Introduction

Isocitrate dehydrogenase (IDH) and telomerase reverse transcriptase promoter (TERTp) mutations highly affect the prognosis and clinical outcome in gliomas.^1-4 While IDH mutant (IDH-mut) gliomas have a better treatment response and outcome, TERTp-only gliomas, who have the TERT mutation without the presence of an IDH mutation, have been reported to have the worst overall survival.² Recent studies have shown that the IDH mutation is associated with tumor angiogenesis, and it could be predicted using rCBV maps obtained from DSC-MRI.^{5, 6} Moreover, TERTp mutation has been associated with the aggressiveness of gliomas⁷, for which DSC-MRI might be useful for prediction. Therefore, this study aims to define possible brain perfusion-based signatures for the identification of IDH and TERT mutations in gliomas using rCBV maps with machine learning algorithms.

Methods

The patient cohort included 106 glioma patients in this IRB approved study (36F/70M, mean age = 47.6 ± 13.5 years, 66 IDH-wild, 40 IDH-mutant, 38 TERTp-mutant, 50 TERTp-wild, 56 TERTp-only). The patients were scanned on a 3T clinical MR scanner (Siemens, Erlangen, Germany) using a 32-channel head coil before surgery. The brain tumor protocol included pre- and post-contrast (gadolinium DTPA) T1-weighted TSE (TR=500 ms, TE=10 ms), T2-weighted TSE (TR=5000 ms, TE=105 ms), and T2*-weighted gradient-echo echo-planar imaging (EPI) dynamic susceptibility contrast (DSC) MRI (TR=1500 ms, TE=30 ms). IDH and TERTp mutations in the tissue were determined by either minisequencing or Sanger sequencing. The whole tumor volumes were segmented on FLAIR images while the necrosis volumes were segmented on T1-weighted post-contrast MRI using 3D slicer version 4.8.1 (http://slicer.org/). The tumor and necrosis images of each subject were registered to the rCBV maps using FMRIB Software Library (FSL; http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/). The histogram properties, including the mean, median, standard deviation, kurtosis and skewness, of the rCBV pixel values within the tumor regions were calculated using an in-house program written in MATLAB 2020a (The MathWorks Inc., Natick, MA). A Mann Whitney U test was applied to detect statistically significant differences between the three molecular subsets of gliomas defined either based on IDH and TERTp mutations or TERTp mutation only. Then, the histogram properties were used as input features and supervised machine learning algorithms were used in the Classification Learner application of MATLAB with five-fold cross-validation. Moreover, an in-house program was written to automatically convert the segmented ROI into an RGB image including the slice with the largest tumor area, and the former and the latter slices (Figure 1), and the features coming from the resultant RGB images were also used for classification. The overall classification accuracy, specificity and sensitivity were calculated.

Results

Using six features coming from the pixel-wise analysis, only the skewness of the rCBV values was statistically significantly different between IDH-mut and IDH wildtype (IDH-wt) gliomas (p<0.01). There were no other statistically significant differences in rCBV values between the molecular subgroups (Table 1). The IDH-mut and IDH-wt gliomas were classified with 84.6% accuracy (sensitivity = 93.7%, specificity = 70.0%) using a linear discriminant analysis on rCBV maps. Additionally, the TERTp mutant (TERTp-mut) and TERTp wildtype (TERTp-wt) subgroups were classified with 81.8% accuracy (sensitivity = 90%, specificity = 75%) using ensemble methods on rCBV maps. Moreover, the classification accuracy of TERTp-only gliomas vs others was 68.2% (sensitivity = 71.4%, specificity = 62.5%) using support vector machines on rCBV maps. On the other hand, using RGB image features resulted in higher classification accuracies. The IDH mutational subgroups were classified with 87.2% accuracy (sensitivity = 85.7%, specificity = 88.9%) using k nearest neighbors, while the TERTp mutational subgroups were classified with 81.8% accuracy (sensitivity = 77.5%, specificity = 86.4%) using the same algorithm. Additionally, the classification accuracy of TERTp-only gliomas vs others was 89.6% (sensitivity = 88.3%, specificity = 91.2%) using bagged trees (Table 2).

Conclusion and Discussion

The results of this study indicated that both IDH and TERTp molecular subsets of gliomas could be classified using rCBV maps acquired from DSC-MRI. Although no statistically significant differences were observed between the histogram features, machine learning methods were able to differentiate the molecular subgroups. Moreover, using three consecutive slices provided more information for the molecular subgroup classification. Future studies will use the RGB images of the rCBV maps in deep learning approaches to identify IDH and TERP mutations on a larger glioma patient cohort.

Acknowledgements

This study was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) grant 216S432.

References

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