Abstract
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 IDH1, EGFR, MGMT, ATRX, TP53 and PTEN 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 IDH1, 0.99/80% for ATRX, 0.79/67% for MGMT, and 0.77/66% for EGFR. The addition of diffusion data to conventional MRI features significantly (p<0.05) increased predictive performance for IDH1, MGMT and ATRX. The overall accuracy of the final model in predicting biomarkers in the test group was 80% (IDH1), 70% (ATRX), 70% (MGMT) and 75% (EGFR).
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.
Diversity of Cortico-descending Projections: Histological and Diffusion MRI Characterization in the Monkey