Attention-Boosted CNN for Improving the Classification of IDH and TERTp Mutation Status in Gliomas Based on Dynamic Susceptibility Contrast MRI

Motivation: Molecular markers, such as IDH and TERTp, have been reported as significant prognostic factors in gliomas. Goal(s): The aim of this study is to predict IDH and TERTp mutational subtypes in gliomas non-invasively using deep-learning applied to rCBV images derived from DSC-MRI. Approach: We proposed a deep-learning approach with attention gates to classify IDH- and TERTp-mutation subgroups of gliomas using rCBV images along with anatomical-MRI. Additionally, Grad-CAM approach was employed to provide an explanation of which image sections played a role in decision-making. Results: Attention-boosted deep learning-based classification model yielded high accuracy rates. GradCAM approach also highlighted the significance of different tumor components. Impact: The proposed attention-boosted deep learning based method might have the potential to assist clinicians in the noninvasive identification of IDH and TERTp mutations at the pre-surgery point and potentially enhance treatment strategies and patient outcomes.