title: [Computer assisted grading of gliomas of the astrocytoma/glioblastoma groups].
abstract: For automated astrocytoma grading morphometric parameters are determined by means of an image analysis system and a special Ki-67(MIB1)/Feulgen-staining method allowing the quantification of the essential characteristics of malignant gliomas: growth pattern, cellularity, proliferation index and nucleus pleomorphism. Based upon a cluster analytical approach a grading scale resembling the WHO-scheme is established which is suitable for automatic glioma grading purposes (HOM-scale). For automatic glioma grading backpropagation neural networks are employed. The results are compared with those of a classical multivariate discriminant classificatory analysis. The presented approach can also be employed for automatic grading of other tumour entities.
authors.affiliation: Abteilung für Neuropathologie, Universitätskliniken des Saarlandes, Homburg/Saar.
authors.forename: H
authors.initials: H
authors.lastname: Kolles
authors.forename: A
authors.initials: A
authors.lastname: von Wangenheim
authors.forename: I
authors.initials: I
authors.lastname: Niedermayer
authors.forename: G H
authors.initials: GH
authors.lastname: Vince
authors.forename: W
authors.initials: W
authors.lastname: Feiden
mesh_terms: D001254:Astrocytoma / Q000145:classification / Q000473:pathology*; D001331:Automation; D016000:Cluster Analysis; D003936:Diagnosis, Computer-Assisted; D016002:Discriminant Analysis; D005909:Glioblastoma / Q000145:classification / Q000473:pathology*; D005910:Glioma / Q000145:classification / Q000473:pathology*; D006801:Humans; D019394:Ki-67 Antigen; D015999:Multivariate Analysis; D009363:Neoplasm Proteins / Q000032:analysis; D009687:Nuclear Proteins / Q000032:analysis; D014944:World Health Organization
chemical_list: D019394:Ki-67 Antigen; D009363:Neoplasm Proteins; D009687:Nuclear Proteins