Nature Communications (May 2022)
A deep learning model and human-machine fusion for prediction of EBV-associated gastric cancer from histopathology
- Xueyi Zheng,
- Ruixuan Wang,
- Xinke Zhang,
- Yan Sun,
- Haohuan Zhang,
- Zihan Zhao,
- Yuanhang Zheng,
- Jing Luo,
- Jiangyu Zhang,
- Hongmei Wu,
- Dan Huang,
- Wenbiao Zhu,
- Jianning Chen,
- Qinghua Cao,
- Hong Zeng,
- Rongzhen Luo,
- Peng Li,
- Lilong Lan,
- Jingping Yun,
- Dan Xie,
- Wei-Shi Zheng,
- Junhang Luo,
- Muyan Cai
Affiliations
- Xueyi Zheng
- Department of Pathology, Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center
- Ruixuan Wang
- School of Computer Science and Engineering, Sun Yat-sen University
- Xinke Zhang
- Department of Pathology, Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center
- Yan Sun
- Department of Pathology, Tianjin Medical University Cancer Institute and Hospital
- Haohuan Zhang
- School of Computer Science and Engineering, Sun Yat-sen University
- Zihan Zhao
- Department of Pathology, Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center
- Yuanhang Zheng
- School of Computer Science and Engineering, Sun Yat-sen University
- Jing Luo
- School of Computer Science and Engineering, Sun Yat-sen University
- Jiangyu Zhang
- Department of Pathology, Affiliated Cancer Hospital & Institute of Guangzhou Medical University
- Hongmei Wu
- Department of Pathology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences
- Dan Huang
- Department of Pathology, Fudan University Shanghai Cancer Center
- Wenbiao Zhu
- Department of Pathology, Meizhou People’s Hospital
- Jianning Chen
- Department of Pathology, The Third Affiliated Hospital, Sun Yat-sen University
- Qinghua Cao
- Department of Pathology, The First Affiliated Hospital, Sun Yat-sen University
- Hong Zeng
- Department of Pathology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University
- Rongzhen Luo
- Department of Pathology, Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center
- Peng Li
- Department of Pathology, Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center
- Lilong Lan
- Department of Pathology, Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center
- Jingping Yun
- Department of Pathology, Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center
- Dan Xie
- Department of Pathology, Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center
- Wei-Shi Zheng
- School of Computer Science and Engineering, Sun Yat-sen University
- Junhang Luo
- Department of Urology, The First Affiliated Hospital, Sun Yat-Sen University
- Muyan Cai
- Department of Pathology, Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center
- DOI
- https://doi.org/10.1038/s41467-022-30459-5
- Journal volume & issue
-
Vol. 13,
no. 1
pp. 1 – 12
Abstract
Epstein–Barr virus-associated gastric cancer shows a robust response to immune checkpoint inhibitors. Here the authors introduce a deep convolutional neural network and its fusion with pathologists for predicting it from histopathology.
