Meta-Analysis Validates SLNB Prediction Tools

Key Takeaways

• Validated sentinel lymph node biopsy (SLNB) positivity models demonstrated strong discriminative performance in a meta-analysis.
• Models using gene expression profiles performed comparably to those using clinicopathologic features.
• The Memorial Sloan Kettering Cancer Center (MSKCC) and Melanoma Institute of Australia (MIA) models were most frequently validated.

A new systematic review and meta-analysis found several externally validated risk prediction models for sentinel lymph node biopsy (SLNB) positivity in melanoma with strong discriminative performance, though significant heterogeneity was observed.

Researchers described 21 unique models for SLNB positivity risk prediction and 20 external validations of eight models after reviewing more than 20 articles. Nine models provided sufficient data to generate individualized patient risk estimates for routine preprocedural use. The analysis included a total of 23,125 patients.

The primary outcome measure (mean pooled C statistic) was 0.78 (95% CI, 0.74 to 0.81), suggesting strong model discrimination. The authors also observed significant heterogeneity (I² = 97.4%), which they said was unexplained despite meta-regression. The Memorial Sloan Kettering Cancer Center (MSKCC) and Melanoma Institute of Australia (MIA) models were most frequently validated [weighted C statistics of 0.73 (95% CI, 0.69 to 0.78) and 0.70 (95% CI, 0.66 to 0.74), respectively]. Models incorporating gene expression profiles showed higher discriminative performance (pooled C statistic, 0.83; 95% CI, 0.76 to 0.90) compared to those relying solely on clinicopathologic features (pooled C statistic, 0.77; 95% CI, 0.73 to 0.81), although the difference was not statistically significant (P = 0.11).

"[We] found several risk prediction models that have been externally validated with strong discriminative performance," they wrote. "These findings suggest that further head-to-head comparisons of risk prediction models are required to identify the best performing nomogram and associations with routine preoperative care."

Source:  Ma B, et al. JAMA Dermatology. Doi:10.1001/jamadermatol.2025.0113