Papillary lesions of the breast: A systematic evaluation of cytologic parameters.

Abstract

The cytologic diagnosis of papillary lesions of the breast is challenging because of the diverse morphology, including epithelial hyperplasia, atypia, low-grade malignancy, and neuroendocrine differentiation; also, traditional malignant features such as necrosis and myoepithelial cell loss can be lacking. Thus, the diagnostic criteria for papillary lesions may differ from those for other breast lesions. This study evaluated various cytologic parameters in a large cohort to identify useful diagnostic features. Cytologic preparations of papillary lesions with histologic follow-up were reviewed for features related to cellularity, epithelial cohesiveness, cellular and stromal architecture, cytomorphology, and background. Corresponding histologic slides were also reviewed. In all, 153 cases were included. Epithelial discohesion, solid and cribriform patterns, atypical nuclear features, and mitoses (P ≤ .001 to P = .017) were associated with malignancy. Cell balls, monolayer sheets, and features of cystic change (P < .001 to P = .016) were associated with benign lesions. Complex (P = .031) and slender (P = .026) papillae and neuroendocrine features (P < .001) were associated with malignancy. Hemorrhage, background, and infiltrating neutrophils (P < .001 to P = .025) were associated with malignancy; fibrotic broad papillary stromal fragments (naked papillary fronds [NPFs]; P = .043) were associated with benignity. The presence of any single parameter, including the absence of myoepithelial cells within epithelial structure, the presence of cytoplasmic granules, an increased amount of cytoplasm, and a nuclear to cytoplasmic (N/C) ratio greater than 0.7, which were identified by principal component analysis, yielded a sensitivity of 95.1% and a specificity of 100.0% in predicting malignancy. Methodological assessment of multiple features is recommended. Myoepithelial cells, cytoplasmic granules, the amount of cytoplasm, and the N/C ratio are key features for diagnosis.