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Risk Prediction Models for Sentinel Node Positivity in Melanoma: A Systematic Review and Meta-Analysis.

Bryan Ma1, Maharshi Gandhi2, Sonia Czyz2

  • 1Division of Dermatology, Department of Medicine, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.

JAMA Dermatology
|March 12, 2025
PubMed
Summary
This summary is machine-generated.

This review identified effective melanoma risk prediction models for sentinel lymph node biopsy (SLNB) positivity. Several models show strong external validation, aiding clinical decisions for SLNB positivity.

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Area of Science:

  • Oncology
  • Medical Informatics
  • Biostatistics

Background:

  • Melanoma staging and treatment decisions often rely on sentinel lymph node biopsy (SLNB) positivity.
  • Accurate risk prediction models are crucial for identifying patients who would benefit from SLNB.

Purpose of the Study:

  • To systematically review and assess the performance of existing risk prediction models for melanoma SLNB positivity.
  • To identify the best-performing models for clinical application.

Main Methods:

  • A comprehensive literature search of Embase and MEDLINE databases was conducted up to May 1, 2024.
  • Included studies developed or validated risk prediction models for melanoma SLNB positivity, with model discrimination as a key metric.
  • Data extraction and synthesis followed established guidelines (DSReview, TRIPOD, PRISMA), with meta-analysis used for pooled estimates.

Main Results:

  • Twenty-one distinct risk prediction models were identified, with 20 external validations for 8 models.
  • The overall pooled weighted C-statistic for all models was 0.78, indicating moderate discriminative ability, though significant heterogeneity was noted.
  • The Memorial Sloan Kettering Cancer Center and Melanoma Institute of Australia models demonstrated strong and comparable external validation performance (C-statistics of 0.73 and 0.70, respectively).
  • Models incorporating gene expression profiles did not show significantly better discrimination than those using only clinicopathologic features.

Conclusions:

  • Several externally validated risk prediction models for melanoma SLNB positivity exhibit strong discriminative performance.
  • These validated models offer valuable tools for preprocedural risk assessment in clinical practice.
  • Further research is warranted to evaluate the impact of implementing these models on patient care and outcomes.