title: Comparison of light microscopic grading and morphometric features in cytological breast cancer specimens.
abstract: A study was set up to investigate correlations between different light microscopic grades and morphometric features in cytological breast cancer specimens and to evaluate the discriminative power of morphometry for the three grades. 76 slides (smears and imprints) were graded independently in two different laboratories, and the 54 unequivocally graded slides were used as a training set for computing classification rules. In all slides, 100 nuclei and their nucleoli were measured on a graphic tablet at a final magnification of 2800x. A total of 43 morphometric features was evaluated. Univariate analysis showed significantly different values for most of the morphometric features for the three grades. Discriminant analysis revealed that a combination of the mean nuclear area and the number of mitoses per slide provided optimal discrimination between grades one and two (87.5% correct classification). One more feature (mean nuclear shape factor) was needed to obtain optimal discrimination between grades two and three (83.3% correct classifications). 16 of the 22 (72.7%) equivocally graded slides could be classified with high probability using these classification rules. We conclude that morphometry has discriminative power for the three grades in cytologic breast cancer specimens and may therefore be a useful instrument for classification of difficult cases.
authors.affiliation: Department of Pathology, Free University Hospital, Amsterdam, The Netherlands.
authors.forename: P J
authors.initials: PJ
authors.lastname: van Diest
authors.forename: E K
authors.initials: EK
authors.lastname: Risse
authors.forename: N W
authors.initials: NW
authors.lastname: Schipper
authors.forename: J P
authors.initials: JP
authors.lastname: Baak
authors.forename: J
authors.initials: J
authors.lastname: Mouriquand
mesh_terms: D001943:Breast Neoplasms / Q000473:pathology*; D003581:Cytodiagnosis / Q000379:methods; D016002:Discriminant Analysis; D005260:Female; D006801:Humans; D011379:Prognosis