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

Author

Listed:
  • Xueyi Zheng

    (Sun Yat-sen University Cancer Center)

  • Ruixuan Wang

    (Sun Yat-sen University)

  • Xinke Zhang

    (Sun Yat-sen University Cancer Center)

  • Yan Sun

    (Tianjin Medical University Cancer Institute and Hospital)

  • Haohuan Zhang

    (Sun Yat-sen University)

  • Zihan Zhao

    (Sun Yat-sen University Cancer Center)

  • Yuanhang Zheng

    (Sun Yat-sen University)

  • Jing Luo

    (Sun Yat-sen University)

  • Jiangyu Zhang

    (Affiliated Cancer Hospital & Institute of Guangzhou Medical University)

  • Hongmei Wu

    (Guangdong Academy of Medical Sciences)

  • Dan Huang

    (Fudan University Shanghai Cancer Center)

  • Wenbiao Zhu

    (Meizhou People’s Hospital)

  • Jianning Chen

    (Sun Yat-sen University)

  • Qinghua Cao

    (Sun Yat-sen University)

  • Hong Zeng

    (Sun Yat-Sen University)

  • Rongzhen Luo

    (Sun Yat-sen University Cancer Center)

  • Peng Li

    (Sun Yat-sen University Cancer Center)

  • Lilong Lan

    (Sun Yat-sen University Cancer Center)

  • Jingping Yun

    (Sun Yat-sen University Cancer Center)

  • Dan Xie

    (Sun Yat-sen University Cancer Center)

  • Wei-Shi Zheng

    (Sun Yat-sen University)

  • Junhang Luo

    (Sun Yat-Sen University)

  • Muyan Cai

    (Sun Yat-sen University Cancer Center)

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.

Suggested Citation

  • 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 , 2022. "A deep learning model and human-machine fusion for prediction of EBV-associated gastric cancer from histopathology," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-30459-5
    DOI: 10.1038/s41467-022-30459-5
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    References listed on IDEAS

    as
    1. Nikhil Naik & Ali Madani & Andre Esteva & Nitish Shirish Keskar & Michael F. Press & Daniel Ruderman & David B. Agus & Richard Socher, 2020. "Deep learning-enabled breast cancer hormonal receptor status determination from base-level H&E stains," Nature Communications, Nature, vol. 11(1), pages 1-8, December.
    2. Wenying Zhou & Yang Yang & Cheng Yu & Juxian Liu & Xingxing Duan & Zongjie Weng & Dan Chen & Qianhong Liang & Qin Fang & Jiaojiao Zhou & Hao Ju & Zhenhua Luo & Weihao Guo & Xiaoyan Ma & Xiaoyan Xie & , 2021. "Ensembled deep learning model outperforms human experts in diagnosing biliary atresia from sonographic gallbladder images," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
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    Cited by:

    1. Darui Jin & Shangying Liang & Artem Shmatko & Alexander Arnold & David Horst & Thomas G. P. Grünewald & Moritz Gerstung & Xiangzhi Bai, 2024. "Teacher-student collaborated multiple instance learning for pan-cancer PDL1 expression prediction from histopathology slides," Nature Communications, Nature, vol. 15(1), pages 1-14, December.

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