AI-assisted Radiomic Model for Cervical Cancer Recurrence Prediction: A Multicenter Retrospective Study with...

Purpose To evaluate the value of a nomogram model incorporating clinical parameters, hematologic inflammatory biomarkers, and MRI radiomic features in predicting postoperative disease-free survival (DFS) in patients with cervical cancer and to explore the biologic mechanism underlying the radiomic signature. Materials and Methods This multicenter retrospective study enrolled 804 patients with cervical cancer (2016-2023), with a median follow-up of 43.7 months. Three-dimensional radiomic features were extracted from pretreatment MRI tumor and 5-mm peritumoral regions. Five machine learning algorithms were tested to construct the optimal radiomics score (Radscore). Multivariable Cox regression was used to develop the nomogram model, with bioinformatic analysis and in vitro experiments for biologic mechanism validation. Results In the clinical prediction cohort (n = 751; median age, 52 years [IQR, 47-58 years]), 164 patients (21.8%) experienced recurrence during follow-up. The random survival forest-based radiomics model achieved optimal predictive performance in the external test set, with 1-, 3-, and 5-year DFS areas under the receiver operating characteristic curves (AUCs) of 0.88 (95% CI: 0.83, 0.94), 0.82 (95% CI: 0.75, 0.90), and 0.89 (95% CI: 0.83, 0.95), respectively. International Federation of Gynecology and Obstetrics stage, squamous cell carcinoma antigen level, systemic inflammation response index, and Radscore were identified as independent prognostic factors for DFS, which were incorporated into the nomogram model, with 1-, 3-, and 5-year DFS AUCs of 0.93 (95% CI: 0.90, 0.97), 0.84 (95% CI: 0.78, 0.90), and 0.86 (95% CI: 0.80, 0.92) in the external test set. A radiomic signature-TRIM29-cell cycle regulatory axis was identified and validated via transcriptomic analysis and in vitro experiments. Conclusion The integrated clinical-radiomic nomogram developed in this study enables accurate noninvasive prediction of postoperative recurrence in patients with cervical cancer. Keywords: MRI, Machine Learning, Radiomics, Prognosis and Prediction, Cervical Cancer, Recurrence, Inflammatory Markers Supplemental material is available for this article. © RSNA, 2026.