Al-Rohil, Rami N.Moore, Jessica L.Patterson, Nathan HeathNicholson, SarahVerbeeck, NicoClaesen, MarcMuhammad, Jameelah Z.Caprioli, Richard M.Norris, Jeremy L.Kantrow, SaraCompton, Margaret2023-10-122023-10-122021-12Al-Rohil RN, Moore JL, Patterson NH, et al. Diagnosis of melanoma by imaging mass spectrometry: Development and validation of a melanoma prediction model. J Cutan Pathol. 2021;48(12):1455-1462. doi:10.1111/cup.14083https://hdl.handle.net/1805/36301Background: The definitive diagnosis of melanocytic neoplasia using solely histopathologic evaluation can be challenging. Novel techniques that objectively confirm diagnoses are needed. This study details the development and validation of a melanoma prediction model from spatially resolved multivariate protein expression profiles generated by imaging mass spectrometry (IMS). Methods: Three board-certified dermatopathologists blindly evaluated 333 samples. Samples with triply concordant diagnoses were included in this study, divided into a training set (n = 241) and a test set (n = 92). Both the training and test sets included various representative subclasses of unambiguous nevi and melanomas. A prediction model was developed from the training set using a linear support vector machine classification model. Results: We validated the prediction model on the independent test set of 92 specimens (75 classified correctly, 2 misclassified, and 15 indeterminate). IMS detects melanoma with a sensitivity of 97.6% and a specificity of 96.4% when evaluating each unique spot. IMS predicts melanoma at the sample level with a sensitivity of 97.3% and a specificity of 97.5%. Indeterminate results were excluded from sensitivity and specificity calculations. Conclusion: This study provides evidence that IMS-based proteomics results are highly concordant to diagnostic results obtained by careful histopathologic evaluation from a panel of expert dermatopathologists.en-USPublisher PolicyMelanomaImaging mass spectrometryProteomicsDiagnostic testArticle