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Abstract #5018

Cross-scale prediction of glioblastoma MGMT methylation status based on deep learning combined with MRI and pathology images

Xusha Wu1, Yibin Xi1, and Hong Yin1
1Xi'an People's Hospital (Xi'an Fourth Hospital), Xi'an, China

Synopsis

Keywords: Tumors (Pre-Treatment), Brain

Motivation: Promoter methylation of MGMT in glioblastoma is associated with beneficial treatment outcomes and prognosis; however, MGMT testing is costly, and molecular detection is not available in some hospitals.

Goal(s): This study aims to construct a model based on MRI and pathological images to predict the status of MGMT.

Approach: MRI and pathological images from patients with glioblastoma and a machine learning model was established to predict the MGMT status.

Results: The combination of MRI and pathological images demonstrated superior predictive performance for MGMT compared to either MRI or pathological images alone.

Impact: This research provides a method for therapeutic decision-making and prognostic prediction in glioblastoma, offering a potential approach for the future integration of multi-information for accurate prediction and clinical application in other tumors.

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