[Computer assisted grading of gliomas of the astrocytoma/glioblastoma groups].
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.
Kolles
H
H
Abteilung für Neuropathologie, Universitätskliniken des Saarlandes, Homburg/Saar.
von Wangenheim
A
A
Niedermayer
I
I
Vince
G H
GH
Feiden
W
W
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
D019394:Ki-67 Antigen; D009363:Neoplasm Proteins; D009687:Nuclear Proteins