Endoscopy 2018; 50(05): 471-478
DOI: 10.1055/s-0043-121569
Original article
© Georg Thieme Verlag KG Stuttgart · New York

Learning curve and competence for volumetric laser endomicroscopy in Barrett’s esophagus using cumulative sum analysis

Arvind J. Trindade
 1  Division of Gastroenterology, Hofstra Northwell School of Medicine, Northwell Health System, Long Island Jewish Medical Center, New Hyde Park, New York, United States
,
Sumant Inamdar
 1  Division of Gastroenterology, Hofstra Northwell School of Medicine, Northwell Health System, Long Island Jewish Medical Center, New Hyde Park, New York, United States
,
Michael S. Smith
 2  Lewis Katz School of Medicine at Temple University, Philadelphia, Pennsylvania, United States
 3  Icahn School of Medicine at Mount Sinai, Mount Sinai West and Mount Sinai St. Luke’s Hospitals, New York, New York, United States
,
Lisa Rosen
 4  Division of Biostatistics, Hofstra Northwell School of Medicine, Northwell Health System, Feinstein Institute for Medical Research, Manhasset, New York, United States
,
Dennis Han
 1  Division of Gastroenterology, Hofstra Northwell School of Medicine, Northwell Health System, Long Island Jewish Medical Center, New Hyde Park, New York, United States
,
Kenneth J. Chang
 5  H. H. Chao Comprehensive Digestive Disease Center, University of California, Irvine Medical Center, Orange, California, United States
,
Cadman L. Leggett
 6  Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, United States
,
Charles J. Lightdale
 7  Division of Digestive and Liver Diseases, Columbia University Medical Center, New York, New York, United States
,
Douglas K. Pleskow
 8  Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States
,
Divyesh V. Sejpal
 1  Division of Gastroenterology, Hofstra Northwell School of Medicine, Northwell Health System, Long Island Jewish Medical Center, New Hyde Park, New York, United States
,
Guillermo J. Tearney
 9  Wellman Center for Photomedicine, Massachusetts General Hospital, Boston, Massachusetts, United States
10  Department of Pathology, Harvard Medical School, Boston, Massachusetts, United States
11  Massachusetts General Hospital, Boston, Massachusetts, United States
12  Harvard-MIT Division of Health Sciences Technology, Cambridge, Massachusetts, United States
,
Rebecca M. Thomas
13  Department of Pathology, Hofstra Northwell School of Medicine, Northwell Health System, Long Island Jewish Medical Center, New Hyde Park, New York, United States
,
Michael B. Wallace
14  Division of Gastroenterology, Mayo Clinic, Jacksonville, Florida, United States
› Author Affiliations
Further Information

Publication History

submitted 22 March 2017

accepted after revision 10 October 2017

Publication Date:
27 November 2017 (eFirst)

Abstract

Background and study aims Little is known about the learning curve for image interpretation in volumetric laser endomicroscopy (VLE) in Barrett’s esophagus (BE). The goal of this study was to calculate the learning curve, competence of image interpretation, sensitivity, specificity, and accuracy of VLE among novice users.

Methods 31 novice users viewed 96 VLE images electronically at three academic institutions after a brief training session. There were 24 images of each histologic type: normal gastric cardia, normal esophageal squamous epithelium, non-neoplastic BE, and neoplastic BE. The users were asked to identify the correct tissue type and level of confidence. The cumulative summation (CUSUM) technique was used to construct a learning curve.

Results 22 (71 %) of the physicians achieved VLE interpretation competency during their 96-slide review. Half of the physicians achieved competency at 65 images (95 % confidence interval [CI] 45 – 85). There was a statistically significant association between confidence in diagnosis and selecting the correct histologic tissue type (P < 0.001). The median accuracy for esophageal squamous epithelium, normal gastric cardia, non-neoplastic BE, and neoplastic BE was 96 % (95 %CI 95 % – 96 %), 95 % (95 %CI 94 % – 96 %), 90 % (95 %CI 88 % – 91 %), 96 % (95 %CI 95 % – 96 %). The overall accuracy was 95 % (95 %CI 93 % – 95 %).

Conclusion The majority of novice users achieved competence in image interpretation of VLE for BE, using a pre-selected image set, with a favorable learning curve after a brief training session. An electronic review of VLE images, prior to real-time use of VLE, is encouraged.