Comparison of Three Clinical Models for Estimating the Risk of Endometrial Cancer in Post-Menopausal Women with Abnormal Vaginal Bleeding

Given the prevalence of abnormal post-menopausal bleeding and the importance of its early examination for ensuring the timely diagnosis of any malignancies, the present study was conducted to investigate uterine pathologies in relation to post-menopausal bleeding patterns and endometrial thickness as observed in transvaginal ultrasounds and to offer models for predicting endometrial cancer.
The present descriptive-analytical cross sectional study was conducted on 112 post-menopausal women presenting to Ayatollah Rouhani Hospital in Babol, Iran. The patients underwent a transvaginal ultrasound and hysteroscopy and their samples were sent for pathological examination. The logistic regression model and the receiver operating characteristic (ROC) curve were used. This study presents 3 models for predicting endometrial cancer, including AM30 [which considers the subject’s amount of bleeding, Menopause age and BMI (Body Mass Index) for assessing her risk of endometrial cancer], AMD30 (which additionally considers the subject’s history of diabetes) and AMDI30 (which additionally considers the subject’s history of internal diseases).
Menopause age, amount of bleeding, BMI, and history of internal diseases were significantly linked to endometrial cancer in post-menopausal women with abnormal bleeding; that is, the variables were higher in this group than in those without cancer (P = 0.007, P = 0.004, P = 0.001, and P = 0.02). The three models defined, i.e. AM30, AMD30, and AMI30 had a high area under the ROC curve and could predict endometrial cancer with a proper sensitivity and specificity in post-menopausal women with vaginal bleeding. There were no statistically significant differences among these models, although the AMI30 model had a higher area under the ROC curve compared to the other two models (P = 0.29).
The present study recommends these three predictive models as alternatives for predicting endometrial cancer in post-menopausal women with vaginal bleeding.