Influential factors and prediction model of mammographic density among Chinese women

Breast density in NF1 women: a retrospective study

Neurofibromatosis type 1 (NF1) is an autosomal dominant condition caused by neurofibromin haploinsufficiency due to pathogenic variants in the NF1 gene. Tumor predisposition has long been associated with NF1, and an increased breast cancer (BC) incidence and reduced survival have been reported in recent years for women with NF1. As breast density is another known independent risk factor for BC, this study aims to evaluate the variability of breast density in patients with NF1 compared to the general population. Mammograms from 98 NF1 women affected by NF1, and enrolled onto our monocentric BC screening program, were compared with those from 300 healthy subjects to verify differences in breast density. Mammograms were independently reviewed and scored by a radiologist and using a Computer-Aided Detection (CAD) software. The comparison of breast density between NF1 patients and controls was performed through Chi-squared test and with multivariable ordinal logistic models adjusted for age, body mass index (BMI), number of pregnancies, and menopausal status.breast density was influenced by BMI and menopausal status in both NF1 patients and healthy subjects. No difference in breast density was observed between NF1 patients and the healthy female population, even after considering the potential confounding factors.Although NF1 and a highly fibroglandular breast are known risk factors of BC, in this study, NF1 patients were shown to have comparable breast density to healthy subjects. The presence of pathogenic variants in the NF1 gene does not influence the breast density value.

Breast cancer risk factors and mammographic density among 12518 average-risk women in rural China

BACKGROUND

Mammographic density (MD) is a strong risk factor for breast cancer. We aimed to evaluate the association between MD and breast cancer related risk factors among average-risk women in rural China.

METHODS

This is a population-based screening study. 12518 women aged 45-64 years with complete MD data from three maternal and childcare hospitals in China were included in the final analysis. ORs and 95%CIs were estimated using generalized logit model by comparing each higher MD (BI-RADS b, c, d) to the lowest group (BI-RADS a). The cumulative logistic regression model was used to estimate the ORtrend (95%CI) and Ptrend by treating MD as an ordinal variable.

RESULTS

Older age (ORtrend = 0.81, 95%CI: 0.79-0.81, per 2-year increase), higher BMI (ORtrend = 0.73, 95%CI: 0.71-0.75, per 2 kg/m2), more births (ORtrend = 0.47, 95%CI: 0.41-0.54, 3 + vs. 0-1), postmenopausal status (ORtrend = 0.42, 95%CI: 0.38-0.46) were associated with lower MD. For parous women, longer duration of breastfeeding was found to be associated with higher MD when adjusting for study site, age, BMI, and age of first full-term birth (ORtrend = 1.53, 95%CI: 1.27-1.85, 25 + months vs. no breastfeeding; ORtrend = 1.45, 95%CI: 1.20-1.75, 19-24 months vs. no breastfeeding), however, the association became non-significant when adjusting all covariates. Associations between examined risk factors and MD were similar in premenopausal and postmenopausal women except for level of education and oral hormone drug usage. Higher education was only found to be associated with an increased proportion of dense breasts in postmenopausal women (ORtrend = 1.08, 95%CI: 1.02-1.15). Premenopausal women who ever used oral hormone drug were less likely to have dense breasts, though the difference was marginally significant (OR = 0.54, P = 0.045). In postmenopausal women, we also found the proportion of dense breasts increased with age at menopause (ORtrend = 1.31, 95%CI: 1.21-1.43).

CONCLUSIONS

In Chinese women with average risk for breast cancer, we found MD was associated with age, BMI, menopausal status, lactation, and age at menopausal. This finding may help to understand the etiology of breast cancer and have implications for breast cancer prevention in China.

Sensing mammographic density using single-sided portable Nuclear Magnetic Resonance

This research paper presents a quantitative approach to sensing mammographic density (MD) using single-sided portable Nuclear Magnetic Resonance (NMR). It focuses on three main techniques: spin-lattice relaxation (recovery) time (T1), spin-spin relaxation (decay) time (T2), and Diffusion (D) techniques by testing whether or not the aforementioned techniques are in agreement with the gold standard and with each other when used for scanning breast tissue specimens with a variety of mammographic densities (MDs). The high mammographic density (HMD), intermediate MD, and low mammographic density (LMD) regions of each slice were identified according to the mammogram images. Subsequently, the grayscale values for these regions were quantified. One region was measured from the first sample while the remaining ones were measured from the second sample. The same areas were then exposed to portable NMR, and the sequences used as following: the stimulated echo sequence for diffusion (D), the Carr-Purcell-Meiboom-Gill (CPMG) sequence for T2, and saturation recovery sequence for T1. The correlations between the grayscale values and NMR techniques were strongly correlated. The Pearson correlation coefficient, R, of T1 (%) versus grayscale value, D (%) versus grayscale value, and T2 (%) versus grayscale value, was 0.91, 0.91, and 0.93, respectively. Furthermore, the relative water content of the breast slices based on T1, T2, and diffusion (D) measurements were strongly in agreement with each other. The Pearson correlation coefficient, R, of D (%) versus T1 (%), D (%) versus T2 (%), and T1 (%) versus T2 (%), was 0.984, 0.966, and 0.9868, respectively. The three pulse sequences can be employed in a portable NMR device to deliver continuous quantitative measurements of MD in breast tissue samples. As a result, the method demonstrated to be acceptable for determining the distribution of MDs among breast tissue samples without the need for additional qualitative analysis.

Factors Influencing Mammographic Density in Asian Women: A Retrospective Cohort Study in the Northeast Region of Peninsular Malaysia

Mammographic density is a significant risk factor for breast cancer. In this study, we identified the risk factors of mammographic density in Asian women and quantified the impact of breast density on the severity of breast cancer. We collected data from Hospital Universiti Sains Malaysia, a research- and university-based hospital located in Kelantan, Malaysia. Multivariable logistic regression was performed to analyse the data. Five significant factors were found to be associated with mammographic density: age (OR: 0.94; 95% CI: 0.92, 0.96), number of children (OR: 0.88; 95% CI: 0.81, 0.96), body mass index (OR: 0.88; 95% CI: 0.85, 0.92), menopause status (yes vs. no, OR: 0.59; 95% CI: 0.42, 0.82), and BI-RADS classification (2 vs. 1, OR: 1.87; 95% CI: 1.22, 2.84; 3 vs. 1, OR: 3.25; 95% CI: 1.86, 5.66; 4 vs. 1, OR: 3.75; 95% CI: 1.88, 7.46; 5 vs. 1, OR: 2.46; 95% CI: 1.21, 5.02; 6 vs. 1, OR: 2.50; 95% CI: 0.65, 9.56). Similarly, the average predicted probabilities were higher among BI-RADS 3 and 4 classified women. Understanding mammographic density and its influencing factors aids in accurately assessing and screening dense breast women.

Evaluation of the association of endometriosis and mammographic breast density, a cross-sectional study

Background

Endometriosis is a common benign but painful gynecologic condition. Studies suggest that the risk of some types of malignancies such as breast cancer is higher in women with endometriosis. Mammographic breast density (MBD) is known as an important predictor for breast cancer. The present study aimed to investigate the potential relationship between endometriosis and MBD.

Methods

This cross-sectional study was conducted on 370 women over 40 years of age. Laparoscopic surgery was carried out for the diagnosis of endometriosis. MBD was classified into four categories according to the ACR BI-RADS classification. Statistical analysis was performed using SPSS software to evaluate the potential association between variables.

Results

The mean age of all participants was 47.2 ± 6.4 years, and most participants (76.8%) were premenopausal. Multivariate analysis of the potential predictors of MBD, including age, body mass index, oral contraceptive consumption, progesterone consumption, family history of breast cancer and endometriosis showed that age (P value = 0.002), history of progesterone consumption (P value = 0.004) and endometriosis (P value = 0.006) were independent factors for MBD.

Conclusion

This study indicated that endometriosis had an inverse association with MBD. Age and history of progesterone use were also independent influential factors for MBD. This finding shows that the positive association between breast cancer and endometriosis is not mediated through MBD.