Metric-based analysis of FTIR data to discriminate tissue types in oral cancer. Issue 9 (17th April 2023)

• Record Type: Journal Article
• Authors: Ellis, Barnaby G.; Ingham, James; Whitley, Conor A.; Al Jedani, Safaa; Gunning, Philip J.; Gardner, Peter; Shaw, Richard J.; Barrett, Steve D.; Triantafyllou, Asterios; Risk, Janet M.; Smith, Caroline I.; Weightman, Peter
• Is Part Of: Analyst. Volume 148:Issue 9(2023)
• Page Start: 1948
• Page End: 1953
• Publication Date: 2023-04-17
• Subjects: Chemistry, Analytic -- Periodicals; 543
• DOI: 10.1039/d3an00258f
• Languages: English
• ISSNs: 0003-2654

Abstract

Abstract : Comparison of (a) H&E image of oral squamous cell carcinoma with (b) ratio of absorbance at 1252 cm −1 /1285 cm −1 and (c) pseudo-coloured labelled image of seven types of tissue found in oral tissue. 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.