Abstract #2416

Classification of low- and high-grade gliomas through multimodal temporal MRI and PET data

Marianna Inglese^1,2, Matteo Ferrante¹, Tommaso Boccato¹, Shah Islam³, Matthew Williams^4,5, Adam D Waldman⁶, Kevin O'Neill⁷, Eric O Aboagye³, and Nicola Toschi^1,8

¹Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy, ²Surgery and Cancer, Imperial College London, London, United Kingdom, ³Department of Surgery and Cancer, Imperial College London, London, United Kingdom, ⁴Computational Oncology Group - Department of Surgery and Cancer, Imperial College London, London, United Kingdom, ⁵Institute for Global Health Innovation, Imperial College London, London, United Kingdom, ⁶Centre for Clinical Brain Sciences, University of Edinburgh, Edimburgh, United Kingdom, ⁷Imperial College Healthcare NHS Trust, London, United Kingdom, ⁸Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Boston, NY, United States

Synopsis

Keywords: Data Processing, Modelling, Deep Learning, convolutional filtersStratifying human brain gliomas using imaging techniques is extremely challenging. Valuable insight into the characterization and classification of gliomas can be provided by integrating two imaging modalities, i.e. ¹⁸F-FPIA PET and MRI. This study introduces a new approach for glioma stratification based on the extraction of temporal features from tissue time activity curves (TACs) extracted from dynamic PET/MRI data. We exploit tissue-specific biochemical properties embedded in the TACs through deep learning and achieve good discrimination results while foregoing pharmacokinetic fitting and hence invasive measurement of the AIF.

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