Digital breast tomosynthesis screening outcomes are sustainable, with significant recall reduction, increasing cancer cases per recalled patients, and a decline in interval cancers.

olfe in 1976 suggested that patterns of breast parenchymal complexity, formed by the x-ray attenuation of fatty, fibroglandular, and stromal tissues (1), are associated with breast cancer risk (2). Breast density ratings, based on the extent of mammographic density, are routinely used clinically to characterize the breast parenchyma. High breast density has been associated with greater risk of breast cancer (3-5). Additionally, breast density has been associated with masking of cancers leading to interval cancers (6) in mammographic screening. Density measures aim to capture the relative amount of fibroglandular tissue in the breast (7); however, they are increasingly considered to be coarse measures, being limited in fully capturing the complexity of the breast parenchymal pattern (8). This has motivated research toward complementing quantitative density measures with more granular characterization of parenchymal complexity and their association to breast cancer risk and detection. Early studies with BRCA1 and BRCA2 (BRCA1/2) carriers have shown that computerized measures of mammographic parenchymal texture from the retroareolar breast region can distinguish BRCA1/2 carriers from low-risk women (9,10). Recent studies of case-control samples from screening populations have also shown that parenchymal texture features (either from the retroareolar region or the entire breast area) are significantly associated with breast cancer independent of breast density (11-15). Nevertheless, to our knowledge, no studies to date have attempted to define distinct imaging phenotypes that reflect intrinsic complexity of the breast parenchymal tissue.

Purpose: With raw digital mammograms (DMs), which retain the relationship with x-ray attenuation of the breast tissue, not being routinely available, processed DMs are often the only viable means to acquire imaging measures. The authors investigate differences in quantitative measures of breast density and parenchymal texture, shown to have value in breast cancer risk assessment, between the two DM representations. Methods: The authors report data from 8458 pairs of bilateral raw ("FOR PROCESSING") and processed ("FOR PRESENTATION") DMs acquired from 4278 women undergoing routine screening evaluation, collected with DM units from two different vendors. Breast dense tissue area and percent density (PD), as well as a range of quantitative descriptors of breast parenchymal texture (statistical, co-occurrence, run-length, and structural descriptors), were measured using previously validated, fully automated software. Feature measurements were compared using matched-pairs Wilcoxon signed-ranks test, correlation (r), and linear-mixed-effects (LME) models, where potential interactions with woman-and system-specific factors were also assessed. The authors also compared texture feature correlations with the established risk factors of the Gail lifetime risk score (r G ) and breast PD (r PD ), and evaluated the within woman intraclass feature correlation (ICC), a measure of bilateral breast-tissue symmetry, in raw versus processed images. Results: All density measures and most of the texture features were strongly (r ≥ 0.6) or moderately (0.4 ≤ r < 0.6) correlated between raw and processed images. However, measurements were significantly different between the two imaging formats (Wilcoxon signed-ranks test, p w < 0.05). The association between measurements varied across features and vendors, and was substantially modified by woman-and system-specific image acquisition factors, such as age, BMI, and mAs/kVp, respectively. The strongest correlation, combined with minimal LME-model interactions, was observed for structural texture features. Overall, texture measures from either image representation were weakly associated with Gail lifetime risk (−0.2 ≤ r G ≤ 0.2), weakly to moderately associated with breast PD (−0.6 ≤ r PD ≤ 0.6), and had overall strong bilateral symmetry (ICC ≥ 0.6). Conclusions: Differences in measures from processed versus raw DM depend highly on the feature, the DM vendor, and image acquisition settings, where structural features appear to be more robust across the different DM settings. The reported findings may serve as a reference in the design of future large-scale studies on mammographic features and breast cancer risk assessment involving multiple DM representations. C

Abstract. An analytical framework is presented for evaluating the equivalence of parenchymal texture features across different full-field digital mammography (FFDM) systems using a physical breast phantom. Phantom images (FOR PROCESSING) are acquired from three FFDM systems using their automated exposure control setting. A panel of texture features, including gray-level histogram, co-occurrence, run length, and structural descriptors, are extracted. To identify features that are robust across imaging systems, a series of equivalence tests are performed on the feature distributions, in which the extent of their intersystem variation is compared to their intrasystem variation via the Hodges-Lehmann test statistic. Overall, histogram and structural features tend to be most robust across all systems, and certain features, such as edge enhancement, tend to be more robust to intergenerational differences between detectors of a single vendor than to intervendor differences. Texture features extracted from larger regions of interest (i.e., >63 pixels 2 ) and with a larger offset length (i.e., >7 pixels), when applicable, also appear to be more robust across imaging systems. This framework and observations from our experiments may benefit applications utilizing mammographic texture analysis on images acquired in multivendor settings, such as in multicenter studies of computer-aided detection and breast cancer risk assessment.

Our results suggest that informative interactions between patterns exist in texture feature maps derived from mammographic images, which can be extracted and summarized via a multichannel CNN architecture toward leveraging the associations of textural measurements to breast cancer risk.