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Diagnostic accuracy of automated ACR BI-RADS breast density classification using deep convolutional neural networks
OBJECTIVES: High breast density is a well-known risk factor for breast cancer. This study aimed to develop and adapt two (MLO, CC) deep convolutional neural networks (DCNN) for automatic breast density classification on synthetic 2D tomosynthesis reconstructions. METHODS: In total, 4605 synthetic 2D...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10289992/ https://www.ncbi.nlm.nih.gov/pubmed/36856841 http://dx.doi.org/10.1007/s00330-023-09474-7 |
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author | Sexauer, Raphael Hejduk, Patryk Borkowski, Karol Ruppert, Carlotta Weikert, Thomas Dellas, Sophie Schmidt, Noemi |
author_facet | Sexauer, Raphael Hejduk, Patryk Borkowski, Karol Ruppert, Carlotta Weikert, Thomas Dellas, Sophie Schmidt, Noemi |
author_sort | Sexauer, Raphael |
collection | PubMed |
description | OBJECTIVES: High breast density is a well-known risk factor for breast cancer. This study aimed to develop and adapt two (MLO, CC) deep convolutional neural networks (DCNN) for automatic breast density classification on synthetic 2D tomosynthesis reconstructions. METHODS: In total, 4605 synthetic 2D images (1665 patients, age: 57 ± 37 years) were labeled according to the ACR (American College of Radiology) density (A-D). Two DCNNs with 11 convolutional layers and 3 fully connected layers each, were trained with 70% of the data, whereas 20% was used for validation. The remaining 10% were used as a separate test dataset with 460 images (380 patients). All mammograms in the test dataset were read blinded by two radiologists (reader 1 with two and reader 2 with 11 years of dedicated mammographic experience in breast imaging), and the consensus was formed as the reference standard. The inter- and intra-reader reliabilities were assessed by calculating Cohen’s kappa coefficients, and diagnostic accuracy measures of automated classification were evaluated. RESULTS: The two models for MLO and CC projections had a mean sensitivity of 80.4% (95%-CI 72.2–86.9), a specificity of 89.3% (95%-CI 85.4–92.3), and an accuracy of 89.6% (95%-CI 88.1–90.9) in the differentiation between ACR A/B and ACR C/D. DCNN versus human and inter-reader agreement were both “substantial” (Cohen’s kappa: 0.61 versus 0.63). CONCLUSION: The DCNN allows accurate, standardized, and observer-independent classification of breast density based on the ACR BI-RADS system. KEY POINTS: • A DCNN performs on par with human experts in breast density assessment for synthetic 2D tomosynthesis reconstructions. • The proposed technique may be useful for accurate, standardized, and observer-independent breast density evaluation of tomosynthesis. |
format | Online Article Text |
id | pubmed-10289992 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-102899922023-06-25 Diagnostic accuracy of automated ACR BI-RADS breast density classification using deep convolutional neural networks Sexauer, Raphael Hejduk, Patryk Borkowski, Karol Ruppert, Carlotta Weikert, Thomas Dellas, Sophie Schmidt, Noemi Eur Radiol Breast OBJECTIVES: High breast density is a well-known risk factor for breast cancer. This study aimed to develop and adapt two (MLO, CC) deep convolutional neural networks (DCNN) for automatic breast density classification on synthetic 2D tomosynthesis reconstructions. METHODS: In total, 4605 synthetic 2D images (1665 patients, age: 57 ± 37 years) were labeled according to the ACR (American College of Radiology) density (A-D). Two DCNNs with 11 convolutional layers and 3 fully connected layers each, were trained with 70% of the data, whereas 20% was used for validation. The remaining 10% were used as a separate test dataset with 460 images (380 patients). All mammograms in the test dataset were read blinded by two radiologists (reader 1 with two and reader 2 with 11 years of dedicated mammographic experience in breast imaging), and the consensus was formed as the reference standard. The inter- and intra-reader reliabilities were assessed by calculating Cohen’s kappa coefficients, and diagnostic accuracy measures of automated classification were evaluated. RESULTS: The two models for MLO and CC projections had a mean sensitivity of 80.4% (95%-CI 72.2–86.9), a specificity of 89.3% (95%-CI 85.4–92.3), and an accuracy of 89.6% (95%-CI 88.1–90.9) in the differentiation between ACR A/B and ACR C/D. DCNN versus human and inter-reader agreement were both “substantial” (Cohen’s kappa: 0.61 versus 0.63). CONCLUSION: The DCNN allows accurate, standardized, and observer-independent classification of breast density based on the ACR BI-RADS system. KEY POINTS: • A DCNN performs on par with human experts in breast density assessment for synthetic 2D tomosynthesis reconstructions. • The proposed technique may be useful for accurate, standardized, and observer-independent breast density evaluation of tomosynthesis. Springer Berlin Heidelberg 2023-03-01 2023 /pmc/articles/PMC10289992/ /pubmed/36856841 http://dx.doi.org/10.1007/s00330-023-09474-7 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Breast Sexauer, Raphael Hejduk, Patryk Borkowski, Karol Ruppert, Carlotta Weikert, Thomas Dellas, Sophie Schmidt, Noemi Diagnostic accuracy of automated ACR BI-RADS breast density classification using deep convolutional neural networks |
title | Diagnostic accuracy of automated ACR BI-RADS breast density classification using deep convolutional neural networks |
title_full | Diagnostic accuracy of automated ACR BI-RADS breast density classification using deep convolutional neural networks |
title_fullStr | Diagnostic accuracy of automated ACR BI-RADS breast density classification using deep convolutional neural networks |
title_full_unstemmed | Diagnostic accuracy of automated ACR BI-RADS breast density classification using deep convolutional neural networks |
title_short | Diagnostic accuracy of automated ACR BI-RADS breast density classification using deep convolutional neural networks |
title_sort | diagnostic accuracy of automated acr bi-rads breast density classification using deep convolutional neural networks |
topic | Breast |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10289992/ https://www.ncbi.nlm.nih.gov/pubmed/36856841 http://dx.doi.org/10.1007/s00330-023-09474-7 |
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