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Abstract #2561

Defining Radiation Target Volumes with AI-Driven Predictions of Glioma Recurrence from MRSI, Diffusion MRI, and Transformers

Harshita Kukreja1, Nate Tran1, Bo Liu1,2, Jacob Ellison1,2, Tiffany Ngan1, Angela Jakary1, Oluwaseun Adegbite1,2, Tracy Luks1, Yan Li1,2, Annette M. Molinaro3, Javier E. Villanueva-Meyer1,3, Steve E. Braunstein4, Hui Lin2,4, and Janine M. Lupo1,2
1Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States, 2UCSF/UC Berkeley Graduate Program in Bioengineering, University of California, San Francisco & Berkeley, San Francisco, CA, United States, 3Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, United States, 4Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, United States

Synopsis

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.

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Keywords