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A modern reproducible method for the histologic grading of astrocytomas with statistical classification tools

  • To investigate whether statistical classification tools can infer the correct World Health Organization (WHO) grade from standardized histologic features in astrocytomas and how these tools compare with GRADO-IGL, an earlier computer-assisted method. A total of 794 human brain astrocytomas were studied between January 1976 and June 2005. The presence of 50 histologic features was rated in 4 categories from 0 (not present) to 3 (abundant) by visual inspection of the sections under a microscope. All tumors were also classified with the corresponding WHO grade between I and IV. We tested the prediction performance of several statistical classification tools (learning vector quantization [LVQ], supervised relevance neural gas [SRNG], support vector machines [SVM], and generalized regression neural network [GRNN]) for this data set. The WHO grade was predicted correctly from histologic features in close to 80% of the cases by 2 modern classifiers (SRNG and SVM), and GRADO-IGL was predicted correctly in > 84% of the cases by a GRNN. A standardized report, based the 50 histologic features, can be used in conjunction with modern classification tools as an objective and reproducible method for histologic grading of astrocytomas.

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Author:Norbert Röhrl, José R. Iglesias-Rozas, Galia WeidlORCiD
URL:https://pubmed.ncbi.nlm.nih.gov/18459585/
Parent Title (English):Analytical and quantitative cytology and histology / the International Academy of Cytology [and] American Society of Cytology
Document Type:Article
Language:English
Year of Completion:2008
Release Date:2023/12/07
GND Keyword:Cytologie; Histologie; Hirntumor
Volume:2008
Issue:30/1
First Page:33
Last Page:8
Urheberrecht:1
Institutes:Einrichtungen / Kompetenzzentrum Künstliche Intelligenz
research focus :Intelligent Systems / Artifical Intelligence and Data Science
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International
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