Raman spectroscopy is a technique that utilises inelastic scattering processes to provide a
biochemical fingerprint that has been shown to successfully discriminate oesophageal
pathologies. The aim of this study was to develop Raman spectroscopy as a clinical tool;
both in vivo for ‘targeted biopsy’, and in ex vivo for ‘automated histopathology’.
Two different Raman probes were evaluated and compared and tissue classification models
generated ex vivo. A preliminary classification model of a novel single collection fibre
probe demonstrated potential for the probe design. Both probes were shown to discriminate
three different oesophageal pathology groups. A cross-validated tissue classification model
(88 samples) discriminated normal, Barrett’s and neoplasia with an overall accuracy of
86.5% with a sensitivity of 83.3-89.5% and specificity of 89.2-97.1%. A novel rapid
Raman mapping technique was evaluated. It was shown that sufficient biochemical
information for pathology diagnosis could be extracted from low signal to noise ratio data
using multivariate analysis providing the dataset was sufficiently large, thus demonstrating
the feasibility of automated histopathology in a clinically realistic time frame. Furthermore,
it was demonstrated that high spatial resolution imaging was not necessarily required for
automated histopathology using novel interpretation of multivariate techniques. A tissue
classification model generated from two rapid Raman maps containing separated substrate,
normal, HGD, luminescence and fibrous connective tissue with an overall training
performance of 97.5% Problems limiting clinical implementation of Raman techniques
were investigated and methods of overcoming devised
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