Prediction Of Isocitrate Dehydrogenase-1 Mutation Status In Astrocytoma By Quantitative Radiomics Analysis Based On Diffusion Tensor Imaging

Purpose:The predictive accuracy of isocitrate dehydrogenase-1(IDH1)mutation status in astrocytoma using radiometric analysis based on diffusion tensor imaging(DTI)was evaluated and was compared with conventional MRI sequences and clinical features.Methods:A number of 69 patients with pathologically confirmed astrocytoma were retrospectively included,of them,there were 37 males and 32 females,aged 7 to 70 years,with an average age of(49.39±15.06)years,22 cases of mutant type and 47 cases of wild type.According to time,the training group and validation group were divided from the data.3D-Slicer software was used for image process and feature extraction.From T1WI,T2WI and DTI,a number of 372 radiomics features were extracted.Comparing the differences between the groups by univariate analysis.The features with statistical significance for the differences were analyzed using the ROC curve to identify the value of discrimination.A recursive feature elimination method based on a random forest model and cross-validation method was used to screen the most feature subsets for constructing radiomics signature.Prediction models for IDH1 gene status was constructed using the following combination of variables:(1)clinical model(based on clinical and morphological features);(2)conventional radiomics model(T1WI,T2WI);(3)DTI-based radiomics model(ADC,FA);(4)Clinical-radiomics model(based on clinical and radiomics)).The models are verified through cross-validation.The receiver operating characteristic curve,clinical decision curve,and impact curve were used to evaluate the predictive performance of the models.Results:Of the total 372 radiomics features,the first-order statistical features and the gray level co-occurrence matrix(GLCM)features provided the most valuable features.The accuracy of the IDH1 genotyping based on ADC-derived optimal feature subset was higher than that of T1WI,T2WI,and FA(accuracy were 0.786±0.121,0.713±0.131,0.674±0.242,and 0.613±0.224 respectively).Among the clinical features,age[OR(95%CI):0.028(0.002-0.336)]and enhancement boundary[OR(95%CI):0.058(0.005-0.664)]were independent risk factors for IDH1 gene mutation.The combined model composed of ADC-based radiomic signature and clinical features performed significantly better than a single clinical model and radiomics model.Conclusions:Histogram and texture features have certain discriminative value for astrocytoma IDH1 gene status.DTI-based radiomics can help improve the predictive accuracy of IDH1 gene status in astrocytoma patients.