Goncalves, Juliana Pereira Lopes and Bollwein, Christine and Schlitter, Anna Melissa and Martin, Benedikt and Maerkl, Bruno and Utpatel, Kirsten and Weichert, Wilko and Schwamborn, Kristina (2021) The Impact of Histological Annotations for Accurate Tissue Classification Using Mass Spectrometry Imaging. METABOLITES, 11 (11): 752. ISSN , 2218-1989
Full text not available from this repository. (Request a copy)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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | RESOLUTION; mass spectrometry imaging; proteomics; histological annotations; supervised classification; on-tissue analysis |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Lehrstuhl für Pathologie |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 16 Sep 2022 07:57 |
| Last Modified: | 16 Sep 2022 07:57 |
| URI: | https://pred.uni-regensburg.de/id/eprint/47525 |
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