A risk prediction model for endometrial hyperplasia/endometrial carcinoma in premenopausal women - Zhen Li; Juan Yin; Yu Liu; Fanqing Zeng (Scientific Reports)
Abstract This study investigated the risk factors for endometrial hyperplasia (EH) and endometrial carcinoma (EC) in premenopausal women. The goal was to establish a nomogram model to predict the risk of EH/EC and quantitative standards in clinical practice, which improved the clinical prognosis of EH/EC patients. Data were collected from premenopausal women with suspected EH/EC who underwent hysteroscopic endometrial biopsy. Patients (n = 1541) were divided into training and validation groups at a 3:1 ratio. Univariable and multivariable logistic regression analyses were conducted to identify risk factors for EH/EC and establish a predictive model. The model’s discrimination was evaluated using the area under the receiver operating characteristic curve (AUC), its calibration was assessed using calibration plots, and its clinical effectiveness was evaluated using decision curve analysis (DCA). The optimal score and probability cutoff values were determined to differentiate between low and high-risk populations, guiding clinical medical practice. BMI, age at menarche, intrauterine device (IUD), diabetes, polycystic ovary syndrome (PCOS), endometrial thickness (ET), and uterine cavity fluid were identified as independent risk factors for EH/EC and were incorporated into the predictive nomogram model. The model demonstrated good discrimination with AUCs of 0.845 and 0.905 in the training and validation sets, respectively. The calibration plots and DCA showed excellent model calibration and clinical effectiveness. EH/EC is significantly associated with BMI, age at menarche, IUD use, diabetes, PCOS, ET, and uterine cavity fluid. The nomogram model can be used to predict the risk of EH/EC in premenopausal women and facilitate rapid screening.