Predicting recurrence and recurrence-free survival in high-grade endometrial cancer using machine learning.

Journal: Journal of surgical oncology
PMID: 35819161

Abstract

OBJECTIVE: To develop machine-learning models to predict recurrence and time-to-recurrence in high-grade endometrial cancer (HGEC) following surgery and tailored adjuvant treatment.

Authors

  • Sabrina Piedimonte
    Division of Gynecologic Oncology, University of Toronto, Toronto, Ontario, Canada.
  • Tomer Feigenberg
    Trillium Health Partners, Missassauga, Toronto, Ontario, Canada.
  • Erik Drysdale
    The Hospital for Sick Children, Toronto, Canada.
  • Janice Kwon
    Vancouver Coastal Health, Vancouver, British Columbia, Canada.
  • Walter H Gotlieb
  • Beatrice Cormier
    Centre Hospitalier Universitaire de Montreal, Montreal, Quebec, Canada.
  • Marie Plante
    Gynecologic Oncology Division, L'Hôtel-Dieu de Québec, Centre Hospitalier Universitaire de Québec, Laval University, Quebec City, Canada. Electronic address: marie.plante@crhdq.ulaval.ca.
  • Susie Lau
  • Limor Helpman
    Juravinski Cancer Center, Hamilton, Quebec, Canada.
  • Marie-Claude Renaud
    Gynecologic Oncology Division, L'Hôtel-Dieu de Québec, Centre Hospitalier Universitaire de Québec, Laval University, Quebec City, Canada.
  • Taymaa May
    University Health Network, Toronto, Ontario, Canada.
  • Danielle Vicus
    Sunnybrook Health Sciences Center, Toronto, Ontario, Canada.

Keywords

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