Nuclear planimetry on the samples from cytologically overlapping proliferative breast lesions.

Differentiation of low-grade breast carcinomas from fibroadenomas with atypia on fine-needle aspiration cytology material may sometimes be problematic. In some cytological samples of these lesions, both the nuclei of cells and patterns of cell clusters display substantial overlapping morphological characteristics. Nuclear morphometry on cytological material is suggested as an ancillary method for the differential diagnosis in many lesions, including breast tumors. Twenty-five cytological samples obtained from patients with breast lesions, which were histopathologically confirmed as grade I ductal carcinomas (n=10), tubular carcinomas (n=5), and fibroadenomas with atypia (n=10), were utilized in this study. Eight geometric features of about 2002 nuclei from these tumors were measured. Discriminant analysis was performed on this data set in order to test the correct classification based on the eight measured variables. Statistical analyses were carried out with two fundamentally different approaches: in the first one, the entire data from all measured nuclei were used for classification. In the second one, a subset of data representing the 10% most deviated values of variables was extracted from the entire dataset to simulate the "selective examination" performed during classical morphologic evaluation. When the entire data was used in discriminant analysis, the overall performance in the correct classification rate was found to be approximately 50%, which was considered an unacceptable value in routine diagnostic practice. In the subset of data constructed with our systematic and reproducible "selection bias", the overall performance of correct classification rate of the same discriminant model improved substantially to 97%. Morphologic examination is actually based on selection. The use of data obtained from all of the cells in morphometry, as previously used in nearly all of the statistical methods, may cause a masking effect in diagnostically important features. Morphometric studies may seem to be useless when this effect is not taken into account. However, with a systematic and reproducible selection of the values with a proper "bias", morphometry may provide some discriminatory information in overlapping lesions.