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

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), multip...

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Veröffentlicht in:Cancers 2021-06, Vol.13 (13), p.3234
Hauptverfasser: Giardina, Gabriel, Micko, Alexander, Bovenkamp, Daniela, Krause, Arno, Placzek, Fabian, Papp, Laszlo, Krajnc, Denis, Spielvogel, Clemens P, Winklehner, Michael, Höftberger, Romana, Vila, Greisa, Andreana, Marco, Leitgeb, Rainer, Drexler, Wolfgang, Wolfsberger, Stefan, Unterhuber, Angelika
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container_issue 13
container_start_page 3234
container_title Cancers
container_volume 13
creator Giardina, Gabriel
Micko, Alexander
Bovenkamp, Daniela
Krause, Arno
Placzek, Fabian
Papp, Laszlo
Krajnc, Denis
Spielvogel, Clemens P
Winklehner, Michael
Höftberger, Romana
Vila, Greisa
Andreana, Marco
Leitgeb, Rainer
Drexler, Wolfgang
Wolfsberger, Stefan
Unterhuber, Angelika
description 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.
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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. 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subjects Adenoma
Biomarkers
Biopsy
Carotenoids
Collagen
Endocrine glands
Lipids
Metabolism
Microscopy
Morphology
Pituitary
Pituitary (anterior)
Pituitary gland
Radiomics
Spatial distribution
Surgery
Tomography
Tumorigenesis
Tumors
title Morpho-Molecular Metabolic Analysis and Classification of Human Pituitary Gland and Adenoma Biopsies Based on Multimodal Optical Imaging
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