Multiparametric MRI texture analysis in prediction of glioma biomarker status: added value of MR diffusion.

BACKGROUND

Early identification of glioma molecular phenotypes can lead to understanding of patient prognosis and treatment guidance. We aimed to develop a multiparametric MRI texture analysis model using a combination of conventional and diffusion MRI to predict a wide range of biomarkers in patients with glioma.

METHODS

In this retrospective study, patients were included if they (1) had diagnosis of gliomas with known , , , , , and status from surgical pathology and (2) had preoperative MRI including FLAIR, T1c+ and diffusion for radiomic texture analysis. Statistical analysis included logistic regression and receiver-operating characteristic (ROC) curve analysis to determine the optimal model for predicting glioma biomarkers. A comparative analysis between ROCs (conventional only vs conventional + diffusion) was performed.

RESULTS

From a total of 111 patients included, 91 (82%) were categorized to training and 20 (18%) to test datasets. Constructed cross-validated model using a combination of texture features from conventional and diffusion MRI resulted in overall AUC/accuracy of 1/79% for , 0.99/80% for , 0.79/67% for , and 0.77/66% for . The addition of diffusion data to conventional MRI features significantly ( < .05) increased predictive performance for , , and . The overall accuracy of the final model in predicting biomarkers in the test group was 80% (), 70% (), 70% (), and 75% ().

CONCLUSION

Addition of MR diffusion to conventional MRI features provides added diagnostic value in preoperative determination of IDH1, MGMT, and ATRX in patients with glioma.