Browsing by Author "Al-Rohil, Rami N."

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    Diagnosis of Melanoma by Imaging Mass Spectrometry: Development and Validation of a Melanoma Prediction Model
    (Wiley, 2021-12) Al-Rohil, Rami N.; Moore, Jessica L.; Patterson, Nathan Heath; Nicholson, Sarah; Verbeeck, Nico; Claesen, Marc; Muhammad, Jameelah Z.; Caprioli, Richard M.; Norris, Jeremy L.; Kantrow, Sara; Compton, Margaret; Pathology and Laboratory Medicine, School of Medicine
    Background: 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.