNBC and rest 116 with other subtypes. First, their clinical-pathological features and morphological characteristics on MRI were assessed, including tumor size, foci quantity, tumor shape, margin, internal enhancement, and time-signal intensity curve types, in addition to the signal intensity on T2-weighted images. Second, whole-lesion apparent diffusion coefficient (ADC) histogram analysis was executed. Finally, both univariate and multivariate regression analyses were applied to identify the most useful variables in separating TNBCs from non-TNBCs, and then their effects were evaluated following receiver operating characteristic curve analysis. RESULT: Multivariate regression analysis indicated that circumscribed margin, rim enhancement, and ADC(90) were important predictors for TNBC. Increased area under curve (AUC) and improved specificity can be obtained when combined s-MRI and DWI (circumscribed margin+rim enhancement+ADC(90)>1.47×10(−3) mm(2)/s) is taken as the criterion, other than s-MRI (circumscribed margin+rim enhancement) alone (s-MRI+DWI vs s-MRI; AUC, 0.833 vs 0.797; specificity, 98.3% vs 89.7%; sensitivity, 68.4% vs 69.7%). CONCLUSION: Circumscribed margin and rim enhancement on s-MRI and ADC(90) are three important elements in detecting TNBC, while ADC histogram analysis can provide additional value in this detection.
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spelling pubmed-67356232019-09-27 Added value of histogram analysis of apparent diffusion coefficient maps for differentiating triple-negative breast cancer from other subtypes of breast cancer on standard MRI Liu, Hong-Li Zong, Min Wei, Han Wang, Cong Lou, Jian-Juan Wang, Si-Qi Zou, Qi-Gui Jiang, Yan-Ni Cancer Manag Res Original Research BACKGROUND: Triple-negative breast cancers generally occur in young women with remarkable potential to be aggressive. It will be of great help to detect this subtype of tumor early. To retrospectively evaluate the performance of histogram analysis of apparent diffusion coefficient (ADC) maps in distinguishing triple-negative breast cancer (TNBC) from other subtypes of breast cancer (non-TNBC), when combined with magnetic resonance imaging (MRI) features. MATERIALS AND METHODS: From February 2014 to December 2018, 192 patients were included in this study taking preoperative standard MRI (s-MRI) and DWI. Seventy-six of them were pathologically confirmed with TNBC and rest 116 with other subtypes. First, their clinical-pathological features and morphological characteristics on MRI were assessed, including tumor size, foci quantity, tumor shape, margin, internal enhancement, and time-signal intensity curve types, in addition to the signal intensity on T2-weighted images. Second, whole-lesion apparent diffusion coefficient (ADC) histogram analysis was executed. Finally, both univariate and multivariate regression analyses were applied to identify the most useful variables in separating TNBCs from non-TNBCs, and then their effects were evaluated following receiver operating characteristic curve analysis. RESULT: Multivariate regression analysis indicated that circumscribed margin, rim enhancement, and ADC(90) were important predictors for TNBC. Increased area under curve (AUC) and improved specificity can be obtained when combined s-MRI and DWI (circumscribed margin+rim enhancement+ADC(90)>1.47×10(−3) mm(2)/s) is taken as the criterion, other than s-MRI (circumscribed margin+rim enhancement) alone (s-MRI+DWI vs s-MRI; AUC, 0.833 vs 0.797; specificity, 98.3% vs 89.7%; sensitivity, 68.4% vs 69.7%). CONCLUSION: Circumscribed margin and rim enhancement on s-MRI and ADC(90) are three important elements in detecting TNBC, while ADC histogram analysis can provide additional value in this detection. Dove 2019-09-06 /pmc/articles/PMC6735623/ /pubmed/31564982 http://dx.doi.org/10.2147/CMAR.S210583 Text en © 2019 Liu et al. http://creativecommons.org/licenses/by-nc/3.0/ This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
spellingShingle Original Research
Liu, Hong-Li
Zong, Min
Wei, Han
Wang, Cong
Lou, Jian-Juan
Wang, Si-Qi
Zou, Qi-Gui
Jiang, Yan-Ni
Added value of histogram analysis of apparent diffusion coefficient maps for differentiating triple-negative breast cancer from other subtypes of breast cancer on standard MRI
title Added value of histogram analysis of apparent diffusion coefficient maps for differentiating triple-negative breast cancer from other subtypes of breast cancer on standard MRI
title_full Added value of histogram analysis of apparent diffusion coefficient maps for differentiating triple-negative breast cancer from other subtypes of breast cancer on standard MRI
title_fullStr Added value of histogram analysis of apparent diffusion coefficient maps for differentiating triple-negative breast cancer from other subtypes of breast cancer on standard MRI
title_full_unstemmed Added value of histogram analysis of apparent diffusion coefficient maps for differentiating triple-negative breast cancer from other subtypes of breast cancer on standard MRI
title_short Added value of histogram analysis of apparent diffusion coefficient maps for differentiating triple-negative breast cancer from other subtypes of breast cancer on standard MRI
title_sort added value of histogram analysis of apparent diffusion coefficient maps for differentiating triple-negative breast cancer from other subtypes of breast cancer on standard mri
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6735623/
https://www.ncbi.nlm.nih.gov/pubmed/31564982
http://dx.doi.org/10.2147/CMAR.S210583
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