Linked Life Data

7534014

Source:http://linkedlifedata.com/resource/pubmed/id/7534014

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
1995-4-13
pubmed:abstractText
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.
pubmed:language
ger
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:issn
0070-4113
pubmed:author
pubmed:issnType
Print
pubmed:volume
78
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
427-31
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
1994
pubmed:articleTitle
[Computer assisted grading of gliomas of the astrocytoma/glioblastoma groups].
pubmed:affiliation
Abteilung für Neuropathologie, Universitätskliniken des Saarlandes, Homburg/Saar.
pubmed:publicationType
Journal Article, Comparative Study, English Abstract