Integrating multi-omics and clinical features to model survival in epithelial ovarian cancer subtypes.

ABSTRACT: Epithelial ovarian cancer (EOC) exhibits significant heterogeneity in clinical outcomes, influenced by histology, age, stage, and molecular characteristics. This study aimed to develop and validate a comprehensive model integrating demographic, clinical, and molecular data from The Cancer Genome Atlas (TCGA) to predict two-year survival outcomes in EOC. The cohort included 2,427 patients with Endometrioid Adenocarcinoma (EA)s and Serous Cystadenocarcinoma (SC) , of whom 1,011 had gene data. Machine learning models, including Logistic Regression, Gradient Boosting Classifier (GBC), Support Vector Machines (SVM), and Random Forest, were trained and evaluated for predictive performance. SVM provided the optimal balance of mortality-class detection and overall performance. While GBC achieved t

SUBMITTER: Eldesouki RE 

PROVIDER: S-EPMC12722313 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature