Mammographic Breast Density Assessed with Fully Automated Method and its Risk for Breast Cancer

Abstract

Objectives:We evaluated the association between breast cancer and breast density (BD) measured using fully automated software. We also evaluated the performance of cancer risk models such as only clinical risk factors, density related measures, and both clinical risk factors and density-related measures for determining cancer risk.Materials and Methods:This is a retrospective case–control study. The data were collected from August 2015 to December 2018. Two hundred fifty women with breast cancer and 400 control subjects were included in this study. We evaluated the BD qualitatively using breast imaging-reporting and data system density and quantitatively using 3D slicer. We also collected clinical factors such as age, familial history of breast cancer, menopausal status, number of births, body mass index, and hormonal replacement therapy use. We calculated the odds ratio (OR) for BD to determine the risk of breast cancer. We performed receiver operating characteristic (ROC) curve to assess the performance of cancer risk models.Results:The OR for the percentage BD for second, third, and fourth quartiles was 1.632 (95% confidence intervals [CI]: 1.102–2.416), 2.756 (95% CI: 1.704–4.458), and 3.163 (95% CI: 1.356–5.61). The area under ROC curve for clinical risk factors only, mammographic density measures, combined mammographic, and clinical risk factors was 0.578 (95% CI: 0.45, 0.64), 0.684 (95% CI: 0.58, 0.75), and 0.724 (95% CI: 0.64, 0.80), respectively.Conclusion:Mammographic BD was found to be positively associated with breast cancer. The density related measures combined clinical risk factors, and density model had good discriminatory power in identifying the cancer risk.

Proton density water fraction as a reproducible MR‐based measurement of breast density

“…While mammographic breast density has received significant attention, assessment of breast FGT has important clinical applications beyond categorizing women as having dense breasts. Incorporation of breast density into risk prediction models has been shown to more accurately predict future risk for breast cancer 5‐8 . Additionally, as more practices offer supplemental screening beyond mammography to patients at higher than average risk of breast cancer, including those women with dense breasts, FGT assessment could more accurately stratify patients into personalized screening regimens.…”

The Prognostic Quality of Risk Prediction Models to Assess the Individual Breast Cancer Risk in Women: An Overview of Reviews

“…Similarly, assessing genetic information as an additional risk factor requires the organisation of cooperations between qualifed centres for medical genetics. 0.58 [39]-0.71 [40] Models assessing the prognostic quality in invasive and/or in situ breast cancer without and with breast density 5 Without breast density: 0.5 [41]-0.65 [42,43]; with breast density: 0.63 [41]-0.72 [44] Models assessing the prognostic quality in invasive and/or in situ breast cancer without and with SNPs 10 Without SNPs: 0.53 [45]-0.79 [46]; SNPs enhanced: 0.60 [45]-0.69 [47] Model assessing the prognostic quality in ER-positive, HER2-negative, invasive, and noninvasive cancers without and with SNPs…”

“…A Swedish model [46] including age, body mass index, hormone replacement therapy, family history of breast cancer, age at menopause, breast density, microcalcifcations, and spaceoccupying lesions as risk factors showed an AUC value above 0.71 for a Caucasian population. Te discriminatory accuracy of the models considering breast density as a risk factor ranged from AUC values of 0.63 [40] to 0.72 [41], depending on whether the absolute area, per cent of the area, or fbroglandular volume of breast density measurement was used. Te models that included a polygenic risk score as a risk factor-except the Japanese model-had AUC values between 0.60 [38] and 0.693 [44].…”