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Purpose: To develop and validate a radiomics nomogram for predicting early recurrence in high-grade serous ovarian cancer (HGSOC) patients. Materials and Methods: From May 2008 to December 2019, 256 eligible HGSOC patients were enrolled and divided into training (n=179) and test cohorts (n=77) in a 7:3 ratio. A radiomics signature (Radscore) was selected by using recursive feature elimination based on support vector machine (SVM-RFE) and building a radiomics model for recurrence prediction. Independent clinical risk factors were generated by uni variable and multivariable Cox regression analyses. A combined model was developed based on the Radscore and independent clinical risk factors and presented as a radiomics nomogram. Its performance was assessed by the AUC, Kaplan-Meier survival analysis and Decision curve analysis. Results: Seven radiomics features were selected. The radiomics model yielded AUCs of 0.715 (95% CI: 0.640, 0.790) and 0.717 (95% CI: 0.600, 0.834) in the training and test cohorts. The clinical model (FIGO stage and residual disease) yielded AUCs of 0.632 and 0.691 in the training and test cohorts. The combined model demonstrated AUCs of 0.749 (95% CI: 0.678, 0.821) and 0.769 (95% CI: 0.662, 0.877) in the training and test cohorts. In the combined model , PFS was significantly shorter in the high-risk group than in the low-risk group (P