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Giardina G, Micko A, Bovenkamp D, et al. Morpho-Molecular Metabolic Analysis and Classification of Human Pituitary Gland and Adenoma Biopsies Based on Multimodal Optical Imaging. Cancers (Basel). 2021;13(13)doi: 10.3390/cancers13133234.
Giardina, G., Micko, A., Bovenkamp, D., Krause, A., Placzek, F., Papp, L., Krajnc, D., Spielvogel, C. P., Winklehner, M., Höftberger, R., Vila, G., Andreana, M., Leitgeb, R., Drexler, W., Wolfsberger, S., & Unterhuber, A. (2021). Morpho-Molecular Metabolic Analysis and Classification of Human Pituitary Gland and Adenoma Biopsies Based on Multimodal Optical Imaging. Cancers, 13(13), . https://doi.org/10.3390/cancers13133234
Giardina, Gabriel, et al. "Morpho-Molecular Metabolic Analysis and Classification of Human Pituitary Gland and Adenoma Biopsies Based on Multimodal Optical Imaging." Cancers vol. 13,13 (2021). doi: https://doi.org/10.3390/cancers13133234
Giardina G, Micko A, Bovenkamp D, Krause A, Placzek F, Papp L, Krajnc D, Spielvogel CP, Winklehner M, Höftberger R, Vila G, Andreana M, Leitgeb R, Drexler W, Wolfsberger S, Unterhuber A. Morpho-Molecular Metabolic Analysis and Classification of Human Pituitary Gland and Adenoma Biopsies Based on Multimodal Optical Imaging. Cancers (Basel). 2021 Jun 29;13(13). doi: 10.3390/cancers13133234. PMID: 34209497; PMCID: PMC8267638.
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Cancers (Basel). 2021 Jun 29;13(13). doi: 10.3390/cancers13133234.

Morpho-Molecular Metabolic Analysis and Classification of Human Pituitary Gland and Adenoma Biopsies Based on Multimodal Optical Imaging.

Cancers

Gabriel Giardina, Alexander Micko, Daniela Bovenkamp, Arno Krause, Fabian Placzek, Laszlo Papp, Denis Krajnc, Clemens P Spielvogel, Michael Winklehner, Romana Höftberger, Greisa Vila, Marco Andreana, Rainer Leitgeb, Wolfgang Drexler, Stefan Wolfsberger, Angelika Unterhuber

Affiliations

  1. Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria.
  2. Department of Neurosurgery, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria.
  3. QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria.
  4. Christian Doppler Laboratory for Applied Metabolomics, Division of Nuclear Medicine, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria.
  5. Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria.
  6. Department of Internal Medicine III, Division of Endocrinology and Metabolism, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria.

PMID: 34209497 PMCID: PMC8267638 DOI: 10.3390/cancers13133234

Abstract

Pituitary adenomas count among the most common intracranial tumors. During pituitary oncogenesis structural, textural, metabolic and molecular changes occur which can be revealed with our integrated ultrahigh-resolution multimodal imaging approach including optical coherence tomography (OCT), multiphoton microscopy (MPM) and line scan Raman microspectroscopy (LSRM) on an unprecedented cellular level in a label-free manner. We investigated 5 pituitary gland and 25 adenoma biopsies, including lactotroph, null cell, gonadotroph, somatotroph and mammosomatotroph as well as corticotroph. First-level binary classification for discrimination of pituitary gland and adenomas was performed by feature extraction via radiomic analysis on OCT and MPM images and achieved an accuracy of 88%. Second-level multi-class classification was performed based on molecular analysis of the specimen via LSRM to discriminate pituitary adenomas subtypes with accuracies of up to 99%. Chemical compounds such as lipids, proteins, collagen, DNA and carotenoids and their relation could be identified as relevant biomarkers, and their spatial distribution visualized to provide deeper insight into the chemical properties of pituitary adenomas. Thereby, the aim of the current work was to assess a unique label-free and non-invasive multimodal optical imaging platform for pituitary tissue imaging and to perform a multiparametric morpho-molecular metabolic analysis and classification.

Keywords: Raman spectroscopy; image analysis; multimodal imaging; multiphoton microscopy; optical coherence tomography; pituitary gland and adenomas; radiomics; second harmonic generation; two-photon excitation fluorescence

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