An Image-Based Model for Assisting in Diagnosing Malignant Esophageal Lesions During Lugol Chromoendoscopic Examination.

IF 3 3区 医学 Q2 GASTROENTEROLOGY & HEPATOLOGY
Mengfei Liu, Zifan Qi, Ren Zhou, Chuanhai Guo, Anxiang Liu, Haijun Yang, Fenglei Li, Liping Duan, Lin Shen, Qi Wu, Zhen Liu, Yaqi Pan, Fangfang Liu, Ying Liu, Huanyu Chen, Zhe Hu, Hong Cai, Zhonghu He, Yang Ke
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Abstract

Introduction: Image-based diagnostic tools that aid endoscopists to biopsy putative esophageal malignant lesions are essential for ensuring the standardization and quality of Lugol chromoendoscopy. But there is no such model available yet.

Methods: We developed a diagnostic model using endoscopic Lugol-unstained lesions (LULs) features and baseline data from 1,099 individuals enrolled from a large-scale population-based ESCC screening cohort. Six hundred three participants from a clinical outpatient cohort were included as the external validation set. High-grade intraepithelial neoplasia and above lesions identified at baseline or within 1 year after screening were defined as outcome. The final model was determined using logistic regression analysis by the Akaike information criterion.

Results: The optimal diagnostic model contained the size, irregularity, sharp border of LUL, age, and body mass index of the participant, with the area under the curve of 0.83 (95% confidence interval [CI]: 0.78-0.87) in the development set, 0.81 (95% CI: 0.77-0.86) in the internal validation set, and 0.87 (95% CI: 0.84-0.90) in the external set. This model stratified individuals with LULs into low-risk, moderate-risk, and high-risk groups based on tertiles of predicted probabilities. The high-risk group accounted for <40% participants but enriched 80.8% and 82.7% of high-grade intraepithelial neoplasia and above cases in the development and external validation sets, respectively, achieving detection ratios 16.2 and 11.0 times higher than the low-risk group.

Discussion: Our model can help maintain consistency and accuracy in detecting esophageal malignancy through Lugol chromoendoscopy, particularly in primary healthcare units in high-risk rural areas.

在lugol色镜检查中辅助诊断食管恶性病变的图像模型。
基于图像的诊断工具有助于内镜医师对假定的食管恶性病变进行活检,这对于确保Lugol色内镜检查的标准化和质量至关重要。但目前还没有这样的模型可用。方法:我们建立了一个诊断模型,使用内镜下lugo -unstained病变(LULs)特征和基线数据,这些数据来自1099个大规模人群ESCC筛查队列。来自临床门诊队列的603名参与者被纳入外部验证集。在基线或筛查后1年内发现的高级别上皮内瘤变及以上(HGINA)病变被定义为结局。采用赤池信息准则进行logistic回归分析,确定最终模型。结果:最优诊断模型包含受试者LUL的大小、不规则性、边缘尖锐、年龄和体重指数,其中发育组曲线下面积为0.83 (95% CI: 0.78 ~ 0.87),内部验证组曲线下面积为0.81 (95% CI: 0.77 ~ 0.86),外部验证组曲线下面积为0.87 (95% CI: 0.84 ~ 0.90)。该模型根据预测概率的分位数将ll患者分为低、中、高风险组。高风险组的参与者比例小于40%,但在开发和外部验证集中分别富集了80.8%和82.7%的HGINA病例,检出率分别比低风险组高16.2倍和11.0倍。讨论:我们的模型可以帮助保持通过Lugol色内窥镜检测食管恶性肿瘤的一致性和准确性,特别是在高危农村地区的初级卫生保健单位。
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来源期刊
Clinical and Translational Gastroenterology
Clinical and Translational Gastroenterology GASTROENTEROLOGY & HEPATOLOGY-
CiteScore
7.00
自引率
0.00%
发文量
114
审稿时长
16 weeks
期刊介绍: Clinical and Translational Gastroenterology (CTG), published on behalf of the American College of Gastroenterology (ACG), is a peer-reviewed open access online journal dedicated to innovative clinical work in the field of gastroenterology and hepatology. CTG hopes to fulfill an unmet need for clinicians and scientists by welcoming novel cohort studies, early-phase clinical trials, qualitative and quantitative epidemiologic research, hypothesis-generating research, studies of novel mechanisms and methodologies including public health interventions, and integration of approaches across organs and disciplines. CTG also welcomes hypothesis-generating small studies, methods papers, and translational research with clear applications to human physiology or disease. Colon and small bowel Endoscopy and novel diagnostics Esophagus Functional GI disorders Immunology of the GI tract Microbiology of the GI tract Inflammatory bowel disease Pancreas and biliary tract Liver Pathology Pediatrics Preventative medicine Nutrition/obesity Stomach.
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