Literature DB >> 28732392

Systematic assessment with I-SCAN magnification endoscopy and acetic acid improves dysplasia detection in patients with Barrett's esophagus.

Gideon Lipman1,2, Raf Bisschops3, Vinay Sehgal1,2, Jacobo Ortiz-Fernández-Sordo4, Rami Sweis2, Jose M Esteban5, Rifat Hamoudi1,6, Matthew R Banks1,2, Krish Ragunath4, Laurence B Lovat1,2, Rehan J Haidry1,2.   

Abstract

Background and study aims Enhanced endoscopic imaging with chromoendoscopy may improve dysplasia recognition in patients undergoing assessment of Barrett's esophagus (BE). This may reduce the need for random biopsies to detect more dysplasia. The aim of this study was to assess the effect of magnification endoscopy with I-SCAN (Pentax, Tokyo, Japan) and acetic acid (ACA) on dysplasia detection in BE using a novel mucosal and vascular classification system. Methods BE segments and suspicious lesions were recorded with high definition white-light and magnification endoscopy enhanced using all I-SCAN modes in combination. We created a novel mucosal and vascular classification system based on similar previously validated classifications for narrow-band imaging (NBI). A total of 27 videos were rated before and after ACA application. Following validation, a further 20 patients had their full endoscopies recorded and analyzed to model use of the system to detect dysplasia in a routine clinical scenario. Results The accuracy of the I-SCAN classification system for BE dysplasia improved with I-SCAN magnification from 69 % to 79 % post-ACA (P = 0.01). In the routine clinical scenario model in 20 new patients, accuracy of dysplasia detection increased from 76 % using a "pull-through" alone to 83 % when ACA and magnification endoscopy were combined (P = 0.047). Overall interobserver agreement between experts for dysplasia detection was substantial (0.69). Conclusions A new I-SCAN classification system for BE was validated against similar systems for NBI with similar outcomes. When used in combination with magnification and ACA, the classification detected BE dysplasia in clinical practice with good accuracy.Trials registered at ISRCTN (58235785). © Georg Thieme Verlag KG Stuttgart · New York.

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Year:  2017        PMID: 28732392     DOI: 10.1055/s-0043-113441

Source DB:  PubMed          Journal:  Endoscopy        ISSN: 0013-726X            Impact factor:   10.093


  4 in total

1.  Development and validation of a risk prediction model to diagnose Barrett's oesophagus (MARK-BE): a case-control machine learning approach.

Authors:  Avi Rosenfeld; David G Graham; Sarah Jevons; Jose Ariza; Daryl Hagan; Ash Wilson; Samuel J Lovat; Sarmed S Sami; Omer F Ahmad; Marco Novelli; Manuel Rodriguez Justo; Alison Winstanley; Eliyahu M Heifetz; Mordehy Ben-Zecharia; Uria Noiman; Rebecca C Fitzgerald; Peter Sasieni; Laurence B Lovat
Journal:  Lancet Digit Health       Date:  2019-12-05

Review 2.  Electronic chromo-endoscopy: technical details and a clinical perspective.

Authors:  Partha Pal; Aniruddha Pratap Singh; Navya D Kanuri; Rupa Banerjee
Journal:  Transl Gastroenterol Hepatol       Date:  2022-01-25

3.  A new artificial intelligence system successfully detects and localises early neoplasia in Barrett's esophagus by using convolutional neural networks.

Authors:  Mohamed Hussein; Juana González-Bueno Puyal; David Lines; Vinay Sehgal; Daniel Toth; Omer F Ahmad; Rawen Kader; Martin Everson; Gideon Lipman; Jacobo Ortiz Fernandez-Sordo; Krish Ragunath; Jose Miguel Esteban; Raf Bisschops; Matthew Banks; Michael Haefner; Peter Mountney; Danail Stoyanov; Laurence B Lovat; Rehan Haidry
Journal:  United European Gastroenterol J       Date:  2022-05-06       Impact factor: 6.866

4.  Machine Learning Creates a Simple Endoscopic Classification System that Improves Dysplasia Detection in Barrett's Oesophagus amongst Non-expert Endoscopists.

Authors:  Vinay Sehgal; Avi Rosenfeld; David G Graham; Gideon Lipman; Raf Bisschops; Krish Ragunath; Manuel Rodriguez-Justo; Marco Novelli; Matthew R Banks; Rehan J Haidry; Laurence B Lovat
Journal:  Gastroenterol Res Pract       Date:  2018-08-29       Impact factor: 2.260

  4 in total

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