Investigating the value of radiomics stemming from DSC quantitative biomarkers in IDH mutation prediction in gliomas

Georgios S Ioannidis; Laura Elin Pigott; Michael Iv; Katarina Surlan-Popovic; Max Wintermark; Sotirios Bisdas; Kostas Marias

doi:10.3389/fneur.2023.1249452

Investigating the value of radiomics stemming from DSC quantitative biomarkers in IDH mutation prediction in gliomas

Front Neurol. 2023 Nov 16:14:1249452. doi: 10.3389/fneur.2023.1249452. eCollection 2023.

Authors

Georgios S Ioannidis^#¹, Laura Elin Pigott^#^{2

3

4}, Michael Iv⁵, Katarina Surlan-Popovic^{6

7}, Max Wintermark⁵, Sotirios Bisdas^{8

9}, Kostas Marias^{1

10}

Affiliations

¹ Computational BioMedicine Laboratory (CBML), Institute of Computer Science, Foundation for Research and Technology-Hellas (FORTH), Heraklion, Greece.
² Institute of Health and Social Care, London South Bank University, London, United Kingdom.
³ Faculty of Brain Science, Queen Square Institute of Neurology, University College London, London, United Kingdom.
⁴ Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery University College London, London, United Kingdom.
⁵ Department of Radiology, Division of Neuroimaging and Neurointervention, Stanford University, Stanford, CA, United States.
⁶ Department of Radiology, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia.
⁷ Department of Neuroradiology, University Medical Centre, Ljubljana, Slovenia.
⁸ Department of Brain Repair and Rehabilitation, Queen Square Institute of Neurology, UCL, London, United Kingdom.
⁹ Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, University College London NHS Foundation Trust, London, United Kingdom.
¹⁰ Department of Electrical and Computer Engineering, Hellenic Mediterranean University, Heraklion, Greece.

^# Contributed equally.

Abstract

Objective: This study aims to assess the value of biomarker based radiomics to predict IDH mutation in gliomas. The patient cohort consists of 160 patients histopathologicaly proven of primary glioma (WHO grades 2-4) from 3 different centers.

Methods: To quantify the DSC perfusion signal two different mathematical modeling methods were used (Gamma fitting, leakage correction algorithms) considering the assumptions about the compartments contributing in the blood flow between the extra- and intra vascular space.

Results: The Mean slope of increase (MSI) and the K₁ parameter of the bidirectional exchange model exhibited the highest performance with (ACC 74.3% AUROC 74.2%) and (ACC 75% AUROC 70.5%) respectively.

Conclusion: The proposed framework on DSC-MRI radiogenomics in gliomas has the potential of becoming a reliable diagnostic support tool exploiting the mathematical modeling of the DSC signal to characterize IDH mutation status through a more reproducible and standardized signal analysis scheme for facilitating clinical translation.

Keywords: IDH mutation; dynamic susceptibility contrast MRI; explainability; generalizability; gliomas; radiogenomics.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This publication was funded by the ICS Internal Grants of the Foundation for Research and Technology-Hellas under the program PCa RADical PATH.