Marianna Inglese1, Shah Islam1, Matthew Grech-Sollars1,2, Giulio Anichini3, James Davies4, Azeem Saleem4,5, Matthew Williams6,7, Kevin S O'Neill3, Adam D Waldman8, and Eric O Aboagye1
1Surgery and Cancer, Imperial College London, London, United Kingdom, 2Imaging, Imperial College London Healthcare NHS Trust, London, United Kingdom, 3Imperial College London Healthcare NHS Trust, London, United Kingdom, 4Invicro Imperial College London, London, United Kingdom, 5Hull York Medical School, Faculty of Health Sciences, University of Hull, Hull, United Kingdom, 6Computational Oncology Group, Department of Surgery and Cancer, Imperial College London, London, United Kingdom, 7Institute for Global Health Innovation, Imperial College London, London, United Kingdom, 8Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
18F-FPIA PET/MRI integrates
two imaging modalities that can provide valuable insight into the
characterization and classification of brain tumours.
10 patients with primary brain
gliomas were recruited to this study. Static and dynamic 18F-FPIA PET,
together with perfusion/diffusion MRI data were post-processed for the
extraction of 3D parametric maps for each subject. Correlations among
parameters were evaluated with Spearman test. Tumour grade prediction was
assessed with a machine learning model.
correlation was found between uptake and influx rate constant of FPIA and MRI
perfusion parameters. The PET/MRI methodology provided 100% accuracy in differentiating
low from high grade tumours.