Purpose: Our study aimed to develop a model to predict distant recurrence in locally advanced cervical cancer, which can be used to select high-risk patients in enriched clinical trials. Patients and Methods: Our study was a retrospective analysis of a multi-institutional cohort of patients treated between 2001 and 2009. According to the order of data submission, data from three institutions were allocated to a model development cohort (n = 434), and data from the remaining two institutions were allocated to an external validation cohort (n = 115). Patient information including [ 18F] fluorodeoxyglucose positron emission tomography (FDG-PET) data and clinical outcome was modeled using competing risk regression analysis to predict 5-year cumulative incidence of distant recurrence. Results: The competing risk analysis revealed that the following four parameters were significantly associated with distant recurrence: pelvic and para-aortic nodal positivity on FDG-PET, nonsquamous cell histology, and pretreatment serum squamous cell carcinoma antigen levels. This four-parameter model showed good discrimination and calibration, with a bootstrap-adjusted concordance index of 0.70. Also, the validation set showed good discrimination with a bootstrap-adjusted concordance index of 0.73. A user-friendly Web-based nomogram predicting 5-year probability of distant recurrence was developed. Conclusion: We have developed a robust model to predict the risk of distant recurrence in patients with locally advanced cervical cancer. Further, we discussed how the selective enrichment of the patient population could facilitate clinical trials of systemic chemotherapy in locally advanced cervical cancer.