Correlation between mammographic findings and corresponding histopathology: Potential predictors for biological characteristics of breast diseases

Abstract

The present study retrospectively evaluated the mammographic findings of 606 Japanese women with breast cancer (median age 50years; range 27-89years) and correlated them with histopathological characteristics. Mammographic findings were evaluated with an emphasis on mass shape, margin, density, calcification, and the presence of architectural distortion; these findings were correlated with histopathological characteristics such as intrinsic subtype, histological grade, lymphovascular invasion, and the Ki-67 labeling index. An irregular mass shape and masses with a spiculated margin were significantly higher in the group of patients with luminal A breast cancer than in patients with masses that were lobular or round, or in tumors with an indistinct or microlobulated periphery (P=0.017, P=0.024, P<0.001, and P=0.001, respectively). Irregular mass shape and spiculated periphery were significantly lower in patients with Grade 3 cancer (P<0.001 for both). In terms of lymphovascular invasion, there were significant differences between oval and irregular or round mass shape (P=0.008 and P=0.034), between tumors with a microlobulated and indistinct periphery (P=0.014), between tumors with a punctate and amorphous or pleomorphic calcification shape (P=0.030 and 0.038), and between the presence and absence of architectural distortion (P=0.027). Equivalent or low-density masses were also higher in Grade 1 breast cancers (P=0.007). There were significant differences in the Ki-67 labeling index between irregular and lobular or round tumors (P<0.001 and P=0.014), as well as between spiculated and indistinct or microlobulated tumors (P<0.001 for both). Significant differences were noted in the mammographic features of different primary breast cancer subtypes. These proposed mammographic diagnostic criteria based on biological characteristics may contribute to a more accurate prediction of biological behavior of breast malignancies. © 2011 Japanese Cancer Association.