Background and aim: Magnifying endoscopy combined with narrow-band imaging (ME-NBI) has been used for
differential diagnosis of various focal lesions. The aim of our study was to evaluate
ME-NBI criteria for cancer diagnosis in superficial depressed gastric lesions in comparison
to conventional white light endoscopy (WLE).
Patients and methods: ME-NBI and WLE images of 100 superficial gastric depressions (55 depressed cancers,
45 benign depressions) were independently evaluated by 11 endoscopists blinded to
the diagnosis in each case. The presence or absence of predefined ME-NBI findings
relating to microvasculature and fine mucosal structure (FMS) was recorded. A general
diagnosis of benign or malignant also had to be given on the basis of a general assessment
of features of color and shape as shown in the ME-NBI and WLE images, respectively,
without regard to any prespecified criteria.
Results: Multivariate and ROC analysis demonstrated that the triad of FMS disappearance, microvascular
dilation, and heterogeneity appeared to be the best combination for diagnosis of gastric
cancer. ME-NBI diagnosis with the triad attained a good specificity (85 %, theoretically
calculated if all of the triad were positive), which was significantly (P < 0.001) superior to WLE general diagnosis (65 %), and comparable with ME-NBI general
diagnosis (80 %). The sensitivities of the three diagnoses (ME-NBI with the triad
69 %, WLE general diagnosis 71 %, ME-NBI general diagnosis 72 %) were comparably moderate.
The κ values (interobserver concordance) for ME-NBI diagnosis with the triad (0.47)
and ME-NBI general diagnosis (0.48) were superior to the κ value for WLE diagnosis
(0.34).
Conclusion: The triad of FMS disappearance, microvascular dilation, and heterogeneity has good
specificity for the diagnosis of superficial depressed gastric carcinoma, but the
sensitivity needs to be improved.
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M. KaiseMD
Department of Endoscopy The Jikei University School of Medicine
3-25-8, Nishishinbashi, Minato-ku Tokyo 105-8461 Japan
Fax: +81-3-34594524
Email: kaise@jikei.ac.jp