Predicting the molecular subtype of breast cancer based on mammography and ultrasound findings

Abstract

Abstract Aim: To determine the correlation between mammography and ultrasound features of breast cancer with molecular subtypes and to calculate the predictive value of these features. Materials and Method: This is a prospective study of consecutive patients with breast cancer presenting between January 2016 and July 2017, who underwent mammography and/or ultrasound of breast and excision of the breast mass. Patients with contralateral breast mass, metastases, h/o prior cancer treatment, and other malignancies were excluded. On mammography, the presence or absence of microcalcification was noted. On ultrasound examination size, margins, microcalcification, posterior acoustic features, vascularity, and axillary nodes were assessed. Margins were categorized into circumscribed and non-circumscribed. Posterior acoustic features were classified into four categories: shadowing, enhancement, mixed, and no changes. Vascularity was assessed based on Adler's index into grades 0, 1, 2, and 3. Grades 0 and 1 were considered low and 2 and 3 were high. Results: Tumors with non-circumscribed margins and posterior acoustic shadowing were likely to be luminal A or B subtype of breast cancer [odds ratio (JR) 5.78; 95% confidence interval (CI) 3.68–9.80; P < 0.0001]. Tumors with non-circumscribed margins, posterior acoustic shadowing, and high vascularity were more likely to be luminal B subtype (JR 2.88; 95% CI 2–4.14; P- <0.0001). Tumors with microcalcification and posterior mixed acoustic pattern were strongly associated to be HER2-positive (JR 5.48; 95% CI 3.06–9.80; P < 0.0001). Tumors with circumscribed margins and posterior acoustic enhancement were highly suggestive of triple-negative breast cancer (JR 7.06; 95% CI 4.64–10.73; P < 0.0001). Conclusion: Microcalcification detected on mammography and certain ultrasound features such as circumscribed or non-circumscribed margins, posterior acoustic features, and vascularity are strongly correlated in predicting the molecular subtypes of breast cancer, and thus may further expand the role of conventional breast imaging.