In International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics
OBJECTIVE : Establishing a prognostic model for endometrial cancer (EC), that individualizes risk and management plan per patient and disease characteristics.
METHODS : this is multicentre retrospective study conducted in 9 European gynaecologic cancer centres. Women with confirmed EC between January 2008 to December 2015 were included. Demographics, disease characteristics, management, and follow-up information were collected. Cancer-specific survival (CSS) and disease-free survival (DFS) at 3 and 5 years comprise the primary outcomes of the study. Machine learning algorithms were applied to patient and disease characteristics. Model I: pre-treatment model. Calculated probability was added to management variables (model II: treatment model), and the second calculated probability was added to perioperative and postoperative variables (model III).
RESULTS : Out of 1,150 women, 1,144 were eligible for 3-year survival analysis and 860 for 5-year survival analysis. Model I, II, and III accuracies of prediction of 5-year CSS were 84.88%/85.47% (in train and test sets), 85.47%/84.88% and 87.35%/86.05%, respectively. Model I predicted 3-year CSS at an accuracy of 91.34%/87.02%. Accuracy of model I, II and III in predicting 5-year DFS were 74.63%/76.72%, 77.03%/76.72%, and 80.61%/77.78%, respectively.
CONCLUSION : Endometrial Cancer Individualised Scoring System (ECISS) is a novel machine learning tool assessing patient-specific survival probability with high accuracy.
Shazly Sherif A, Coronado Pluvio J, Yılmaz Ercan, Melekoglu Rauf, Sahin Hanifi, Giannella Luca, Ciavattini Andrea, Carpini Giovanni Delli, Di Giuseppe Jacopo, Yordanov Angel, Karakadieva Konstantina, Nedelcheva Nevena Milenova, Vasileva-Slaveva Mariela, Alcazar Juan Luis, Chacon Enrique, Manzour Nabil, Vara Julio, Karaman Erbil, Karaaslan Onur, Hacıoğlu Latif, Korkmaz Duygu, Onal Cem, Knez Jure, Ferrari Federico, Hosni Esraa M, Mahmoud Mohamed E, Elassall Gena M, Abdo Mohamed S, Mohamed Yasmin I, Abdelbadie Amr S
2022-Dec-26
artificial intelligence, disease-free survival, overall survival, uterine cancer
