Characterizing impact of positive lymph node number in endometrial cancer using machine-learning: A better prognostic indicator than FIGO staging?

Abstract

Authors

• Colton Ladbury
  Department of Radiation Oncology, City of Hope National Medical Center, Duarte, CA, USA.
• Richard Li
  Department of Radiation Oncology, City of Hope National Medical Center, Duarte, CA, USA.
• Jay Shiao
  Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, USA.
• Jason Liu
  Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America.
• Mihaela Cristea
  Department of Medical Oncology, City of Hope National Medical Center, Duarte, CA, USA.
• Ernest Han
  Department of Gynecologic Oncology, City of Hope National Medical Center, Duarte, CA, USA.
• Thanh Dellinger
  Department of Gynecologic Oncology, City of Hope National Medical Center, Duarte, CA, USA.
• Stephen Lee
  Department of Gynecologic Oncology, City of Hope National Medical Center, Duarte, CA, USA.
• Edward Wang
  Department of Medical Oncology, City of Hope National Medical Center, Duarte, CA, USA.
• Christine Fisher
  Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, USA.
• Yi-Jen Chen
  Department of Obstetrics and Gynecology, Taipei Veterans General Hospital, Taipei, Taiwan; School of Medicine, National Yang-Ming University, Taipei, Taiwan. Electronic address: chenyj@vghtpe.gov.tw.
• Arya Amini
  Department of Radiation Oncology, City of Hope National Medical Center, Duarte, CA, USA.
• Tyler Robin
  Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, USA.
• Scott Glaser
  Department of Radiation Oncology, City of Hope National Medical Center, Duarte, CA, USA. Electronic address: sglaser@coh.org.

Keywords

External Resources