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Digital subtraction of temporally sequential mammograms for improved detection and classification of microcalcifications
BACKGROUND: Our aim was to demonstrate that automated detection and classification of breast microcalcifications, according to Breast Imaging Reporting and Data System (BI-RADS) categorisation, can be improved with the subtraction of sequential mammograms as opposed to using the most recent image on...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8440760/ https://www.ncbi.nlm.nih.gov/pubmed/34519867 http://dx.doi.org/10.1186/s41747-021-00238-w |
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author | Loizidou, Kosmia Skouroumouni, Galateia Pitris, Costas Nikolaou, Christos |
author_facet | Loizidou, Kosmia Skouroumouni, Galateia Pitris, Costas Nikolaou, Christos |
author_sort | Loizidou, Kosmia |
collection | PubMed |
description | BACKGROUND: Our aim was to demonstrate that automated detection and classification of breast microcalcifications, according to Breast Imaging Reporting and Data System (BI-RADS) categorisation, can be improved with the subtraction of sequential mammograms as opposed to using the most recent image only. METHODS: One hundred pairs of mammograms were retrospectively collected from two temporally sequential rounds. Fifty percent of the images included no (BI-RADS 1) or benign (BI-RADS 2) microcalcifications. The remaining exhibited suspicious findings (BI-RADS 4-5) in the recent image. Mammograms cannot be directly subtracted, due to tissue changes over time and breast deformation during mammography. To overcome this challenge, optimised preprocessing, image registration, and postprocessing procedures were developed. Machine learning techniques were employed to eliminate false positives (normal tissue misclassified as microcalcifications) and to classify the true microcalcifications as BI-RADS benign or suspicious. Ninety-six features were extracted and nine classifiers were evaluated with and without temporal subtraction. The performance was assessed by measuring sensitivity, specificity, accuracy, and area under the curve (AUC) at receiver operator characteristics analysis. RESULTS: Using temporal subtraction, the contrast ratio improved ~ 57 times compared to the most recent mammograms, enhancing the detection of the radiologic changes. Classifying as BI-RADS benign versus suspicious microcalcifications, resulted in 90.3% accuracy and 0.87 AUC, compared to 82.7% and 0.81 using just the most recent mammogram (p = 0.003). CONCLUSION: Compared to using the most recent mammogram alone, temporal subtraction is more effective in the microcalcifications detection and classification and may play a role in automated diagnosis systems. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s41747-021-00238-w. |
format | Online Article Text |
id | pubmed-8440760 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-84407602021-10-01 Digital subtraction of temporally sequential mammograms for improved detection and classification of microcalcifications Loizidou, Kosmia Skouroumouni, Galateia Pitris, Costas Nikolaou, Christos Eur Radiol Exp Original Article BACKGROUND: Our aim was to demonstrate that automated detection and classification of breast microcalcifications, according to Breast Imaging Reporting and Data System (BI-RADS) categorisation, can be improved with the subtraction of sequential mammograms as opposed to using the most recent image only. METHODS: One hundred pairs of mammograms were retrospectively collected from two temporally sequential rounds. Fifty percent of the images included no (BI-RADS 1) or benign (BI-RADS 2) microcalcifications. The remaining exhibited suspicious findings (BI-RADS 4-5) in the recent image. Mammograms cannot be directly subtracted, due to tissue changes over time and breast deformation during mammography. To overcome this challenge, optimised preprocessing, image registration, and postprocessing procedures were developed. Machine learning techniques were employed to eliminate false positives (normal tissue misclassified as microcalcifications) and to classify the true microcalcifications as BI-RADS benign or suspicious. Ninety-six features were extracted and nine classifiers were evaluated with and without temporal subtraction. The performance was assessed by measuring sensitivity, specificity, accuracy, and area under the curve (AUC) at receiver operator characteristics analysis. RESULTS: Using temporal subtraction, the contrast ratio improved ~ 57 times compared to the most recent mammograms, enhancing the detection of the radiologic changes. Classifying as BI-RADS benign versus suspicious microcalcifications, resulted in 90.3% accuracy and 0.87 AUC, compared to 82.7% and 0.81 using just the most recent mammogram (p = 0.003). CONCLUSION: Compared to using the most recent mammogram alone, temporal subtraction is more effective in the microcalcifications detection and classification and may play a role in automated diagnosis systems. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s41747-021-00238-w. Springer International Publishing 2021-09-14 /pmc/articles/PMC8440760/ /pubmed/34519867 http://dx.doi.org/10.1186/s41747-021-00238-w Text en © The Author(s) 2021 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 | Original Article Loizidou, Kosmia Skouroumouni, Galateia Pitris, Costas Nikolaou, Christos Digital subtraction of temporally sequential mammograms for improved detection and classification of microcalcifications |
title | Digital subtraction of temporally sequential mammograms for improved detection and classification of microcalcifications |
title_full | Digital subtraction of temporally sequential mammograms for improved detection and classification of microcalcifications |
title_fullStr | Digital subtraction of temporally sequential mammograms for improved detection and classification of microcalcifications |
title_full_unstemmed | Digital subtraction of temporally sequential mammograms for improved detection and classification of microcalcifications |
title_short | Digital subtraction of temporally sequential mammograms for improved detection and classification of microcalcifications |
title_sort | digital subtraction of temporally sequential mammograms for improved detection and classification of microcalcifications |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8440760/ https://www.ncbi.nlm.nih.gov/pubmed/34519867 http://dx.doi.org/10.1186/s41747-021-00238-w |
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