Increased mammographic breast density is a moderate independent risk factor for breast cancer, with findings of published studies in which quantitative methods of assessment were used showing a positive association. Breast density may be quantified by using visual assessment or planimetry. Although the category definitions vary, the odds ratio for developing breast cancer for the most dense compared with the least dense breast tissue categories ranges from 1.8 to 6.0, with most studies yielding an odds ratio of 4.0 or greater. Plausible explanations for the association of breast density with increased breast cancer risk may be the development of premalignant lesions such as atypical ductal hyperplasia, elevated growth factors, or increased estrogen production within the breast due to overactive aromatase. The amount of breast density may be due in part to genetic heredity. However, unlike other risk factors, breast density may be influenced. Specifically, breast density is very hormonally responsive and potentially may be influenced by lifestyle factors such as alcohol intake and diet. Assessment of breast density may become useful in risk assessment and prevention decisions.
Importance: Improved screening methods for women with dense breasts are needed because of their increased risk of breast cancer and of failed early diagnosis by screening mammography. Objective: To compare the screening performance of abbreviated breast MRI (AB-MR), and digital breast tomosynthesis (DBT) in women with dense breasts. Design, Setting, and Participants: Cross-sectional study with longitudinal follow-up at 48 academic, community hospital, and private practice sites in the US and Germany, conducted between December 2016 and November 2017, that included average-risk women aged 40-75 years with heterogeneously dense or extremely dense breasts undergoing routine screening. Follow up ascertainment of cancer diagnoses was complete through September 12 th , 2019. Exposure: All women underwent screening by both DBT and AB-MR, performed in randomized order and read independently to avoid interpretation bias. Main outcome measures: The primary endpoint was the invasive cancer detection rate. Secondary outcomes included sensitivity, specificity, the additional-imaging-recommendation-rate, and positive predictive value (PPV) of biopsy, using invasive cancer and DCIS to define a positive reference standard. All outcomes are reported at the participant level. Pathology of core or surgical biopsy was the reference standard for cancer detection rate and PPV; interval cancers reported until the next annual screen were included in the reference standard for sensitivity and specificity. Results: Among 1516 enrolled women, 1444 (median age 54, range 40-75) completed both examinations and were included in the analysis. The reference standard was positive for invasive cancer with or without DCIS in 17 women, and for DCIS alone in another 6. No interval cancers were observed during follow-up. AB-MR detected all 17 women with invasive cancer, and 5/6 women with DCIS. DBT detected 7/17 women with invasive cancer, and 2/6 women with DCIS. The invasive-cancer-detection-rate was 11.8 per 1000 women [95% CI 7.4-18.8] for AB-MR versus 4.8 per 1000 women [95% CI 2.4-10.0] for DBT, a difference of 7 per 1000 women [95% CI for the difference 2.2-11.6] (exact McNemar p=0.002). For detection of invasive cancer and Comstock et al.
Abstract. Volumetric breast composition measurements generally require accurate imaging physics data. In this paper we describe a new method (Volpara™) that uses relative (as opposed to absolute) physics modeling together with additional information derived from the image to substantially reduce the dependence on imaging physics data. Results on 2,217 GE digital images, from a diversity of sites, show encouraging agreement with MRI data, as well as robustness to noise and errors in the imaging physics data.
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