Generalisable deep learning method for mammographic density prediction across imaging techniques and self-reported race.

ABSTRACT:

Background

Breast density is an important risk factor for breast cancer complemented by a higher risk of cancers being missed during screening of dense breasts due to reduced sensitivity of mammography. Automated, deep learning-based prediction of breast density could provide subject-specific risk assessment and flag difficult cases during screening. However, there is a lack of evidence for generalisability across imaging techniques and, importantly, across race.

Methods

This study used a large, racially diverse dataset with 69,697 mammographic studies comprising 451,642 individual images from 23,057 female participants. A deep learning model was developed for four-class BI-RADS density prediction. A comprehensive performance evaluation assessed the generalisability across t

SUBMITTER: Khara G 

PROVIDER: S-EPMC10876691 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature