Source:http://linkedlifedata.com/resource/pubmed/id/19199266
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| Predicate | Object |
|---|---|
| rdf:type | |
| lifeskim:mentions | |
| pubmed:issue |
4
|
| pubmed:dateCreated |
2009-2-9
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| pubmed:abstractText |
Hormone therapy with tamoxifen has long been the established adjuvant treatment for node-positive, estrogen-receptor-positive breast cancer in postmenopausal women. Since 30-40% of these patients fail to respond, reliableoutcome prediction is necessary for successful treatment allocation. Using pathobiological variables (available in mostclinical records: tumor size, nodal involvement, estrogen and progesterone receptor content) from 596 patients recruitedat a comprehensive cancer center, we developed a prediction model which we validated in an independent cohort of 175patients recruited at a general hospital. Calculated at 3 and 4 years of follow-up, the discrimination indices were 0.716[confidence limits (CL) 0.641, 0.752] and 0.714 (CL 0.650, 0.750) for the training data, and 0.726 (CL 0.591, 0.769) and0.677 (CL 0.580, 0.745) for the testing data. Waiting for more effective approaches from genomic and proteomic studies, amodel based on consolidated pathobiological variables routinely assessed at relatively low costs may be considered as thereference for assessing the gain of new markers over traditional ones, thus substantially improving the conventional use ofprognostic criteria.
|
| pubmed:language |
eng
|
| pubmed:journal | |
| pubmed:citationSubset |
IM
|
| pubmed:chemical |
http://linkedlifedata.com/resource/pubmed/chemical/Antineoplastic Agents, Hormonal,
http://linkedlifedata.com/resource/pubmed/chemical/Receptors, Estrogen,
http://linkedlifedata.com/resource/pubmed/chemical/Receptors, Progesterone,
http://linkedlifedata.com/resource/pubmed/chemical/Tamoxifen,
http://linkedlifedata.com/resource/pubmed/chemical/Tumor Markers, Biological
|
| pubmed:status |
MEDLINE
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| pubmed:issn |
0393-6155
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| pubmed:author | |
| pubmed:issnType |
Print
|
| pubmed:volume |
23
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| pubmed:owner |
NLM
|
| pubmed:authorsComplete |
Y
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| pubmed:pagination |
199-206
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| pubmed:meshHeading |
pubmed-meshheading:19199266-Adult,
pubmed-meshheading:19199266-Aged,
pubmed-meshheading:19199266-Aged, 80 and over,
pubmed-meshheading:19199266-Antineoplastic Agents, Hormonal,
pubmed-meshheading:19199266-Breast Neoplasms,
pubmed-meshheading:19199266-Chemotherapy, Adjuvant,
pubmed-meshheading:19199266-Cohort Studies,
pubmed-meshheading:19199266-Female,
pubmed-meshheading:19199266-Humans,
pubmed-meshheading:19199266-Middle Aged,
pubmed-meshheading:19199266-Models, Statistical,
pubmed-meshheading:19199266-Neoplasm Recurrence, Local,
pubmed-meshheading:19199266-Nomograms,
pubmed-meshheading:19199266-Postmenopause,
pubmed-meshheading:19199266-Predictive Value of Tests,
pubmed-meshheading:19199266-Receptors, Estrogen,
pubmed-meshheading:19199266-Receptors, Progesterone,
pubmed-meshheading:19199266-Tamoxifen,
pubmed-meshheading:19199266-Tumor Markers, Biological
|
| pubmed:articleTitle |
A prediction model for breast cancer recurrence after adjuvant hormone therapy.
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| pubmed:affiliation |
Istituto di Statistica Medica e Biometria, Universita' degli Studi di Milano, Milan, Italy. elia.biganzoli@istitutotumori.mi.it
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| pubmed:publicationType |
Journal Article,
Research Support, Non-U.S. Gov't
|