Cancer tissue classification using supervised machine learning applied to maldi mass spectrometry imaging
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(Published version)
Date
2021
Authors
Mittal, P.
Condina, M.R.
Klingler-Hoffmann, M.
Kaur, G.
Oehler, M.K.
Sieber, O.M.
Palmieri, M.
Kommoss, S.
Brucker, S.
McDonnell, M.D.
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Journal article
Citation
Cancers, 2021; 13(21)
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Paul Mittal, Mark R. Condina, Manuela Klingler-Hoffmann, Gurjeet Kaur, Martin K. Oehler, Oliver M. Sieber
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Abstract
Matrix assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) can determine the spatial distribution of analytes such as protein distributions in a tissue section according to their mass-to-charge ratio. Here, we explored the clinical potential of machine learning (ML) applied to MALDI MSI data for cancer diagnostic classification using tissue microarrays (TMAs) on 302 colorectal (CRC) and 257 endometrial cancer (EC)) patients. ML based on deep neural networks discriminated colorectal tumour from normal tissue with an overall accuracy of 98% in balanced cross-validation (98.2% sensitivity and 98.6% specificity). Moreover, our machine learning approach predicted the presence of lymph node metastasis (LNM) for primary tumours of EC with an accuracy of 80% (90% sensitivity and 69% specificity). Our results demonstrate the capability of MALDI MSI for complementing classic histopathological examination for cancer diagnostic applications.
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Data source: Supplementary information, https://doi.org/10.3390/cancers13215388
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© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).