Risk Prediction Models for Sentinel Node Positivity in Melanoma

Importance There is a need to identify the best performing risk prediction model for sentinel lymph node biopsy (SLNB) positivity in melanoma. Objective To comprehensively review the characteristics and discriminative performance of existing risk prediction models for SLNB positivity in melanoma. Data Sources Embase and MEDLINE were searched from inception to May 1, 2024, for English language articles. Study Selection All studies that either developed or validated a risk prediction model (defined as any calculator that combined more than 1 variable to provide a patient estimate for probability of melanoma SLNB positivity) with a corresponding measure of model discrimination were considered for inclusion by 2 reviewers, with disagreements adjudicated by a third reviewer. Data Extraction and Synthesis Data were extracted in duplicate according to Data Extraction for Systematic Reviews of Prediction Modeling Studies, Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis, and Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guidelines. Effects were pooled using random-effects meta-analysis. Main Outcome and Measures The primary outcome was the mean pooled C statistic. Heterogeneity was assessed using the I 2 statistic. Results In total, 23 articles describing the development of 21 different risk prediction models for SLNB positivity, 20 external validations of 8 different risk prediction models, and 9 models that included sufficient information to obtain individualized patient risk estimates in routine preprocedural clinical practice were identified. Among all risk prediction models, the pooled weighted C statistic was 0.78 (95% CI, 0.74-0.81) with significant heterogeneity ( I 2 = 97.4%) that was not explained in meta-regression. The Memorial Sloan Kettering Cancer Center and Melanoma Institute of Australia models were most frequently externally validated with both having strong and comparable discriminative performance (pooled weighted C statistic, 0.73; 95% CI, 0.69-0.78 vs pooled weighted C statistic, 0.70; 95% CI, 0.66-0.74). Discrimination was not significantly different between models that included gene expression profiles (pooled C statistic, 0.83; 95% CI, 0.76-0.90) and those that only used clinicopathologic features (pooled C statistic, 0.77; 95% CI, 0.73-0.81) ( P = .11). Conclusions and Relevance This systematic review and meta-analysis found several risk prediction models that have been externally validated with strong discriminative performance. Further research is needed to evaluate the associations of their implementation with preprocedural care.