R andomized controlled trials have shown that screening mammography reduces breast cancer mortality by r educing the incidence of advanced cancer (1,2). However, mammography has reduced sensitivity in the detection of breast cancers in breasts with radiologically dense and complex tissue (3,4). These cancers discovered within 12 months after normal screening mammograms are defined as interval cancers, and the reduction of mammographic sensitivity from breast density is commonly called masking. Roughly 13% of breast cancers diagnosed in the United States are interval cancers (3). Interval cancers usually have more aggressive tumor biology and are typically discovered at an advanced stage (3-6). It is therefore important to identify women who have a high risk for interval breast cancer and provide additional prevention strategies such as supplemental screenings (3).Previous studies have shown breast density is both a risk factor for breast cancer and a masking factor of interval breast cancer. One study of more than 547 women found that the cancer detection rate with mammography was 80% in women with predominantly fatty breasts and 30% in women with extremely dense breasts (7). A larger study of more than 240 000 women found that combined relative risks of incident breast cancer showed positive correlation with the percentage breast density, reporting relative risks

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ObjectiveThis study examined whether body shape and composition obtained by three‐dimensional optical (3DO) scanning improved the prediction of metabolic syndrome (MetS) prevalence compared with BMI and demographics.MethodsA diverse ambulatory adult population underwent whole‐body 3DO scanning, blood tests, manual anthropometrics, and blood pressure assessment in the Shape Up! Adults study. MetS prevalence was evaluated based on 2005 National Cholesterol Education Program criteria, and prediction of MetS involved logistic regression to assess (1) BMI, (2) demographics‐adjusted BMI, (3) 85 3DO anthropometry and body composition measures, and (4) BMI + 3DO + demographics models. Receiver operating characteristic area under the curve (AUC) values were generated for each predictive model.ResultsA total of 501 participants (280 female) were recruited, with 87 meeting the criteria for MetS. Compared with the BMI model (AUC = 0.819), inclusion of age, sex, and race increased the AUC to 0.861, and inclusion of 3DO measures further increased the AUC to 0.917. The overall integrated discrimination improvement between the 3DO + demographics and the BMI model was 0.290 (p < 0.0001) with a net reclassification improvement of 0.214 (p < 0.0001).ConclusionsBody shape measures from an accessible 3DO scan, adjusted for demographics, predicted MetS better than demographics and/or BMI alone. Risk classification in this population increased by 29% when using 3DO scanning.