Intra-and peri-tumoral radiomics model for predicting the response to concurrent chemoradiotherapy in cervical squamous cell carcinoma based on dynamic contrast-enhanced MRI / 实用放射学杂志

ABSTRACT

Objective To investigate the correlation between intra-and peri-tumoral radiomics features and the response to con-current chemoradiotherapy(CCRT)in cervical squamous cell carcinoma,and to explore the difference of predictive performance between 2D and 3D radiomics models.Methods The imaging data of 132 patients were analyzed retrospectively and randomly divided into training set(n=92)and validation set(n=40).Radiomics features were extracted based on the dynamic contrast-enhanced magnetic resonance imaging(DCE-MRI),the correlation analysis and least absolute shrinkage and selection operator(LASSO)algorithm were used for dimensionality reduction and screening,then the radiomics score was calculated and the logistic model was constructed.The receiver operating characteristic(ROC)curve,internal validation of Bootstrap and Brier score were used to evaluate the discrimina-tion and calibration of the model,and the improvement in predictive performance of 3D model compared with 2D model was evaluated by the integrated discrimination improvement(IDI).Results In the training set,the ROC curve showed that the area under the curve(AUC)of the models(2D-intratumoral,3D-intratumoral,3D-peritumoral,3D-combined)ranged from 0.774 to 0.893.The internal validation of Bootstrap showed the AUC were 0.772,0.860,0.847 and 0.888,respectively,while in the validation set,the AUC were 0.757,0.849,0.824 and 0.887,respectively.The Brier scores indicated that the models were well calibrated.In the training set and the validation set,the IDI values were 0.155 and 0.179,respectively,and the differences were statistically significant(P<0.05).Conclusion The radiomics analysis based on the tumor volume can fully explore the tumor heterogeneity.The intra-and peri-tumoral radiomics combined model shows the best predictive performance,which is important to assist clinicians in developing individualized therapies.