Abstract #1726

The Diagnostic Performance of Multiparametric MRI Radiomics for Classification of Untreated Adult Gliomas

Amirah Faisal Alsaedi^1,2, Jasmina Panovska-Griffiths³, Xavier Golay², and Sotirios Bisdas^2,4

¹Department of Radiology Technology, Taibah University, Medina, Saudi Arabia, ²Department of Brain Repair & Rehabilitation, UCL, Queen Square, Institute of Neurology, London, United Kingdom, ³Department of Applied Health Research, UCL, London, United Kingdom, ⁴Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, University College Hospitals NHS Trust, London, United Kingdom

This study aimed to assess the diagnostic performance of multiparametric MRI radiomics for glioma class prediction according to the WHO 2016 classification. Histogram features were extracted from prospectively acquired multiparametric MRI (pCASL, DSC-MRI, DCE-MRI, and DWI) in 32 patients with primary gliomas. The uncombined significant features of ASL, ADC, DSC, and DCE, revealed diagnostic performances varying from low (44% ) to fair (86%) and unable to predict all the histomolecular classes. However, combining them for each MRI method, independently, enhanced the diagnostic accuracy up to 100% and predict all the classes. This alludes the use of multimodal radiomics for glioma classification.

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