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Multi-parameter ultrasonography-based predictive model for breast cancer diagnosis

OBJECTIVES: To develop, validate, and evaluate a predictive model for breast cancer diagnosis using conventional ultrasonography (US), shear wave elastography (SWE), and contrast-enhanced US (CEUS). MATERIALS AND METHODS: This retrospective study included 674 patients with 674 breast lesions. The da...

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Autores principales: Chen, Jing, Ma, Ji, Li, Chunxiao, Shao, Sihui, Su, Yijin, Wu, Rong, Yao, Minghua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9714455/
https://www.ncbi.nlm.nih.gov/pubmed/36465370
http://dx.doi.org/10.3389/fonc.2022.1027784
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author Chen, Jing
Ma, Ji
Li, Chunxiao
Shao, Sihui
Su, Yijin
Wu, Rong
Yao, Minghua
author_facet Chen, Jing
Ma, Ji
Li, Chunxiao
Shao, Sihui
Su, Yijin
Wu, Rong
Yao, Minghua
author_sort Chen, Jing
collection PubMed
description OBJECTIVES: To develop, validate, and evaluate a predictive model for breast cancer diagnosis using conventional ultrasonography (US), shear wave elastography (SWE), and contrast-enhanced US (CEUS). MATERIALS AND METHODS: This retrospective study included 674 patients with 674 breast lesions. The data, a main and an independent datasets, were divided into three cohorts. Cohort 1 (80% of the main dataset; n = 448) was analyzed by logistic regression analysis to identify risk factors and establish the predictive model. The area under the receiver operating characteristic curve (AUC) was analyzed in Cohort 2 (20% of the main dataset; n = 119) to validate and in Cohort 3 (the independent dataset; n = 107) to evaluate the predictive model. RESULTS: Multivariable regression analysis revealed nine independent breast cancer risk factors, including age > 40 years; ill-defined margin, heterogeneity, rich blood flow, and abnormal axillary lymph nodes on US; enhanced area enlargement, contrast agent retention, and irregular shape on CEUS; mean SWE higher than the cutoff value (P < 0.05 for all). The diagnostic performance of the model was good, with AUC values of 0.847, 0.857, and 0.774 for Cohorts 1, 2, and 3, respectively. The model increased the diagnostic specificity (from 31% to 81.3% and 7.3% to 73.1% in cohorts 2 and 3, respectively) without a significant loss in sensitivity (from 100.0% to 90.1% and 100.0% to 81.8% in cohorts 2 and 3, respectively). CONCLUSION: The multi-parameter US-based model showed good performance in breast cancer diagnosis, improving specificity without a significant loss in sensitivity. Using the model could reduce unnecessary biopsies and guide clinical diagnosis and treatment.
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spelling pubmed-97144552022-12-02 Multi-parameter ultrasonography-based predictive model for breast cancer diagnosis Chen, Jing Ma, Ji Li, Chunxiao Shao, Sihui Su, Yijin Wu, Rong Yao, Minghua Front Oncol Oncology OBJECTIVES: To develop, validate, and evaluate a predictive model for breast cancer diagnosis using conventional ultrasonography (US), shear wave elastography (SWE), and contrast-enhanced US (CEUS). MATERIALS AND METHODS: This retrospective study included 674 patients with 674 breast lesions. The data, a main and an independent datasets, were divided into three cohorts. Cohort 1 (80% of the main dataset; n = 448) was analyzed by logistic regression analysis to identify risk factors and establish the predictive model. The area under the receiver operating characteristic curve (AUC) was analyzed in Cohort 2 (20% of the main dataset; n = 119) to validate and in Cohort 3 (the independent dataset; n = 107) to evaluate the predictive model. RESULTS: Multivariable regression analysis revealed nine independent breast cancer risk factors, including age > 40 years; ill-defined margin, heterogeneity, rich blood flow, and abnormal axillary lymph nodes on US; enhanced area enlargement, contrast agent retention, and irregular shape on CEUS; mean SWE higher than the cutoff value (P < 0.05 for all). The diagnostic performance of the model was good, with AUC values of 0.847, 0.857, and 0.774 for Cohorts 1, 2, and 3, respectively. The model increased the diagnostic specificity (from 31% to 81.3% and 7.3% to 73.1% in cohorts 2 and 3, respectively) without a significant loss in sensitivity (from 100.0% to 90.1% and 100.0% to 81.8% in cohorts 2 and 3, respectively). CONCLUSION: The multi-parameter US-based model showed good performance in breast cancer diagnosis, improving specificity without a significant loss in sensitivity. Using the model could reduce unnecessary biopsies and guide clinical diagnosis and treatment. Frontiers Media S.A. 2022-11-17 /pmc/articles/PMC9714455/ /pubmed/36465370 http://dx.doi.org/10.3389/fonc.2022.1027784 Text en Copyright © 2022 Chen, Ma, Li, Shao, Su, Wu and Yao https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Chen, Jing
Ma, Ji
Li, Chunxiao
Shao, Sihui
Su, Yijin
Wu, Rong
Yao, Minghua
Multi-parameter ultrasonography-based predictive model for breast cancer diagnosis
title Multi-parameter ultrasonography-based predictive model for breast cancer diagnosis
title_full Multi-parameter ultrasonography-based predictive model for breast cancer diagnosis
title_fullStr Multi-parameter ultrasonography-based predictive model for breast cancer diagnosis
title_full_unstemmed Multi-parameter ultrasonography-based predictive model for breast cancer diagnosis
title_short Multi-parameter ultrasonography-based predictive model for breast cancer diagnosis
title_sort multi-parameter ultrasonography-based predictive model for breast cancer diagnosis
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9714455/
https://www.ncbi.nlm.nih.gov/pubmed/36465370
http://dx.doi.org/10.3389/fonc.2022.1027784
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