The Impact of Histological Annotations for Accurate Tissue Classification Using Mass Spectrometry Imaging

Knowing the precise location of analytes in the tissue has the potential to provide information about the organs’ function and predict its behavior. It is especially powerful when used in diagnosis and prognosis prediction of pathologies, such as cancer. Spatial proteomics, in particular mass spectr...

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Autores principales:	Juliana Pereira Lopes Gonçalves, Christine Bollwein, Anna Melissa Schlitter, Benedikt Martin, Bruno Märkl, Kirsten Utpatel, Wilko Weichert, Kristina Schwamborn
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	mass spectrometry imaging proteomics histological annotations supervised classification on-tissue analysis Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/908038b2ece0454da997ba8e0f977186
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spelling	oai:doaj.org-article:908038b2ece0454da997ba8e0f9771862021-11-25T18:20:39ZThe Impact of Histological Annotations for Accurate Tissue Classification Using Mass Spectrometry Imaging10.3390/metabo111107522218-1989https://doaj.org/article/908038b2ece0454da997ba8e0f9771862021-10-01T00:00:00Zhttps://www.mdpi.com/2218-1989/11/11/752https://doaj.org/toc/2218-1989Knowing the precise location of analytes in the tissue has the potential to provide information about the organs’ function and predict its behavior. It is especially powerful when used in diagnosis and prognosis prediction of pathologies, such as cancer. Spatial proteomics, in particular mass spectrometry imaging, together with machine learning approaches, has been proven to be a very helpful tool in answering some histopathology conundrums. To gain accurate information about the tissue, there is a need to build robust classification models. We have investigated the impact of histological annotation on the classification accuracy of different tumor tissues. Intrinsic tissue heterogeneity directly impacts the efficacy of the annotations, having a more pronounced effect on more heterogeneous tissues, as pancreatic ductal adenocarcinoma, where the impact is over 20% in accuracy. On the other hand, in more homogeneous samples, such as kidney tumors, histological annotations have a slenderer impact on the classification accuracy.Juliana Pereira Lopes GonçalvesChristine BollweinAnna Melissa SchlitterBenedikt MartinBruno MärklKirsten UtpatelWilko WeichertKristina SchwambornMDPI AGarticlemass spectrometry imagingproteomicshistological annotationssupervised classificationon-tissue analysisMicrobiologyQR1-502ENMetabolites, Vol 11, Iss 752, p 752 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	mass spectrometry imaging proteomics histological annotations supervised classification on-tissue analysis Microbiology QR1-502
spellingShingle	mass spectrometry imaging proteomics histological annotations supervised classification on-tissue analysis Microbiology QR1-502 Juliana Pereira Lopes Gonçalves Christine Bollwein Anna Melissa Schlitter Benedikt Martin Bruno Märkl Kirsten Utpatel Wilko Weichert Kristina Schwamborn The Impact of Histological Annotations for Accurate Tissue Classification Using Mass Spectrometry Imaging
description	Knowing the precise location of analytes in the tissue has the potential to provide information about the organs’ function and predict its behavior. It is especially powerful when used in diagnosis and prognosis prediction of pathologies, such as cancer. Spatial proteomics, in particular mass spectrometry imaging, together with machine learning approaches, has been proven to be a very helpful tool in answering some histopathology conundrums. To gain accurate information about the tissue, there is a need to build robust classification models. We have investigated the impact of histological annotation on the classification accuracy of different tumor tissues. Intrinsic tissue heterogeneity directly impacts the efficacy of the annotations, having a more pronounced effect on more heterogeneous tissues, as pancreatic ductal adenocarcinoma, where the impact is over 20% in accuracy. On the other hand, in more homogeneous samples, such as kidney tumors, histological annotations have a slenderer impact on the classification accuracy.
format	article
author	Juliana Pereira Lopes Gonçalves Christine Bollwein Anna Melissa Schlitter Benedikt Martin Bruno Märkl Kirsten Utpatel Wilko Weichert Kristina Schwamborn
author_facet	Juliana Pereira Lopes Gonçalves Christine Bollwein Anna Melissa Schlitter Benedikt Martin Bruno Märkl Kirsten Utpatel Wilko Weichert Kristina Schwamborn
author_sort	Juliana Pereira Lopes Gonçalves
title	The Impact of Histological Annotations for Accurate Tissue Classification Using Mass Spectrometry Imaging
title_short	The Impact of Histological Annotations for Accurate Tissue Classification Using Mass Spectrometry Imaging
title_full	The Impact of Histological Annotations for Accurate Tissue Classification Using Mass Spectrometry Imaging
title_fullStr	The Impact of Histological Annotations for Accurate Tissue Classification Using Mass Spectrometry Imaging
title_full_unstemmed	The Impact of Histological Annotations for Accurate Tissue Classification Using Mass Spectrometry Imaging
title_sort	impact of histological annotations for accurate tissue classification using mass spectrometry imaging
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/908038b2ece0454da997ba8e0f977186
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The Impact of Histological Annotations for Accurate Tissue Classification Using Mass Spectrometry Imaging

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