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Integrated Signature of Multiple Indices for Preoperative Predictive Stratification of Endometrial Hyperplasia
Rong Yang1, Yong Tian1, Yu Xiang1
1Department of Obstetrics and Gynecology, Central Hospital of Enshi Tujia and Miao Autonomous Prefecture, Enshi Clinical College of Wuhan University, Enshi, Hubei, People's Republic of China.
Objective:
To develop a comprehensive predictive model incorporating multi-dimensional parameters for the accurate preoperative stratification of endometrial hyperplasia subtypes.
Methods:
A retrospective cohort study was conducted involving 1822 patients with endometrial lesions. We gathered candidate variables spanning demographic, reproductive, hematological, hepatic and renal, coagulation, ultrasonographic, and pathological domains. Utilizing a two-step feature selection approach (variance thresholding combined with lasso regression), we constructed and compared three logistic regression models.
Results:
Sixteen key features were identified, encompassing pathological, clinical, and inflammatory indicators. Especially, the integrated model (Model 3) demonstrated the highest diagnostic efficacy, with an AUC of 0.923 (training set: sensitivity 87.2%, specificity 85.6%) and 0.905 (validation set: sensitivity 84.5%, specificity 83.2%), significantly outperforming the single-domain model.
Conclusion:
The predictive model, developed based on multidimensional clinical indicators, effectively categorized the preoperative subtypes of endometrial hyperplasia, thereby offering a practical tool for clinical decision-making and underscoring the pivotal role of inflammation in the progression of pathological lesions.