Keywords: Tumors (Pre-Treatment), MR-Guided Radiotherapy, Glioma, Deep Learning
Motivation: Presurgery MR scans have a higher percentage of tumor voxels and can be used as a better signal to predict tumor progression using AI-driven models.
Goal(s): We show deep learning can be used to predict tumor progression in patients diagnosed with glioblastoma multiforme (GBM) using a combination of anatomical, diffusion, and metabolic MRI scans done prior to surgery.
Approach: Convolutional Neural Networks (CNNs) and Vision Transformers are trained to predict tumor ROI of the progression lesion using presurgery MR scans.
Results: Our methods perform better than standard of care in both inclusion of the tumor and exclusion of the normal brain.
Impact: Our results highlight the potential value of deep learning in future RT treatment planning with presurgery MRI scans. Vision transformers perform at par (if not better) with CNNs suggesting opportunities for future work into their use in progression prediction.
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