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 ex vivotissue sections with long acquisition times, and measurements can take many hours. To enable translation of this technology to a clinical setting, either as an adjunct for pathologists or an in vivoprobe for point of care testing, measurement times must be reduced. By using Renishaw’s Streamline™ technologywhich combines a line focusing technique and pixel binning, it is possible to collect Raman spectral measurements 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.
Method Tissue was collected from the oesophagus in patients undergoing endoscopy of open surgery on the oesophagus. Specimens were collected from patients with Barrett’s oesophagus (BO), dysplasia and adenocarcinoma, and snap frozen in liquid nitrogen. 8 µm 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 s per pixel, at 1.1 µm spatial resolution. Classification models were constructed to discriminate pathology subtypes.
Results Advanced multivariate statistic analysis tools were used to develop pathology classification models, which were then tested using leave-one-out cross-validation. The sensitivity and specificity of this pathology classification model using Raman Spectroscopy to discriminate dysplasia/adenocarcinoma from Barrett’s oesophagus produced sensitivity and specificities >75% and >75%, respectively.
Conclusion By combining multivariate statistical analysis with StreamlineTM 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 both providing a clinical tool for pathologists and clinicians.
Disclosure of interest None Declared.
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