Development of a combined model incorporating clinical characteristics and magnetic resonance imaging features to enhance the predictive value of a prognostic model for locally advanced cervical cancer.

Objective: This study aimed to develop non-invasive predictive tools based on clinical characteristics and magnetic resonance imaging (MRI) features to predict survival in patients with locally advanced cervical cancer (LACC), thereby facilitating clinical decision-making. Methods: We conducted a retrospective analysis of clinical and MRI data from LACC patients who underwent radical radiotherapy at our center between September 2012 and May 2020. Prognostic predictors were identified using single-factor and multifactor Cox analyses. Clinical and MRI models were established based on relevant features, and combined models were created by incorporating MRI factors into the clinical model. The predictive performance of the models was evaluated using the area under the curve (AUC), consistency index (C-index), and decision curve analysis (DCA). Results: The study included 175 LACC patients. Multivariate Cox analysis revealed that patients with FIGO IIA-IIB stage, ECOG score 0-1, CYFRA 21-1<7.7 ng/ml, ADC ≥ 0.79 mm^2/s, and Kep ≥ 4.23 minutes had a more favorable survival prognosis. The clinical models, incorporating ECOG, FIGO staging, and CYFRA21-1, outperformed individual prognostic factors in predicting 5-year overall survival (AUC: 0.803) and 5-year progression-free survival (AUC: 0.807). The addition of MRI factors to the clinical model (AUC: 0.803 for 5-year overall survival) increased the AUC of the combined model to 0.858 (P=0.011). Similarly, the combined model demonstrated a superior predictive ability for 5-year progression-free survival, with an AUC of 0.849, compared to the clinical model (AUC: 0.807) and the MRI model (AUC: 0.673). Furthermore, the C-index of the clinical models for overall survival and progression-free survival were 0.763 and 0.800, respectively. Upon incorporating MRI factors, the C-index of the combined model increased to 0.826 for overall survival and 0.843 for progression-free survival. The DCA further supported the superior prognostic performance of the combined model. Conclusion: Our findings indicate that ECOG, FIGO staging, and CYFRA21-1 in clinical characteristics, as well as ADC and Kep values in MRI features, are independent prognostic factors for LACC patients undergoing radical radiotherapy. The combined models provide enhanced predictive ability in assessing the risk of patient mortality and disease progression.