Fully Automatic Assessment of Background Parenchymal Enhancement on Breast MRI Using Machine-Learning Models.

Journal of Magnetic Resonance Imaging : JMRI
Yoonho NamSung Hun Kim

Abstract

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 (T1 W) 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 T1 W images and the segmented FGT mask. Two radiologists independently assessed the categories of FGT and BPE. A deep-learning-based algorithm was designed to segment and measure the volume of whole breast and FGT and classify the grade of BPE. Dice similarity coefficients (DSC) and Spearman correlation analysis were used to compare the volumes from the manual and deep-learning-based segmentations. Kappa statistics were used for agreement analysis. Comparison of area under t...Continue Reading

References

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