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Multicenter Study
. 2023 Jun;161(3):760-768.
doi: 10.1002/ijgo.14639. Epub 2023 Jan 19.

Endometrial Cancer Individualized Scoring System (ECISS): A machine learning-based prediction model of endometrial cancer prognosis

Sherif A Shazly  1 , Pluvio J Coronado  2 , Ercan Yılmaz  3 , Rauf Melekoglu  3 , Hanifi Sahin  3 , Luca Giannella  4 , Andrea Ciavattini  4 , Giovanni Delli Carpini  4 , Jacopo Di Giuseppe  4 , Angel Yordanov  5 , Konstantina Karakadieva  5 , Nevena Milenova Nedelcheva  5 , Mariela Vasileva-Slaveva  5 , Juan Luis Alcazar  6 , Enrique Chacon  6 , Nabil Manzour  6 , Julio Vara  6 , Erbil Karaman  7 , Onur Karaaslan  7 , Latif Hacıoğlu  7 , Duygu Korkmaz  7 , Cem Onal  8 , Jure Knez  9 , Federico Ferrari  10 , Esraa M Hosni  11 , Mohamed E Mahmoud  11 , Gena M Elassall  11 , Mohamed S Abdo  11 , Yasmin I Mohamed  11 , Amr S Abdelbadie  12 ; Middle-Eastern College of Obstetricians and Gynaecologists (MCOG) Muti-Center Studies (MCS) office and Artificial Intelligence Unit (AI)
Affiliations
Multicenter Study

Endometrial Cancer Individualized Scoring System (ECISS): A machine learning-based prediction model of endometrial cancer prognosis

Sherif A Shazly et al. Int J Gynaecol Obstet. 2023 Jun.

Abstract

Objective: To establish a prognostic model for endometrial cancer (EC) that individualizes a risk and management plan per patient and disease characteristics.

Methods: 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).

Results: 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.

Conclusion: The Endometrial Cancer Individualized Scoring System (ECISS) is a novel machine learning tool assessing patient-specific survival probability with high accuracy.

Keywords: Artificial intelligence; Disease-free survival; Overall survival; Uterine cancer.

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References

REFERENCES

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