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Ultrasound-based radiomics model for predicting molecular biomarkers in breast cancer

BACKGROUND: Breast cancer (BC) is the most common cancer in women and is highly heterogeneous. BC can be classified into four molecular subtypes based on the status of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and proliferation marker protein...

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Autores principales: Xu, Rong, You, Tao, Liu, Chen, Lin, Qing, Guo, Quehui, Zhong, Guodong, Liu, Leilei, Ouyang, Qiufang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10424446/
https://www.ncbi.nlm.nih.gov/pubmed/37583930
http://dx.doi.org/10.3389/fonc.2023.1216446
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author Xu, Rong
You, Tao
Liu, Chen
Lin, Qing
Guo, Quehui
Zhong, Guodong
Liu, Leilei
Ouyang, Qiufang
author_facet Xu, Rong
You, Tao
Liu, Chen
Lin, Qing
Guo, Quehui
Zhong, Guodong
Liu, Leilei
Ouyang, Qiufang
author_sort Xu, Rong
collection PubMed
description BACKGROUND: Breast cancer (BC) is the most common cancer in women and is highly heterogeneous. BC can be classified into four molecular subtypes based on the status of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and proliferation marker protein Ki-67. However, they can only be obtained by biopsy or surgery, which is invasive. Radiomics can noninvasively predict molecular expression via extracting the image features. Nevertheless, there is a scarcity of data available regarding the prediction of molecular biomarker expression using ultrasound (US) images in BC. OBJECTIVES: To investigate the prediction performance of US radiomics for the assessment of molecular profiling in BC. METHODS: A total of 342 patients with BC who underwent preoperative US examination between January 2013 and December 2021 were retrospectively included. They were confirmed by pathology and molecular subtype analysis of ER, PR, HER2 and Ki-67. The radiomics features were extracted and four molecular models were constructed through support vector machine (SVM). Pearson correlation coefficient heatmaps are employed to analyze the relationship between selected features and their predictive power on molecular expression. The receiver operating characteristic curve was used for the prediction performance of US radiomics in the assessment of molecular profiling. RESULTS: 359 lesions with 129 ER- and 230 ER+, 163 PR- and 196 PR+, 265 HER2- and 94 HER2+, 114 Ki-67- and 245 Ki-67+ expression were included. 1314 features were extracted from each ultrasound image. And there was a significant difference of some specific radiomics features between the molecule positive and negative groups. Multiple features demonstrated significant association with molecular biomarkers. The area under curves (AUCs) were 0.917, 0.835, 0.771, and 0.896 in the training set, while 0.868, 0.811, 0.722, and 0.706 in the validation set to predict ER, PR, HER2, and Ki-67 expression respectively. CONCLUSION: Ultrasound-based radiomics provides a promising method for predicting molecular biomarker expression of ER, PR, HER2, and Ki-67 in BC.
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spelling pubmed-104244462023-08-15 Ultrasound-based radiomics model for predicting molecular biomarkers in breast cancer Xu, Rong You, Tao Liu, Chen Lin, Qing Guo, Quehui Zhong, Guodong Liu, Leilei Ouyang, Qiufang Front Oncol Oncology BACKGROUND: Breast cancer (BC) is the most common cancer in women and is highly heterogeneous. BC can be classified into four molecular subtypes based on the status of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and proliferation marker protein Ki-67. However, they can only be obtained by biopsy or surgery, which is invasive. Radiomics can noninvasively predict molecular expression via extracting the image features. Nevertheless, there is a scarcity of data available regarding the prediction of molecular biomarker expression using ultrasound (US) images in BC. OBJECTIVES: To investigate the prediction performance of US radiomics for the assessment of molecular profiling in BC. METHODS: A total of 342 patients with BC who underwent preoperative US examination between January 2013 and December 2021 were retrospectively included. They were confirmed by pathology and molecular subtype analysis of ER, PR, HER2 and Ki-67. The radiomics features were extracted and four molecular models were constructed through support vector machine (SVM). Pearson correlation coefficient heatmaps are employed to analyze the relationship between selected features and their predictive power on molecular expression. The receiver operating characteristic curve was used for the prediction performance of US radiomics in the assessment of molecular profiling. RESULTS: 359 lesions with 129 ER- and 230 ER+, 163 PR- and 196 PR+, 265 HER2- and 94 HER2+, 114 Ki-67- and 245 Ki-67+ expression were included. 1314 features were extracted from each ultrasound image. And there was a significant difference of some specific radiomics features between the molecule positive and negative groups. Multiple features demonstrated significant association with molecular biomarkers. The area under curves (AUCs) were 0.917, 0.835, 0.771, and 0.896 in the training set, while 0.868, 0.811, 0.722, and 0.706 in the validation set to predict ER, PR, HER2, and Ki-67 expression respectively. CONCLUSION: Ultrasound-based radiomics provides a promising method for predicting molecular biomarker expression of ER, PR, HER2, and Ki-67 in BC. Frontiers Media S.A. 2023-07-31 /pmc/articles/PMC10424446/ /pubmed/37583930 http://dx.doi.org/10.3389/fonc.2023.1216446 Text en Copyright © 2023 Xu, You, Liu, Lin, Guo, Zhong, Liu and Ouyang 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
Xu, Rong
You, Tao
Liu, Chen
Lin, Qing
Guo, Quehui
Zhong, Guodong
Liu, Leilei
Ouyang, Qiufang
Ultrasound-based radiomics model for predicting molecular biomarkers in breast cancer
title Ultrasound-based radiomics model for predicting molecular biomarkers in breast cancer
title_full Ultrasound-based radiomics model for predicting molecular biomarkers in breast cancer
title_fullStr Ultrasound-based radiomics model for predicting molecular biomarkers in breast cancer
title_full_unstemmed Ultrasound-based radiomics model for predicting molecular biomarkers in breast cancer
title_short Ultrasound-based radiomics model for predicting molecular biomarkers in breast cancer
title_sort ultrasound-based radiomics model for predicting molecular biomarkers in breast cancer
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10424446/
https://www.ncbi.nlm.nih.gov/pubmed/37583930
http://dx.doi.org/10.3389/fonc.2023.1216446
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