窄带成像
分级(工程)
医学
内窥镜检查
放射科
肠化生
普通外科
医学物理学
内科学
发育不良
土木工程
工程类
作者
Wenlu Niu,Leheng Liu,Zhiya Dong,Xiongzhu Bu,Francis Y. Yao,JING WANG,Xiaowan Wu,Congying Chen,Tiancheng Mao,Yulun Wu,Yan Lin,Xinjian Wan,Hui Zhou
标识
DOI:10.1016/j.dld.2024.02.001
摘要
Patients with stage III or IV of operative link for gastric intestinal metaplasia assessment (OLGIM) are at a higher risk of gastric cancer (GC). We aimed to construct a deep learning (DL) model based on magnifying endoscopy with narrow-band imaging (ME-NBI) to evaluate OLGIM staging.This study included 4473 ME-NBI images obtained from 803 patients at three endoscopy centres. The endoscopic expert marked intestinal metaplasia (IM) regions on endoscopic images of the target biopsy sites. Faster Region-Convolutional Neural Network model was used to grade IM lesions and predict OLGIM staging.The diagnostic performance of the model for IM grading in internal and external validation sets, as measured by the area under the curve (AUC), was 0.872 and 0.803, respectively. The accuracy of this model in predicting the high-risk stage of OLGIM was 84.0%, which was not statistically different from that of three junior (71.3%, p = 0.148) and three senior endoscopists (75.3%, p = 0.317) specially trained in endoscopic images corresponding to pathological IM grade, but higher than that of three untrained junior endoscopists (64.0%, p = 0.023).This DL model can assist endoscopists in predicting OLGIM staging using ME-NBI without biopsy, thereby facilitating screening high-risk patients for GC.
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