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Detection and quantification of breast arterial calcifications on mammograms: a deep learning approach
OBJECTIVE: Breast arterial calcifications (BAC) are a sex-specific cardiovascular disease biomarker that might improve cardiovascular risk stratification in women. We implemented a deep convolutional neural network for automatic BAC detection and quantification. METHODS: In this retrospective study,...
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/PMC10511622/ https://www.ncbi.nlm.nih.gov/pubmed/37160426 http://dx.doi.org/10.1007/s00330-023-09668-z |
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author | Mobini, Nazanin Codari, Marina Riva, Francesca Ienco, Maria Giovanna Capra, Davide Cozzi, Andrea Carriero, Serena Spinelli, Diana Trimboli, Rubina Manuela Baselli, Giuseppe Sardanelli, Francesco |
author_facet | Mobini, Nazanin Codari, Marina Riva, Francesca Ienco, Maria Giovanna Capra, Davide Cozzi, Andrea Carriero, Serena Spinelli, Diana Trimboli, Rubina Manuela Baselli, Giuseppe Sardanelli, Francesco |
author_sort | Mobini, Nazanin |
collection | PubMed |
description | OBJECTIVE: Breast arterial calcifications (BAC) are a sex-specific cardiovascular disease biomarker that might improve cardiovascular risk stratification in women. We implemented a deep convolutional neural network for automatic BAC detection and quantification. METHODS: In this retrospective study, four readers labelled four-view mammograms as BAC positive (BAC+) or BAC negative (BAC−) at image level. Starting from a pretrained VGG16 model, we trained a convolutional neural network to discriminate BAC+ and BAC− mammograms. Accuracy, F1 score, and area under the receiver operating characteristic curve (AUC-ROC) were used to assess the diagnostic performance. Predictions of calcified areas were generated using the generalized gradient-weighted class activation mapping (Grad-CAM++) method, and their correlation with manual measurement of BAC length in a subset of cases was assessed using Spearman ρ. RESULTS: A total 1493 women (198 BAC+) with a median age of 59 years (interquartile range 52–68) were included and partitioned in a training set of 410 cases (1640 views, 398 BAC+), validation set of 222 cases (888 views, 89 BAC+), and test set of 229 cases (916 views, 94 BAC+). The accuracy, F1 score, and AUC-ROC were 0.94, 0.86, and 0.98 in the training set; 0.96, 0.74, and 0.96 in the validation set; and 0.97, 0.80, and 0.95 in the test set, respectively. In 112 analyzed views, the Grad-CAM++ predictions displayed a strong correlation with BAC measured length (ρ = 0.88, p < 0.001). CONCLUSION: Our model showed promising performances in BAC detection and in quantification of BAC burden, showing a strong correlation with manual measurements. CLINICAL RELEVANCE STATEMENT: Integrating our model to clinical practice could improve BAC reporting without increasing clinical workload, facilitating large-scale studies on the impact of BAC as a biomarker of cardiovascular risk, raising awareness on women’s cardiovascular health, and leveraging mammographic screening. KEY POINTS: • We implemented a deep convolutional neural network (CNN) for BAC detection and quantification. • Our CNN had an area under the receiving operator curve of 0.95 for BAC detection in the test set composed of 916 views, 94 of which were BAC+ . • Furthermore, our CNN showed a strong correlation with manual BAC measurements (ρ = 0.88) in a set of 112 views. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00330-023-09668-z. |
format | Online Article Text |
id | pubmed-10511622 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-105116222023-09-22 Detection and quantification of breast arterial calcifications on mammograms: a deep learning approach Mobini, Nazanin Codari, Marina Riva, Francesca Ienco, Maria Giovanna Capra, Davide Cozzi, Andrea Carriero, Serena Spinelli, Diana Trimboli, Rubina Manuela Baselli, Giuseppe Sardanelli, Francesco Eur Radiol Imaging Informatics and Artificial Intelligence OBJECTIVE: Breast arterial calcifications (BAC) are a sex-specific cardiovascular disease biomarker that might improve cardiovascular risk stratification in women. We implemented a deep convolutional neural network for automatic BAC detection and quantification. METHODS: In this retrospective study, four readers labelled four-view mammograms as BAC positive (BAC+) or BAC negative (BAC−) at image level. Starting from a pretrained VGG16 model, we trained a convolutional neural network to discriminate BAC+ and BAC− mammograms. Accuracy, F1 score, and area under the receiver operating characteristic curve (AUC-ROC) were used to assess the diagnostic performance. Predictions of calcified areas were generated using the generalized gradient-weighted class activation mapping (Grad-CAM++) method, and their correlation with manual measurement of BAC length in a subset of cases was assessed using Spearman ρ. RESULTS: A total 1493 women (198 BAC+) with a median age of 59 years (interquartile range 52–68) were included and partitioned in a training set of 410 cases (1640 views, 398 BAC+), validation set of 222 cases (888 views, 89 BAC+), and test set of 229 cases (916 views, 94 BAC+). The accuracy, F1 score, and AUC-ROC were 0.94, 0.86, and 0.98 in the training set; 0.96, 0.74, and 0.96 in the validation set; and 0.97, 0.80, and 0.95 in the test set, respectively. In 112 analyzed views, the Grad-CAM++ predictions displayed a strong correlation with BAC measured length (ρ = 0.88, p < 0.001). CONCLUSION: Our model showed promising performances in BAC detection and in quantification of BAC burden, showing a strong correlation with manual measurements. CLINICAL RELEVANCE STATEMENT: Integrating our model to clinical practice could improve BAC reporting without increasing clinical workload, facilitating large-scale studies on the impact of BAC as a biomarker of cardiovascular risk, raising awareness on women’s cardiovascular health, and leveraging mammographic screening. KEY POINTS: • We implemented a deep convolutional neural network (CNN) for BAC detection and quantification. • Our CNN had an area under the receiving operator curve of 0.95 for BAC detection in the test set composed of 916 views, 94 of which were BAC+ . • Furthermore, our CNN showed a strong correlation with manual BAC measurements (ρ = 0.88) in a set of 112 views. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00330-023-09668-z. Springer Berlin Heidelberg 2023-05-09 2023 /pmc/articles/PMC10511622/ /pubmed/37160426 http://dx.doi.org/10.1007/s00330-023-09668-z 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 | Imaging Informatics and Artificial Intelligence Mobini, Nazanin Codari, Marina Riva, Francesca Ienco, Maria Giovanna Capra, Davide Cozzi, Andrea Carriero, Serena Spinelli, Diana Trimboli, Rubina Manuela Baselli, Giuseppe Sardanelli, Francesco Detection and quantification of breast arterial calcifications on mammograms: a deep learning approach |
title | Detection and quantification of breast arterial calcifications on mammograms: a deep learning approach |
title_full | Detection and quantification of breast arterial calcifications on mammograms: a deep learning approach |
title_fullStr | Detection and quantification of breast arterial calcifications on mammograms: a deep learning approach |
title_full_unstemmed | Detection and quantification of breast arterial calcifications on mammograms: a deep learning approach |
title_short | Detection and quantification of breast arterial calcifications on mammograms: a deep learning approach |
title_sort | detection and quantification of breast arterial calcifications on mammograms: a deep learning approach |
topic | Imaging Informatics and Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10511622/ https://www.ncbi.nlm.nih.gov/pubmed/37160426 http://dx.doi.org/10.1007/s00330-023-09668-z |
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