Optimal Breast Density Characterization Using a Three-Dimensional Automated Breast Densitometry System

Dense breasts are a risk factor for breast cancer. Assessment of breast density is important and radiologist-dependent. We objectively measured mammographic density using the three-dimensional automatic mammographic density measurement device Volpara™ and examined the criteria for combined use of ultrasonography (US). Of 1227 patients who underwent primary breast cancer surgery between January 2019 and April 2021 at our hospital, 441 were included. A case series study was conducted based on patient age, diagnostic accuracy, effects of mammography (MMG) combined with US, size of invasion, and calcifications. The mean density of both breasts according to the Volpara Density Grade (VDG) was 0–3.4% in 2 patients, 3.5–7.4% in 55 patients, 7.5–15.4% in 173 patients, and ≥15.5% in 211 patients. Breast density tended to be higher in younger patients. Diagnostic accuracy of MMG tended to decrease with increasing breast density. US detection rates were not associated with VDG on MMG and were favorable at all densities. The risk of a non-detected result was high in patients without malignant suspicious calcifications. Supplementary use of US for patients without suspicious calcifications on MMG and high breast density, particularly ≥25.5%, could improve the breast cancer detection rate.

Performance of TyrerCuzick Model for Breast Cancer Risk Assessment among Pakistan’s Females

Background: Breast cancer incidence is highest in Pakistan among Asian countries. The known risk factors are family history, hormonal exposure, benign proliferative diseases, and high mammographic density which are included in the TyrerCuzick model. The model needs validation studies to implement in prediction, screening, and prevention strategies among different populations. This study aims to validate the TyrerCuzick model for Pakistan's females. Methods and Materials: A total of 317 biopsy-proven breast cancer patients from the breast surgery clinic at Liaquat National Hospital were included. The 10 years risk score is calculated by applying the TyrerCuzick model software. Subcategories of low risk <2%, moderate risk 2-7%, and high risk >8% were identified. Further risk group stratification is done to find the association with individual factors i.e., age group, menopausal status, family history, and mammographic density. Results: The mean TyrerCuzick score was low to moderate i.e. 2.23±1.66. The score was distributed as low risk 174(54.9%), moderate risk 137(43.2%), and high risk 6(1.9%). Low risk was observed among 116(81.7%) of less than 50 years old, 105(78.9%) premenopausal, 113(59.8%) with no family history, and 120 patients (59.7%) with low mammographic density. Most of the moderate risk was found in 113(64.6%) of more than 50 years old, 109(60.2%) with postmenopausal, 24(61.5%) with family history, 58(50%) with high mammographic density respectively. Conclusion: The TyrerCuzick model can predict risk for developing breast cancer among Pakistan’s femalesclose to accurate among older age, postmenopausal, family history of breast cancer, and high mammographic density.

Mammary collagen architecture and its association with mammographic density and lesion severity among women undergoing image-guided breast biopsy

Background Elevated mammographic breast density is a strong breast cancer risk factor with poorly understood etiology. Increased deposition of collagen, one of the main fibrous proteins present in breast stroma, has been associated with increased mammographic density. Collagen fiber architecture has been linked to poor outcomes in breast cancer. However, relationships of quantitative collagen fiber features assessed in diagnostic biopsies with mammographic density and lesion severity are not well-established. Methods Clinically indicated breast biopsies from 65 in situ or invasive breast cancer cases and 73 frequency matched-controls with a benign biopsy result were used to measure collagen fiber features (length, straightness, width, alignment, orientation and density (fibers/µm2)) using second harmonic generation microscopy in up to three regions of interest (ROIs) per biopsy: normal, benign breast disease, and cancer. Local and global mammographic density volumes were quantified in the ipsilateral breast in pre-biopsy full-field digital mammograms. Associations of fibrillar collagen features with mammographic density and severity of biopsy diagnosis were evaluated using generalized estimating equation models with an independent correlation structure to account for multiple ROIs within each biopsy section. Results Collagen fiber density was positively associated with the proportion of stroma on the biopsy slide (p < 0.001) and with local percent mammographic density volume at both the biopsy target (p = 0.035) and within a 2 mm perilesional ring (p = 0.02), but not with global mammographic density measures. As severity of the breast biopsy diagnosis increased at the ROI level, collagen fibers tended to be less dense, shorter, straighter, thinner, and more aligned with one another (p < 0.05). Conclusions Collagen fiber density was positively associated with local, but not global, mammographic density, suggesting that collagen microarchitecture may not translate into macroscopic mammographic features. However, collagen fiber features may be markers of cancer risk and/or progression among women referred for biopsy based on abnormal breast imaging.