Diagnosis of breast cancer by measuring nuclear disorder using planar graphs.

Objective: To achieve a classifier of breast lesions to distinguish benign from malignant mammary lesions by quantifying nuclear disorder in epithelial cell groups from smears obtained by fine needle aspiration.

Study Design: The study included 95 cases of breast cancer (289 groups) and 47 of benign breast lesions (150 groups), diagnosed by cytology. Information from planar graphs (mean of nuclear distances, standard deviation, maximum and minimum distance between nuclei) was used, and an algorithm constructed for this purpose was applied.

Automated segmentation of cell nuclei in fine needle aspirates of the breast.

Objective: To achieve a classifier of breast lesions to discriminate between benign and malignant cases of cytologic smears with automated segmentation image analysis techniques.

Study Design: The techniques were applied to images of epithelial cell nuclei from cytologic smears obtained by fine needle aspiration. The images of the nuclei were taken from 95 cases of malignant lesions and 47 benign (approximately 25 nuclei per case), and 28 nuclear variables were measured.