A combined model based on clinical, radiomics, and deep transfer learning features for differentiating endometrial hyperplasia with polyps from endometrial cancer

Abstract

Objective This study aimed to develop an ultrasound-based deep learning radiomics nomogram for differentiating endometrial hyperplasia with polyps from endometrial cancer in endometrial lesions and to explore its clinical diagnostic efficacy. Methods We retrospectively collected clinical data from patients who underwent transvaginal ultrasound examinations in Suzhou Ninth People’s Hospital and Jiangsu Shengze Hospital from January 2020 to October 2024. Various machine learning models were developed using clinical data, handcrafted radiomics features, and deep learning features, with the highest AUC model selected as optimal. Univariate and stepwise multivariate analyses identified significant clinical features, which were combined with deep learning radiomics to create a Combined Model. The model’s predictive performance was evaluated using ROC, calibration, and decision curves, along with a deep learning radiomics nomogram. Results This retrospective study of 340 patients from two hospitals included 149 endometrial cancer cases. Among them, 305 patients were from Suzhou Ninth People’s Hospital and 35 were from Jiangsu Shengze Hospital. The 305 patients from Suzhou Ninth People’s Hospital were divided into a training cohort and an internal validation cohort in a 7:3 ratio, comprising 213 and 92 patients, respectively. The 35 patients from Shengze Hospital served as the external testing cohort. Multivariate analysis confirmed four independent predictors: Testosterone, Estradiol, Abnormal Bleeding, and Menopausal Status, used to build the final Clinical Model. The effectiveness of the models was assessed by comparing the area under the receiver operating characteristic curve (AUC) based on the internal validation cohort. The results showed that the Combined Model achieved an AUC of 0.991 (95% confidence interval [CI]: 0.980 - 1.000) on the training cohort, 0.94 (95% CI: 0.892 - 0.987) on the internal validation cohort, and 0.955 (95% CI: 0.882 - 1.000) on the external testing cohort. Calibration curves and DeLong test confirmed the Combined Model’s superior accuracy and performance over single-modality models, with DCA showing highest net benefit, and nomogram enabling individualized risk stratification. Conclusion The ultrasound-based combined model has high clinical diagnostic value for diagnosing endometrial cancer and simple endometrial hyperplasia with polyps.