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Fully Automatic Assessment of Background Parenchymal Enhancement on Breast MRI Using Machine-Learning Models
Yoonho Nam1,2, Ga Eun Park1, Junghwa Kang2
1Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
Background:
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).
Purpose:
To develop and evaluate a machine-learning algorithm for breast FGT segmentation and BPE classification.
Study Type:
Retrospective.
Population:
A total of 794 patients with breast cancer, 594 patients assigned to the development set, and 200 patients to the test set.
Field Strength/Sequence:
3T and 1.5T; T2 -weighted, fat-saturated T1 -weighted (T1 W) with dynamic contrast enhancement (DCE).
Assessment:
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.
Statistical Tests:
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 the receiver operating characteristic (ROC) curves (AUC) and F1 scores were calculated to evaluate the performance of BPE classification.
Results:
The mean (±SD) DSC for manual and deep-learning segmentations was 0.85 ± 0.11. The correlation coefficient for FGT volume from manual- and deep-learning-based segmentations was 0.93. Overall accuracy of manual segmentation and deep-learning segmentation in BPE classification task was 66% and 67%, respectively. For binary categorization of BPE grade (minimal/mild vs. moderate/marked), overall accuracy increased to 91.5% in manual segmentation and 90.5% in deep-learning segmentation; the AUC was 0.93 in both methods.
Data Conclusion:
This deep-learning-based algorithm can provide reliable segmentation and classification results for BPE.
Level Of Evidence:
3 TECHNICAL EFFICACY STAGE: 2.
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