Next Article in Journal
Solvent-Free C-3 Coupling of Azaindoles with Cyclic Imines
Previous Article in Journal
Chemical Compositions of Propolis from China and the United States and their Antimicrobial Activities Against Penicillium notatum
Previous Article in Special Issue
Detection of Pirimiphos-Methyl in Wheat Using Surface-Enhanced Raman Spectroscopy and Chemometric Methods
Open AccessArticle

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

1
Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria
2
Department of Neurosurgery, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria
3
Institute of Neurology, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria
4
Department of Internal Medicine III, Division of Endocrinology and Metabolism, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Dmitry Kurouski
Molecules 2019, 24(19), 3577; https://doi.org/10.3390/molecules24193577
Received: 13 September 2019 / Revised: 30 September 2019 / Accepted: 3 October 2019 / Published: 4 October 2019
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. View Full-Text
Keywords: 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 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
Show Figures

Figure 1

MDPI and ACS Style

Bovenkamp, D.; Micko, A.; Püls, J.; Placzek, F.; Höftberger, R.; Vila, G.; Leitgeb, R.; Drexler, W.; Andreana, M.; Wolfsberger, S.; Unterhuber, A. Line Scan Raman Microspectroscopy for Label-Free Diagnosis of Human Pituitary Biopsies. Molecules 2019, 24, 3577.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop