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Abstract #0425

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

Julia Cluceru1,2, Yannet Interian3, Joanna Phillips4, Devika Nair1, Susan Chang4, Paula Alcaide-Leon1, Javier E. Villanueva-Meyer1, and Janine Lupo1,5
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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