Imaging features of automated breast volume scanner: Correlation with molecular subtypes of breast cancer

Abstract Objectives To investigate the correlation between the imaging features obtained by an automated breast volume scanner (ABVS) and molecular subtypes of breast cancer. Methods We examined 303 malignant breast tumours by ABVS for specific imaging features and by immunohistochemical analysis to determine the molecular subtype. ABVS imaging features, including retraction phenomenon, shape, margins, echogenicity, post-acoustic features, echogenic halo, and calcifications were analysed by univariate and multivariate logistic regression analyses to determine the significant predictive factors of the molecular subtypes. Results By univariate logistic regression analysis, the predictive factors of the Luminal-A subtype (n = 128) were retraction phenomenon (odds ratio [OR] = 10.188), post-acoustic shadowing (OR = 5.112), and echogenic halo (OR = 3.263, P P P  = 0.035), and post-acoustic enhancement (OR = 3.641, P  = 0.008). The predictors for the Triple-Negative subtype (n = 47) were absence of retraction phenomenon (OR = 5.884), post-acoustic enhancement (OR = 5.255, P P  = 0.002), and absence of calcifications (OR = 3.363, P  = 0.001). Predictors for the Luminal-B subtype (n = 89) had a relatively lower association (OR ≤ 2.328). By multivariate logistic regression analysis, retraction phenomenon was the strongest independent predictor for the Luminal-A subtype (OR = 9.063, P P Conclusions ABVS imaging features, especially retraction phenomenon, have a strong correlation with the molecular subtypes, expanding the scope of ultrasound in identifying breast cancer subtypes with confidence.