Files and links (1)
Published, Version of Record (VoR)CC BY-NC V4.0, Open
Journal article
A prognostic system for epithelial ovarian carcinomas using machine learning
Acta obstetricia et gynecologica Scandinavica, v 100(8), pp 1511-1519
01 Aug 2021
PMID: 33665831
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Introduction Integrating additional factors into the International Federation of Gynaecology 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 x 10(-7)). Nevertheless, there remained a strong inter-system association between EACCD and FIGO (rank correlation = 0.9480, p = 6.1 x 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.
Conclusions EACCD 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.
Metrics
Details
- Title
- A prognostic system for epithelial ovarian carcinomas using machine learning
- Creators
- Philip M. Grimley - Uniformed Services University of the Health SciencesZhenqiu Liu - Penn State Cancer InstituteKathleen M. Darcy - Uniformed Services University of the Health SciencesMatthew T. Hueman - Walter Reed National Military Medical CenterHuan Wang - George Washington UniversityLi Sheng - Drexel UniversityDonald E. Henson - Uniformed Services University of the Health SciencesDechang Chen - Uniformed Services University of the Health Sciences
- Publication Details
- Acta obstetricia et gynecologica Scandinavica, v 100(8), pp 1511-1519
- Publisher
- Wiley
- Number of pages
- 9
- Grant note
- grant "Creating Prognostic Systems for Cancer" - John P. Murtha Cancer Center Research Program grant "Four Diamonds Fund from Penn State University" - Penn State University grant "Using Dendrograms to Create Prognostic Systems for Cancer" - John P. Murtha Cancer Center Research Program
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Mathematics
- Web of Science ID
- WOS:000630050500001
- Scopus ID
- 2-s2.0-85102643778
- Other Identifier
- 991019169653204721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Obstetrics & Gynecology