PT  - JOURNAL ARTICLE
AU  - Zhigang Song
AU  - Shuangmei Zou
AU  - Weixun Zhou
AU  - Yong Huang
AU  - Liwei Shao
AU  - Jing Yuan
AU  - Xiangnan Gou
AU  - Wei Jin
AU  - Zhanbo Wang
AU  - Xin Chen
AU  - Xiaohui Ding
AU  - Jinhong Liu
AU  - Chunkai Yu
AU  - Calvin Ku
AU  - Cancheng Liu
AU  - Zhuo Sun
AU  - Gang Xu
AU  - Yuefeng Wang
AU  - Xiaoqing Zhang
AU  - Dandan Wang
AU  - Shuhao Wang
AU  - Wei Xu
AU  - Richard C. Davis
AU  - Huaiyin Shi
TI  - Clinically applicable histopathological diagnosis system for gastric cancer detection using deep learning
AID  - 10.1101/2020.01.30.927749
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.01.30.927749
4099  - http://biorxiv.org/content/early/2020/01/31/2020.01.30.927749.short
4100  - http://biorxiv.org/content/early/2020/01/31/2020.01.30.927749.full
AB  - Gastric cancer is among the malignant tumors with the highest incidence and mortality rates. Early detection and accurate histopathological diagnosis of gastric cancer are essential factors that can help increase the chances of successful treatment. While the worldwide shortage of pathologists has imposed burdens on the current histopathology service, it also offers a unique opportunity for the use of artificial intelligence assistance systems to alleviate the workload and increase diagnostic accuracy. To the best of our knowledge, there has not been a clinically applicable histopathological assistance system with high accuracy, and can generalize to whole slide images created with diverse digital scanner models from different hospitals. Here, we report the clinically applicable artificial intelligence assistance system developed at the Chinese PLA General Hospital, China, using a deep convolutional neural network trained with 2,123 pixel-level annotated whole slide images. The model achieved a sensitivity near 100% and an average specificity of 80.6% on a real world test dataset, which included 3,212 whole slide images digitalized with three scanner models. We showed that the system would aid pathologists in improving diagnostic accuracy and preventing misdiagnosis. Moreover, we demonstrated that our system could perform robustly with 1,582 whole slide images from two other medical centers. Our study proves the feasibility and benefits of using histopathological artificial intelligence assistance systems in routine practice scenarios.