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Line Scan Raman Microspectroscopy for Label-Free Diagnosis of Human Pituitary Biopsies

Pituitary adenomas are neoplasia of the anterior pituitary gland and can be subdivided into hormone-producing tumors (lactotroph, corticotroph, gonadotroph, somatotroph, thyreotroph or plurihormonal) and hormone-inactive tumors (silent or null cell adenomas) based on their hormonal status. We theref...

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Autores principales: Bovenkamp, Daniela, Micko, Alexander, Püls, Jeremias, Placzek, Fabian, Höftberger, Romana, Vila, Greisa, Leitgeb, Rainer, Drexler, Wolfgang, Andreana, Marco, Wolfsberger, Stefan, Unterhuber, Angelika
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6804209/
https://www.ncbi.nlm.nih.gov/pubmed/31590270
http://dx.doi.org/10.3390/molecules24193577
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author Bovenkamp, Daniela
Micko, Alexander
Püls, Jeremias
Placzek, Fabian
Höftberger, Romana
Vila, Greisa
Leitgeb, Rainer
Drexler, Wolfgang
Andreana, Marco
Wolfsberger, Stefan
Unterhuber, Angelika
author_facet Bovenkamp, Daniela
Micko, Alexander
Püls, Jeremias
Placzek, Fabian
Höftberger, Romana
Vila, Greisa
Leitgeb, Rainer
Drexler, Wolfgang
Andreana, Marco
Wolfsberger, Stefan
Unterhuber, Angelika
author_sort Bovenkamp, Daniela
collection PubMed
description Pituitary adenomas are neoplasia of the anterior pituitary gland and can be subdivided into hormone-producing tumors (lactotroph, corticotroph, gonadotroph, somatotroph, thyreotroph or plurihormonal) and hormone-inactive tumors (silent or null cell adenomas) based on their hormonal status. We therefore developed a line scan Raman microspectroscopy (LSRM) system to detect, discriminate and hyperspectrally visualize pituitary gland from pituitary adenomas based on molecular differences. By applying principal component analysis followed by a k-nearest neighbor algorithm, specific hormone states were identified and a clear discrimination between pituitary gland and various adenoma subtypes was achieved. The classifier yielded an accuracy of 95% for gland tissue and 84–99% for adenoma subtypes. With an overall accuracy of 92%, our LSRM system has proven its potential to differentiate pituitary gland from pituitary adenomas. LSRM images based on the presence of specific Raman bands were created, and such images provided additional insight into the spatial distribution of particular molecular compounds. Pathological states could be molecularly differentiated and characterized with texture analysis evaluating Grey Level Cooccurrence Matrices for each LSRM image, as well as correlation coefficients between LSRM images.
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spelling pubmed-68042092019-11-18 Line Scan Raman Microspectroscopy for Label-Free Diagnosis of Human Pituitary Biopsies Bovenkamp, Daniela Micko, Alexander Püls, Jeremias Placzek, Fabian Höftberger, Romana Vila, Greisa Leitgeb, Rainer Drexler, Wolfgang Andreana, Marco Wolfsberger, Stefan Unterhuber, Angelika Molecules Article Pituitary adenomas are neoplasia of the anterior pituitary gland and can be subdivided into hormone-producing tumors (lactotroph, corticotroph, gonadotroph, somatotroph, thyreotroph or plurihormonal) and hormone-inactive tumors (silent or null cell adenomas) based on their hormonal status. We therefore developed a line scan Raman microspectroscopy (LSRM) system to detect, discriminate and hyperspectrally visualize pituitary gland from pituitary adenomas based on molecular differences. By applying principal component analysis followed by a k-nearest neighbor algorithm, specific hormone states were identified and a clear discrimination between pituitary gland and various adenoma subtypes was achieved. The classifier yielded an accuracy of 95% for gland tissue and 84–99% for adenoma subtypes. With an overall accuracy of 92%, our LSRM system has proven its potential to differentiate pituitary gland from pituitary adenomas. LSRM images based on the presence of specific Raman bands were created, and such images provided additional insight into the spatial distribution of particular molecular compounds. Pathological states could be molecularly differentiated and characterized with texture analysis evaluating Grey Level Cooccurrence Matrices for each LSRM image, as well as correlation coefficients between LSRM images. MDPI 2019-10-04 /pmc/articles/PMC6804209/ /pubmed/31590270 http://dx.doi.org/10.3390/molecules24193577 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Bovenkamp, Daniela
Micko, Alexander
Püls, Jeremias
Placzek, Fabian
Höftberger, Romana
Vila, Greisa
Leitgeb, Rainer
Drexler, Wolfgang
Andreana, Marco
Wolfsberger, Stefan
Unterhuber, Angelika
Line Scan Raman Microspectroscopy for Label-Free Diagnosis of Human Pituitary Biopsies
title Line Scan Raman Microspectroscopy for Label-Free Diagnosis of Human Pituitary Biopsies
title_full Line Scan Raman Microspectroscopy for Label-Free Diagnosis of Human Pituitary Biopsies
title_fullStr Line Scan Raman Microspectroscopy for Label-Free Diagnosis of Human Pituitary Biopsies
title_full_unstemmed Line Scan Raman Microspectroscopy for Label-Free Diagnosis of Human Pituitary Biopsies
title_short Line Scan Raman Microspectroscopy for Label-Free Diagnosis of Human Pituitary Biopsies
title_sort line scan raman microspectroscopy for label-free diagnosis of human pituitary biopsies
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6804209/
https://www.ncbi.nlm.nih.gov/pubmed/31590270
http://dx.doi.org/10.3390/molecules24193577
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