A prognostic system for epithelial ovarian carcinomas using machine learning

作者：Philip M. Grimley, Zhenqiu Liu, Kathleen M. Darcy, Matthew T. Hueman, Huan Wang, Sheng Li, Donald E. Henson, Dechang Chen · 发表于：Acta Obstetricia Et Gynecologica Scandinavica · 年份：2021 · DOI：10.1111/aogs.14137 · 被引用次数：20 · 研究领域：AI in cancer detection、Ovarian cancer diagnosis and treatment、Ferroptosis and cancer prognosis

Abstract Introduction Integrating 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 methods Cancer‐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. Results Dataset 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 remain...