影像组学联合临床病理特征预测早期宫颈癌无病生存期的效能OACSTPCD

Objective:To determine the optimal preoperative magnetic resonance imaging(MRI)radiomics parameters and compare the values in predicting disease-free survival(DFS)of early-stage cervical cancer patients with clinicopathological factors.Methods:A total of 186 patients with early-stage cervical cancers(preoperative FIGO stageⅠB-ⅡA),who underwent radical hysterectomy with lymphadenectomy during January 2013 and June 2018,were retrospectively reviewed.Multi-sequence MR data,clinicopathologic data,and DFS data were collected;least-absolute shrinkage and selection operator(LASSO)and the Cox proportional hazard regression model were applied to construct a Rad-score.Clinicopathological models(with significant clinicopathological features),radiomics models(Rad-scores for T1CE,DWI,T2WI,T1CE+DWI,T1CE+T2WI,DWI+T2WI,and T1CE+DWI+T2WI),and combined models(the Rad-score model combined with significant clinicopathological features)were compared using a multivariable Cox model.Results:The T1CE radiomics model was the most stable and optimal model(C-index for training,0.798;for validation,0.758)of all the radiomics models.The radiomics model of T1CE showed higher prognostic performance than the clinicopathological model(C-index for training,0.746;for validation,0.641).The combined model(histological type of adenocarcinoma,lymph-node metastasis together with Rad-score for T1CE)showed the highest prognostic performance in estimating DFS(C-index for training,0.848;for validation,0.784).Conclusion:An MRI-derived Rad-score of T1CE combined with a clinicopathological model is optimal in predicting DFS in early-stage cervical cancers.