Rapid identification of endometrial hyperplasia and endometrial endometrioid cancer in young women

PURPOSE: We investigated endometrial hyperplasia (EH) and endometrial endometrioid cancer (EEC) and developed a nomogram model to predict the EH/EEC risk and improve patients’ clinical prognosis. METHODS: Data were collected from young females (age: ≤ 40 years) who complained of abnormal uterine bleeding (AUB) or abnormal ultrasound endometrial echoes. The patients were randomly divided into training and validation cohorts at a 7:3 ratio. The risk factors for EH/EEC were determined through the optimal subset regression analysis and a prediction model was developed. We used the concordance-index (C-index), and calibration plots in the training and validation sets to assess the prediction model. We drew the ROC curve in the validation set and calculated the area under the curve (AUC), as well as its accuracy, sensitivity, specificity, negative predictive value, and positive predictive value, and finally, converted the nomogram into a web page dynamic nomogram. RESULTS: Predictors included in the nomogram model were body mass index (BMI), polycystic ovary syndrome (PCOS), anemia, infertility, menostaxis, AUB type, and endometrial thickness. The C-index of the model in the training and validation sets were 0.863 and 0.858. The nomogram model had good discriminatory power and was well-calibrated. According to the prediction model, the AUC of EH/EC, EH without atypia, and AH/EC were 0.889, 0.867, and 0.956, respectively. CONCLUSIONS: The nomogram of EH/EC is significantly associated with risk factors, namely BMI, PCOS, anemia, infertility, menostaxis, AUB type, and endometrial thickness. The nomogram model can be used to predict the EH/EC risk and rapidly screen risk factors in a women population with high risk.