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[The value of analysis of quantitative radiomics based on DTI in predicting astrocytoma IDH1 mutation].

Objective: Non-invasive prediction of IDH1 mutations by establishing a quantitative radiographic model based on DTI-based whole-tumor texture analysis. Methods: Preoperative MRI images of patients with surgically confirmed astrocytoma were collected in the First Affiliated Hospital of Soochow University from February 2016 to June 2019, including T(1)WI, T(2)WI, DTI, and T(1)-contrast enhancement images.A total of 38 patients were included, consisting of 12 mutants and 26 wilds, 20 males and 18 females, the average age was (49±15) years old.The ROIs were drawn on each level of the T(2)WI image using MaZda software and copied to the ADC and FA maps to extract texture feature parameters. The LASSO regression was used to determine the best radiomics features, radiological scores were calculated, and binary Logistic regression was used to construct a prediction model, then the ROC curve was used to analyze the diagnostic efficiency and the calibration curve was used to evaluate model prediction performance. Results: The four most valuable radiomics features were determined by LASSO regression, and then the radiomics scores and Logistic regression models of each patient were established. The radiomics scores of the wild and mutant groups were 2.3±0.3 and 1.8±0.4. There were significant differences between the groups (P<0.05). The ROC curve analysis showed an AUC of 0.837 with sensitivity and specificity of 91.7% and 61.5%, respectively. The Logistic regression model had good predictive performance with AUC of 0.907, sensitivity and specificity of 91.7% and 84.6%. Conclusions: DTI-based whole tumor radiomics model is benefit for predicting astrocytoma IDH1 mutations.