Machine learning-based prognosis prediction for serous ovarian cancer using the SEER database and data from a single center in China.

ABSTRACT:

Background

Ovarian cancer, particularly serous ovarian cancer, is the leading cause of death among gynecological malignancies. Despite advances in treatment, prognosis remains poor due to the tumor's heterogeneity and the frequent late-stage diagnosis, making survival a critical concern for patients. However, there is a lack of accurate clinical prognostic models to guide treatment decisions. Therefore, this study aimed to develop and validate a robust prognostic model for serous ovarian cancer using machine learning.

Methods

Data for this study were obtained from the Surveillance, Epidemiology, and End Results (SEER) database (2010-2021) and Yancheng Dafeng People's Hospital (2012-2020). We used univariate and multivariate Cox regression analyses to identify independent ris

SUBMITTER: Chen H 

PROVIDER: S-EPMC12432648 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature