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Gonçalves JPL, Bollwein C, Schlitter AM, et al. The Impact of Histological Annotations for Accurate Tissue Classification Using Mass Spectrometry Imaging. Metabolites. 2021;11(11)doi: 10.3390/metabo11110752.
Gonçalves, J. P. L., Bollwein, C., Schlitter, A. M., Martin, B., Märkl, B., Utpatel, K., Weichert, W., & Schwamborn, K. (2021). The Impact of Histological Annotations for Accurate Tissue Classification Using Mass Spectrometry Imaging. Metabolites, 11(11), . https://doi.org/10.3390/metabo11110752
Gonçalves, Juliana Pereira Lopes, et al. "The Impact of Histological Annotations for Accurate Tissue Classification Using Mass Spectrometry Imaging." Metabolites vol. 11,11 (2021). doi: https://doi.org/10.3390/metabo11110752
Gonçalves JPL, Bollwein C, Schlitter AM, Martin B, Märkl B, Utpatel K, Weichert W, Schwamborn K. The Impact of Histological Annotations for Accurate Tissue Classification Using Mass Spectrometry Imaging. Metabolites. 2021 Oct 30;11(11). doi: 10.3390/metabo11110752. PMID: 34822410; PMCID: PMC8624953.
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Metabolites. 2021 Oct 30;11(11). doi: 10.3390/metabo11110752.

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

Metabolites

Juliana Pereira Lopes Gonçalves, Christine Bollwein, Anna Melissa Schlitter, Benedikt Martin, Bruno Märkl, Kirsten Utpatel, Wilko Weichert, Kristina Schwamborn

Affiliations

  1. Institute of Pathology, School of Medicine, Technical University of Munich, Trogerstraße 18, 81675 Munich, Germany.
  2. German Cancer Consortium (DKTK), Partner Site Munich, 80336 Munich, Germany.
  3. General Pathology and Molecular Diagnostics, Medical Faculty, University Hospital of Augsburg, 86156 Augsburg, Germany.
  4. Institute of Pathology, University of Regensburg, 93053 Regensburg, Germany.
  5. Comprehensive Cancer Center Munich (CCCM), Marchioninistraße 15, 81377 Munich, Germany.

PMID: 34822410 PMCID: PMC8624953 DOI: 10.3390/metabo11110752

Abstract

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.

Keywords: histological annotations; mass spectrometry imaging; on-tissue analysis; proteomics; supervised classification

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