Linked Life Data

1329052

Source:http://linkedlifedata.com/resource/pubmed/id/1329052

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4-5
pubmed:dateCreated
1992-11-12
pubmed:abstractText
The usefulness of quantitative nuclear image features (QNI) for the histological classification of lung carcinomas was investigated. As no clear distinction could be established between the distributions of these features for the nuclei of squamous cell, adenocarcinoma, and large cell carcinoma, the attention was restricted to the discrimination between small cell lung carcinoma (SCLC) and non-small cell carcinoma (NSCLC). This discrimination is the crucial one in discussions about the choice of treatment. The differences between SCLC and NSCLC are statistically highly significant for various QNI features. The use of more than one QNI feature hardly raised the discriminatory performance with respect to the distinction between SCLC and NSCLC. Inferences were made about the probability and confidence interval of SCLC for a given QNI feature. It is concluded that in cases of uncertainty or disagreement, nuclear characteristics are useful for the discrimination between SCLC and NSCLC.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
0344-0338
pubmed:author
pubmed:issnType
Print
pubmed:volume
188
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
531-5
pubmed:dateRevised
2004-11-17
pubmed:meshHeading
pubmed:year
1992
pubmed:articleTitle
Further evaluation of quantitative nuclear image features for classification of lung carcinomas.
pubmed:affiliation
Department of Pathology, University Hospital, Maastricht, The Netherlands.
pubmed:publicationType
Journal Article