Objective To evaluate the value of clinical-radiomics model based on reduced field-of-view(r-FOV)ADC map in predicting clinical stage of cervical cancer.Methods Eighty-nine patients with pathologically proven cervical cancer who underwent pre-treatment conventional MR sequences,r-FOV DWI and full field-of-view(f-FOV)DWI were ret-rospectively reviewed.3D Slicer software was used to manually delineate the region of interest of tumor layer by layer on the sagittal r-FOV ADC map,and then radiomics features of the lesions were extracted.Independent sample t test or Mann-Whitney U test was used to preliminarily screen out the radiomics features with statistically significant differences between early and advanced cervical cancer groups.Least absolute shrinkage and selection operator(LASSO)regression analysis and a 10-fold cross-validation method were used to screen the optimal radiomics features.Multivariate Logistic regression a-nalysis was used to construct the prediction model including clinical factors and radiomics features.Receiver operating char-acteristic(ROC)curve was used to evaluate the predictive efficacy of the model.Decision curve analysis(DC A)was used to evaluate the clinical application value of the model.Results A total of 851 radiomics features were extracted,and 16 optimal radiomics features were finally screened.On multivariate Logistic regression analysis,radiomics features,patient age and menopause were selected into the nomogram,and then they were used to construct clinical-radiomics model for predic-ting early and advanced cervical cancer.The area under the curve(AUC)of clinical-radiomics model was 0.998 and 0.777 in the training set and test set,respectively.The decision curve analysis(DCA)shows that the threshold value of the models discrimination ability is 0.18.Conclusion The clinical-radiomics model based on reduced field-of-view(r-FOV)ADC map has high value in predicting clinical stage of cervical cancer.
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