دورية أكاديمية
Line Scan Raman Microspectroscopy for Label-Free Diagnosis of Human Pituitary Biopsies
| العنوان: | Line Scan Raman Microspectroscopy for Label-Free Diagnosis of Human Pituitary Biopsies |
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| المؤلفون: | Daniela Bovenkamp, Alexander Micko, Jeremias Püls, Fabian Placzek, Romana Höftberger, Greisa Vila, Rainer Leitgeb, Wolfgang Drexler, Marco Andreana, Stefan Wolfsberger, Angelika Unterhuber |
| المصدر: | Molecules, Vol 24, Iss 19, p 3577 (2019) |
| بيانات النشر: | MDPI AG, 2019. |
| سنة النشر: | 2019 |
| المجموعة: | LCC:Organic chemistry |
| مصطلحات موضوعية: | raman spectroscopy, line scan raman microspectroscopy, pituitary gland, pituitary adenoma, principal component analysis, k-nearest neighbor classifier, texture analysis, grey level cooccurrence matrix, correlation coefficients, Organic chemistry, QD241-441 |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1420-3049 24193577 |
| Relation: | https://www.mdpi.com/1420-3049/24/19/3577; https://doaj.org/toc/1420-3049 |
| DOI: | 10.3390/molecules24193577 |
| URL الوصول: | https://doaj.org/article/d37faf1b07db4e93b29b749e59bfcfe3 |
| رقم الأكسشن: | edsdoj.37faf1b07db4e93b29b749e59bfcfe3 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 14203049 24193577 |
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| DOI: | 10.3390/molecules24193577 |