  Background: Distinction of IDH mutant and wildtype GBMs is challenging on
MRI, since conventional imaging shows considerable overlap. While few studies
employed deep-learning in a mixed low/high grade glioma population, a
GBM-specific model is still lacking in the literature. Our objective was to
develop a deep-learning model for IDH prediction in GBM by using Convoluted
Neural Networks (CNN) on multiparametric MRI. Methods: We included 100 adult
patients with pathologically proven GBM and IDH testing. MRI data included:
morphologic sequences, rCBV and ADC maps. Tumor area was obtained by a bounding
box function on the axial slice with widest tumor extension on T2 images and
was projected on every sequence. Data was split into training and test (80:20)
sets. A 4 block 2D - CNN architecture was implemented for IDH prediction on
every MRI sequence. IDH mutation probability was calculated with softmax
activation function from the last dense layer. Highest performance was
calculated accounting for model accuracy and categorical cross-entropy loss
(CCEL) in the test cohort. Results: Our model achieved the following
performance: T1 (accuracy 77%, CCEL 1.4), T2 (accuracy 67%, CCEL 2.41), FLAIR
(accuracy 77%, CCEL 1.98), MPRAGE (accuracy 66%, CCEL 2.55), rCBV (accuracy
83%, CCEL 0.64). ADC achieved lower performance. Conclusion: We built a
GBM-tailored deep-learning model for IDH mutation prediction, achieving
accuracy of 83% with rCBV maps. High predictivity of perfusion images may
reflect the known correlation between IDH, hypoxia inducible factor (HIF) and
neoangiogenesis. This model may set a path for non-invasive evaluation of IDH
mutation in GBM.
