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Beyond Breast Density: Radiomic Phenotypes Enhance Assessment of Breast Cancer Risk.

B density, or breast composition, reflects the amount of fibroglandular tissue in relation to the amount of fatty tissue in the breast. It varies greatly throughout a woman’s life, influenced by endogenous (eg, age, parity, body mass index, ethnicity) and exogenous factors (eg, smoking, alcohol, obesity, sedentary lifestyle, oral contraception, hormone replacement therapy) (1). On the mammogram, fatty components appear radiolucent, whereas fibroglandular components appear radiopaque. Unfortunately, women with dense breasts have a lower sensitivity for breast cancer detection because normal breast tissue may overlap with tissue asymmetries and underlying tumors on the mammogram. Women with dense breasts face a higher rate of false-negative and falsepositive findings as well as recall rates (1,2). Nevertheless, the risk for breast cancer associated with dense breasts cannot be attributed merely to the masking bias that reduces the sensitivity of mammography. The fibroglandular components consist of epithelial and glandular structures where most breast cancers originate. Thus, having dense breasts on its own elevates the risk of developing breast cancer, that is, the more epithelial tissue, the greater the chance that cancer may arise in one of the epithelial cells. Several studies have yielded consistent findings that breast density is an independent and strong risk factor for breast cancer (1–3). Compared with other much stronger but far less common risk factors, such as a mutation carrier or high-risk status, breast density significantly contributes to cancer risk. This emphasizes the potential for risk prediction and stratification of breast density. Yet breast cancer risk estimation tools, such as the Gail and Tyrer-Cuzick models (4,5) that currently do not include breast density, have not been able to reliably identify women at higher risk of developing breast cancer within the general population. Ideally, more accurate risk assessment models would be developed that could easily be adopted in clinical routine. Assessment of breast density has shown some value for estimating breast cancer risk, yet this approach is rather crude and highly subjective to interand intrarater variability (6). In contrast, radiomics analysis of mammographic breast data allows a sophisticated characterization of the complexity and morphologic distribution of the breast parenchymal patterns. Radiomics analysis is defined as the conversion of medical images to higher-dimensional mineable data by using computer classification algorithms. Radiomics data can be correlated with variables of interest (eg, patient characteristics, patient outcomes, and other “omics” data) for a noninvasive and cost-effective analysis allowing decision support and enabling precision medicine (7). Initial research shows that radiomics parenchymal features are associated with breast cancer independent of breast density and therefore have the potential to augment breast density in assessing a woman’s risk of developing cancer (8,9). In this issue of Radiology, Kontos and colleagues expand on this initial body of evidence, showing that radiomic phenotypes reflect the intrinsic properties of mammographic parenchymal complexity and have an independent association to breast cancer (10). They demonstrate that unlike a simplistic global mammographic breast density assessment, radiomics texture features can capture the subtle spatial distribution of the parenchymal complexity patterns. Therefore, radiomics can provide information beyond mammographic density and established risk factors in association to breast cancer. Kontos et al measured breast density by using publicly available, fully automated software (LIBRA, version 1.0.3). Radiomics analysis was performed to extract parenchymal texture by using a custom-developed software that uses a lattice-based strategy. The authors used a cross-sectional sample of 2029 women screened with digital mammography. Unsupervised clustering was applied to identify and reproduce phenotypes of parenchymal complexity in a separate training set of 1339 women and a test set of 690 women. Four distinct radiomic phenotypes of mammographic parenchymal complexity were identified: low, low to intermediate, intermediate to high, and high complexity. Each of these had different associations with age, body mass index, breast density, and other established risk factors. Breast density was not strongly correlated with phenotype category (R2 = 0.24), indicating the radiomics parameters provided unique information. The most intriguing finding was that radiomic phenotypes of mammographic parenchymal complexity showed an independent association with breast cancer, both in unadjusted and in models accounting for breast percent density and body mass index. Beyond Breast Density: Radiomic Phenotypes Enhance Assessment of Breast Cancer Risk