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Additive value of texture analysis based on breast MRI for distinguishing between benign and malignant non-mass enhancement in premenopausal women

BACKGROUND: Non-mass enhancement (NME) is a diagnostic dilemma and highly reliant on the experience of the radiologists. Texture analysis (TA) could serve as an objective method to quantify lesion characteristics. However, it remains unclear what role TA plays in a predictive model based on routine...

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Autores principales: Tan, Yu, Mai, Hui, Huang, Zhiqing, Zhang, Li, Li, Chengwei, Wu, Songxin, Huang, Huang, Tang, Wen, Liu, Yongxi, Jiang, Kuiming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7953679/
https://www.ncbi.nlm.nih.gov/pubmed/33706695
http://dx.doi.org/10.1186/s12880-021-00571-x
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author Tan, Yu
Mai, Hui
Huang, Zhiqing
Zhang, Li
Li, Chengwei
Wu, Songxin
Huang, Huang
Tang, Wen
Liu, Yongxi
Jiang, Kuiming
author_facet Tan, Yu
Mai, Hui
Huang, Zhiqing
Zhang, Li
Li, Chengwei
Wu, Songxin
Huang, Huang
Tang, Wen
Liu, Yongxi
Jiang, Kuiming
author_sort Tan, Yu
collection PubMed
description BACKGROUND: Non-mass enhancement (NME) is a diagnostic dilemma and highly reliant on the experience of the radiologists. Texture analysis (TA) could serve as an objective method to quantify lesion characteristics. However, it remains unclear what role TA plays in a predictive model based on routine MRI characteristics. The purpose of this study was to explore the value of TA in distinguishing between benign and malignant NME in premenopausal women. METHODS: Women in whom NME was histologically proven (n = 147) were enrolled (benign: 58; malignant: 89) was retrospective. Then, 102 and 45 patients were classified as the training and validation groups, respectively. Scanning sequences included Fat-suppressed T2-weighted and fat-suppressed contrast-enhanced T1-weighted which were acquired on a 1.5T MRI system. Clinical and routine MR characteristics (CRMC) were evaluated by two radiologists according to the Breast Imaging and Reporting and Data system (2013). Texture features were extracted from all post-contrast sequences in the training group. The combination model was built and then assessed in the validation group. Pearson’s chi-square test and Mann–Whitney U test were used to compare categorical variables and continuous variables, respectively. Logistic regression analysis and receiver operating characteristic curve were employed to assess the diagnostic performance of CRMC, TA, and their combination model in NME diagnosis. RESULTS: The combination model showed superior diagnostic performance in differentiating between benign and malignant NME compared to that of CRMC or TA alone (AUC, 0.887 vs 0.832 vs 0.74). Moreover, compared to CRMC, the model showed high specificity (72.5% vs 80%). The results obtained in the validation group confirmed the model was promising. CONCLUSIONS: With the combined use of TA and CRMC could afford an improved diagnostic performance in differentiating between benign and malignant NME. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12880-021-00571-x.
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spelling pubmed-79536792021-03-12 Additive value of texture analysis based on breast MRI for distinguishing between benign and malignant non-mass enhancement in premenopausal women Tan, Yu Mai, Hui Huang, Zhiqing Zhang, Li Li, Chengwei Wu, Songxin Huang, Huang Tang, Wen Liu, Yongxi Jiang, Kuiming BMC Med Imaging Research Article BACKGROUND: Non-mass enhancement (NME) is a diagnostic dilemma and highly reliant on the experience of the radiologists. Texture analysis (TA) could serve as an objective method to quantify lesion characteristics. However, it remains unclear what role TA plays in a predictive model based on routine MRI characteristics. The purpose of this study was to explore the value of TA in distinguishing between benign and malignant NME in premenopausal women. METHODS: Women in whom NME was histologically proven (n = 147) were enrolled (benign: 58; malignant: 89) was retrospective. Then, 102 and 45 patients were classified as the training and validation groups, respectively. Scanning sequences included Fat-suppressed T2-weighted and fat-suppressed contrast-enhanced T1-weighted which were acquired on a 1.5T MRI system. Clinical and routine MR characteristics (CRMC) were evaluated by two radiologists according to the Breast Imaging and Reporting and Data system (2013). Texture features were extracted from all post-contrast sequences in the training group. The combination model was built and then assessed in the validation group. Pearson’s chi-square test and Mann–Whitney U test were used to compare categorical variables and continuous variables, respectively. Logistic regression analysis and receiver operating characteristic curve were employed to assess the diagnostic performance of CRMC, TA, and their combination model in NME diagnosis. RESULTS: The combination model showed superior diagnostic performance in differentiating between benign and malignant NME compared to that of CRMC or TA alone (AUC, 0.887 vs 0.832 vs 0.74). Moreover, compared to CRMC, the model showed high specificity (72.5% vs 80%). The results obtained in the validation group confirmed the model was promising. CONCLUSIONS: With the combined use of TA and CRMC could afford an improved diagnostic performance in differentiating between benign and malignant NME. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12880-021-00571-x. BioMed Central 2021-03-12 /pmc/articles/PMC7953679/ /pubmed/33706695 http://dx.doi.org/10.1186/s12880-021-00571-x Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Tan, Yu
Mai, Hui
Huang, Zhiqing
Zhang, Li
Li, Chengwei
Wu, Songxin
Huang, Huang
Tang, Wen
Liu, Yongxi
Jiang, Kuiming
Additive value of texture analysis based on breast MRI for distinguishing between benign and malignant non-mass enhancement in premenopausal women
title Additive value of texture analysis based on breast MRI for distinguishing between benign and malignant non-mass enhancement in premenopausal women
title_full Additive value of texture analysis based on breast MRI for distinguishing between benign and malignant non-mass enhancement in premenopausal women
title_fullStr Additive value of texture analysis based on breast MRI for distinguishing between benign and malignant non-mass enhancement in premenopausal women
title_full_unstemmed Additive value of texture analysis based on breast MRI for distinguishing between benign and malignant non-mass enhancement in premenopausal women
title_short Additive value of texture analysis based on breast MRI for distinguishing between benign and malignant non-mass enhancement in premenopausal women
title_sort additive value of texture analysis based on breast mri for distinguishing between benign and malignant non-mass enhancement in premenopausal women
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7953679/
https://www.ncbi.nlm.nih.gov/pubmed/33706695
http://dx.doi.org/10.1186/s12880-021-00571-x
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