The authors have developed a technique to produce 3D anthropomorphic breast phantoms with known ground truth, yielding highly realistic x-ray images. Such phantoms may serve both qualitative and quantitative performance assessments for 2D and 3D breast x-ray imaging systems.

Purpose:To evaluate inter-and intrareader agreement in breast percent density (PD) estimation on clinical digital mammograms and central digital breast tomosynthesis (DBT) projection images. Materials and Methods:This HIPAA-compliant study had institutional review board approval; all patients provided informed consent. Breast PD estimation was performed on the basis of anonymized digital mammograms and central DBT projections in 39 women (mean age, 51 years; range, 31-80 years).All women had recently detected abnormalities or biopsyproved cancers. PD was estimated by three experienced readers on the mediolateral oblique views of the contralateral breasts by using software; each reader repeated the estimation after 2 months. Spearman correlations of interand intrareader and intermodality PD estimates, as well as statistics between categoric PD estimates, were computed. Results:High correlation ( ϭ 0.91) was observed between PD estimates on digital mammograms and those on central DBT projections, averaged over all estimations; the corresponding coefficient (0.79) indicated substantial agreement. Mean interreader agreement for PD estimation on central DBT projections ( ϭ 0.85 Ϯ 0.05 [standard deviation]) was significantly higher (P Ͻ .01) than that for PD estimation on digital mammograms ( ϭ 0.75 Ϯ 0.05); the corresponding coefficients indicated substantial ( ϭ 0.65 Ϯ 0.12) and moderate ( ϭ 0.55 Ϯ 0.14) agreement for central DBT projections and digital mammograms, respectively. Conclusion:High correlation between PD estimates on digital mammograms and those on central DBT projections suggests the latter could be used until a method for PD estimation based on three-dimensional reconstructed images is introduced. Moreover, clinical PD estimation is possible with reduced radiation dose, as each DBT projection was acquired by using about 22% of the dose for a single mammographic projection. Note: This copy is for your personal, non-commercial use only. To order presentation-ready copies for distribution to your colleagues or clients, use the Radiology Reprints form at the end of this article. Identification of women with an increased risk of breast cancer is of high importance, because they may benefit from modified screening and diagnosis protocols (1). Current clinical standards for breast cancer risk estimation, the Gail (2) and Claus (3) statistical models, are used to predict the absolute risk of breast cancer over a defined age interval on the basis of standard risk factors (4), including age, age at menarche, age at first full-term pregnancy, number of previous biopsies with a benign result, and number of firstdegree relatives with breast cancer. These models perform well on a population level but are limited in the prediction of individual cancer incidence (5) because the standard risk factors are practically nonmodifiable and cannot reflect changes in risk over time.Breast density is considered to be an independent risk factor for cancer (6). It is also indicative of changes in modifiable risk factors (7-11). ...

Rationale and Objectives-Studies have demonstrated a relationship between mammographic parenchymal texture and breast cancer risk. Although promising, texture analysis in mammograms is limited by tissue superimposition. Digital breast tomosynthesis (DBT) is a novel tomographic xray breast imaging modality that alleviates the effect of tissue superimposition, offering superior parenchymal texture visualization compared to mammography. Our study investigates the potential advantages of DBT parenchymal texture analysis for breast cancer risk estimation.Materials and Methods-DBT and digital mammography (DM) images of 39 women were analyzed. Texture features, shown in studies with mammograms to correlate with cancer risk, were computed from the retroareolar breast region. We compared the relative performance of DBT and DM texture features in correlating with two measures of breast cancer risk: (i) the Gail and Claus risk estimates, and (ii) mammographic breast density. Linear regression was performed to model the association between texture features and increasing levels of risk.Results-No significant correlation was detected between parenchymal texture and the Gail and Claus risk estimates. Significant correlations were observed between texture features and breast density. Overall, the DBT texture features demonstrated stronger correlations with breast percent density (PD) than DM (p ≤0.05). When dividing our study population in groups of increasing breast PD, the DBT texture features appeared to be more discriminative, having regression lines with overall lower p-values, steeper slopes, and higher R 2 estimates.Conclusion-Although preliminary, our results suggest that DBT parenchymal texture analysis could provide more accurate characterization of breast density patterns, which could ultimately improve breast cancer risk estimation.