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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...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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MDPI
2019
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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. |
format | Online Article Text |
id | pubmed-6804209 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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