Abstract #0526

Defining radiation target volumes for glioblastoma from predictions of tumor recurrence with AI and diffusion & metabolic MRI

Nate Tran^1,2,3, Jacob Ellison^1,2,3, Tracy Luks¹, Yan Li^1,3, Angela Jakary¹, Oluwaseun Adegbite^1,2, Ozan Genc^1,3, Bo Liu^1,2, Hui Lin^2,4, Javier Villanueva-Meyer^1,3, Olivier Morin⁴, Steve Braunstein⁴, Nicholas Butowski⁵, Jennifer Clarke⁵, Susan M. Chang⁵, and Janine M. Lupo^1,2,3

¹Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States, ²UC Berkeley - UCSF Graduate Program in Bioengineering, University of California, San Francisco, San Francisco, CA, United States, ³Center for Intelligent Imaging, University of California, San Francisco, San Francisco, CA, United States, ⁴Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, United States, ⁵Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States

Synopsis

Keywords: Tumors, RadiotherapyUsing pre-radiotherapy anatomical, diffusion, and metabolic MRI from 42 patients newly-diagnosed with GBM, we first used Random Forest models to identify voxels that later exhibit either contrast-enhancing or T2 lesion progression. We then applied convolutional encoder-decoder neural networks to pre-radiotherapy imaging to segment subsequent tumor progression and found that the resulting predicted region better covered the actual tumor progression while sparing normal brain compared to the standard uniform 2cm expansion of the anatomical lesion to define the radiation target volume. This shows that multi-parametric MRI with deep learning has the potential to assist in future RT treatment planning.

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