Endometrial Cancer Individualized Scoring System (ECISS): A machine learning-based prediction model of endometrial cancer prognosis.
Artificial intelligence
Disease-free survival
Overall survival
Uterine cancer
Journal
International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics
ISSN: 1879-3479
Titre abrégé: Int J Gynaecol Obstet
Pays: United States
ID NLM: 0210174
Informations de publication
Date de publication:
Jun 2023
Jun 2023
Historique:
revised:
02
12
2022
received:
12
09
2022
accepted:
16
12
2022
medline:
17
5
2023
pubmed:
27
12
2022
entrez:
26
12
2022
Statut:
ppublish
Résumé
To establish a prognostic model for endometrial cancer (EC) that individualizes a risk and management plan per patient and disease characteristics. A multicenter retrospective study conducted in nine European gynecologic cancer centers. 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: pretreatment 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). Of 1150 women, 1144 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%. Accuracies of models I, II, and III in predicting 5-year DFS were 74.63%/76.72%, 77.03%/76.72%, and 80.61%/77.78%, respectively. The Endometrial Cancer Individualized Scoring System (ECISS) is a novel machine learning tool assessing patient-specific survival probability with high accuracy.
Types de publication
Multicenter Study
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
760-768Informations de copyright
© 2022 International Federation of Gynecology and Obstetrics.
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