Linked Life Data

16254686

Source:http://linkedlifedata.com/resource/pubmed/id/16254686

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2005-12-5
pubmed:abstractText
The objective of this study is to compare the predictive accuracy of a neural network (NN) model versus the standard Cox proportional hazard model. Data about the 3811 patients included in this study were collected within the 'El Alamo' Project, the largest dataset on breast cancer (BC) in Spain. The best prognostic model generated by the NN contains as covariates age, tumour size, lymph node status, tumour grade and type of treatment. These same variables were considered as having prognostic significance within the Cox model analysis. Nevertheless, the predictions made by the NN were statistically significant more accurate than those from the Cox model (p < 0.0001). Seven different time intervals were also analyzed to find that the NN predictions were much more accurate than those from the Cox model in particular in the early intervals between 1-10 and 11-20 months, and in the later one considered from 61 months to maximum follow-up time (MFT). Interestingly, these intervals contain regions of high relapse risk that have been observed in different studies and that are also present in the analyzed dataset.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Dec
pubmed:issn
0167-6806
pubmed:author
pubmed:issnType
Print
pubmed:volume
94
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
265-72
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2005
pubmed:articleTitle
Improvement of breast cancer relapse prediction in high risk intervals using artificial neural networks.
pubmed:affiliation
Departamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga, Málaga, Spain.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't