Search

Background: Identifying metastatic melanoma in the sentinel lymph node (SLN) is important because 80% of SLN biopsies are negative and 11% of patients develop complications. The neutrophil-to-lymphocyte ratio (NLR), a biomarker of micrometastatic disease, could improve prediction models for SLN status. We externally validated existing models and developed 'MelRisk' prognostic score to better predict SLN metastasis., Methods: The models were externally validated using data from a multicenter cohort study of 1,251 adults. Additionally, we developed and internally validated a new prognostic score `MelRisk', using candidate predictors derived from the extant literature., Results: The Karakousis model had a C-statistic of 0.58 (95% CI, 0.54-0.62). The Sondak model had a C-statistic of 0.57 (95% CI 0.53-0.61). The MIA model had a C-statistic of 0.60 (95% CI. 0.56-0.64). Our 'MelRisk' model (which used Breslow thickness, ulceration, age, anatomical site, and the NLR) showed an adjusted C-statistic of 0.63 (95% CI, 0.56-0.64)., Conclusion: Our prediction tool is freely available in the Google Play Store and Apple App Store, and we invite colleagues to externally validate its performance ., (Copyright © 2021 British Association of Plastic, Reconstructive and Aesthetic Surgeons. Published by Elsevier Ltd. All rights reserved.)

Purpose: Genetic testing has shifted from academic laboratories with expertise in specific genes to commercial laboratories that offer tests of a diverse array of genes. The purpose of this comparative study was to determine whether one academic laboratory's model of variant interpretation is similar to that of several commercial laboratories., Methods: The Collagen Diagnostic Laboratory (CDL) received, over a 14-month period, 38 requests to interpret variants originally identified by an outside laboratory (OL). The interpretations by the OL and CDL were compared and discrepancies were assessed., Results: Interpretations from the OL and CDL were concordant in 11 inquiries (29%); discrepancies were moderate in 11 instances (29%) and significant in 16 (42%). Factors that caused discrepancies included the following: (i) private data were not shared in a public database (n = 9); (ii) publicly available allele frequency data were not referenced and used as evidence (n = 5); and (iii) important aspects of protein structure and function were not taken into account (n = 13)., Conclusion: Comprehensive interpretation of sequence variants depends on good functional tests and well-curated variant databases. Provision of clinical information to the clinical laboratory, mandatory submission of identified variants with phenotype data to common resources, and collaboration between clinical laboratories and recognized experts is likely to improve consistency in variant interpretation among clinical laboratories.Genet Med 18 1, 20-24.