J Gynecol Oncol.  2019 Jul;30(4):e65. 10.3802/jgo.2019.30.e65.

Prediction of survival outcomes in patients with epithelial ovarian cancer using machine learning methods

Affiliations
  • 1Department of Obstetrics and Gynecology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
  • 2Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University School of Medicine, Seoul, Korea. sungwseo@skku.edu
  • 3Department of Obstetrics and Gynecology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
  • 4Department of Electronics and Information System, Ghent University, Ghent, Belgium.
  • 5Department of Orthopedic Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.

Abstract


OBJECTIVES
The aim of this study was to develop a new prognostic classification for epithelial ovarian cancer (EOC) patients using gradient boosting (GB) and to compare the accuracy of the prognostic model with the conventional statistical method.
METHODS
Information of EOC patients from Samsung Medical Center (training cohort, n=1,128) was analyzed to optimize the prognostic model using GB. The performance of the final model was externally validated with patient information from Asan Medical Center (validation cohort, n=229). The area under the curve (AUC) by the GB model was compared to that of the conventional Cox proportional hazard regression analysis (CoxPHR) model.
RESULTS
In the training cohort, the AUC of the GB model for predicting second year overall survival (OS), with the highest target value, was 0.830 (95% confidence interval [CI]=0.802-0.853). In the validation cohort, the GB model also showed high AUC of 0.843 (95% CI=0.833-0.853). In comparison, the conventional CoxPHR method showed lower AUC (0.668 (95% CI=0.617-0.719) for the training cohort and 0.597 (95% CI=0.474-0.719) for the validation cohort) compared to GB. New classification according to survival probability scores of the GB model identified four distinct prognostic subgroups that showed more discriminately classified prediction than the International Federation of Gynecology and Obstetrics staging system.
CONCLUSION
Our novel GB-guided classification accurately identified the prognostic subgroups of patients with EOC and showed higher accuracy than the conventional method. This approach would be useful for accurate estimation of individual outcomes of EOC patients.

Keyword

Machine Learning; CA-125 Antigen; Ovarian Neoplasms; Prognosis; Survival

MeSH Terms

Area Under Curve
CA-125 Antigen
Chungcheongnam-do
Classification
Cohort Studies
Gynecology
Humans
Machine Learning*
Methods*
Obstetrics
Ovarian Neoplasms*
Prognosis
CA-125 Antigen
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