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

Nat Commun. 2022 May 19;13(1):2790. doi: 10.1038/s41467-022-30459-5.


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

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


  • Immune Checkpoint Inhibitors