Annals of Surgical Oncology

, Volume 19, Issue 1, pp 287–293

Validation of Statistical Predictive Models Meant to Select Melanoma Patients for Sentinel Lymph Node Biopsy

Authors

    • Department of SurgeryUniversity of Michigan Health System
  • John D. Rice
    • Biostatistics Core of the University of Michigan Comprehensive Cancer Center
  • Kent A. Griffith
    • Biostatistics Core of the University of Michigan Comprehensive Cancer Center
  • Lori Lowe
    • Department of PathologyUniversity of Michigan
  • Sandra L. Wong
    • Department of SurgeryUniversity of Michigan Health System
  • Alfred E. Chang
    • Department of SurgeryUniversity of Michigan Health System
  • Timothy M. Johnson
    • Department of SurgeryUniversity of Michigan Health System
    • Department of DermatologyUniversity of Michigan Health System
  • Jeremy M. G. Taylor
    • Biostatistics Core of the University of Michigan Comprehensive Cancer Center
Melanomas

DOI: 10.1245/s10434-011-1979-6

Cite this article as:
Sabel, M.S., Rice, J.D., Griffith, K.A. et al. Ann Surg Oncol (2012) 19: 287. doi:10.1245/s10434-011-1979-6

Abstract

Introduction

To identify melanoma patients at sufficiently low risk of nodal metastases who could avoid sentinel lymph node biopsy (SLNB), several statistical models have been proposed based upon patient/tumor characteristics, including logistic regression, classification trees, random forests, and support vector machines. We sought to validate recently published models meant to predict sentinel node status.

Methods

We queried our comprehensive, prospectively collected melanoma database for consecutive melanoma patients undergoing SLNB. Prediction values were estimated based upon four published models, calculating the same reported metrics: negative predictive value (NPV), rate of negative predictions (RNP), and false-negative rate (FNR).

Results

Logistic regression performed comparably with our data when considering NPV (89.4 versus 93.6%); however, the model’s specificity was not high enough to significantly reduce the rate of biopsies (SLN reduction rate of 2.9%). When applied to our data, the classification tree produced NPV and reduction in biopsy rates that were lower (87.7 versus 94.1 and 29.8 versus 14.3, respectively). Two published models could not be applied to our data due to model complexity and the use of proprietary software.

Conclusions

Published models meant to reduce the SLNB rate among patients with melanoma either underperformed when applied to our larger dataset, or could not be validated. Differences in selection criteria and histopathologic interpretation likely resulted in underperformance. Statistical predictive models must be developed in a clinically applicable manner to allow for both validation and ultimately clinical utility.

Copyright information

© Society of Surgical Oncology 2011