Brain tumour differentiation: rapid stratified serum diagnostics via attenuated total reflection Fourier-transform infrared spectroscopy

Hands, J.R. and Clemens, G. and Stables, Ryan and Ashton, K. and Brodbelt, A. and Davis, C. and Dawson, T.P. and Jenkinson, M.D. and Lea, R.W. and Walker, C. and Baker, M.J. (2016) Brain tumour differentiation: rapid stratified serum diagnostics via attenuated total reflection Fourier-transform infrared spectroscopy. Journal of Neuro-Oncology, 127 (3). pp. 463-472. ISSN 0167594X (ISSN)

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Abstract

The ability to diagnose cancer rapidly with high sensitivity and specificity is essential to exploit advances in new treatments to lead significant reductions in mortality and morbidity. Current cancer diagnostic tests observing tissue architecture and specific protein expression for specific cancers suffer from inter-observer variability, poor detection rates and occur when the patient is symptomatic. A new method for the detection of cancer using 1 μl of human serum, attenuated total reflection - Fourier transform infrared spectroscopy and pattern recognition algorithms is reported using a 433 patient dataset (3897 spectra). To the best of our knowledge, we present the largest study on serum mid-infrared spectroscopy for cancer research. We achieve optimum sensitivities and specificities using a Radial Basis Function Support Vector Machine of between 80.0 and 100% for all strata and identify the major spectral features, hence biochemical components, responsible for the discrimination within each stratum. We assess feature fed-SVM analysis for our cancer versus non-cancer model and achieve 91.5 and 83.0% sensitivity and specificity respectively. We demonstrate the use of infrared light to provide a spectral signature from human serum to detect, for the first time, cancer versus non-cancer, metastatic cancer versus organ confined, brain cancer severity and the organ of origin of metastatic disease from the same sample enabling stratified diagnostics depending upon the clinical question asked. © 2016, The Author(s).

Item Type:	Article
Identification Number:	10.1007/s11060-016-2060-x
Dates:	Date Event 13 February 2016 Published Online 22 January 2016 Accepted
Uncontrolled Keywords:	ATR-FTIR, Cancer, Diagnostics, Glioma, Rapid, Serum, Spectroscopy
Subjects:	CAH11 - computing > CAH11-01 - computing > CAH11-01-04 - software engineering CAH02 - subjects allied to medicine > CAH02-05 - medical sciences > CAH02-05-04 - anatomy, physiology and pathology
Divisions:	Faculty of Computing, Engineering and the Built Environment Faculty of Computing, Engineering and the Built Environment > College of Computing
Depositing User:	Users 18 not found.
Date Deposited:	08 Nov 2016 11:29
Last Modified:	22 Mar 2023 12:01
URI:	https://www.open-access.bcu.ac.uk/id/eprint/491

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