Diagnosis of melanoma by imaging mass spectrometry: Development and validation of a melanoma prediction model.

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Al-Rohil, RN; Moore, JL; Patterson, NH; Nicholson, S; Verbeeck, N; Claesen, M; Muhammad, JZ; Caprioli, RM; Norris, JL; Kantrow, S; Compton, M; Robbins, J; Alomari, AK

Published Date

  • December 2021

Published In

Volume / Issue

  • 48 / 12

Start / End Page

  • 1455 - 1462

PubMed ID

  • 34151458

Pubmed Central ID

  • PMC8595555

Electronic International Standard Serial Number (EISSN)

  • 1600-0560

Digital Object Identifier (DOI)

  • 10.1111/cup.14083

Language

  • eng

Conference Location

  • United States