The pathological risk score: A new deep learning-based signature for predicting survival in cervical cancer.

Journal: Cancer medicine
Published Date:

Abstract

PURPOSE: To develop and validate a deep learning-based pathological risk score (RS) with an aim of predicting patients' prognosis to investigate the potential association between the information within the whole slide image (WSI) and cervical cancer prognosis.

Authors

  • Chi Chen
    Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, China.
  • Yuye Cao
    Department of Obstetrics and Gynecology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
  • Weili Li
    Department of Obstetrics and Gynecology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
  • Zhenyu Liu
    School of Electronic Information, Hangzhou Dianzi University, Hangzhou 310018, China.
  • Ping Liu
    Department of Cardiology, the Second Hospital of Shandong University, 250033 Jinan, Shandong, China.
  • Xin Tian
    Cancer Hospital Chinese Academy of Medical Sciences (Shenzhen Hospital), Shenzhen, 518000, China. Electronic address: 947952187@qq.com.
  • Caixia Sun
    CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, China; Key Laboratory of Big Data-Based Precision Medicine, Ministry of Industry and Information Technology, Beihang University, Beijing, China.
  • Wuliang Wang
    Department of Obstetrics and Gynecology, The Second Affiliated Hospital of He' nan Medical University, Zhengzhou, China.
  • Han Gao
    Zhejiang Construction Investment Environment Engineering Co, Ltd., Hangzhou, 310013, PR China.
  • Shan Kang
    Department of Gynecology, Fourth Hospital Hebei Medical University, Shijiazhuang, China.
  • Shaoguang Wang
    Department of Gynecology, Yantai Yuhuangding Hospital, Yantai, China.
  • Jingying Jiang
    Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, China.
  • Chunlin Chen
    Department of Obstetrics and Gynecology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
  • Jie Tian
    CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.