Keywords: Diagnosis/Prediction, Data Processing
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.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords