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
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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