Abstract #0525

Differentiation of recurrent tumor from post-treatment changes in Glioblastoma patients using Deep Learning and Restriction Spectrum Imaging

Louis Gagnon¹, Diviya Gupta¹, Nathan S White², Vaness Goodwill³, Carrie McDonald⁴, Thomas Beaumont⁵, Tyler M Seibert^1,4,6, Jona Hattangadi-Gluth⁴, Santosh Kesari⁷, Jessica Schulte⁸, David Piccioni⁸, Anders M Dale^1,8, Nikdokht Farid¹, and Jeffrey D Rudie¹

¹Department of Radiology, University of California San Diego, La Jolla, CA, United States, ²Cortechs.ai, San Diego, CA, United States, ³Department of Pathology, University of California San Diego, La Jolla, CA, United States, ⁴Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, United States, ⁵Department of Neurological Surgery, University of California San Diego, La Jolla, CA, United States, ⁶Department of Bioengineering, University of California San Diego, La Jolla, CA, United States, ⁷Department of Translational Neurosciences, Pacific Neuroscience Institute and Saint John’s Cancer Institute at Providence Saint Johns’ Health Center, Santa Monica, CA, United States, ⁸Department of Neuroscience, University of California San Diego, La Jolla, CA, United States

Synopsis

Keywords: Tumors, Diffusion/other diffusion imaging techniques

Differentiating recurrent tumor from post-treatment changes is challenging in glioblastoma. Using restriction spectrum imaging (RSI) and deep learning, we were able to accurately identify and segment residual and recurrent enhancing and non-enhancing cellular tumor in post-treatment brain MRIs. Including RSI in the deep learning model improved tumor segmentation due to the ability of RSI to separate cellular tumor from peritumoral edema and treatment related enhancement. The volume of cellular tumor was also predictive of survival. Our results suggest that combining deep learning and RSI may identify recurrent tumor in glioblastoma patients, which could improve targeted treatments and guide clinical decision-making.

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.

Click here for more information on becoming a member.