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PWE-079 Raman Spectroscopycancer Diagnostic for Pathology of Barrett’s Oesophagus
  1. H Barr1,
  2. M Isabelle2,
  3. O Old1,
  4. G Lloyd2,
  5. K Lau3,
  6. J Dorney4,
  7. A Lewis5,
  8. T Geraint5,
  9. N Shepherd6,
  10. I Bell7,
  11. N Stone4,
  12. C Kendall8
  1. 1Surgery
  2. 2Biophotonics, Gloucestershire Royal Hospital, Gloucester
  3. 3Renishaw PLC, Renishaw PLC, Wotten under Edge
  4. 4Physics, University of Exeter, Exeter
  5. 5Department of Cell and Developmental Biology, University College, London
  6. 6Pathology, Gloucestershire Royal hospital, Gloucester
  7. 7Renishaw, Renishaw PLC, Wotten under Edge
  8. 8Biophonotics Unit, Gloucestershire Royal Hospital, Gloucester, UK

Abstract

Introduction Raman spectroscopy has been shown to accurately classify tissue pathology in a variety of conditions and organ systems. Much of this work has been performed using Raman microspectrometers on tissue sections.

Despite the demonstrated potential as an accurate cancer diagnostic tool, Raman spectroscopy (RS) is yet to be adopted by the clinic for histopathology reviews. The Stratified Medicine through Advanced Raman Technologies (SMART) consortium has begun to address some of the hurdles (e.g. tissue sample preparation, data collection, pre-processing and transferability) in its adoption for cancer diagnosis. SMART is a multicentre industry-academic collaboration with the aim of developing a pathology platform for advanced diagnosis, using developments in hardware and software. Renishaw’s Streamline™ Raman technology enables collection of Raman spectral much faster without compromising signal to noise.

This study aims to assess the ability of this technique to accurately classify tissue pathology, using an oesophageal model.

Methods Specimens were collected from patients with Barrett’s oesophagus (BO), dysplasia and adenocarcinoma, and snap frozen in liquid nitrogen. 8μm tissue sections were prepared onto calcium fluoride slides, with contiguous sections stained with haematoxylin and eosin (H&E) for histological comparison. Raman spectra were collected across homogeneous regions of tissue pathology, using Streamline™ acquisitions of 60 seconds/line, at 1.1μm spatial resolution.

Results Advanced multivariate statistical analysis tools were used to develop pathology classification models, which were then tested using leave-one-out cross-validation. Each sample was then classified using a ‘voting classification’ for all pixels from one sample. The sensitivity and specificity of this pathology classification model using Raman Spectroscopy to discriminate dysplasia/adenocarcinoma from Barrett’s oesophagus produced sensitivity and specificities >80%.

Conclusion By combining multivariate statistical analysis with Streamline™ Raman acquisition of spectral data, we have demonstrated good sensitivities and specificities. This study illustrates the potential of non-invasive rapid Raman spectral mapping measurements and development of a robust and validated oesophageal classification model that are able to classify tissue pathology.

Disclosure of Interest None Declared

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