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

Mitra A, Conway C, Walker C, Cook M, Powell B, Lobo S, Chan M, Kissin M, Layer G, Smallwood J, Ottensmeier C, Stanley P, Peach H, Chong H, Elliott F, Iles MM, Nsengimana J, Barrett JH, Bishop DT, Newton-Bishop JA - Br. J. Cancer (2010)

Bottom Line: Osteopontin expression best predicted SNB positivity (P=2.4 × 10⁻⁷), remaining significant in multivariable analysis.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%).Use of these models should be tested in other data sets in order to improve predictive and prognostic data for patients.

View Article: PubMed Central - PubMed

Affiliation: Section 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.

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Related in: MedlinePlus

Receiver operating characteristic (ROC) analysis of predictors of SNB positivity for tumour subset group. Green: Breslow, mitotic rate, tumour site, age sex and SPP1 expression, AUC=72.6%. Red: Breslow, mitotic rate, tumour site, age and sex, AUC=68.6%. Black: SPP1 expression alone, AUC=65.7%. Blue: Breslow thickness alone, AUC=60.9%.
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fig1: Receiver operating characteristic (ROC) analysis of predictors of SNB positivity for tumour subset group. Green: Breslow, mitotic rate, tumour site, age sex and SPP1 expression, AUC=72.6%. Red: Breslow, mitotic rate, tumour site, age and sex, AUC=68.6%. Black: SPP1 expression alone, AUC=65.7%. Blue: Breslow thickness alone, AUC=60.9%.

Mentions: Using the total study group, we found that SNB positivity was best predicted by a model including thickness, mitotic count, tumour site, age and sex, giving an area under the curve (AUC) of 68.0%, compared with using a model using thickness alone, which gave an AUC of 58.0%. Figure 1 shows the receiver operating characteristic (ROC) curves for prediction of SNB positivity if osteopontin is included in the prognostic models using patients from the tumour subset. Osteopontin expression gave a better AUC (65.7%) than Breslow thickness alone (60.9%) in the tumour subset. Use of clinico-pathological features (thickness, mitotic count, site, age and sex) increased the AUC to 68.6%. However the best AUC of 72.6% was seen using a combination of osteopontin expression and the clinico-pathological variables together.


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

Mitra A, Conway C, Walker C, Cook M, Powell B, Lobo S, Chan M, Kissin M, Layer G, Smallwood J, Ottensmeier C, Stanley P, Peach H, Chong H, Elliott F, Iles MM, Nsengimana J, Barrett JH, Bishop DT, Newton-Bishop JA - Br. J. Cancer (2010)

Receiver operating characteristic (ROC) analysis of predictors of SNB positivity for tumour subset group. Green: Breslow, mitotic rate, tumour site, age sex and SPP1 expression, AUC=72.6%. Red: Breslow, mitotic rate, tumour site, age and sex, AUC=68.6%. Black: SPP1 expression alone, AUC=65.7%. Blue: Breslow thickness alone, AUC=60.9%.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2967048&req=5

fig1: Receiver operating characteristic (ROC) analysis of predictors of SNB positivity for tumour subset group. Green: Breslow, mitotic rate, tumour site, age sex and SPP1 expression, AUC=72.6%. Red: Breslow, mitotic rate, tumour site, age and sex, AUC=68.6%. Black: SPP1 expression alone, AUC=65.7%. Blue: Breslow thickness alone, AUC=60.9%.
Mentions: Using the total study group, we found that SNB positivity was best predicted by a model including thickness, mitotic count, tumour site, age and sex, giving an area under the curve (AUC) of 68.0%, compared with using a model using thickness alone, which gave an AUC of 58.0%. Figure 1 shows the receiver operating characteristic (ROC) curves for prediction of SNB positivity if osteopontin is included in the prognostic models using patients from the tumour subset. Osteopontin expression gave a better AUC (65.7%) than Breslow thickness alone (60.9%) in the tumour subset. Use of clinico-pathological features (thickness, mitotic count, site, age and sex) increased the AUC to 68.6%. However the best AUC of 72.6% was seen using a combination of osteopontin expression and the clinico-pathological variables together.

Bottom Line: Osteopontin expression best predicted SNB positivity (P=2.4 × 10⁻⁷), remaining significant in multivariable analysis.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%).Use of these models should be tested in other data sets in order to improve predictive and prognostic data for patients.

View Article: PubMed Central - PubMed

Affiliation: Section 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.

Show MeSH
Related in: MedlinePlus