Deep learning model for diagnosing early gastric cancer using preoperative computed tomography images

医学 粘膜下层 接收机工作特性 阶段(地层学) 深度学习 癌症 淋巴结 放射科 计算机断层摄影术 卷积神经网络 人工智能 内科学 计算机科学 生物 古生物学
作者
Qingwen Zeng,Zongfeng Feng,Yanyan Zhu,Yang Zhang,Xufeng Shu,Ahao Wu,Lianghua Luo,Yi Cao,Jianbo Xiong,Hong Li,Fuqing Zhou,Zhigang Jie,Yi Tu,Zhengrong Li
出处
期刊:Frontiers in Oncology [Frontiers Media SA]
卷期号:12 被引量:2
标识
DOI:10.3389/fonc.2022.1065934
摘要

Background Early gastric cancer (EGC) is defined as a lesion restricted to the mucosa or submucosa, independent of size or evidence of regional lymph node metastases. Although computed tomography (CT) is the main technique for determining the stage of gastric cancer (GC), the accuracy of CT for determining tumor invasion of EGC was still unsatisfactory by radiologists. In this research, we attempted to construct an AI model to discriminate EGC in portal venous phase CT images. Methods We retrospectively collected 658 GC patients from the first affiliated hospital of Nanchang university, and divided them into training and internal validation cohorts with a ratio of 8:2. As the external validation cohort, 93 GC patients were recruited from the second affiliated hospital of Soochow university. We developed several prediction models based on various convolutional neural networks, and compared their predictive performance. Results The deep learning model based on the ResNet101 neural network represented sufficient discrimination of EGC. In two validation cohorts, the areas under the curves (AUCs) for the receiver operating characteristic (ROC) curves were 0.993 (95% CI: 0.984-1.000) and 0.968 (95% CI: 0.935-1.000), respectively, and the accuracy was 0.946 and 0.914. Additionally, the deep learning model can also differentiate between mucosa and submucosa tumors of EGC. Conclusions These results suggested that deep learning classifiers have the potential to be used as a screening tool for EGC, which is crucial in the individualized treatment of EGC patients.
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