Establishment and Applicability of a Diagnostic System for Advanced Gastric Cancer T Staging Based on a Faster Region-Based Convolutional Neural Network

Frontiers in Oncology

Jilin Hu¹  Xianxiang Zhang¹  Maoshen Zhang¹  Yuan Gao¹  Longbo Zheng¹  Dongsheng Wang¹  Xunying Zhang¹  Xiaoming Zhou²  Tianye Niu³  Yun Lu⁴  Shuai Li⁵ 

[1] Department of Gastroenterology Surgery, Affiliated Hospital of Qingdao University, Qingdao, China;Department of Radiology, Affiliated Hospital of Qingdao University, Qingdao, China;Nuclear and Radiological Engineering and Medical Physics Programs, Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, United States;Shandong Key Laboratory of Digital Medicine and Computer Assisted Surgery, Qingdao, China;State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China;

关键词: convolutional neural network;    advanced gastric cancer;    T staging;    faster RCNN;    artificial intelligence;   

DOI  :  10.3389/fonc.2020.01238

来源: DOAJ

【 摘 要 】

Background: The accurate prediction of the tumor infiltration depth in the gastric wall based on enhanced CT images of gastric cancer is crucial for screening gastric cancer diseases and formulating treatment plans. Convolutional neural networks perform well in image segmentation. In this study, a convolutional neural network was used to construct a framework for automatic tumor recognition based on enhanced CT images of gastric cancer for the identification of lesion areas and the analysis and prediction of T staging of gastric cancer.Methods: Enhanced CT venous phase images of 225 patients with advanced gastric cancer from January 2017 to June 2018 were retrospectively collected. Ftable LabelImg software was used to identify the cancerous areas consistent with the postoperative pathological T stage. The training set images were enhanced to train the Faster RCNN detection model. Finally, the accuracy, specificity, recall rate, F1 index, ROC curve, and AUC were used to quantify the classification performance of T staging on this system.Results: The AUC of the Faster RCNN operating system was 0.93, and the recognition accuracies for T2, T3, and T4 were 90, 93, and 95%, respectively. The time required to automatically recognize a single image was 0.2 s, while the interpretation time of an imaging expert was ~10 s.Conclusion: In enhanced CT images of gastric cancer before treatment, the application of Faster RCNN to diagnosis the T stage of gastric cancer has high accuracy and feasibility.

【 授权许可】

Unknown