Whole-tumor radiomics analysis of DKI and DTI may improve the prediction of genotypes for astrocytomas: A preliminary study

Purpose: To test whether the whole-tumor radiomics analysis of DKI and DTI images could predict IDH and MGMTmet genotypes of astrocytomas. Method: Sixty-two astrocytomas were enrolled. 364 radiomics features of whole tumor were extracted from mean-kurtosis (MK), and mean-diffusivity (MD) images, respectively. The multivariable logistic regression was used to select the most meaningful radiomics features for predicting IDH and MGMTmet genotypes. A radiomics model was built by logistic linear regression. A combined model was established based on selected radiomic, radiological and clinical features. To assess the difference between the models, the Z-test was performed. Results: The radiomics model built using the three most informative radiomics features for each genotype yielded an AUC of 0.831 ((95 % confidence interval [CI]: 0.721-0.918) for predicting IDH genotype, and 0.835 (95 %CI: 0.686-0.951) for MGMTmet genotype. A combined model for predicting IDH based on the radiomics score, age, and degree of edema reached an AUC of 0.885 (95 %CI: 0.802-0.955) and a combined model for predicting MGMTmet based on radiomics score and edema degree reached an AUC of 0.859 (95 %CI: 0.751-0.945) which was not significantly higher than the radiomics only model (P = 0.081). Conclusions: The radiomics models via an objective whole-tumor analysis of MK and MD maps were independent imaging biomarkers for predicting IDH and MGMTmet genotypes, and the combined model further improved the performance for IDH, but not for MGMTmet.