Radiomics-clinical model construction and performance evaluation
glioma
REGRESSION, GLIOMA, HYPERPLASIA
To obtain the regression coefficients, the optimal radiomics features were put into a logistic model. The Rad-score for each patient was subsequently calculated with a linear method based on the different weighting coefficient. Finally, the clinical features and Rad-score were filter out with logistics univariate analysis, the variables that showed statistically significant distinction were retained (P < 0.05), and logistics multivariate analysis was performed to determine the independent predictors (P < 0.05). The radiomics-clinical model for distinguishing glial cell hyperplasia from glioma was then constructed with these independent predictors. The performance could be evaluated through the calibration curve and AUC of training set and validation set. In order to further confirm the clinical value of the radiomics-clinical model, decision curve analysis was performed.