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Article

Automated Cancer Diagnostics via Analysis of Optical and Chemical Images by Deep and Shallow Learning

1
Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK
2
Faculty of Pharmaceutical Sciences, University of Iceland, Hofsvallagata 53, 107 Reykjavik, Iceland
3
Biomedical Center, School of Health Sciences, University of Iceland, 101 Reykjavik, Iceland
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Department of Pathology, Landspitali the National University Hospital, Hringbraut, 101 Reykjavik, Iceland
5
Faculty of Medicine, University of Iceland, Vatnsmyrarvegur 16, 101 Reykjavik, Iceland
*
Authors to whom correspondence should be addressed.
Academic Editor: Ala F. Nassar
Metabolites 2022, 12(5), 455; https://doi.org/10.3390/metabo12050455
Received: 20 April 2022 / Revised: 10 May 2022 / Accepted: 13 May 2022 / Published: 18 May 2022
(This article belongs to the Special Issue Advances in Ambient Ionization Techniques for Mass Spectrometry)
Optical microscopy has long been the gold standard to analyse tissue samples for the diagnostics of various diseases, such as cancer. The current diagnostic workflow is time-consuming and labour-intensive, and manual annotation by a qualified pathologist is needed. With the ever-increasing number of tissue blocks and the complexity of molecular diagnostics, new approaches have been developed as complimentary or alternative solutions for the current workflow, such as digital pathology and mass spectrometry imaging (MSI). This study compares the performance of a digital pathology workflow using deep learning for tissue recognition and an MSI approach utilising shallow learning to annotate formalin-fixed and paraffin-embedded (FFPE) breast cancer tissue microarrays (TMAs). Results show that both deep learning algorithms based on conventional optical images and MSI-based shallow learning can provide automated diagnostics with F1-scores higher than 90%, with the latter intrinsically built on biochemical information that can be used for further analysis. View Full-Text
Keywords: mass spectrometry imaging; DESI-MSI; deep learning; shallow learning; FFPE; diagnostics mass spectrometry imaging; DESI-MSI; deep learning; shallow learning; FFPE; diagnostics
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MDPI and ACS Style

Isberg, O.G.; Giunchiglia, V.; McKenzie, J.S.; Takats, Z.; Jonasson, J.G.; Bodvarsdottir, S.K.; Thorsteinsdottir, M.; Xiang, Y. Automated Cancer Diagnostics via Analysis of Optical and Chemical Images by Deep and Shallow Learning. Metabolites 2022, 12, 455. https://doi.org/10.3390/metabo12050455

AMA Style

Isberg OG, Giunchiglia V, McKenzie JS, Takats Z, Jonasson JG, Bodvarsdottir SK, Thorsteinsdottir M, Xiang Y. Automated Cancer Diagnostics via Analysis of Optical and Chemical Images by Deep and Shallow Learning. Metabolites. 2022; 12(5):455. https://doi.org/10.3390/metabo12050455

Chicago/Turabian Style

Isberg, Olof Gerdur, Valentina Giunchiglia, James S. McKenzie, Zoltan Takats, Jon Gunnlaugur Jonasson, Sigridur Klara Bodvarsdottir, Margret Thorsteinsdottir, and Yuchen Xiang. 2022. "Automated Cancer Diagnostics via Analysis of Optical and Chemical Images by Deep and Shallow Learning" Metabolites 12, no. 5: 455. https://doi.org/10.3390/metabo12050455

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