Department of Gastroenterology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Department of Gastroenterology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Key Laboratory of Pancreatic Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Dig Liver Dis. 2024 Sep;56(9):1565-1571. doi: 10.1016/j.dld.2024.02.001. Epub 2024 Feb 23.
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.
OLGIM 评估（operative link for gastric intestinal metaplasia assessment）Ⅲ期或Ⅳ期的患者胃癌（gastric cancer，GC）风险较高。我们旨在构建一种基于放大内镜窄带成像（magnifying endoscopy with narrow-band imaging，ME-NBI）的深度学习（deep learning，DL）模型，以评估 OLGIM 分期。
本研究纳入了来自 3 个内镜中心的 803 例患者的 4473 张 ME-NBI 图像。内镜专家在目标活检部位的内镜图像上标记肠上皮化生（intestinal metaplasia，IM）区域。使用快速区域卷积神经网络（Faster Region-Convolutional Neural Network，Faster R-CNN）模型对 IM 病变进行分级，并预测 OLGIM 分期。
模型在内部和外部验证集的 IM 分级诊断性能，由曲线下面积（area under the curve，AUC）衡量，分别为 0.872 和 0.803。该模型预测 OLGIM 高危分期的准确率为 84.0%，与 3 名专门接受过对应病理 IM 分级内镜图像培训的初级（71.3%，p=0.148）和 3 名高级内镜医生（75.3%，p=0.317）无统计学差异，但高于 3 名未经培训的初级内镜医生（64.0%，p=0.023）。
该 DL 模型可以帮助内镜医生使用 ME-NBI 预测 OLGIM 分期，而无需进行活检，从而有利于筛选 GC 的高危患者。
