Predicting the prognosis of epithelial ovarian cancer patients based on deep learning models

作者：Zihan Li, Jiao Wang, Yixin Zhang, Zhen Yang, Fanchen Zhou, Xueting Bai, Qian Zhang, Wenchong Zhen, Rongxuan Xu, Wei Wu, Zhihan Yao, Xiaofeng Li, Yiming Yang · 发表于：Frontiers in Oncology · 年份：2025 · DOI：10.3389/fonc.2025.1592746 · 被引用次数：3 · 研究领域：Ovarian cancer diagnosis and treatment、AI in cancer detection、Ferroptosis and cancer prognosis

Background: Epithelial ovarian cancer(EOC) has a higher mortality and morbidity rate than other types, and it has a dramatic impact on the survival of ovarian cancer(OC) patients. Therefore, investigating, developing and validating prognostic models to predict overall survival(OS) in patients with epithelial ovarian cancer represents an area of research with significant clinical implications. Methods: Patients with a confirmed diagnosis of epithelial ovarian cancer from 2010 to 2017 in The Surveillance, Epidemiology, and End Results(SEER) database were identified for enrollment based on inclusion and exclusion criteria(N=10902). Patients with epithelial ovarian cancer diagnosed from 2010 to 2022 were selected from Dalian Municipal Central Hospital as an external validation cohort based on the same criteria (N=116). COX proportional risk regression for screening independent prognostic factors. Survival outcomes were compared between different risk subgroups based on Kaplan-Meier analysis. Three predictive models were developed using machine learning(ML) techniques, and another was a nomogram based on COX proportional risk regression for estimating 3-year and 5-year overall survival in patients with epithelial ovarian cancer. Evaluation of several models based on multiple metrics including C-index, ROC curve, calibration curve and decision curve analysis (DCA). Results: Through univariate and multivariate COX proportional risk regression analyses, we selected 12 significantly i...