Linked Life Data

15382668

Source:http://linkedlifedata.com/resource/pubmed/id/15382668

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2004-9-22
pubmed:abstractText
Several studies have described malignancy-associated changes (MACs) of chromatin arrangement in the nuclei of apparently normal cells adjacent to and distant from an invasive cancer area. MAC assessment is a hard task, since it requires a deep knowledge of morphologic features of chromatin arrangement. The aim of this work is to verify the reproducibility of the subjective evaluation of the expert on the basis of a decision support system (DSS) that automatically and objectively reproduces MAC diagnosis. A set of 61 patients with suspected clinical diagnosis for lung cancer has been taken into account. The scientist who first described MAC defined each patient as MAC positive or negative on the basis of the MAC diagnosis performed on all cells of the related cytologic sample. A DSS based on an artificial neural network has been set up to learn the relation between 14 morphometric and texture parameters, computed on each nucleus by image processing techniques, with the MAC diagnosis of the expert on each cell. The results show that an objective automatic assessment on MAC by the DSS can effectively support the MAC diagnosis. The method adopted in this approach may be also appropriate for other problems, where an automatic classification of visually inspected patterns of biological micro- and submicrostructure is needed.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
1536-1241
pubmed:author
pubmed:issnType
Print
pubmed:volume
2
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
118-23
pubmed:dateRevised
2004-11-17
pubmed:meshHeading
pubmed:year
2003
pubmed:articleTitle
A decision support system to detect morphologic changes of chromatin arrangement in normal-appearing cells.
pubmed:affiliation
Department of Communication, Computer and System Sciences (DIST), University of Genoa, via Opera Pia 13, 16145 Genoa, Italy. roberto.sacile@unige.it
pubmed:publicationType
Journal Article, Clinical Trial, Randomized Controlled Trial, Validation Studies