Abstract #2743

Tissue-level probabilistic mapping of treatment-induced effects in recurrent glioblastoma

Jacob Ellison^1,2,3, Nate Tran^1,2,3, Julia Cluceru^1,2, Joanna Phillips^4,5, Anny Shai⁵, Devika Nair¹, Annette Molinaro⁵, Valentina Pedoia^1,2,3, Yan Li^1,2,3, Javier Villanueva-Meyer¹, Mitchel Berger⁵, Shawn Hervey-Jumper⁵, Manish Aghi⁵, Susan Chang⁵, and Janine Lupo^1,2,3

¹Radiology and Biomedical Imaging, UCSF, San Francisco, CA, United States, ²Center for Intelligent Imaging, UCSF, San Francisco, CA, United States, ³Graduate Group in Bioengineering, UCSF - UC Berkeley, San Francisco, CA, United States, ⁴Brain Tumor Research Center, UCSF, San Fransisco, CA, United States, ⁵Neurological Surgery, UCSF, San Francisco, CA, United States

Synopsis

Treatment-induced effects can mimic tumor recurrence and pose a challenge to accurately assessing treatment response. We aim to provide a machine learning framework and identify important imaging features for discriminating treatment-induced injury from recurrent glioblastoma at biopsy level resolution. Our best model performs with a mean AUC of .77+/-0.11 across 4 fold cross-validation of 108 tissue samples. rCBV, choline-to-NAA index (CNI), and normalized lipid levels were the top three most import features in distinguishing treatment effects from recurrent tumor.

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.