A predictive model for recurrence in patients with borderline ovarian tumor based on neural multi-task logistic regression.

Journal: BMC cancer
PMID: 39962474

Abstract

BACKGROUND: Effective management of patients with borderline ovarian tumor (BOT) requires the timely identification of those at a higher risk of recurrence. Artificial neural networks have been successfully used in many areas of clinical event prediction, significantly affecting clinical decisions and practice.

Authors

  • Qiulin Ye
    Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, 110004, People's Republic of China.
  • Yue Qi
    Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, 110004, People's Republic of China. qiy1@sj-hospital.org.
  • 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.
  • Xiao Li
    Department of Inner Mongolia Clinical Medicine College, Inner Mongolia Medical University, Hohhot, Inner Mongolia, China.
  • Qian Guo
    State Key Laboratory for Turbulence and Complex Systems and Department of Biomedical Engineering, College of Engineering, Peking University, Beijing 100871, Beijing, China.
  • Danye Zhang
    Department of Obstetrics and Gynaecology, Shengjing Hospital of China Medical University, No. 36 Sanhao Street, Heping District, Shenyang, Liaoning, 110004, China.
  • Bei Lin
    Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, 110004, People's Republic of China. linbei88@hotmail.com.

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