Linked Life Data

19357014

Source:http://linkedlifedata.com/resource/pubmed/id/19357014

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
2009-4-9
pubmed:abstractText
An impressive number of publications refer to prognostic and predictive factors in lung cancer. TNM classification and performance status significantly influence the choice of treatment and strongly predict patients' survival. Depending on the population studied (small cell or non-small cell cancer, operable or not) other independent factors improve the prediction of prognosis; they are clinical, biological, radiological or molecular and pertain to the tumor or the patient. Molecular targeted therapies development has renewed the interest towards predictive factors. New strategies are developed to explore individual response to treatment such as EGFR tyrosine-kinase inhibitors, without success for anti-angiogenic treatments. Conventional cytotoxic agents may also be customized with predictive factors (i.e. ERCC1 or RRM1). Large multicenter studies are needed to validate new independent prognostic factors and increase our current knowledge aiming at separating patients who will really benefit from therapies of those who will only experience the side effects.
pubmed:language
fre
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Apr
pubmed:issn
1769-6917
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
96
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
391-404
pubmed:dateRevised
2009-11-19
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
[Prognostic and predictive factors in lung cancer].
pubmed:affiliation
Département de médecine, Institut Gustave-Roussy, 39, rue Camille-Desmoulins, 94800 Villejuif, France.
pubmed:publicationType
Journal Article, English Abstract, Review