Omer Faruk BAYRAKTAR, Erhan Huseyin COMERT, Ümran Karabulut DOĞAN, Özlem Karabay AKGÜL
Eastern Journal of Medicine (EJM) - 2026;31(4):644-651
Endometrial polyps are a common structural cause of abnormal uterine bleeding (AUB) in reproductive-aged women. However, managing cases with equivocal ultrasound findings remains a clinical challenge. This prospective study evaluated whether clinical bleeding characteristics, combined with endometrial thickness measured by transvaginal ultrasonography, improve risk stratification for difficult-to-diagnose endometrial polyps. This prospective observational study included 1,073 reproductive-aged women presenting with AUB who underwent endometrial sampling. To focus on diagnostically challenging cases, patients with unequivocally identified intrauterine space-occupying lesions on ultrasonography were excluded. Based on histopathology, patients were classified as having an endometrial polyp (n=453) or benign endometrium (n=620). Clinical characteristics, bleeding patterns, oral contraceptive (OCP) use, and ultrasonographic findings were recorded. Multivariable logistic regression adjusted for age, BMI, parity, and cycle phase identified independent variables associated with polyps. ROC analysis assessed diagnostic performance. Endometrial thickness was higher in patients with polyps (10.56+/-4.63 vs 9.28+/-4.00 mm, p < 0.001) and remained an independent predictor (OR 1.071, 95% CI 1.038-1.106, p < 0.001). Premenstrual bleeding showed a strong positive association (OR 3.572, p < 0.001), whereas postmenstrual bleeding (OR 0.506, p < 0.001) and OCP use (OR 0.757, p=0.045) were inversely associated. Endometrial thickness alone demonstrated limited performance (AUC =0.582). However, the combined multivariable clinical model yielded fair discrimination (AUC=0.686) with higher specificity (77.4%). Endometrial thickness and specific bleeding patterns are independent variables associated with endometrial polyps. While the standalone diagnostic value of ultrasound is limited in equivocal cases, integrating clinical bleeding characteristics improves risk stratification to guide clinical decisions.
