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@article{2416243,
author = {Biros, Marek and Kvak, Daniel and Dandar, Jakub and Hruby, Robert and Janu, Eva and Atakhanova, Anora and Alandantari, Mugahed A},
article_location = {Basel},
article_number = {11},
doi = {http://dx.doi.org/10.3390/diagnostics14111117},
keywords = {BI-RADS; breast density; computer-aided diagnosis; deep learning; full-field digital mammography; medical image processing},
language = {eng},
issn = {2075-4418},
journal = {Diagnostics},
title = {Enhancing Accuracy in Breast Density Assessment Using Deep Learning: A Multicentric, Multi-Reader Study},
url = {https://www.mdpi.com/2075-4418/14/11/1117},
volume = {14},
year = {2024}
}
TY - JOUR
ID - 2416243
AU - Biros, Marek - Kvak, Daniel - Dandar, Jakub - Hruby, Robert - Janu, Eva - Atakhanova, Anora - Al-antari, Mugahed A
PY - 2024
TI - Enhancing Accuracy in Breast Density Assessment Using Deep Learning: A Multicentric, Multi-Reader Study
JF - Diagnostics
VL - 14
IS - 11
SP - 1-14
EP - 1-14
PB - MPDI
SN - 20754418
KW - BI-RADS
KW - breast density
KW - computer-aided diagnosis
KW - deep learning
KW - full-field digital mammography
KW - medical image processing
UR - https://www.mdpi.com/2075-4418/14/11/1117
N2 - The evaluation of mammographic breast density, a critical indicator of breast cancer risk, is traditionally performed by radiologists via visual inspection of mammography images, utilizing the Breast Imaging-Reporting and Data System (BI-RADS) breast density categories. However, this method is subject to substantial interobserver variability, leading to inconsistencies and potential inaccuracies in density assessment and subsequent risk estimations. To address this, we present a deep learning-based automatic detection algorithm (DLAD) designed for the automated evaluation of breast density. Our multicentric, multi-reader study leverages a diverse dataset of 122 full-field digital mammography studies (488 images in CC and MLO projections) sourced from three institutions. We invited two experienced radiologists to conduct a retrospective analysis, establishing a ground truth for 72 mammography studies (BI-RADS class A: 18, BI-RADS class B: 43, BI-RADS class C: 7, BI-RADS class D: 4). The efficacy of the DLAD was then compared to the performance of five independent radiologists with varying levels of experience. The DLAD showed robust performance, achieving an accuracy of 0.819 (95% CI: 0.736-0.903), along with an F1 score of 0.798 (0.594-0.905), precision of 0.806 (0.596-0.896), recall of 0.830 (0.650-0.946), and a Cohen's Kappa (kappa) of 0.708 (0.562-0.841). The algorithm achieved robust performance that matches and in four cases exceeds that of individual radiologists. The statistical analysis did not reveal a significant difference in accuracy between DLAD and the radiologists, underscoring the model's competitive diagnostic alignment with professional radiologist assessments. These results demonstrate that the deep learning-based automatic detection algorithm can enhance the accuracy and consistency of breast density assessments, offering a reliable tool for improving breast cancer screening outcomes.
ER -
BIROS, Marek, Daniel KVAK, Jakub DANDAR, Robert HRUBY, Eva JANU, Anora ATAKHANOVA and Mugahed A AL-ANTARI. Enhancing Accuracy in Breast Density Assessment Using Deep Learning: A Multicentric, Multi-Reader Study. \textit{Diagnostics}. Basel: MPDI, 2024, vol.~14, No~11, p.~1-14. ISSN~2075-4418. Available from: https://dx.doi.org/10.3390/diagnostics14111117.
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