Objectives: We evaluated radiotherapy planning CT-based radiomics for predicting clinical endpoints [tumor complete response (CR), 5-year overall survival (OS), hypohemoglobin, and leucopenia] after intensity-modulated radiation therapy (IMRT) in locally advanced cervical cancer (LACC). Methods: This study retrospectively collected 257 LACC patients treated with IMRT from 2014 to 2017. Patients were allocated into the training/validation sets (3:1 ratio) using proportional random sampling, resulting in the same proportion of groups in the two sets. We extracted 254 radiomic features from each of the gross target volume (GTV), pelvis, and sacral vertebrae in planning CT images. The sequentially backward elimination support vector machine algorithm was used for feature selection and endpoint prediction. Model performance was evaluated using area under the curve (AUC). Results: A combination of 10 clinicopathological parameters and 34 radiomic features achieved the best performance for predicting CR [validation balanced accuracy: 80.79%]. For OS, 54 radiomic features showed good prediction accuracy [validation balanced accuracy: 85.75%], and the threshold value of their scores can stratify patients into the low-risk and high-risk groups (P<0.001). The clinical and radiomic models were also predictive of hypohemoglobin and severe leucopenia [validation balanced accuracies: 70.96% and 69.93%]. Conclusion: This study demonstrated that combining clinicopathological parameters with CT-based radiomics had good predictive value for treatment outcomes and hematologic toxicities to radiotherapy in LACC. The prediction of clinical endpoints prior to radiotherapy may assist the radiation therapists to select the optimal therapeutic strategy with the minimal toxicity and best curative effect.