Assessing breast density using the chemical-shift encoding-based proton density fat fraction in 3-T MRI

Area of Science:

• Radiology and Medical Imaging
• Biomarkers and Diagnostic Tools
• Oncology and Breast Cancer Research

Background:

• Breast density is a significant risk factor for breast cancer.
• Current breast density assessment methods have limitations.
• There is a need for non-ionizing, quantitative breast density evaluation.

Purpose of the Study:

• To establish proton density fat fraction (PDFF) as a quantitative biomarker for breast fat tissue concentration using MRI.
• To correlate mean breast PDFF with mammographic breast density measurements.
• To assess the potential of PDFF for routine clinical use in breast MRI.

Main Methods:

• Retrospective analysis of 3-T MRI data from 193 women using a six-echo chemical shift encoding water-fat sequence.
• Water-fat separation and calculation of PDFF and T2* values.
• Semi-automated breast segmentation for PDFF and T2* determination in the whole breast and fibroglandular tissue.
• Classification of breast density using American College of Radiology (ACR) categories (A-D) based on mammography and MRI.

Main Results:

• PDFF showed a strong negative correlation with mammographic and MRI-based breast density (Spearman rho: -0.74, p < .001).
• Significant distinctions in PDFF were observed across all four ACR categories.
• Mean T2* of fibroglandular tissue correlated with increasing ACR categories (Spearman rho: 0.34, p < .001).
• PDFF of fibroglandular tissue correlated with patient age (Pearson rho: 0.56, p = .03).

Conclusions:

• Breast PDFF provides an automated, quantitative measurement of tissue fat concentration comparable to mammographic density estimations.
• PDFF is a promising tool for accurate, user-independent, and non-ionizing breast density assessment.
• Combined with T2*, PDFF can help track breast tissue composition changes for individualized breast cancer risk assessment.