Cross-Field Strength and Multi-Vendor Validation of MagDensity for MRI-based Quantitative Breast Density Analysis

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Abstract Purpose Breast density (BD) is a significant risk factor for breast cancer, yet current assessment methods lack automation, quantification, and cross-platform consistency. This study aims to evaluate MagDensity, a novel magnetic resonance imaging (MRI)-based quantitative BD measure, for its validity and reliability across different imaging platforms. Methods Ten healthy volunteers participated in this prospective study, undergoing fat-water MRI scans on three scanners: 3T Siemens Prisma, 3T Siemens Biograph mMR, and 1.5T GE Signa. Great effort was made to schedule all scans within a narrow three-hour window on the same day to minimize any potential intraday variations, highlighting the logistical challenges involved. BD was assessed using the MagDensity technique, which included combining magnitude and phase images, applying a fat-water separation technique, employing an automated whole-breast segmentation algorithm, and quantifying the volumetric water fraction. The agreement between measures was analyzed using mean differences, two-tailed t-tests, Pearson’s correlation coefficients, and Bland-Altman plots. Results No statistically significant differences in BD measurements by MagDensity within the same field strength and vendor (3T Siemens), with high correlation (Pearson’s r > 0.99) and negligible mean differences (< 0.2%). Cross-platform comparison between the 3T Siemens and the 1.5T GE scanners showed mean differences of < 5%. After linear calibration, these variations were reduced to insignificant levels, yielding a strong correlation (Pearson’s r > 0.97) and mean differences within ±0.2%. Conclusion MagDensity, an MRI-based BD measure, exhibits robustness and reliability across diverse scanner models, vendors, and field strengths, marking a promising advancement towards standardizing BD measurements across multiple MRI platforms. It provides a valuable tool for monitoring subtle longitudinal changes in BD, which is vital for breast cancer prevention and personalized treatment strategies.

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