Convolutional neural network for the diagnosis of early gastric cancer based on magnifying narrow band imaging

作者：Lan Li, Yishu Chen, Zhe Shen, Xuequn Zhang, Jianzhong Sang, Yong Ding, Xiaoyun Yang, ­Jun Li­, Ming Chen, Chaohui Jin, Chunlei Chen, Chaohui Yu · 发表于：Gastric Cancer · 年份：2019 · DOI：10.1007/s10120-019-00992-2 · 被引用次数：218 · 研究领域：Gastric Cancer Management and Outcomes、Helicobacter pylori-related gastroenterology studies、Colorectal Cancer Screening and Detection

BACKGROUND: Magnifying endoscopy with narrow band imaging (M-NBI) has been applied to examine early gastric cancer by observing microvascular architecture and microsurface structure of gastric mucosal lesions. However, the diagnostic efficacy of non-experts in differentiating early gastric cancer from non-cancerous lesions by M-NBI remained far from satisfactory. In this study, we developed a new system based on convolutional neural network (CNN) to analyze gastric mucosal lesions observed by M-NBI. METHODS: A total of 386 images of non-cancerous lesions and 1702 images of early gastric cancer were collected to train and establish a CNN model (Inception-v3). Then a total of 341 endoscopic images (171 non-cancerous lesions and 170 early gastric cancer) were selected to evaluate the diagnostic capabilities of CNN and endoscopists. Primary outcome measures included diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. RESULTS: The sensitivity, specificity, and accuracy of CNN system in the diagnosis of early gastric cancer were 91.18%, 90.64%, and 90.91%, respectively. No significant difference was spotted in the specificity and accuracy of diagnosis between CNN and experts. However, the diagnostic sensitivity of CNN was significantly higher than that of the experts. Furthermore, the diagnostic sensitivity, specificity and accuracy of CNN were significantly higher than those of the non-experts. CONCLUSIONS: Our CNN system showed...