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Toward improving fine needle aspiration cytology by applying Raman microspectroscopy

Titelangaben

Becker-Putsche, Melanie ; Bocklitz, Thomas ; Clement, Joachim H. ; Rösch, Petra ; Popp, Jürgen:
Toward improving fine needle aspiration cytology by applying Raman microspectroscopy.
In: Journal of Biomedical Optics. Bd. 18 (2013) Heft 4 . - 047001.
ISSN 1560-2281
DOI: https://doi.org/10.1117/1.JBO.18.4.047001

Abstract

Medical diagnosis of biopsies performed by fine needle aspiration has to be very reliable. Therefore, pathologists/cytologists need additional biochemical information on single cancer cells for an accurate diagnosis. Accordingly, we applied three different classification models for discriminating various features of six breast cancer cell lines by analyzing Raman microspectroscopic data. The statistical evaluations are implemented by linear discriminant analysis (LDA) and support vector machines (SVM). For the first model, a total of 61,580 Raman spectra from 110 single cells are discriminated at the cell-line level with an accuracy of 99.52 using an SVM. The LDA classification based on Raman data achieved an accuracy of 94.04 by discriminating cell lines by their origin (solid tumor versus pleural effusion). In the third model, Raman cell spectra are classified by their cancer subtypes. LDA results show an accuracy of 97.45 and specificities of 97.78, 99.11, and 98.97 for the subtypes basal-like, HER2+/ER, and luminal, respectively. These subtypes are confirmed by gene expression patterns, which are important prognostic features in diagnosis. This work shows the applicability of Raman spectroscopy and statistical data handling in analyzing cancer-relevant biochemical information for advanced medical diagnosis on the single-cell level.

Weitere Angaben

Publikationsform: Artikel in einer Zeitschrift
Begutachteter Beitrag: Ja
Institutionen der Universität: Fakultäten > Fakultät für Mathematik, Physik und Informatik > Institut für Informatik > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie - Univ.-Prof. Dr. Thomas Wilhelm Bocklitz
Titel an der UBT entstanden: Nein
Themengebiete aus DDC: 500 Naturwissenschaften und Mathematik > 530 Physik
Eingestellt am: 22 Mai 2023 12:34
Letzte Änderung: 22 Mai 2023 12:34
URI: https://eref.uni-bayreuth.de/id/eprint/76272