Classification of bladder cancer cell lines using Raman spectroscopy: a comparison of excitation wavelength, sample substrate and statistical algorithms

Laura T. Kerr; Aine Adams; Shirley O'Dea; Katarina Domijan; Ivor Cullen; Bryan M. Hennelly

doi:10.1117/12.2051780

8 May 2014 Classification of bladder cancer cell lines using Raman spectroscopy: a comparison of excitation wavelength, sample substrate and statistical algorithms

Laura T. Kerr, Aine Adams, Shirley O'Dea, Katarina Domijan, Ivor Cullen, Bryan M. Hennelly

Author Affiliations +

Proceedings Volume 9129, Biophotonics: Photonic Solutions for Better Health Care IV; 91290E (2014) https://doi.org/10.1117/12.2051780
Event: SPIE Photonics Europe, 2014, Brussels, Belgium

Abstract

Raman microspectroscopy can be applied to the urinary bladder for highly accurate classification and diagnosis of bladder cancer. This technique can be applied in vitro to bladder epithelial cells obtained from urine cytology or in vivo as an optical biopsy" to provide results in real-time with higher sensitivity and specificity than current clinical methods. However, there exists a high degree of variability across experimental parameters which need to be standardised before this technique can be utilized in an everyday clinical environment. In this study, we investigate different laser wavelengths (473 nm and 532 nm), sample substrates (glass, fused silica and calcium fluoride) and multivariate statistical methods in order to gain insight into how these various experimental parameters impact on the sensitivity and specificity of Raman cytology.

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Laura T. Kerr, Aine Adams, Shirley O'Dea, Katarina Domijan, Ivor Cullen, and Bryan M. Hennelly "Classification of bladder cancer cell lines using Raman spectroscopy: a comparison of excitation wavelength, sample substrate and statistical algorithms", Proc. SPIE 9129, Biophotonics: Photonic Solutions for Better Health Care IV, 91290E (8 May 2014); https://doi.org/10.1117/12.2051780

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KEYWORDS

Raman spectroscopy

Bladder cancer

Bladder

Principal component analysis

Silica

Diagnostics

Cell biology

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