Self‐Sampling for CDO1 / CELF4 Methylation Accurately Identifies Endometrial Cancer in a Resource‐Limited Region: A Prospective Study From Gansu

ABSTRACT

Timely diagnosis and risk stratification of endometrial cancer (EC) remain difficult in resource‐limited regions. This prospective diagnostic accuracy and feasibility study evaluated whether self‐collected cervical specimens combined with CDO1/CELF4 methylation testing, defined as the CISENDO assay, could identify histologically confirmed EC in symptomatic women from Gansu, northwest China. Of 302 screened women, 154 participants with self‐collected specimens and histopathological diagnoses formed the primary analytic cohort; 35 had EC and 119 had non‐EC diagnoses. An exploratory paired subset included 35 participants with both self‐ and physician‐collected specimens. Diagnostic performance was assessed using receiver operating characteristic analysis and logistic regression, while paired sampling was evaluated using correlation, Bland–Altman analysis, and classification concordance. In self‐collected specimens, CISENDO showed strong discrimination for EC at manufacturer‐defined cutoffs (AUC 0.834). Bland–Altman mean differences were −1.05 ΔCt for CDO1 and −1.73 ΔCt for CELF4. CISENDO classifications were concordant in 31/35 paired specimens (88.6%), with positive agreement of 84.6%, negative agreement of 90.9%, and Cohen's κ of 0.76. CISENDO positivity remained the strongest independent predictor of EC after adjustment (adjusted OR 85.08; 95% CI 12.21–592.71; p  < 0.001). Endometrial thickness showed modest discrimination (AUC 0.646) and did not improve performance when combined with CISENDO. Self‐collected CISENDO testing showed strong discrimination for histologically confirmed EC and may provide a feasible molecular triage and risk‐stratification approach for symptomatic women in underserved settings. This approach could support decentralized sample collection and reduce dependence on immediate specialist‐based evaluation, particularly in geographically dispersed, resource‐constrained populations.