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Abstract #0423

Using anatomic and diffusion MRI with deep convolutional neural networks to distinguish treatment-induced injury from recurrent glioblastoma

Julia Cluceru1,2, Paula Alcaide-Leon1, Valentina Pedoia1, Joanna Phillips3, Devika Nair1, Yannet Interian4, Susan Chang5, Javier E. Villanueva-Meyer1, Tracy Luks1, Annette Molinaro5, Mitchel Berger5, and Janine Lupo1,6
1Radiology and Biomedical Imaging, UCSF, San Francisco, CA, United States, 2Bioengineering and Therapeutic Sciences, UCSF, San Francisco, CA, United States, 3UCSF, Neurological Surgery, CA, United States, 4Data Science, USF, San Francisco, CA, United States, 5Neurological Surgery, UCSF, San Francisco, CA, United States, 6Graduate Program in Bioengineering, UCSF/UC Berkeley, San Francisco and Berkeley, CA, United States

In this study, we leverage a promising new centrally restricted diffusion pattern1 together with modern advances in deep learning to create a novel method for detecting treatment-related injury in the context of suspected recurrent glioblastoma. We report a 5-fold cross-validation average AUC ROC of 0.83 +/- 0.2 for the classification of lesions into two categories: those induced by treatment, and those that are true incidences of recurrent glioblastoma.

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