Analytic studies of foam cells from breast cancer precursors.
A preliminary study of foam cells from nipple aspirate fluid demonstrated the ability of image analysis to discriminate categories of breast disease. Foam cell images numbering 471 were collected from nipple aspirate samples representing three to six cases of each of the four following disease categories based on breast tissue diagnosis: benign, nonproliferative; hyperplasia; atypical hyperplasia; and cancer. Twenty-two shape and density parameters were measured for each cell image. Using multivariate analysis, eight nuclear and three cytoplasmic parameters showed significant differences (P less than 0.005) when tested among cell populations from the breast disease categories. Linear stepwise discriminant analysis enabled construction of a three-parameter model that was optimal for distinguishing among cell populations from the four categories of breast disease. The means of all twenty-three parameters were then evaluated on a per-patient basis. A second three-parameter model was constructed that distinguished, with 100% accuracy, patients with proliferative disease from those with nonproliferative disease. Grouping disease categories and comparing patients whose diagnosis was benign or hyperplasia versus atypical hyperplasia or malignant, the model placed patients in the correct group 83% of the time.
D001943:Breast Neoplasms / Q000473:pathology*; D002467:Cell Nucleus / Q000648:ultrastructure; D002843:Chromatin / Q000648:ultrastructure; D005260:Female; D005434:Flow Cytometry / Q000379:methods; D005487:Foam Cells / Q000166:cytology*; D006801:Humans; D008264:Macrophages / Q000166:cytology*; D009558:Nipples / Q000473:pathology; D011230:Precancerous Conditions / Q000473:pathology*; D013194:Staining and Labeling
D002843:Chromatin