Oesophageal Cancer Diagnostics using Multi-Centre Raman Spectroscopy


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Martin Isabelle1,Catherine Kendall2,Nick Stone3,Geraint Thomas4,Gavin Lloyd2,Neil Shepherd5,Manuel Rodriguez-Justo4,Ian Bell1,Hugh Barr2
1Spectroscopy Products Division, Renishaw plc,2Biophotonics Research Unit, Gloucestershire Hospitals NHSFT,3Biomedical Spectroscopy, Physics and Astronomy, University of Exeter,4Department of Cell and Developmental Biology, University College London,5Gloucestershire Hospitals NHSFT

Abstract

Background

Raman spectroscopy (RS) has been used as a non-invasive, label-free, non-destructive optical technique that can be used to analyse molecular changes in cells, tissues or biofluids, that are either the cause or the effect of pathological diseases. Despite clear evidence of the potential of Raman spectroscopy as an accurate diagnostic tool in a range of cancer and non-cancer diseases, RS has yet to make the wide translation from research to clinic. A barrier blocking clinical implementation is evidence that RS is capable of providing comparable results or diagnoses across multiple sites utilising multiple instruments that are suitable for use in a clinical environment.

This research will highlight the design of the workflow integration, hardware and software to aid in the integration of Raman imaging into routine disease diagnosis for oesophageal cancer and support histopathology and surgical activities to reduce tissue processing time, save hospitals money and labour, and reduce the time to patient diagnosis.

Method

Three Renishaw benchtop RA816 series Raman spectrometers (located at three different geographical locations) were used to collect Raman spectra of tissue sections from patients with Barrett’s oesophagus, dysplasia and adenocarcinoma with consensus pathology review.

Methods to minimise instrument and sampling variations between sites were developed. Classification models were developed to discriminate normal squamous (NSq) tissue versus adenocarcinoma (AC) and discrimination between five pathology groups.

Results

Site classification performance achieved good sensitivities and specificities with NSq v AC sensitivity of 92-100% & specificity of 96-100% and independent validation sensitivity of 95-100% & specificity of 83-100%. 

Conclusion

This study illustrates the potential of developing validated oesophageal classification models constructed from data measured at three remote instrument sites with potential application to other tissue types. Compared to some other molecular and imaging diagnostics techniques, RS is non-invasive, fast, low-cost and can achieve high chemical specificity in biological samples.