Literature DB >> 3753894

Computerized interactive morphometry as a potentially useful tool for the classification of non-Hodgkin's lymphomas.

A Marchevsky, J Gil, D Silage.   

Abstract

The use of a simple form of Computerized Interactive Morphometry (CIM) is proposed as a tool to achieve a reproducible classification of non-Hodgkin's lymphomas. This system combines a random sampling method for cells with simple size measurements and additional subjective criteria such as a shape, mitotic counts, and follicular or diffuse features. In this system, which utilizes a high resolution touch screen as interactive peripheral, the video image of the specimen is superimposed to a computer generated reference system which consists of a test area and four fixed points for random sampling of cells and a series of concentric circles to serve as internal standard for nuclear size; the computer tabulates and facilitates data processing. Forty-four lymphoid lesions have been characterized with the CIM system and specific criteria for diagnoses according to the Working Formulation of non-Hodgkin's lymphomas for clinical usage are derived. Studies of inter- and intraobserver variations in data collection are discussed, and a diagnostic algorithm that categorizes non-Hodgkin's lymphomas according to the relative proportions of various lymphoid cells and densities of mitotic counts is proposed. The potential applications of touch screen-based CIM for the study of malignant lymphomas and its practical technical advantages over other quantitative systems based on either gray-level analysis or tracings of cell contours on photographs or digitizer pads are emphasized.

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Year:  1986        PMID: 3753894     DOI: 10.1002/1097-0142(19860415)57:8<1544::aid-cncr2820570818>3.0.co;2-4

Source DB:  PubMed          Journal:  Cancer        ISSN: 0008-543X            Impact factor:   6.860


  1 in total

1.  Classification of individual lung cancer cell lines based on DNA methylation markers: use of linear discriminant analysis and artificial neural networks.

Authors:  Alberto M Marchevsky; Jeffrey A Tsou; Ite A Laird-Offringa
Journal:  J Mol Diagn       Date:  2004-02       Impact factor: 5.568

  1 in total

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