Quantification of Breast Arterial Calcification in Mammograms Using a UNet-Based Deep Learning for Detecting Cardiovascular Disease.

Abstract

Background: Breast arterial calcification (BAC) is increasingly recognized as a significant indicator of cardiovascular risk, necessitating improvements in detection and quantification methods through mammographic screening.

Purpose: To develop and validate a deep-learning model capable of detecting, segmenting, and quantifying BAC in mammograms, improving mammographic screening for cardiovascular risk assessment.

Materials and methods: We conducted a retrospective study using mammograms from 369 patients. The study utilized a modified U-Net architecture that incorporates Hausdorff loss, Dice loss, and Binary Cross-Entropy (BCE) loss for segmentation and subsequent quantification. The model's performance was assessed using the Dice score, BCE loss for segmentation accuracy, linear fit, and Bland-Altman analysis for quantification accuracy.

Results: Our model achieved high segmentation accuracy with Dice scores of 0.90 for the training set and 0.89 for the validation set. Quantification reliability was validated through Bland-Altman analysis, showing a mean difference of -0.98 mg of calcium in the training set. The model also demonstrated high classification accuracy with F1 scores of 0.97 and 0.93 for validation and training sets, respectively, in BAC detection.

Conclusion: The deep-learning framework substantially improves BAC detection, segmentation, and quantification in mammograms, advancing the accuracy and efficiency of cardiovascular risk screening. This study supports the potential for integrated dual-purpose screening in women's healthcare.