A Predictive Model for Endometrial Carcinoma Based on Hysteroscopic Data.

Objective: The purpose is to establish a model to predict endometrial carcinoma and assess its value in the preliminary diagnosis of endometrial carcinoma. Methods: The data of 381 patients undergoing hysteroscopy were incorporated into the model, including 282 cases in the training cohort and 99 cases in the validation cohort. Significant morphological indexes were selected using the chi-square test and subjected to the binary logistic regression analysis. Besides, the scoring interval was set, and the nomogram of the prediction model was established. Model calibration curves were drawn using the data from the validation cohort. The study was approved by the Ethics Committee of the Affiliated Sir Run Run Hospital of Nanjing Medical University, and written informed consent was obtained from the patients. Results: The sensitivity, specificity, positive predictive value, and negative predictive value of the model were 96.7%, 92.3%, 77.3%, and 99.0%, respectively. Analysis of the receiver operating characteristic curve in the training cohort showed an area under the curve of 0.984 (95% CI: 0.974-0.995). The receiver operating characteristic curve in the validation cohort revealed an area under the curve of 0.976 (95% CI: 0.950-1.000). The calibration curve indicated that the probability in the actual setting was consistent with that predicted by the nomogram in the training cohort. Conclusion: Our model has high sensitivity and specificity in predicting endometrial carcinoma, and helps clinicians to make accurate diagnosis.