Objective:To investigate the value of machine-learning-based radiomics in predicting disease-free survival (DFS) and overall survival (OS) after concurrent chemoradiotherapy in patients with locally advanced cervical cancer.MethodsThree-dimensional radiomics parameters of the primary lesion and its surrounding 5 cm region in T2-weighted imaging (T2WI) sequences of all patients were measured. Six machine learning methods were used to construct the optimal radiomics model and to analyse its incremental value for existing clinical markers.ResultsData of 632 patients with locally advanced cervical cancer who underwent concurrent chemoradiotherapy with continuous follow-up in two centres were retrospectively analysed. And 552 patients from centre 1 served as the training set and 80 patients from centre 2 served as the validation set. In the prediction of DFS, the combined tumor and peritumor randomised survival forest model showed the best predictive efficacy, with 1-year, 3-year and 5-year area under curve (AUC) of 0.955, 0.906, 0.970, and 0.781, 0.885, 0.836 in the training and validation sets, respectively. In the prediction of OS, the combined tumor and peritumor Deepsurv model showed the best predictive efficacy, with 1-year, 3-year and 5-year AUC of 0.977, 0.939, 0.933, and 0.846, 0.875, 0.808 in the training and validation sets, respectively.ConclusionMachine learning-based radiomics model helps to predict DFS and OS after concurrent chemoradiotherapy in cervical cancer patients, and the combination of radiomics and clinical indicators has higher predictive efficacy, which can provide a reliable basis for diagnostic decision-making and prognostic prediction in cervical cancer patients.