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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...

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Autores principales: Sexauer, Raphael, Hejduk, Patryk, Borkowski, Karol, Ruppert, Carlotta, Weikert, Thomas, Dellas, Sophie, Schmidt, Noemi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2023
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.
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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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