Survival outcome prediction in cervical cancer: Cox models vs deep-learning model.

Journal: American journal of obstetrics and gynecology
Published Date: Apr 1, 2019

Abstract

BACKGROUND: Historically, the Cox proportional hazard regression model has been the mainstay for survival analyses in oncologic research. The Cox proportional hazard regression model generally is used based on an assumption of linear association. However, it is likely that, in reality, there are many clinicopathologic features that exhibit a nonlinear association in biomedicine.

Authors

  • Koji Matsuo
    Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of Southern California, Los Angeles, CA; Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA. Electronic address: koji.matsuo@med.usc.edu.
  • Sanjay Purushotham
    University of Southern California, Los Angeles, CA, USA.
  • Bo Jiang
    Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing 210037, China. 111501206@njfu.edu.cn.
  • Rachel S Mandelbaum
    Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of Southern California, Los Angeles, CA.
  • Tsuyoshi Takiuchi
    Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of Southern California, Los Angeles, CA.
  • Yan Liu
    Department of Clinical Microbiology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, 200072, People's Republic of China.
  • Lynda D Roman
    Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of Southern California, Los Angeles, CA; Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA.

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