COMPUTER-ASSISTED PATHOLOGY OF INTRAEPITHELIAL ADENOCARCINOMA AND RELATED LESIONS - 3-D DISTRIBUTION, STRUCTURAL-ABERRATION AND DISCRIMINATION

To discriminate among intraepithelial neoplasms, we have been relying on tissue microscopy, but pathologists' subjectivity sometimes impairs diagnosis. Even an individual;pathologist is sometimes unable to repr oduce exactly his or her own previous diagnosis. Are various atypical lesions classifiable in a reproducible way, and if they are, how? The reliability of a diagnosis will be strengthened if we can define the ' 'natural'' categories inherent in cells or tissues. Morphometry and st atistical analysis using a computer can provide answers.Atypia, a morp hological feature of carcinoma, is essentially multivariate. Quantific ation of a tissue feature requires reducing it to a set of ten or more quantities, including size, shape and position of the nucleus, nucleo lus, and the cell itself. The grade of aberration from the norm can be assessed only by a synthetic approach, using a computer for multivari ate cluster analysis. This classification has been attempted in adenoc arcinoma and related lesions of the lung and pancreas. The categories thus established are reproducible, because the lesions fall into disti nct divisions according to their forms. We can also examine the organ distribution of intraepithelial neoplasms by three dimensional (3-D) c omputer-assisted mapping. To reach a higher level of reliability, as m any meaningful features as possible should be taken into account. Part icularly, we emphasize the significance of architectural pattern as a biomarker for intraepithelial glandular neoplasms. Computer-aided 3-D structural analysis visualizes the basic skeleton of these neoplasms a round which the cells adhere. Instead of the dichotomous tree pattern of normal glands, the tumors basically harbor a 3-D network, tubular o r porous, which increasingly deviates from the norm along with the tra nsition from adenoma to well to moderately to poorly differentiated ad enocarcinoma. This structural aberration, if recognizable on 2-D secti onal images, will serve as a surrogate endpoint biomarker for glandula r tumors. (C) 1995 Wiley-Liss, Inc.