ArticleJournal of cutaneous pathology2021
Diagnosis of melanoma by imaging mass spectrometry: Development and validation of a melanoma prediction model.
Article in Journal of cutaneous pathology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed, 12 citations in OpenAlex.
- Enhancing the Sensitivity of Mass Spectrometry Imaging through Spatial Signal Averaging.Journal of the American Society for Mass Spectrometry · 2026Article
- Photocleavable Mass-Tagged Oligonucleotide Probes for Multiplexed and Multiomic Tissue Imaging of Targeted Transcripts.Journal of the American Society for Mass Spectrometry · 2025Article
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- Prospective on Imaging Mass Spectrometry in Clinical Diagnostics.Molecular & cellular proteomics : MCP · 2023Review
- Imaging Mass Spectrometry for the Classification of Melanoma Based onInternational journal of molecular sciences · 2023Article
Corrections and comments
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Authors and funding
13 authors at 7 institutions in 1 country.
Funding
Abstract
backgroundThe 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).
methodsThree 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.
resultsWe 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.
conclusionThis 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.
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Registered trials
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