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

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.

SUBMITTER: Paik ES

PROVIDER: S-EPMC6543110 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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

Paik E Sun ES Lee Jeong Won JW Park Jeong Yeol JY Kim Ju Hyun JH Kim Mijung M Kim Tae Joong TJ Choi Chel Hun CH Kim Byoung Gie BG Bae Duk Soo DS Seo Sung Wook SW

Journal of gynecologic oncology 20190311 4

<h4>Objectives</h4>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.<h4>Methods</h4>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 ...[more]

PMID: 31074247

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Project description:IntroductionIntegrating additional factors into the International Federation of Gynecology and Obstetrics (FIGO) staging system is needed for accurate patient classification and survival prediction. In this study, we tested machine learning as a novel tool for incorporating additional prognostic parameters into the conventional FIGO staging system for stratifying patients with epithelial ovarian carcinomas and evaluating their survival.Material and methodsCancer-specific survival data for epithelial ovarian carcinomas were extracted from the Surveillance, Epidemiology, and End Results (SEER) program. Two datasets were constructed based upon the year of diagnosis. Dataset 1 (39 514 cases) was limited to primary tumor (T), regional lymph nodes (N) and distant metastasis (M). Dataset 2 (25 291 cases) included additional parameters of age at diagnosis (A) and histologic type and grade (H). The Ensemble Algorithm for Clustering Cancer Data (EACCD) was applied to generate prognostic groups with depiction in dendrograms. C-indices provided dendrogram cutoffs and comparisons of prediction accuracy.ResultsDataset 1 was stratified into nine epithelial ovarian carcinoma prognostic groups, contrasting with 10 groups from FIGO methodology. The EACCD grouping had a slightly higher accuracy in survival prediction than FIGO staging (C-index = 0.7391 vs 0.7371, increase in C-index = 0.0020, 95% confidence interval [CI] 0.0012-0.0027, p = 1.8 × 10-7 ). Nevertheless, there remained a strong inter-system association between EACCD and FIGO (rank correlation = 0.9480, p = 6.1 × 10-15 ). Analysis of Dataset 2 demonstrated that A and H could be smoothly integrated with the T, N and M criteria. Survival data were stratified into nine prognostic groups with an even higher prediction accuracy (C-index = 0.7605) than when using only T, N and M.ConclusionsEACCD was successfully applied to integrate A and H with T, N and M for stratification and survival prediction of epithelial ovarian carcinoma patients. Additional factors could be advantageously incorporated to test the prognostic impact of emerging diagnostic or therapeutic advances.

Dataset Information

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

Publications

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

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