Deep Learning Can Differentiate IDH-Mutant from IDH-Wild GBM

Abstract

Isocitrate dehydrogenase (IDH) mutant and wildtype glioblastoma multiforme (GBM) often show overlapping features on magnetic resonance imaging (MRI), representing a diagnostic challenge. Deep learning showed promising results for IDH identification in mixed low/high grade glioma populations; however, a GBM-specific model is still lacking in the literature. Our aim was to develop a GBM-tailored deep-learning model for IDH prediction by applying convoluted neural networks (CNN) on multiparametric MRI. We selected 100 adult patients with pathologically demonstrated WHO grade IV gliomas and IDH testing. MRI sequences included: MPRAGE, T1, T2, FLAIR, rCBV and ADC. The model consisted of a 4-block 2D CNN, applied to each MRI sequence. Probability of IDH mutation was obtained from the last dense layer of a softmax activation function. Model performance was evaluated in the test cohort considering categorical cross-entropy loss (CCEL) and accuracy. Calculated performance was: rCBV (accuracy 83%, CCEL 0.64), T1 (accuracy 77%, CCEL 1.4), FLAIR (accuracy 77%, CCEL 1.98), T2 (accuracy 67%, CCEL 2.41), MPRAGE (accuracy 66%, CCEL 2.55). Lower performance was achieved on ADC maps. We present a GBM-specific deep-learning model for IDH mutation prediction, with a maximal accuracy of 83% on rCBV maps. Highest predictivity achieved on perfusion images possibly reflects the known link between IDH and neoangiogenesis through the hypoxia inducible factor.

Keywords: CBV; GBM; IDH; MRI; artificial intelligence; deep learning; high grade glioma.

Figure 1

FLAIR images (above) and post-contrast MPRAGE images (below) of four patients with glioblastoma multiforme (GBM) from our cohort. Patient 1 (a,e) presented an expansile left frontal lobe GBM with typical features of rim-enhancement and central necrosis (arrow on image e); pathology confirmed isocitrate dehydrogenase (IDH)-wildtype. Patient 2 (b,f) presented a diffuse non-enhancing infiltrative GBM of the right temporo-insular region; pathology confirmed IDH mutation. Patient 3 (c,g) and 4 (d,h) demonstrated ‘borderline’ features, not typical for any IDH status; pathology confirmed IDH mutation for patient 3 and IDH-wild type for patient 4.

Figure 2

Proposed convoluted neural networks (CNN) architecture to predict IDH status. N = number of Conv-2D operations; F = number of trainable filters.

Figure 3

ROC curve for testing set from k-fold cross validation (k = 5) training on T1-weighted magnetic resonance imaging (MRI) sequence (a), T2-weighted MRI sequence (b), FLAIR MRI sequence (c), MPRAGE MRI sequence (d), rCBV MRI sequence (e), ADC MRI sequence (f).

Figure 4

Boxplot on k-fold cross validation (k = 5) for sensitivity (a), specificity (b), test accuracy (c) and test loss (d) with all MRI sequences.

Figure 5

Examples of correctly predicted outcome (IDH mutation) from our CNN model. Images above show MPRAGE (a), FLAIR (b), T2 (c), T1 (d), ADC (e) and rCBV (f) sequences of a patient with IDH mutated GBM. Images below show MPRAGE (g), FLAIR (h), T2 (i), T1 (j) ADC (k) and rCBV (l) sequences of a patient with IDH-wild-type GBM.

Figure 6

Examples of incorrectly predicted outcome (IDH mutation) from our CNN model. Images above show MPRAGE (a), FLAIR (b), T2 (c), T1 (d), ADC (e) and rCBV (f) sequences of a patient with IDH mutated GBM predicted as wild type (false negative). Images below show MPRAGE (g), FLAIR (h), T2 (i), T1 (j) ADC (k) and rCBV (l) sequences of a patient with IDH-wild-type GBM predicted as mutated (false positive).