Abstract #3463

1H-MRS and machine learning for predicting voxel-wise histopathology of tumor cells in newly-diagnosed glioma patients

Nate Tran¹, Jacob Ellison¹, Oluwaseun Adegbite¹, James Golden¹, Yan Li¹, Joanna Phillips², Devika Nair¹, Anny Shai², Annette Molinaro², Valentina Pedoia¹, Javier Villanueva-Meyer¹, Mitchel Berger², Shawn Hervey-Jumper², Aghi Manish², Susan Chang², and Janine Lupo¹

¹Radiology & Biomedical Imaging, University of California, San Francisco, SAN FRANCISCO, CA, United States, ²Neurological Surgery, University of California, San Francisco, SAN FRANCISCO, CA, United States

Synopsis

Using spectrum obtained at the spatial location of 549 tissue samples from 261 newly diagnosed patients with glioma, we trained and tested an support vector regression (SVR) model on individual metabolites, and a 1D-CNN model on the whole spectrum, to predict tumor biology such as cellularity, Ki-67, and tumor aggressiveness. A regression based 1D-CNN model using the entire spectrum pre-trained on a similar classification task outperformed the SVR model using metabolite peak heights.

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