Glioblastoma multiforme (GBM) is the most common malignant brain tumor. MGMT promoter methylation is associated with beneficial chemotherapy. We extract deep features from a pre-trained deep neural network model via transfer learning and generate an effective feature vector model together with radiomics features for an optimal pretreatment prediction of MGMT promoter methylation status. The deep feature set achieved the higher predictive accuracy of 0.86 and 0.70 for validation and test group comparing to handcrafted radiomics feature and combined feature sets. The deep feature model may serve as a potential imaging biomarker for pretreatment prediction of MGMT methylation in GBM.
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