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 |
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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. |
doi_str_mv | 10.3390/cancers13133234 |
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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.</description><identifier>ISSN: 2072-6694</identifier><identifier>EISSN: 2072-6694</identifier><identifier>DOI: 10.3390/cancers13133234</identifier><identifier>PMID: 34209497</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Adenoma ; Biomarkers ; Biopsy ; Carotenoids ; Collagen ; Endocrine glands ; Lipids ; Metabolism ; Microscopy ; Morphology ; Pituitary ; Pituitary (anterior) ; Pituitary gland ; Radiomics ; Spatial distribution ; Surgery ; Tomography ; Tumorigenesis ; Tumors</subject><ispartof>Cancers, 2021-06, Vol.13 (13), p.3234</ispartof><rights>2021 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 (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2021 by the authors. 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c421t-50c9e149d7459e9f9386e054b063befffa4594c8d4aa7531135a49ceeb9a58363</citedby><cites>FETCH-LOGICAL-c421t-50c9e149d7459e9f9386e054b063befffa4594c8d4aa7531135a49ceeb9a58363</cites><orcidid>0000-0002-1251-3001 ; 0000-0002-9049-9989 ; 0000-0002-5449-745X ; 0000-0003-1918-1959 ; 0000-0002-0131-4111 ; 0000-0003-1786-4297 ; 0000-0001-9105-3519 ; 0000-0002-4409-8324 ; 0000-0002-8212-0545</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8267638/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8267638/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34209497$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Giardina, Gabriel</creatorcontrib><creatorcontrib>Micko, Alexander</creatorcontrib><creatorcontrib>Bovenkamp, Daniela</creatorcontrib><creatorcontrib>Krause, Arno</creatorcontrib><creatorcontrib>Placzek, Fabian</creatorcontrib><creatorcontrib>Papp, Laszlo</creatorcontrib><creatorcontrib>Krajnc, Denis</creatorcontrib><creatorcontrib>Spielvogel, Clemens P</creatorcontrib><creatorcontrib>Winklehner, Michael</creatorcontrib><creatorcontrib>Höftberger, Romana</creatorcontrib><creatorcontrib>Vila, Greisa</creatorcontrib><creatorcontrib>Andreana, Marco</creatorcontrib><creatorcontrib>Leitgeb, Rainer</creatorcontrib><creatorcontrib>Drexler, Wolfgang</creatorcontrib><creatorcontrib>Wolfsberger, Stefan</creatorcontrib><creatorcontrib>Unterhuber, Angelika</creatorcontrib><title>Morpho-Molecular Metabolic Analysis and Classification of Human Pituitary Gland and Adenoma Biopsies Based on Multimodal Optical Imaging</title><title>Cancers</title><addtitle>Cancers (Basel)</addtitle><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.</description><subject>Adenoma</subject><subject>Biomarkers</subject><subject>Biopsy</subject><subject>Carotenoids</subject><subject>Collagen</subject><subject>Endocrine glands</subject><subject>Lipids</subject><subject>Metabolism</subject><subject>Microscopy</subject><subject>Morphology</subject><subject>Pituitary</subject><subject>Pituitary (anterior)</subject><subject>Pituitary gland</subject><subject>Radiomics</subject><subject>Spatial distribution</subject><subject>Surgery</subject><subject>Tomography</subject><subject>Tumorigenesis</subject><subject>Tumors</subject><issn>2072-6694</issn><issn>2072-6694</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNpdkUFP3DAQha2qqCDg3FtlqZdeAnbsxPGl0rJqAYkVHOg5mjiTxcixUztB4h_0Z9cLFFEsjWyNv3ny8yPkM2cnQmh2asAbjIkLLkQp5AdyUDJVFnWt5cc3531ynNI9y0sIrmr1iewLWTIttTogfzYhTneh2ASHZnEQ6QZn6IKzhq48uMdkEwXf07WDlOxgDcw2eBoGerGM4OmNnRc7Q3yk527H7WrVow8j0DMbpmQx0TNI2NM8tlncbMfQg6PX05zFHL0cYWv99ojsDeASHr_sh-TXzx-364vi6vr8cr26Kows-VxUzGjkUvdKVhr1oEVTI6tkx2rR4TAMkPvSNL0EUJXgXFQgtUHsNFSNqMUh-f6sOy3diL1BP0dw7RTtmE20AWz7_423d-02PLRNWataNFng24tADL8XTHM72mTQZfcYltSWlWwkY6pRGf36Dr0PS8y_-kTpUjUVZ5k6faZMDClFHF4fw1m7C7p9F3Se-PLWwyv_L1bxFyG7pzs</recordid><startdate>20210629</startdate><enddate>20210629</enddate><creator>Giardina, Gabriel</creator><creator>Micko, Alexander</creator><creator>Bovenkamp, Daniela</creator><creator>Krause, Arno</creator><creator>Placzek, Fabian</creator><creator>Papp, Laszlo</creator><creator>Krajnc, Denis</creator><creator>Spielvogel, Clemens P</creator><creator>Winklehner, Michael</creator><creator>Höftberger, Romana</creator><creator>Vila, Greisa</creator><creator>Andreana, Marco</creator><creator>Leitgeb, Rainer</creator><creator>Drexler, Wolfgang</creator><creator>Wolfsberger, Stefan</creator><creator>Unterhuber, Angelika</creator><general>MDPI AG</general><general>MDPI</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7T5</scope><scope>7TO</scope><scope>7XB</scope><scope>8FE</scope><scope>8FH</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>H94</scope><scope>HCIFZ</scope><scope>LK8</scope><scope>M2O</scope><scope>M7P</scope><scope>MBDVC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-1251-3001</orcidid><orcidid>https://orcid.org/0000-0002-9049-9989</orcidid><orcidid>https://orcid.org/0000-0002-5449-745X</orcidid><orcidid>https://orcid.org/0000-0003-1918-1959</orcidid><orcidid>https://orcid.org/0000-0002-0131-4111</orcidid><orcidid>https://orcid.org/0000-0003-1786-4297</orcidid><orcidid>https://orcid.org/0000-0001-9105-3519</orcidid><orcidid>https://orcid.org/0000-0002-4409-8324</orcidid><orcidid>https://orcid.org/0000-0002-8212-0545</orcidid></search><sort><creationdate>20210629</creationdate><title>Morpho-Molecular Metabolic Analysis and Classification of Human Pituitary Gland and Adenoma Biopsies Based on Multimodal Optical Imaging</title><author>Giardina, Gabriel ; 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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.</abstract><cop>Switzerland</cop><pub>MDPI AG</pub><pmid>34209497</pmid><doi>10.3390/cancers13133234</doi><orcidid>https://orcid.org/0000-0002-1251-3001</orcidid><orcidid>https://orcid.org/0000-0002-9049-9989</orcidid><orcidid>https://orcid.org/0000-0002-5449-745X</orcidid><orcidid>https://orcid.org/0000-0003-1918-1959</orcidid><orcidid>https://orcid.org/0000-0002-0131-4111</orcidid><orcidid>https://orcid.org/0000-0003-1786-4297</orcidid><orcidid>https://orcid.org/0000-0001-9105-3519</orcidid><orcidid>https://orcid.org/0000-0002-4409-8324</orcidid><orcidid>https://orcid.org/0000-0002-8212-0545</orcidid><oa>free_for_read</oa></addata></record> |
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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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