Integrating epigenetic and metabolic indicators for non-invasive endometrial cancer triage: a machine learning approach.

objectiveAbnormal uterine bleeding (AUB) is a primary symptom indicative of endometrial cancer (EC), yet its diagnosis still primarily relies on invasive biopsy. This study aimed to develop and validate a non-invasive, interpretable machine learning (ML) model integrating local epigenetic (exfoliated cell CDO1 methylation) and systemic metabolic indicators for EC triage.

methodsCDO1 was identified as a potential biomarker via bioinformatics. Its diagnostic performance was assessed using quantitative methylation-specific PCR (qMSP) in a clinical cohort of 267 women with AUB. Subsequently, a multimodal ML framework was developed that incorporated logistic regression (LR), support vector machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) algorithms to integrate methylation data with clinical metabolic profiles. Model interpretability was ensured by SHapley Additive exPlanations (SHAP) analysis, with final testing in an internal holdout cohort.

resultsCDO1 hypermethylation was identified as a significant epigenetic alteration associated with metabolic pathways in EC. Within our clinical cohort, CDO1 methylation emerged as a strong independent risk factor, with an odds ratio (OR) of 12.76 and a 95% confidence interval (CI) ranging from 6.31 to 25.80. Using LASSO regression, four critical predictive variables were identified: CDO1 methylation, menopausal status, diabetes and age. After integrating CDO1 methylation and metabolic profiles, the support vector machine model demonstrated stable performance in the validation set, achieving an area under the curve (AUC) of 0.775 (95% CI: 0.669-0.880), with a sensitivity of 0.800 and a specificity of 0.733. SHAP analysis was used to elucidate the contribution of each feature to the predictive model. The finalized SVM-based model's diagnostic utility was further confirmed in an internal holdout cohort, yielding an AUC of 0.904 (95% CI: 0.838-0.969), with a sensitivity of 0.833 and a specificity of 0.905, demonstrating its preliminary clinical reliability. Using our model with a cutoff of 0.647 in the internal holdout cohort (n = 160), 24 patients were triaged for biopsy, with 2 EC cases missed, representing a clinical false-negative rate of 16.7%. The finalized SVM-based predictive model was developed into a free online risk calculator ( https://phw1996.shinyapps.io/EC_risk/ ) for real-time clinical triage.

conclusionsOur SVM-based model offers a non-invasive and highly specific approach for the triage of patients experiencing AUB. By correlating localized molecular changes with systemic metabolic dysregulation, this tool supports personalized risk stratification and clinical triage. It is designed for relative risk stratification, and external recalibration is required for clinical absolute risk estimation.