Linked Life Data

20879266

Source:http://linkedlifedata.com/resource/pubmed/id/20879266

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
Pt 1
pubmed:dateCreated
2010-9-30
pubmed:abstractText
Conventional whole-heart CAC quantification has been demonstrated to be insufficient in predicting coronary events, especially in accurately predicting near-term coronary events in high-risk adults. In this paper, we propose a lesion-specific CAC quantification framework to improve CAC's near-term predictive value in intermediate to high-risk populations with a novel multiple instance support vector machines (MISVM) approach. Our method works on data sets acquired with clinical imaging protocols on conventional CT scanners without modifying the CT hardware or updating the imaging protocol. The calcific lesions are quantified by geometric information, density, and some clinical measurements. A MISVM model is built to predict cardiac events, and moreover, to give a better insight of the characterization of vulnerable or culprit lesions in CAC. Experimental results on 31 patients showed significant improvement of the predictive value with the ROC analysis, the net reclassification improvement evaluation, and the leave-one-out validation against the conventional methods.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:author
pubmed:volume
13
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
484-92
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Lesion-specific coronary artery calcium quantification for predicting cardiac event with multiple instance support vector machines.
pubmed:affiliation
Rutgers University, Piscataway, NJ 08854, USA.
pubmed:publicationType
Journal Article