Predicting glioma IDH mutation using multi-parametric MRI and fractal analysis

This study aims to investigate the effectiveness of applying fractal analysis to pre-operative MRI images for prediction of glioma IDH mutation status. IDH mutation has been shown to provide more prognostic and therapeutic benefits to patients, so predicting it before surgery can provide useful information for planning the proper treatments. This study utilized the UCSF-PDGM dataset from the Cancer Image Archive. We used the modified box counting method to compute the fractal dimension of segmented tumor regions in pre- and post-contrast T1-weighted MRI. The results showed that the FD provided clear differentiation between tumor grades, with higher FD correlated to higher tumor grade. Additionally, FD demonstrated clear separation between IDH wildtype and IDH mutated tumors. Enhanced differentiation based on FD was observed with post-contrast T1-weighted images. Significant p-values from the Wilcoxon rank sum test validated the potential of using fractal analysis. The AUC of ROC for IDH mutation prediction reached 0.88 for both pre- and post-contrast T1-weighted images. In conclusion, this study shows fractal analysis is a promising technique for glioma IDH mutation prediction. Future work will include studies using more advanced MRI imaging contrasts as well as combination of multi-parametric images.