Abstract #2733

A quantitative multiparametric 18F-FPIA PET/MRI study for the characterization of primary brain gliomas

Marianna Inglese¹, Shah Islam¹, Matthew Grech-Sollars^1,2, Giulio Anichini³, James Davies⁴, Azeem Saleem^4,5, Matthew Williams^6,7, 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.

A strong 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.

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