Data Analysis for Comparative Histometry in Pathology - II.
Application of an Inductive Knowledge-based System for Classification of
Gliomas based on Karyometric Data
Affiliations: Edinger Institut für Neurologie, Klinikum der
Johann Wolfgang Goethe-Universität, D-60528 Frankfurt/Main | Institut für Biometrie, Medizinische Hochschule,
D-30625 Hannover
Note: [] Address for correspondence: Dr. R.Nafe Edinger Institut für
Neurologie Klinikum der Johann Wolfgang Goethe-Universität
Deutschordenstraße 46 D-60528 Frankfurt / Main, Germany
Note: []
Abstract: A knowledge-based system has been used for analysis of morphometric
data obtained from 218 neuroepithelial tumors. These tumors have been
investigated using standardized morphometric procedures including nuclear area,
shape factors, proliferation index Ki-67. It was intended to perform a
comprehensive data analysis using different grouping variables ("tumortype" and
"tumorgrade"). Additionally, non-discrete numerical variables ("mean nuclear
area", "proliferation index" and "patient´s age") have been tested
instead of grouping variables in order to find out, whether different ranges
for those variables are exhibited and show significant differences between the
cases. Several important informations were provided by the implementation of
the knowledge-based system, which are difficult to obtain by means of
descriptive data analysis or by means of statistical analysis only. These
include for instance a separation of cases with considerably high proliferation
index showing a higher age of these patients. Especially, relationships between
several variables could be obtained, which could not be detected in
conventional statistical data analysis. The information provided by the system
has a direct morphological relevance for the evaluation of the tumors. Its use
for data analysis of morphometric studies has to be considered as useful,
especially when many quantitative variables are under investigation.
