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Multicenter Study
. 2011 Dec;18(13):3593-600.
doi: 10.1245/s10434-011-1826-9. Epub 2011 Jun 7.

Factors predictive of the status of sentinel lymph nodes in melanoma patients from a large multicenter database

Richard L White Jr  1 , Gregory D Ayers, Virginia H Stell, Shouluan Ding, Jeffrey E Gershenwald, Jonathan C Salo, Barbara A Pockaj, Richard Essner, Mark Faries, Kim James Charney, Eli Avisar, Axel Hauschild, Friederike Egberts, Bruce J Averbook, Carlos A Garberoglio, John T Vetto, Merrick I Ross, David Chu, Vijay Trisal, Harald Hoekstra, Eric Whitman, Harold J Wanebo, Daniel Debonis, Michael Vezeridis, Aaron Chevinsky, Mohammed Kashani-Sabet, Yu Shyr, Lynne Berry, Zhiguo Zhao, Seng-Jaw Soong, Stanley P L Leong; Sentinel Lymph Node Working Group
Affiliations
Multicenter Study

Factors predictive of the status of sentinel lymph nodes in melanoma patients from a large multicenter database

Richard L White Jr et al. Ann Surg Oncol. 2011 Dec.

Abstract

Background: Numerous predictive factors for cutaneous melanoma metastases to sentinel lymph nodes have been identified; however, few have been found to be reproducibly significant. This study investigated the significance of factors for predicting regional nodal disease in cutaneous melanoma using a large multicenter database.

Methods: Seventeen institutions submitted retrospective and prospective data on 3463 patients undergoing sentinel lymph node (SLN) biopsy for primary melanoma. Multiple demographic and tumor factors were analyzed for correlation with a positive SLN. Univariate and multivariate statistical analyses were performed.

Results: Of 3445 analyzable patients, 561 (16.3%) had a positive SLN biopsy. In multivariate analysis of 1526 patients with complete records for 10 variables, increasing Breslow thickness, lymphovascular invasion, ulceration, younger age, the absence of regression, and tumor location on the trunk were statistically significant predictors of a positive SLN.

Conclusions: These results confirm the predictive significance of the well-established variables of Breslow thickness, ulceration, age, and location, as well as consistently reported but less well-established variables such as lymphovascular invasion. In addition, the presence of regression was associated with a lower likelihood of a positive SLN. Consideration of multiple tumor parameters should influence the decision for SLN biopsy and the estimation of nodal metastatic disease risk.

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Conflict of interest statement

CONFLICT OF INTEREST Mr. Ayers, Ms. Stell, Drs. Soong, Ding, Salo, Pockaj, Essner, Faries, Averbook, Avisar, Egberts, Garberoglio, Ross, Chu, Trisal, Hoekstra, Wanebo, DeBonis, Vezeridis, Chevinsky, Shyr, Berry, and Zhoa indicate no potential conflicts of interest.

Figures

FIG. 1
FIG. 1
Association between ulceration (P <0.0001) and positive SLNs stratified by Breslow thickness; total n = 2781. Numbers above the bars represent the percentage of patients with positive SLNs in each category; numbers below the x-axis labels represent the number of patients in each category. P-value for independent effect of ulceration in a multivariable logistic regression model containing both predictor variables
FIG. 2
FIG. 2
Associations among lymphovascular invasion (LVI; P <0.0001), ulceration (P <0.003), and positive SLNs by Breslow thickness; total n = 1836. Numbers above the bars represent the percentage of patients with positive SLNs in each category; numbers below the x-axis labels represent the number of patients in each category. P-values for independent effect of ulceration and lymphovascular invasion in a multivariable logistic regression model containing all 3 predictor variables
FIG. 3
FIG. 3
Associations among regression (P <0.008), ulceration (P <0.003) and positive SLNs by Breslow thickness; total n = 2003. Numbers above the bars represent the percentage of patients with positive SLNs in each category; numbers below the x-axis labels represent the number of patients in each category. P-values for independent effect of ulceration and regression in a multivariable logistic regression model containing all 3 predictor variables
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