Multi-institutional classification of fibroglandular tissue and background parenchymal enhancement in breast MRI using deep learning.

Abstract

Purpose: The amount of fibroglandular tissue (FGT) and background parenchymal enhancement (BPE) are evaluated as part of the BI-RADS reporting standard for the diagnosis of breast cancer in magnetic resonance imaging (MRI). BPE is associated with breast cancer risk and affects the diagnostic accuracy of breast MRI readings but is subject to very high inter-reader variability.

Approach: We developed a fully automatic observer-independent method to measure the amount of FGT and BPE based on breast, FGT, and lesion masks, which classifies each into four classes based on thresholds applied to those scores. This retrospective study involved 2840 patients from seven institutions for whom T1-weighted dynamic contrast-enhanced breast MRI sequences with and without fat suppression were acquired using 1.5 or 3T scanners. FGT and BPE assessments for model development and evaluation were performed according to the BI-RADS guidelines by radiologists from the respective institutions and by a radiographer if the assessments were missing.

Results: The performance of the models was compared using four-class accuracy and a paired t-test ( $\alpha =0.05$ ). Our automatic classification of FGT and BPE in a multi-institutional setting achieved a four-class accuracy of 0.535 for FGT and 0.504 for BPE ( $\mathrm{random}=0.25$ ). The calibration of the classification thresholds for each site individually resulted in a significant improvement, reaching an accuracy of 0.669 ( $p<0.05$ ) for FGT and 0.565 ( $p<0.05$ ) for BPE. The area under curve of receiver operating characterstic (ROC-AUC) to classify FGT and BPE into low versus high is 0.883 for FGT and 0.757 for BPE when computed across all sites. The averaged per-site AUCs are 0.926 for FGT and 0.810 for BPE.

Conclusion: FGT and BPE classification perform significantly better when classification thresholds are selected per clinical site.