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Automated measurement and classification models with objectivity and reproducibility are required for accurate evaluation of the breast cancer risk of fibroglandular tissue (FGT) and background parenchymal enhancement (BPE).

To develop and evaluate a machine‐learning algorithm for breast FGT segmentation and BPE classification.

Retrospective.

A total of 794 patients with breast cancer, 594 patients assigned to the development set, and 200 patients to the test set.

3T and 1.5T; T₂‐weighted, fat‐saturated T₁‐weighted (T₁W) with dynamic contrast enhancement (DCE).

Manual segmentation was performed for the whole breast and FGT regions in the contralateral breast. The BPE region was determined by thresholding using the subtraction of the pre‐ and postcontrast T₁W images and the segmented FGT mask. Two …