{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T05:05:29Z","timestamp":1782277529507,"version":"3.54.5"},"reference-count":59,"publisher":"American Medical Association (AMA)","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAMA Dermatol"],"published-print":{"date-parts":[[2025,5,1]]},"abstract":"<jats:sec id=\"ab-doi250003-4\"><jats:title>Importance<\/jats:title><jats:p>There is a need to identify the best performing risk prediction model for sentinel lymph node biopsy (SLNB) positivity in melanoma.<\/jats:p><\/jats:sec><jats:sec id=\"ab-doi250003-5\"><jats:title>Objective<\/jats:title><jats:p>To comprehensively review the characteristics and discriminative performance of existing risk prediction models for SLNB positivity in melanoma.<\/jats:p><\/jats:sec><jats:sec id=\"ab-doi250003-6\"><jats:title>Data Sources<\/jats:title><jats:p>Embase and MEDLINE were searched from inception to May 1, 2024, for English language articles.<\/jats:p><\/jats:sec><jats:sec id=\"ab-doi250003-7\"><jats:title>Study Selection<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec id=\"ab-doi250003-8\"><jats:title>Data Extraction and Synthesis<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec id=\"ab-doi250003-9\"><jats:title>Main Outcome and Measures<\/jats:title><jats:p>The primary outcome was the mean pooled C statistic. Heterogeneity was assessed using the <jats:italic>I<jats:sup>2<\/jats:sup><\/jats:italic> statistic.<\/jats:p><\/jats:sec><jats:sec id=\"ab-doi250003-10\"><jats:title>Results<\/jats:title><jats:p>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 (<jats:italic>I<jats:sup>2<\/jats:sup><\/jats:italic>\u2009=\u200997.4%) 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) (<jats:italic>P<\/jats:italic>\u2009=\u2009.11).<\/jats:p><\/jats:sec><jats:sec id=\"ab-doi250003-11\"><jats:title>Conclusions and Relevance<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec>","DOI":"10.1001\/jamadermatol.2025.0113","type":"journal-article","created":{"date-parts":[[2025,3,12]],"date-time":"2025-03-12T15:01:45Z","timestamp":1741791705000},"page":"523","source":"Crossref","is-referenced-by-count":8,"title":["Risk Prediction Models for Sentinel Node Positivity in Melanoma"],"prefix":"10.1001","volume":"161","author":[{"given":"Bryan","family":"Ma","sequence":"first","affiliation":[{"name":"Division of Dermatology, Department of Medicine, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maharshi","family":"Gandhi","sequence":"additional","affiliation":[{"name":"Department of Medicine, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sonia","family":"Czyz","sequence":"additional","affiliation":[{"name":"Department of Medicine, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jocelyn","family":"Jia","sequence":"additional","affiliation":[{"name":"Division of Dermatology, Department of Medicine, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brian","family":"Rankin","sequence":"additional","affiliation":[{"name":"Division of Dermatology, Department of Medicine, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Selena","family":"Osman","sequence":"additional","affiliation":[{"name":"Division of Dermatology, Department of Medicine, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eva Lindell","family":"Jonsson","sequence":"additional","affiliation":[{"name":"Department of Surgery, Arthur J.E. 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