Random forest-based model for the recurrence prediction of borderline ovarian tumor: clinical development and validation.

Journal: Journal of cancer research and clinical oncology
PMID: 40349260

Abstract

PURPOSE: This study aims to develop an effective machine learning (ML)-based predictive model for the recurrence of borderline ovarian tumor (BOT), and provide the guidelines of accurate clinical diagnosis and precise treatment for patients.

Authors

  • Liheng Yan
    College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning, China.
  • Qiulin Ye
    Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, 110004, People's Republic of China.
  • Baole Shi
    College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning, China.
  • Juanjuan Liu
    Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, 110004, People's Republic of China.
  • Yuexin Hu
    Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, 110004, People's Republic of China.
  • Ouxuan Liu
    Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, 110004, People's Republic of China.
  • Xiao Li
    Department of Inner Mongolia Clinical Medicine College, Inner Mongolia Medical University, Hohhot, Inner Mongolia, China.
  • Bei Lin
    Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, 110004, People's Republic of China. linbei88@hotmail.com.
  • Yue Qi
    Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, 110004, People's Republic of China. qiy1@sj-hospital.org.

Keywords

External Resources

Popular Topics
Recent Journals