Tissue-level probabilistic mapping of treatment-induced effects in recurrent glioblastoma
Jacob Ellison1,2,3, Nate Tran1,2,3, Julia Cluceru1,2, Joanna Phillips4,5, Anny Shai5, Devika Nair1, Annette Molinaro5, Valentina Pedoia1,2,3, Yan Li1,2,3, Javier Villanueva-Meyer1, Mitchel Berger5, Shawn Hervey-Jumper5, Manish Aghi5, Susan Chang5, and Janine Lupo1,2,3
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
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.
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