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

Journal: Nature communications
PMID: 35589792

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
    Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
  • Ruixuan Wang
    School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, P. R. China. wangruix5@mail.sysu.edu.cn.
  • 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, 510060, Guangzhou, China.
  • Yan Sun
    Department of Biochemistry, Albert Einstein College of Medicine, New York, NY, United States.
  • Haohuan Zhang
    School of Computer Science and Engineering, Sun Yat-sen University, 510006, Guangzhou, China.
  • Zihan Zhao
    Department of Urology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Institute of Urology, Nanjing University, 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China.
  • Yuanhang Zheng
    School of Computer Science and Engineering, Sun Yat-sen University, 510006, Guangzhou, China.
  • Jing Luo
    Department of Ophthalmology, The Second Xiangya Hospital of Central South University, 139 Middle Renmin RD, Changsha, Hunan, China.
  • Jiangyu Zhang
    Department of Pathology, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China zhenyuliu@gdut.edu.cn superchina2000@foxmail.com.
  • Hongmei Wu
    Department of Pathology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 510080, Guangzhou, China.
  • Dan Huang
    Department of Anesthesiology, The Second Affiliated Hospital of Soochow University, Suzhou 215004, China.; Department of Anesthesiology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai 200127, China.
  • Wenbiao Zhu
    Department of Pathology, Meizhou People's Hospital, 514011, Meizhou, China.
  • Jianning Chen
    Department of Pathology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Qinghua Cao
    Department of Pathology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
  • Hong Zeng
    School of Computer Science and Technology, Hangzhou Dianzi University, China.
  • 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, 510060, Guangzhou, China.
  • Peng Li
    WuXi AppTec Co, Shanghai, China.
  • 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, 510060, Guangzhou, China.
  • Jingping Yun
    Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou, China.
  • Dan Xie
  • Wei-Shi Zheng
    School of Information Science and Technology, Sun Yat-sen University, Guangzhou 510006, China; Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, China. Electronic address: wszheng@ieee.org.
  • Junhang Luo
    Department of Urology, The First Affiliated Hospital, Sun Yat-Sen University, 510080, Guangzhou, China. luojunh@mail.sysu.edu.cn.
  • Muyan Cai

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