To develop and endoscopic Raman spectroscopy probe for delivery down and channel in an endoscope to make near instant assessments of the condition of the oesophagus without the need for expensive and distressing tissue removal (biopsies).
The investigators have already shown that it is possible to tell the difference between healthy and diseased tissue in the laboratory by looking at the light emitted by tissue when illuminated with a low power laser. The investigators intend to use this technique, known as "Raman Spectroscopy" to tell if tissue in the oesophagus is cancerous, healthy or at some stage before full blown cancer, which is termed pre-cancer. It has been shown in the laboratory that this method will be at least as accurate as the conventional methods used now, but will provide the surgeon with instant results without the delay and cost of a laboratory analysis by pathologists. A miniature probe has been developed that slides through a channel in the endoscope (telescope) to the surface of the oesophagus to make near instant assessments of its condition without the need for expensive and distressing tissue removal (biopsies). This project plans to move this technique from the laboratory to the clinic by demonstrating that the method is safe and reliable for use in real patients.
Study Type
INTERVENTIONAL
Allocation
NON_RANDOMIZED
Purpose
DIAGNOSTIC
Masking
NONE
Enrollment
25
Oesophageal endoscopy with biopsy
Gloucestershire Hospitals NHS Foundation Trust
Gloucester, Gloucestershire, United Kingdom
Device safety testing (no detectable damage in biopsy samples when reviewed by histopathology)
Testing of the device for clinical application to demonstrate its use is safe and that is able to acquire diagnostic-quality (see outcome 2) spectra in less than 5 seconds from within the oesophagus. Samples illuminated will be biopsied and sent for routine histopathological analysis.
Time frame: 6 months
Diagnostic model developed (algorithm able to discriminate disease with >50% specificity and >50% sensitivity)
Computer model using multivariate analysis able to discriminate between diseased and non-diseased tissue. The target specificity and sensitivity are low due to small study group sizes.
Time frame: 6 months
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