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Br J Cancer. 2010 Oct 12;103(8):1229-36. doi: 10.1038/sj.bjc.6605849. Epub 2010 Sep 21.

Melanoma sentinel node biopsy and prediction models for relapse and overall survival.

Author information

  • 1Section of Epidemiology and Biostatistics, Leeds Institute of Molecular Medicine, St James's University Hospital, Beckett Street, Leeds LS97TF, UK. a.mitra@leeds.ac.uk

Abstract

BACKGROUND:

To optimise predictive models for sentinal node biopsy (SNB) positivity, relapse and survival, using clinico-pathological characteristics and osteopontin gene expression in primary melanomas.

METHODS:

A comparison of the clinico-pathological characteristics of SNB positive and negative cases was carried out in 561 melanoma patients. In 199 patients, gene expression in formalin-fixed primary tumours was studied using Illumina's DASL assay. A cross validation approach was used to test prognostic predictive models and receiver operating characteristic curves were produced.

RESULTS:

Independent predictors of SNB positivity were Breslow thickness, mitotic count and tumour site. Osteopontin expression best predicted SNB positivity (P=2.4 × 10⁻⁷), remaining significant in multivariable analysis. Osteopontin expression, combined with thickness, mitotic count and site, gave the best area under the curve (AUC) to predict SNB positivity (72.6%). Independent predictors of relapse-free survival were SNB status, thickness, site, ulceration and vessel invasion, whereas only SNB status and thickness predicted overall survival. Using clinico-pathological features (thickness, mitotic count, ulceration, vessel invasion, site, age and sex) gave a better AUC to predict relapse (71.0%) and survival (70.0%) than SNB status alone (57.0, 55.0%). In patients with gene expression data, the SNB status combined with the clinico-pathological features produced the best prediction of relapse (72.7%) and survival (69.0%), which was not increased further with osteopontin expression (72.7, 68.0%).

CONCLUSION:

Use of these models should be tested in other data sets in order to improve predictive and prognostic data for patients.

PMID:
20859289
[PubMed - indexed for MEDLINE]
PMCID:
PMC2967048
Free PMC Article

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