A deep learning model and human-machine fusion for prediction of EBV-associated gastric cancer from histopathology.

Abstract

Epstein-Barr virus-associated gastric cancer (EBVaGC) shows a robust response to immune checkpoint inhibitors. Therefore, a cost-efficient and accessible tool is needed for discriminating EBV status in patients with gastric cancer. Here we introduce a deep convolutional neural network called EBVNet and its fusion with pathologists for predicting EBVaGC from histopathology. The EBVNet yields an averaged area under the receiver operating curve (AUROC) of 0.969 from the internal cross validation, an AUROC of 0.941 on an external dataset from multiple institutes and an AUROC of 0.895 on The Cancer Genome Atlas dataset. The human-machine fusion significantly improves the diagnostic performance of both the EBVNet and the pathologist. This finding suggests that our EBVNet could provide an innovative approach for the identification of EBVaGC and may help effectively select patients with gastric cancer for immunotherapy.

• Authors: 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
• Journal: Nature communications (Nat Commun) Vol. 13 Issue 1 Pg. 2790 (05 19 2022) ISSN: 2041-1723 [Electronic] England
• PMID: 35589792 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)
• Copyright: © 2022. The Author(s).

Chemical References

• Immune Checkpoint Inhibitors

Topics

• Deep Learning
• Epstein-Barr Virus Infections
• Herpesvirus 4, Human (genetics)
• Humans
• Immune Checkpoint Inhibitors
• Stomach Neoplasms (genetics, pathology)