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OC-068 Blue light imaging for barrett’s neoplasia classification (blinc): the development and validation of a new endoscopic classification system to identify barrett’s neoplasia
  1. S Subramaniam,
  2. K Kandiah1,
  3. F Chedgy2,
  4. R Bhattacharyya3,
  5. P Basford4,
  6. G Longcroft-Wheaton1,
  7. P Bhandari1
  1. 1Department of Gastroenterology, PORTSMOUTH HOSPITALS NHS TRUST, Portsmouth
  2. 2Department of Gastroenterology, Brighton and Sussex University Hospitals NHS Trust, Brighton
  3. 3Department of Gastroenterology, Royal Hampshire County Hospital, Winchester
  4. 4Department of Gastroenterology, Western Sussex Hospitals NHS Trust, Chichester, UK

Abstract

Introduction Neoplasia in Barrett’s can be subtle and difficult to identify. Blue light imaging (BLI) by Fujifilm is a novel advanced endoscopic technology that provides high intensity contrast imaging for superior visualisation of mucosal surface and vessel patterns. This can improve the identification of Barrett’s neoplasia. To date there is no formal classification system that enables the characterisation of neoplastic and non-neoplastic Barrett’s for BLI. The aim of our study was to develop and validate a classification to identify Barrett’s neoplasia using BLI.

Method 3 expert endoscopists formed a working group to identify criteria characterising neoplastic and non-neoplastic Barrett’s on BLI using a modified Delphi method. A simple classification system utilising pit, vessel pattern and colour was developed using a database of 40 images. 6 experienced endoscopists then assessed a library containing 45 images of neoplastic and non-neoplastic Barrett’s using the proposed criteria. Sensitivity, specificity, positive (PPV) and negative predictive values (NPV) were calculated to assess its performance. The same parameters were then evaluated for each component criteria.

Results The BLINC criteria are as follows:

The table below shows the overall sensitivity, specificity, PPV and NPV of the classification in the identification of Barrett’s neoplasia.

When each category in the classification was analysed separately the predictive values of pit and vessel pattern in neoplasia characterisation were high compared to colour.

Conclusion We have developed the first internally validated simple classification system for the diagnosis of Barrett’s neoplasia using BLI. The classification criteria demonstrated high sensitivity and specifity. We aim to use the proposed classification in future studies for real time optical diagnosis of Barrett’s neoplasia.

Disclosure of Interest S. Subramaniam: None Declared, K. Kandiah: None Declared, F. Chedgy: None Declared, R. Bhattacharyya: None Declared, P. Basford: None Declared, G. Longcroft-Wheaton: None Declared, P. Bhandari Conflict with: Receives educational grants from Fujifilm, Olympus, Pentax

  • Barrett’s Oesophagus
  • Blue light imaging
  • Neoplasia

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