Mammographic density as marker of increased risk of breast cancer

‘Mammographic density’ (MD) is a concept that has entered medical practice since 2017. as a marker of breast cancer risk factor (BC) according to the international classifiation of NCCN. The term reflcts the degree of severity of benign diffuse breast dysplasia in women of post-reproductive age. MD is determined by the ratio of stromal, epithelial, and adipose tissue. According to the literature, in young women, high MD limits the possibilities of X-ray mammography, reducing its effectiveness in oncomammoscreening, leading to the detection of advanced forms of breast cancer. Post-reproductive women with high MD are more likely to develop breast cancer than those with low MD. In this regard, MD is of particular interest for studying its role in oncogenesis. Recent molecular genetic studies of the differences between high and low MD explain the main biological reasons why post-reproductive women with dense breast structure are at a higher risk of developing breast cancer. The aim is to identify the factors that inflence the relationship of MD with the risk of developing breast cancer based on a comparative analysis of molecular genetic studies and radiological manifestations of MD of different severity and to identify the factors that contribute to the formation of MD variants.

Factors Associated With The Prevalence And Severity of Nausea While Undergoing Radiation Among Women Diagnosed With Early-Stage Breast Cancer

Purpose. The purpose of this study was to estimate radiation-induced nausea (RIN) prevalence and severity among 183 women with early-stage breast cancer and to identify its predictors. Methods. Among participants who underwent radiotherapy, a case-control design compared those who experienced RIN to those who did not. Nausea was measured weekly and operationalized on an 11-point scale with ‘0’ representing “no nausea” and ’10’ representing the “worst nausea ever experienced.” Participants self-reported these symptoms while undergoing radiotherapy. Predictor variables were identified using multivariable binary logistic regression for RIN prevalence and multiple linear regression for RIN severity. Results. Over forty percent (n=75) of participants undergoing radiotherapy experienced RIN, with a mean nausea severity rating of 3.27/10. Significant predictors of RIN prevalence were higher pain levels (p<0.0001), history of motion sickness (p=0.024), and younger age (p=0.032). Higher pain levels (p<0.0001), younger age (p=0.038) and history of postoperative nausea (p=0.042) were significant predictors of increased RIN severity. Conclusions. The RIN prevalence of 41.0 percent among study participants was higher than previously reported for patients undergoing breast radiotherapy. This could be due to the collection of weekly self-reported data that quantified RIN severity. Younger age, history of nausea, and higher average pain levels should be identified as potential RIN risk factors among patients with early-stage breast cancer. Risk factor identification at the onset of radiotherapy would allow for increased prophylactic mitigation of RIN.

Automated percent mammographic density, mammographic texture variation, and risk of breast cancer: a nested case-control study

Percent mammographic density (PMD) is a strong breast cancer risk factor, however, other mammographic features, such as V, the standard deviation (SD) of pixel intensity, may be associated with risk. We assessed whether PMD, automated PMD (APD), and V, yielded independent associations with breast cancer risk. We included 1900 breast cancer cases and 3921 matched controls from the Nurses’ Health Study (NHS) and the NHSII. Using digitized film mammograms, we estimated PMD using a computer-assisted thresholding technique. APD and V were determined using an automated computer algorithm. We used logistic regression to generate odds ratios (ORs) and 95% confidence intervals (CIs). Median time from mammogram to diagnosis was 4.1 years (interquartile range: 1.6–6.8 years). PMD (OR per SD:1.52, 95% CI: 1.42, 1.63), APD (OR per SD:1.32, 95% CI: 1.24, 1.41), and V (OR per SD:1.32, 95% CI: 1.24, 1.40) were positively associated with breast cancer risk. Associations for APD were attenuated but remained statistically significant after mutual adjustment for PMD or V. Women in the highest quartile of both APD and V (OR vs Q1/Q1: 2.49, 95% CI: 2.02, 3.06), or PMD and V (OR vs Q1/Q1: 3.57, 95% CI: 2.79, 4.58) had increased breast cancer risk. An automated method of PMD assessment is feasible and yields similar, but somewhat weaker, estimates to a manual measure. PMD, APD and V are each independently, positively associated with breast cancer risk. Women with dense breasts and greater texture variation are at the highest relative risk of breast cancer.

Asymmetric Dense Breast is an Independent Breast Cancer Risk Factor

Objectives: There are multiple known breast cancer risk factors, but most women with breast cancer do not have any of them, so there should be some other unknown risk factors. We hypothesized that asymmetric breast densities could be another breast cancer risk factor. Method: In this study, we defined two case and control groups with 136 women with breast cancer and 136 who did not have breast cancer, respectively. Any different type of asymmetry in either breast was recorded in both groups. Result: The frequency of focal asymmetry in cases was 47 (34.6%), which was statistically more significant than in the control group (28 (20.6%)) (p=0.010). There were three (2.9%) and five (3.7%) global asymmetries in the case and control groups, respectively (p=0.735). The frequency of one view asymmetry in the case and control groups was not significant (16 (11.8%) and 9 (6.6%) respectively) (p=0.142). In the case group, 59 (43.4%) women had at least one type of asymmetry, compared to 41 (30.1%) in the control group (p=0.02). We identify focal asymmetries (likelihood ratio, 1.215; p=0.027) is risk factors for breast cancer. Conclusion: Breast density asymmetry is a breast cancer risk factor that could be scored, thus enhancing risk stratification for screening and prevention.

Relation of Quantitative Histologic and Radiologic Breast Tissue Composition Metrics with Invasive Breast Cancer Risk

Background Benign breast disease (BBD) is a strong breast cancer risk factor but identifying patients that might develop invasive breast cancer remains a challenge. Methods By applying machine-learning to digitized H&E-stained biopsies and computer-assisted thresholding to mammograms obtained circa BBD diagnosis, we generated quantitative tissue composition metrics and determined their association with future invasive breast cancer diagnosis. Archival breast biopsies and mammograms were obtained for women (18-86 years of age) in a case-control study, nested within a cohort of 15,395 BBD patients from Kaiser Permanente Northwest (1970-2012), followed through mid-2015. Cases (n = 514) who developed incident invasive breast cancer and controls (n = 514) were matched on BBD diagnosis age and plan membership duration. All statistical tests were 2-sided. Results Increasing epithelial area on the BBD biopsy was associated with increasing breast cancer risk (Odds ratio [OR]Q4 vs Q1=1.85, 95% confidence interval [CI] = 1.13-3.04; Ptrend=0.02). Conversely, increasing stroma was associated with decreased risk in non-proliferative, but not proliferative, BBD (Pheterogeneity=0.002). Increasing epithelium-to-stroma proportion [ORQ4 vs Q1=2.06, 95% CI = 1.28-3.33; Ptrend=0.002) and percent mammographic density (MBD) (ORQ4 vs Q1=2.20, 95% CI = 1.20-4.03; Ptrend=0.01) were independently and strongly predictive of increased breast cancer risk. In combination, women with high epithelium-to-stroma proportion/high MBD had substantially higher risk than those with low epithelium-to-stroma proportion/low MBD [OR = 2.27, 95% CI = 1.27-4.06; Ptrend=0.005), particularly among women with non-proliferative (Ptrend=0.01) versus proliferative (Ptrend=0.33) BBD. Conclusion Among BBD patients, increasing epithelium-to-stroma proportion on BBD biopsies and percent MBD at BBD diagnosis were independently and jointly associated with increasing breast cancer risk. These findings were particularly striking for women with non-proliferative disease (comprising approximately 70% of all BBD patients), for whom relevant predictive biomarkers are lacking.

Relation of Quantitative Histologic and Radiologic Breast Tissue Composition Metrics with Invasive Breast Cancer Risk

PurposeBenign breast disease (BBD) is a strong breast cancer risk factor but identifying patients that might develop invasive breast cancer remains a challenge.MethodsBy applying machine-learning to digitized H&E-stained biopsies and computer-assisted thresholding to mammograms obtained circa BBD diagnosis, we generated quantitative tissue composition metrics and determined their association with future invasive breast cancer diagnosis. Archival breast biopsies and mammograms were obtained for women (18-86 years) in a case-control study, nested within a cohort of 15,395 BBD patients from Kaiser Permanente Northwest (1970-2012), followed through mid-2015. Cases (n=514) who developed incident invasive breast cancer and controls (n=514) were matched on BBD diagnosis age and plan membership duration.ResultsIncreasing epithelial area on the BBD biopsy was associated with increasing breast cancer risk [Odds ratio(OR) 95% confidence interval(CI) Q4 vs Q1=1.85(1.13-3.04);Ptrend=0.02]. Conversely, increasing stroma was associated with decreased risk in non-proliferative, but not proliferative, BBD (Pheterogeneity=0.002). Increasing epithelium-to-stroma proportion [OR(95%CI)Q4 vs Q1=2.06(1.28-3.33);Ptrend=0.002] and percent mammographic density (MBD) [OR(95%CI)Q4 vs Q1=2.20(1.20-4.03);Ptrend=0.01] were independently and strongly predictive of increased breast cancer risk. In combination, women with high epithelium-to-stroma proportion/high MBD had substantially higher risk than those with low epithelium-to-stroma proportion/low MBD [OR(95%CI)=2.27(1.27-4.06);Ptrend=0.005], particularly among women with non-proliferative [Ptrend=0.01] versus proliferative [Ptrend=0.33] BBD.ConclusionAmong BBD patients, increasing epithelium-to-stroma proportion on BBD biopsies and percent MBD at BBD diagnosis were independently and jointly associated with increasing breast cancer risk. These findings were particularly striking for women with non-proliferative disease (comprising ∼70% of all BBD patients), for whom relevant predictive biomarkers are lacking.

Assessment of breast carcinoma by correlating BI-RADS scoring with mammographic density

Background: Breast density assessed by mammogram expressed in percentage of density of breast tissue reflects variations in breast tissue composition and is strongly associated with increased risk of breast cancer. The BI-RADS density method was created to indicate whether a mammogram represents a negative, benign or suspected malignant finding. To assess breast carcinoma by correlating breast imaging-reporting and data system (BI-RADS) scoring with mammographic density.Methods: A total of 100 consecutive female patients with breast lump were assessed. The findings of the radiological examination and the histopathology results were subsequently analyzed to study the details of the breast disease in the group surveyed. BI-RADS classifications of breast density was extracted from mammography reports.Results: Majority of patients were having BI-RADS score 4 (33%) followed by BI-RADS score 5 (30%). Majority of the patients were having percentage breast density 4 (35%) followed by Percentage breast density 3 (28%). BI-RADS score and percentage breast density had statistically significant correlation (p<0.05).Conclusions: The BI-RADS score and percentage breast density by mammography had statistically significant correlation. Mammographic density is a strong breast cancer risk factor.