Automated image cytometry can allow concurrent quantification of several parameters in each individual cell within a population, opening new possibilities for diagnosis and prognosis. In this study, the authors investigated the capacity of this method for performing a bivariate analysis of DNA ploidy and synthesis in fine-needle samplings obtained without aspiration from breast tumors. METHODS. Samplings from 25 unselected cases of ductal infiltrative breast adenocarcinoma and 2 cases of fibroadenoma were analyzed. For each case, 3-5 slides (containing approximately 1000 cells each) were quantified to assess experimental precision. Ploidy was determined by fluorescent staining of DNA using 4,6-diamidino-2-phenylindole (DAPI). Contaminating lymphocytes were taken as internal controls to calculate DNA indices. DNA synthesis was analyzed by immunofluorescent detection of 5-bromodeoxyuridine (BrdU) incorporation. Measurements were compared with flow cytometric data obtained from the same patients. RESULTS. Relative error in determination of DNA indices was generally below 5%. Determination of proliferation indices were more variable, with a mean relative error of 25%. Two different populations of BrdU positive cells were detected systematically, one in the diploid and another in the aneuploid fraction. For both cytometric methods, DNA indices were similar in all 27 cases, whereas BrdU labeling indices showed no significant correlation in 13 cases. The remaining cases were not comparable due to lack of flow cytometric data. Labeling indices obtained by image cytometry did not reveal any significant correlation with Scarff-Bloom-Richardson grading or clinical staging.
CONCLUSIONS. Automated image cytometry allows concurrent measurement of ploidy and cell proliferation within individual breast carcinoma cells. Statistical
reliability can be reached with a relative small number of cells (1000), which is crucial for samples in which the cell number is too low for flow cytometry analysis.
Visual control for artifact elimination and better characterization of cell populations makes this a powerful tool for tumor cell investigation. Automated image cytometry allows the obtainment of valuable prognostic parameters of traditional flow cytometry with the relatively small number of cells obtained in aspiration procedures.