Research

Raman spectroscopy and advanced mathematical modelling in the discrimination of human thyroid cell lines

Andrew T Harris¹ , Manjree Garg² , Xuebin B Yang¹ , Sheila E Fisher^3,4 , Jennifer Kirkham¹ , D Alastair Smith² , Dominic P Martin-Hirsch⁵ and Alec S High⁶

¹  Department of Oral Biology, Level 6 Worsley Building, University of Leeds, Clarendon Way, Leeds, LS2 9LU, UK

²  Avacta Group plc, York Biocentre, York Science Park, York, UK

³  Section of Experimental Therapeutics, Leeds Institute of Molecular Medicine, University of Leeds, Leeds, UK

⁴  School of Health Studies, University of Bradford, Bradford, UK

⁵  Department of Ear Nose and Throat/Head and Neck Surgery, Calderdale and Huddersfield NHS Trust, Huddersfield, UK

⁶  Department of Pathology Leeds Dental Institute, University of Leeds, Leeds, UK

author email corresponding author email

Head & Neck Oncology 2009, 1:38doi:10.1186/1758-3284-1-38

Published:	28 October 2009

Abstract

Raman spectroscopy could offer non-invasive, rapid and an objective nature to cancer diagnostics. However, much work in this field has focused on resolving differences between cancerous and non-cancerous tissues, and lacks the reproducibility and interpretation to be put into clinical practice. Much work is needed on basic cellular differences between malignancy and normal. This would allow the establishment of a clinically relevant cellular based model to translate to tissue classification. Raman spectroscopy provides a very detailed biochemical analysis of the target material and to 'unlock' this potential requires sophisticated mathematical modelling such as neural networks as an adjunct to data interpretation. Commercially obtained cancerous and non-cancerous cells, cultured in the laboratory were used in Raman spectral measurements. Data trends were visualised through PCA and then subjected to neural network analysis based on self-organising maps; consisting of m maps, where m is the number of classes to be recognised. Each map approximates the statistical distribution of a given class. The neural network analysis provided a 95% accuracy for identification of the cancerous cell line and 92% accuracy for normal cell line. In this preliminay study we have demonstrated th ability to distinguish between "normal" and cancerous commercial cell lines. This encourages future work to establish the reasons underpinning these spectral differences and to move forward to more complex systems involving tissues. We have also shown that the use of sophisticated mathematical modelling allows a high degree of discrimination of 'raw' spectral data.