Abstract #0425

Automatic stratification of gliomas into WHO 2016 molecular subtypes using diffusion-weighted imaging and a pre-trained deep neural network

Julia Cluceru^1,2, Yannet Interian³, Joanna Phillips⁴, Devika Nair¹, Susan Chang⁴, Paula Alcaide-Leon¹, Javier E. Villanueva-Meyer¹, and Janine Lupo^1,5

¹Radiology and Biomedical Imaging, UCSF, San Francisco, CA, United States, ²Bioengineering and Therapeutic Sciences, UCSF, San Francisco, CA, United States, ³Data Science, USF, San Francisco, CA, United States, ⁴UCSF, Neurological Surgery, CA, United States, ⁵Graduate Program in Bioengineering, UCSF/UC Berkeley, San Francisco and Berkeley, CA, United States

In this abstract, we use diffusion and anatomical MR imaging together with a pre-trained RGB ImageNet to classify patients into major genetic entities defined by the WHO. We achieved 91% accuracy on our validation set with high per-class accuracy, precision, and recall; and 81% accuracy on a separate test dataset.

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