Literature DB >> 29174712

Lugol Chromoendoscopy Detects Esophageal Dysplasia With Low Levels of Sensitivity in a High-Risk Region of China.

Jingjing Li1, Ruiping Xu2, Mengfei Liu1, Hong Cai1, Changqi Cao3, Fangfang Liu1, Fenglei Li4, Chuanhai Guo1, Yaqi Pan1, Zhonghu He5, Yang Ke6.   

Abstract

BACKGROUND & AIMS: Chromoendoscopy with Lugol dye is used to screen for early-stage esophageal squamous dysplasia (ESD) and esophageal cancer. However, the sensitivity with which Lugol chromoendoscopy detects ESD or esophageal cancer has not been fully assessed in large populations in China.
METHODS: From 2012 to 2016, a total of 15,264 residents in rural Hua County, Henan Province, which is a high-incidence area of esophageal cancer in China, were screened by Lugol chromoendoscopy. Biopsies were collected from endoscopically visualized lesions, identified before and after Lugol chromoendoscopy, and analyzed histologically. Biopsies were also collected from standard sites in the esophagus (28 and 33 cm distal to the incisors) if no abnormalities were found. We calculated the sensitivity with which Lugol chromoendoscopy detects esophageal dysplasia and carcinoma, using findings from biopsy analysis as the reference standard.
RESULTS: A total 586 participants were found by biopsy analysis to have ESD or more severe lesions. After endoscopy images were reviewed twice, Lugol chromoendoscopy sensitivity values for the detection of mild, moderate, and severe dysplasia, and esophageal cancer, were 45.9%, 55.3%, 87.0%, and 97.7%, respectively. ESDs were most frequently missed by Lugol chromoendoscopy in younger patients and men with moderate levels of dysplasia.
CONCLUSION: In a screening analysis of a general population in China, we found Lugol chromoendoscopy to identify individuals with ESD with lower levels of sensitivity (46%-87%) than previously believed, although it identified patients with esophageal cancer with almost 98% sensitivity. Prospective studies are needed to evaluate the clinical significance of esophageal lesions that are not detected by endoscopy.
Copyright © 2018 AGA Institute. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Accuracy; Early Detection; Esophageal Cancer; Imaging

Mesh:

Substances:

Year:  2017        PMID: 29174712     DOI: 10.1016/j.cgh.2017.11.031

Source DB:  PubMed          Journal:  Clin Gastroenterol Hepatol        ISSN: 1542-3565            Impact factor:   11.382


  6 in total

Review 1.  Chromoendoscopy: role in modern endoscopic imaging.

Authors:  Rajvinder Singh; Keng Hoong Chiam; Florencia Leiria; Leonardo Zorron Cheng Tao Pu; Kun Cheong Choi; Mariana Militz
Journal:  Transl Gastroenterol Hepatol       Date:  2020-07-05

2.  Lack of Iodine Staining Lugol's Chromoendoscopy Predicts Squamous Neoplastic Progression in a High-risk Region of China: Implications for East and West.

Authors:  Cary C Cotton; Yash A Choksi
Journal:  Clin Gastroenterol Hepatol       Date:  2019-11-08       Impact factor: 11.382

3.  Development and Validation of an Automatic Image-Recognition Endoscopic Report Generation System: A Multicenter Study.

Authors:  Jun-Yan Qu; Zhen Li; Jing-Ran Su; Ming-Jun Ma; Chang-Qin Xu; Ai-Jun Zhang; Cheng-Xia Liu; Hai-Peng Yuan; Yan-Liu Chu; Cui-Cui Lang; Liu-Ye Huang; Lin Lu; Yan-Qing Li; Xiu-Li Zuo
Journal:  Clin Transl Gastroenterol       Date:  2020-12-22       Impact factor: 4.488

4.  Clinical Significance of Monitoring Circulating Free DNA and Plasma Heat Shock Protein 90alpha in Patients with Esophageal Squamous Cell Carcinoma.

Authors:  Qiang Zhao; Congxiu Miao; Qingpu Lu; Weipeng Wu; Yuan He; Shouxin Wu; Huimin Liu; Changhong Lian
Journal:  Cancer Manag Res       Date:  2021-03-05       Impact factor: 3.989

5.  Diagnosis of Esophageal Lesions by Multi-Classification and Segmentation Using an Improved Multi-Task Deep Learning Model.

Authors:  Suigu Tang; Xiaoyuan Yu; Chak-Fong Cheang; Zeming Hu; Tong Fang; I-Cheong Choi; Hon-Ho Yu
Journal:  Sensors (Basel)       Date:  2022-02-15       Impact factor: 3.576

6.  Automatic classification of esophageal lesions in endoscopic images using a convolutional neural network.

Authors:  Gaoshuang Liu; Jie Hua; Zhan Wu; Tianfang Meng; Mengxue Sun; Peiyun Huang; Xiaopu He; Weihao Sun; Xueliang Li; Yang Chen
Journal:  Ann Transl Med       Date:  2020-04
  6 in total

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