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Breast non-mass-like lesions on contrast-enhanced ultrasonography: Feature analysis, breast image reporting and data system classification assessment
BACKGROUND: Breast non-mass-like lesions (NMLs) account for 9.2% of all breast lesions. The specificity of the ultrasound diagnosis of NMLs is low, and it cannot be objectively classified according to the 5(th) Edition of the Breast Imaging Reporting and Data System (BI-RADS). Contrast-enhanced ultr...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Baishideng Publishing Group Inc
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7052556/ https://www.ncbi.nlm.nih.gov/pubmed/32149054 http://dx.doi.org/10.12998/wjcc.v8.i4.700 |
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author | Xu, Ping Yang, Min Liu, Yong Li, Yan-Ping Zhang, Hong Shao, Guang-Rui |
author_facet | Xu, Ping Yang, Min Liu, Yong Li, Yan-Ping Zhang, Hong Shao, Guang-Rui |
author_sort | Xu, Ping |
collection | PubMed |
description | BACKGROUND: Breast non-mass-like lesions (NMLs) account for 9.2% of all breast lesions. The specificity of the ultrasound diagnosis of NMLs is low, and it cannot be objectively classified according to the 5(th) Edition of the Breast Imaging Reporting and Data System (BI-RADS). Contrast-enhanced ultrasound (CEUS) can help to differentiate and classify breast lesions but there are few studies on NMLs alone. AIM: To analyze the features of benign and malignant breast NMLs in grayscale ultrasonography (US), color Doppler flow imaging (CDFI) and CEUS, and to explore the efficacy of the combined diagnosis of NMLs and the effect of CEUS on the BI-RADS classification of NMLs. METHODS: A total of 51 breast NMLs verified by pathology were analyzed in our hospital from January 2017 to April 2019. All lesions were examined by US, CDFI and CEUS, and their features from those examinations were analyzed. With pathology as the gold standard, binary logic regression was used to analyze the independent risk factors for malignant breast NMLs, and a regression equation was established to calculate the efficiency of combined diagnosis. Based on the regression equation, the combined diagnostic efficiency of US combined with CEUS (US + CEUS) was determined. The initial BI-RADS-US classification of NMLs was adjusted according to the independent risk factors identified by CEUS, and the diagnostic efficiency of CEUS combined with BI-RADS (CEUS + BI-RADS) was calculated based on the results. ROC curves were drawn to compare the diagnostic values of the three methods, including US, US + CEUS, and CEUS + BI-RADS, for benign and malignant NMLs. RESULTS: Microcalcification, enhancement time, enhancement intensity, lesion scope, and peripheral blood vessels were significantly different between benign and malignant NMLs. Among these features, microcalcification, higher enhancement, and lesion scope were identified as independent risk factors for malignant breast NMLs. When US, US + CEUS, and CEUS + BI-RADS were used to identify the benign and malignant breast NMLs, their sensitivity rates were 82.6%, 91.3%, and 87.0%, respectively; their specificity rates were 71.4%, 89.2%, and 92.9%, respectively; their positive predictive values were 70.4%, 87.5%, and 90.9%, respectively; their negative predictive values were 83.3%, 92.6%, and 89.7%, respectively; their accuracy rates were 76.5%, 90.2%, and 90.2%, respectively; and their corresponding areas under ROC curves were 0.752, 0.877 and 0.903, respectively. Z tests showed that the area under the ROC curve of US was statistically smaller than that of US + CEUS and CEUS + BI-RADS, and there was no statistical difference between US + CEUS and CEUS + BI-RADS. CONCLUSION: US combined with CEUS can improve diagnostic efficiency for NMLs. The adjustment of the BI-RADS classification according to the features of contrast-enhanced US of NMLs enables the diagnostic results to be simple and intuitive, facilitates the management of NMLs, and effectively reduces the incidence of unnecessary biopsy. |
format | Online Article Text |
id | pubmed-7052556 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Baishideng Publishing Group Inc |
record_format | MEDLINE/PubMed |
spelling | pubmed-70525562020-03-06 Breast non-mass-like lesions on contrast-enhanced ultrasonography: Feature analysis, breast image reporting and data system classification assessment Xu, Ping Yang, Min Liu, Yong Li, Yan-Ping Zhang, Hong Shao, Guang-Rui World J Clin Cases Retrospective Study BACKGROUND: Breast non-mass-like lesions (NMLs) account for 9.2% of all breast lesions. The specificity of the ultrasound diagnosis of NMLs is low, and it cannot be objectively classified according to the 5(th) Edition of the Breast Imaging Reporting and Data System (BI-RADS). Contrast-enhanced ultrasound (CEUS) can help to differentiate and classify breast lesions but there are few studies on NMLs alone. AIM: To analyze the features of benign and malignant breast NMLs in grayscale ultrasonography (US), color Doppler flow imaging (CDFI) and CEUS, and to explore the efficacy of the combined diagnosis of NMLs and the effect of CEUS on the BI-RADS classification of NMLs. METHODS: A total of 51 breast NMLs verified by pathology were analyzed in our hospital from January 2017 to April 2019. All lesions were examined by US, CDFI and CEUS, and their features from those examinations were analyzed. With pathology as the gold standard, binary logic regression was used to analyze the independent risk factors for malignant breast NMLs, and a regression equation was established to calculate the efficiency of combined diagnosis. Based on the regression equation, the combined diagnostic efficiency of US combined with CEUS (US + CEUS) was determined. The initial BI-RADS-US classification of NMLs was adjusted according to the independent risk factors identified by CEUS, and the diagnostic efficiency of CEUS combined with BI-RADS (CEUS + BI-RADS) was calculated based on the results. ROC curves were drawn to compare the diagnostic values of the three methods, including US, US + CEUS, and CEUS + BI-RADS, for benign and malignant NMLs. RESULTS: Microcalcification, enhancement time, enhancement intensity, lesion scope, and peripheral blood vessels were significantly different between benign and malignant NMLs. Among these features, microcalcification, higher enhancement, and lesion scope were identified as independent risk factors for malignant breast NMLs. When US, US + CEUS, and CEUS + BI-RADS were used to identify the benign and malignant breast NMLs, their sensitivity rates were 82.6%, 91.3%, and 87.0%, respectively; their specificity rates were 71.4%, 89.2%, and 92.9%, respectively; their positive predictive values were 70.4%, 87.5%, and 90.9%, respectively; their negative predictive values were 83.3%, 92.6%, and 89.7%, respectively; their accuracy rates were 76.5%, 90.2%, and 90.2%, respectively; and their corresponding areas under ROC curves were 0.752, 0.877 and 0.903, respectively. Z tests showed that the area under the ROC curve of US was statistically smaller than that of US + CEUS and CEUS + BI-RADS, and there was no statistical difference between US + CEUS and CEUS + BI-RADS. CONCLUSION: US combined with CEUS can improve diagnostic efficiency for NMLs. The adjustment of the BI-RADS classification according to the features of contrast-enhanced US of NMLs enables the diagnostic results to be simple and intuitive, facilitates the management of NMLs, and effectively reduces the incidence of unnecessary biopsy. Baishideng Publishing Group Inc 2020-02-26 2020-02-26 /pmc/articles/PMC7052556/ /pubmed/32149054 http://dx.doi.org/10.12998/wjcc.v8.i4.700 Text en ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved. http://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 | Retrospective Study Xu, Ping Yang, Min Liu, Yong Li, Yan-Ping Zhang, Hong Shao, Guang-Rui Breast non-mass-like lesions on contrast-enhanced ultrasonography: Feature analysis, breast image reporting and data system classification assessment |
title | Breast non-mass-like lesions on contrast-enhanced ultrasonography: Feature analysis, breast image reporting and data system classification assessment |
title_full | Breast non-mass-like lesions on contrast-enhanced ultrasonography: Feature analysis, breast image reporting and data system classification assessment |
title_fullStr | Breast non-mass-like lesions on contrast-enhanced ultrasonography: Feature analysis, breast image reporting and data system classification assessment |
title_full_unstemmed | Breast non-mass-like lesions on contrast-enhanced ultrasonography: Feature analysis, breast image reporting and data system classification assessment |
title_short | Breast non-mass-like lesions on contrast-enhanced ultrasonography: Feature analysis, breast image reporting and data system classification assessment |
title_sort | breast non-mass-like lesions on contrast-enhanced ultrasonography: feature analysis, breast image reporting and data system classification assessment |
topic | Retrospective Study |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7052556/ https://www.ncbi.nlm.nih.gov/pubmed/32149054 http://dx.doi.org/10.12998/wjcc.v8.i4.700 |
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