Mammographic Parenchymal Patterns as an Imaging Marker of Endogenous Hormonal Exposure: A Preliminary Study in a High-Risk Population

Rationale and Objectives: Parenchymal texture patterns have been previously associated with breast cancer risk, yet their underlying biological determinants remain poorly understood. Here, we investigate the potential of mammographic parenchymal texture as a phenotypic imaging marker of endogenous hormonal exposure. Materials and Methods: A retrospective cohort study was performed. Digital mammography (DM) images in the craniocaudal (CC) view from 297 women, 154 without breast cancer and 143 with unilateral breast cancer, were analyzed. Menopause status was used as a surrogate of cumulative endogenous hormonal exposure. Parenchymal texture features were extracted and mammographic percent density (MD%) was computed using validated computerized methods. Univariate and multivariable logistic regression analysis was performed to assess the association between texture features and menopause status, after adjusting for MD% and hormonally related confounders. The receiver operating characteristic (ROC) area under the curve (AUC) of each model was estimated to evaluate the degree of association between the extracted mammographic features and menopause status. Results: Coarseness, gray-level correlation, and fractal dimension texture features have a significant independent association with menopause status in the cancer-affected population; skewness and fractal dimension exhibit a similar association in the cancer-free population (P < .05). The ROC AUC of the logistic regression model including all texture features was 0.70 (P < .05) for cancer-affected and 0.63 (P < .05) for cancer-free women. Texture features retained significant association with menopause status (P < .05) after adjusting for MD%, age at menarche, ethnicity, contraception use, hormone replacement therapy, parity, and age at first birth. Conclusion: Mammographic texture patterns may reflect the effect of endogenous hormonal exposure on the breast tissue and may capture such effects beyond mammographic density. Differences in texture features between pre- and postmenopausal women are more pronounced in the cancer-affected population, which may be attributed to an increased association to breast cancer risk. Texture features could ultimately be incorporated in breast cancer risk assessment models as markers of hormonal exposure.