scholarly journals Endoscopic Classification of Intestinal Metaplasia

Author(s):  
Byoung Wook Bang ◽  
Hyung Gil Kim
Gut ◽  
2020 ◽  
Vol 69 (10) ◽  
pp. 1762-1768 ◽  
Author(s):  
Pedro Marcos ◽  
Gisela Brito-Gonçalves ◽  
Diogo Libânio ◽  
Inês Pita ◽  
Rui Castro ◽  
...  

ObjectivesTo assess the value of endoscopic grading of gastric intestinal metaplasia (EGGIM), operative link on gastritis assessment (OLGA) and operative link on gastric intestinal metaplasia (OLGIM) on risk stratification for early gastric neoplasia (EGN) and to investigate other factors possibly associated with its development.DesignSingle centre, case–control study including 187 patients with EGN treated endoscopically and 187 age-matched and sex-matched control subjects. Individuals were classified according to EGGIM, OLGA and OLGIM systems. EGN risk according to gastritis stages and other clinical parameters was further evaluated.ResultsMore patients with EGN had EGGIM of ≥5 than control subjects (68.6% vs 13.3%, p<0.001). OLGA and OLGIM stages III/IV were more prevalent in patients with EGN than in control subjects (68% vs 11%, p<0.001, and 61% vs 3%, p<0.001, respectively). The three systems were the only parameters significantly related to the risk of EGN in multivariate analysis: for EGGIM 1–4 (adjusted OR (AOR) 12.9, 95% CI 1.4 to 118.6) and EGGIM 5–10 (AOR 21.2, 95% CI 5.0 to 90.2); for OLGA I/II (AOR 5.0, 95% CI 0.56 to 44.5) and OLGA III/IV (AOR 11.1, 95% CI 3.7 to 33.1); for OLGIM I/II (AOR 11.5, 95% CI 4.1 to 32.3) and OLGIM III/IV (AOR 16.0, 95% CI 7.6 to 33.4).ConclusionThis study confirms the role of histological assessment as an independent risk factor for gastric cancer (GC), but it is the first study to show that an endoscopic classification of gastric intestinal metaplasia is highly associated with that outcome. After further prospective validation, this classification may be appropriate for GC risk stratification and may simplify every day practice by reducing the need for biopsies.


2021 ◽  
Vol 160 (6) ◽  
pp. S-376
Author(s):  
Eladio Rodriguez-Diaz ◽  
Gyorgy Baffy Wai-Kit Lo ◽  
Hiroshi Mashimo ◽  
Aparna Repaka ◽  
Alexander Goldowsky ◽  
...  

2021 ◽  
Vol 09 (06) ◽  
pp. E955-E964
Author(s):  
Ganggang Mu ◽  
Yijie Zhu ◽  
Zhanyue Niu ◽  
Shigang Ding ◽  
Honggang Yu ◽  
...  

Abstract Background and study aims Endoscopy plays a crucial role in diagnosis of gastritis. Endoscopists have low accuracy in diagnosing atrophic gastritis with white-light endoscopy (WLE). High-risk factors (such as atrophic gastritis [AG]) for carcinogenesis demand early detection. Deep learning (DL)-based gastritis classification with WLE rarely has been reported. We built a system for improving the accuracy of diagnosis of AG with WLE to assist with this common gastritis diagnosis and help lessen endoscopist fatigue. Methods We collected a total of 8141 endoscopic images of common gastritis, other gastritis, and non-gastritis in 4587 cases and built a DL -based system constructed with UNet + + and Resnet-50. A system was developed to sort common gastritis images layer by layer: The first layer included non-gastritis/common gastritis/other gastritis, the second layer contained AG/non-atrophic gastritis, and the third layer included atrophy/intestinal metaplasia and erosion/hemorrhage. The convolutional neural networks were tested with three separate test sets. Results Rates of accuracy for classifying non-atrophic gastritis/AG, atrophy/intestinal metaplasia, and erosion/hemorrhage were 88.78 %, 87.40 %, and 93.67 % in internal test set, 91.23 %, 85.81 %, and 92.70 % in the external test set ,and 95.00 %, 92.86 %, and 94.74 % in the video set, respectively. The hit ratio with the segmentation model was 99.29 %. The accuracy for detection of non-gastritis/common gastritis/other gastritis was 93.6 %. Conclusions The system had decent specificity and accuracy in classification of gastritis lesions. DL has great potential in WLE gastritis classification for assisting with achieving accurate diagnoses after endoscopic procedures.


Endoscopy ◽  
2018 ◽  
Vol 50 (08) ◽  
pp. C8-C8 ◽  
Author(s):  
Marietta Iacucci ◽  
Cristina Trovato ◽  
Marco Daperno ◽  
Oluseyi Akinola ◽  
David Greenwald ◽  
...  

Esophagus ◽  
2009 ◽  
Vol 6 (4) ◽  
pp. 243-248 ◽  
Author(s):  
Sachiko Yamamoto ◽  
Ryu Ishihara ◽  
Hiroyasu Iishi ◽  
Noriya Uedo ◽  
Yoji Takeuchi ◽  
...  

2008 ◽  
Vol 67 (1) ◽  
pp. 169-172 ◽  
Author(s):  
Tomonori Yano ◽  
Hironori Yamamoto ◽  
Keijiro Sunada ◽  
Tomohiko Miyata ◽  
Michiko Iwamoto ◽  
...  

2002 ◽  
Vol 56 (5) ◽  
pp. 675-680 ◽  
Author(s):  
Hwan Y. Yoo ◽  
Joseph A. Eustace ◽  
Sumita Verma ◽  
Lin Zhang ◽  
Mary Harris ◽  
...  

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