Issue 9, 2023

Metric-based analysis of FTIR data to discriminate tissue types in oral cancer

Abstract

A machine learning algorithm (MLA) has predicted the prognosis of oral potentially malignant lesions and discriminated between lymph node tissue and metastatic oral squamous cell carcinoma (OSCC). The MLA analyses metrics, which are ratios of Fourier transform infrared absorbances, and identifies key wavenumbers that can be associated with molecular biomarkers. The wider efficacy of the MLA is now shown in the more complex primary OSCC tumour setting, where it is able to identify seven types of tissue. Three epithelial and four non-epithelial tissue types were discriminated from each other with sensitivities between 82% and 96% and specificities between 90% and 99%. The wavenumbers involved in the five best discriminating metrics for each tissue type were tightly grouped, indicating that small changes in the spectral profiles of the different tissue types are important. The number of samples used in this study was small, but the information will provide a basis for further, larger investigations.

Graphical abstract: Metric-based analysis of FTIR data to discriminate tissue types in oral cancer

Supplementary files

Article information

Article type
Paper
Submitted
16 Feb 2023
Accepted
08 Apr 2023
First published
10 Apr 2023
This article is Open Access
Creative Commons BY license

Analyst, 2023,148, 1948-1953

Metric-based analysis of FTIR data to discriminate tissue types in oral cancer

B. G. Ellis, J. Ingham, C. A. Whitley, S. Al Jedani, P. J. Gunning, P. Gardner, R. J. Shaw, S. D. Barrett, A. Triantafyllou, J. M. Risk, C. I. Smith and P. Weightman, Analyst, 2023, 148, 1948 DOI: 10.1039/D3AN00258F

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements