Enhancing Accuracy in Breast Density Assessment Using Deep Learning: A Multicentric, Multi-Reader Study

Varování

Publikace nespadá pod Filozofickou fakultu, ale pod Lékařskou fakultu. Oficiální stránka publikace je na webu muni.cz.

Autoři	BIROS Marek KVAK Daniel DANDAR Jakub HRUBY Robert JANU Eva ATAKHANOVA Anora AL-ANTARI Mugahed A
Rok publikování	2024
Druh	Článek v odborném periodiku
Časopis / Zdroj	Diagnostics
Fakulta / Pracoviště MU	Lékařská fakulta
Citace
www	https://www.mdpi.com/2075-4418/14/11/1117
Doi	http://dx.doi.org/10.3390/diagnostics14111117
Klíčová slova	BI-RADS; breast density; computer-aided diagnosis; deep learning; full-field digital mammography; medical image processing
Popis	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.