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Objective: Ovarian cancer is one of the most common causes of death in gynecological tumors, and its most common type is epithelial ovarian cancer (EOC). This study aimed to establish a radiomics signature based on ultrasound images to predict the histopathological types of EOC. Methods: Overall, 265 patients with EOC who underwent preoperative ultrasonography and surgery were eligible. They were randomly sorted into two cohorts (training cohort: test cohort = 7:3). We outlined the region of interest of the tumor on the ultrasound images of the lesion. Then, the radiomics features were extracted. Clinical, Rad-score and combined models were constructed based on the least absolute shrinkage, selection operator, and logistic regression analysis. The performance of the models was evaluated using receiver operating characteristic curves and decision curve analysis (DCA). A nomogram was formulated based on the combined prediction model. Results: The combined model had good performance in predicting EOC histopathological types, with an AUC of 0.83 (95% CI: 0.77–0.90) and 0.82 (95% CI: 0.71–0.93) in the training and test cohorts, respectively. The calibration curves showed that the nomogram estimation was consistent with the actual observations. DCA also verified the clinical value of the combined model. Conclusions: The combined model containing clinical and ultrasound radiomics features showed an excellent performance in predicting type I and II EOC. Advances in knowledge: This study presents the first application of ultrasound radiomics features to distinguish EOC histopathological types. The proposed clinical-radiomics nomogram could help gynecologists noninvasively identify EOC types before surgery.

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