Cancer Tissue Classification Using Supervised Machine Learning Applied to MALDI Mass Spectrometry Imaging

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...

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Autores principales:	Paul Mittal, Mark R. Condina, Manuela Klingler-Hoffmann, Gurjeet Kaur, Martin K. Oehler, Oliver M. Sieber, Michelle Palmieri, Stefan Kommoss, Sara Brucker, Mark D. McDonnell, Peter Hoffmann
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	colorectal cancer (CRC) endometrial cancer (EC) lymph node metastasis (LNM) machine learning (ML) matrix assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
Acceso en línea:	https://doaj.org/article/89c98a6a933741a8a47c161c0e4ac318
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spelling	oai:doaj.org-article:89c98a6a933741a8a47c161c0e4ac3182021-11-11T15:29:45ZCancer Tissue Classification Using Supervised Machine Learning Applied to MALDI Mass Spectrometry Imaging10.3390/cancers132153882072-6694https://doaj.org/article/89c98a6a933741a8a47c161c0e4ac3182021-10-01T00:00:00Zhttps://www.mdpi.com/2072-6694/13/21/5388https://doaj.org/toc/2072-6694Matrix 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.Paul MittalMark R. CondinaManuela Klingler-HoffmannGurjeet KaurMartin K. OehlerOliver M. SieberMichelle PalmieriStefan KommossSara BruckerMark D. McDonnellPeter HoffmannMDPI AGarticlecolorectal cancer (CRC)endometrial cancer (EC)lymph node metastasis (LNM)machine learning (ML)matrix assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI)Neoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENCancers, Vol 13, Iss 5388, p 5388 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	colorectal cancer (CRC) endometrial cancer (EC) lymph node metastasis (LNM) machine learning (ML) matrix assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
spellingShingle	colorectal cancer (CRC) endometrial cancer (EC) lymph node metastasis (LNM) machine learning (ML) matrix assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Paul Mittal Mark R. Condina Manuela Klingler-Hoffmann Gurjeet Kaur Martin K. Oehler Oliver M. Sieber Michelle Palmieri Stefan Kommoss Sara Brucker Mark D. McDonnell Peter Hoffmann Cancer Tissue Classification Using Supervised Machine Learning Applied to MALDI Mass Spectrometry Imaging
description	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.
format	article
author	Paul Mittal Mark R. Condina Manuela Klingler-Hoffmann Gurjeet Kaur Martin K. Oehler Oliver M. Sieber Michelle Palmieri Stefan Kommoss Sara Brucker Mark D. McDonnell Peter Hoffmann
author_facet	Paul Mittal Mark R. Condina Manuela Klingler-Hoffmann Gurjeet Kaur Martin K. Oehler Oliver M. Sieber Michelle Palmieri Stefan Kommoss Sara Brucker Mark D. McDonnell Peter Hoffmann
author_sort	Paul Mittal
title	Cancer Tissue Classification Using Supervised Machine Learning Applied to MALDI Mass Spectrometry Imaging
title_short	Cancer Tissue Classification Using Supervised Machine Learning Applied to MALDI Mass Spectrometry Imaging
title_full	Cancer Tissue Classification Using Supervised Machine Learning Applied to MALDI Mass Spectrometry Imaging
title_fullStr	Cancer Tissue Classification Using Supervised Machine Learning Applied to MALDI Mass Spectrometry Imaging
title_full_unstemmed	Cancer Tissue Classification Using Supervised Machine Learning Applied to MALDI Mass Spectrometry Imaging
title_sort	cancer tissue classification using supervised machine learning applied to maldi mass spectrometry imaging
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/89c98a6a933741a8a47c161c0e4ac318
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Cancer Tissue Classification Using Supervised Machine Learning Applied to MALDI Mass Spectrometry Imaging

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