BI-RADS v2025 Update: Glandular Tissue Component on Ultrasound

The BI-RADS Version 2025 update introduces a big sonographic innovation: glandular tissue component (GTC). Whereas mammographic density and MRI fibroglandular tissue (FGT) measure the overall volume of tissue, GTC provides a specific assessment of the internal composition of the breast parenchyma on ultrasound (US).

Prognostic: No longer is standardizing US tissue composition merely a descriptive endeavor. As Paola Minichetti, MD, and her co-authors from the University of Udine broke it down in their recent AJR review, it is predictive.

• Risk Assessed: Higher GTC levels are associated with increased breast cancer risk, as well as higher abnormal interpretation rates during supplemental screening ultrasound.
• Clinically Consistent: GTC moves US away from purely qualitative “habit-based” reporting toward a lexicon harmonized across multidisciplinary teams.
• AI at the Ready: By defining GTC formally, BI-RADS creates a structured target for future AI-driven automated quantification and objective data mining.

Percentage: GTC represents the ratio of glandular structures—lobules and terminal ducts forming terminal ductal lobular units (TDLUs)—to the total fibroglandular tissue, expressed as a percentage. Minichetti et al. categorize GTC into four tiers:

• Minimal: <25% glandular tissue (predominantly fibrous)
• Mild: 25–49% glandular tissue
• Moderate: 50–74% glandular tissue
• Marked: ≥75% glandular tissue (the most glandular-rich category).

And for simplified reporting, these can be grouped into low GTC (minimal/mild) and high GTC (moderate/marked).

Personalized: On US, GTC is assessed qualitatively after a whole-breast scan by distinguishing isoechoic glandular tissue from hyperechoic fibrous tissue. While fibrous tissue contributes to the architecture, TDLUs represent the actual glandular component being quantified.

RadFYI: Ultimately, GTC transforms breast US from a tool used primarily for lesion characterization into a quantitative framework for personalized risk stratification vis-à-vis improved multidisciplinary communication.