Progression-Free Survival Prediction for Locally Advanced Cervical Cancer After Chemoradiotherapy With MRI-based Radiomics

A significant proportion of locally advanced cervical cancer (LACC) patients experience disease progression post chemoradiotherapy (CRT). Currently existing clinical variables are suboptimal predictors of treatment response. This study reported a radiomics-based model leveraging information extracted from magnetic resonance (MR) T2-weighted image (T2WI) to predict the progression-free survival (PFS) for LACC following CRT. Radiomics features were extracted from pre-treatment MR T2WI in 105 LACC patients. Following pre-feature selection and a step forward feature selection method, an optimal feature set was determined with a Cox proportional hazard (CPH) model. The PFS predictions were generated through a radiomics-clinical combined model utilized five repeated nested 5-fold cross-validation (5-fold CV). Disease progression risk was stratified into high- and low-risk groups based on the predicted PFS and assessed by Kaplan-Meier analysis. The radiomics texture feature extracted from MR T2WI significantly predict PFS in LACC after CRT. In comparison with the model using clinical variables alone, the radiomics-clinical combined model achieves significantly improved performance in testing patient cohort, achieving higher C-index (0.748 vs 0.655) and area under the curve (0.798 vs 0.660 for 2-year PFS). Meanwhile, the proposed method significantly differentiated the high- and low-risk patients groups for disease progression (P < 0.001). An MR T2WI-based radiomics and clinical combined model provided improved prognostic capabilities in predicting the PFS for LACC patients treated with CRT, outperforming a model using clinical variables alone. The incorporation of MR T2WI-based radiomics is promising in assisting in personalized management in LACC, indicating the potential of MR T2WI radiomics as imaging biomarker. •An MR T2WI based predictive model was able to predict progression-free survival for LACC after chemoradiotherapy.•Combining radiomics and clinical features improved progression-free survival prediction over using clinical variables alone.•The study outcomes support the role of MR T2WI-based radiomics as a potential imaging biomarker.