Research Papers: Sensing

Toward improving fine needle aspiration cytology by applying Raman microspectroscopy

[+] Author Affiliations
Melanie Becker-Putsche, Thomas Bocklitz, Petra Rösch

University of Jena, Institute of Physical Chemistry and Abbe Center of Photonics, Helmholtzweg 4, 07743 Jena, Germany

Joachim Clement

University Hospital Jena, Department of Hematology and Oncology, Clinic for Internal Medicine II, Erlanger Allee 101, 07740 Jena, Germany

Jürgen Popp

University of Jena, Institute of Physical Chemistry and Abbe Center of Photonics, Helmholtzweg 4, 07743 Jena, Germany

Institute of Photonic Technology, Albert-Einstein-Strasse 9, 07745 Jena, Germany

J. Biomed. Opt. 18(4), 047001 (Apr 01, 2013). doi:10.1117/1.JBO.18.4.047001
History: Received December 4, 2012; Revised March 7, 2013; Accepted March 7, 2013
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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.

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© 2013 Society of Photo-Optical Instrumentation Engineers

Citation

Melanie Becker-Putsche ; Thomas Bocklitz ; Joachim Clement ; Petra Rösch and Jürgen Popp
"Toward improving fine needle aspiration cytology by applying Raman microspectroscopy", J. Biomed. Opt. 18(4), 047001 (Apr 01, 2013). ; http://dx.doi.org/10.1117/1.JBO.18.4.047001


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