Abstract #0423

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

Julia Cluceru^1,2, 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 Lupo^1,6

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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