4.8 Article

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

Journal

NATURE COMMUNICATIONS
Volume 13, Issue 1, Pages -

Publisher

NATURE PORTFOLIO
DOI: 10.1038/s41467-022-30459-5

Keywords

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Funding

  1. National Key R&D Program of China [2017YFC1309000]
  2. National Natural Science Foundation of China [81730072, 81672407, 81872001, 82172646, 62071502, U21A20471, U1811461]
  3. Guangdong Key Research and Development Program [2020B1111190001]
  4. Natural Science Foundation of Guangdong Province [2020B1515120085]
  5. Key-Area Research and Development Program of Guangzhou [202007030004]
  6. Tianjin Medical University Cancer Institute and Hospital, Fudan University Shanghai Cancer Center, Guangdong Provincial People's Hospital
  7. Affiliated Cancer Hospital & Institute of Guangzhou Medical University

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This study introduces a deep convolutional neural network called EBVNet and its fusion with pathologists for predicting Epstein-Barr virus-associated gastric cancer (EBVaGC) from histopathology. EBVNet shows high accuracy in discriminating EBVaGC and the human-machine fusion significantly improves diagnostic performance.
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. 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.

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