Abstract #2556

Quantitative Susceptibility Mapping for non-invasive glioma grading: a pilot study

Giulia Debiasi^1,2, Ben Statton³, Simran Kukran⁴, Sarah Cardona⁵, Katherine Wourms⁵, Lesley Honeyfield⁵, Lillie Pakzad-Shahabi^6,7, Luke Dixon^5,6, Matthew Williams^6,7, Rebecca Quest^4,5, Marco Castellaro², Alessandra Bertoldo^2,8, and Matthew Grech-Sollars^9,10

¹Department of Surgery, Oncology and Gastroenterology, University of Padova, Padova, Italy, ²Department of Information Engineering, University of Padova, Padova, Italy, ³Medical Research Council, London Institute of Medical Sciences, Imperial College London, London, United Kingdom, ⁴Department of Bioengineering, Imperial College London, London, United Kingdom, ⁵Department of Imaging, Imperial College Healthcare NHS Trust, London, United Kingdom, ⁶Department of Surgery and Cancer, Imperial College London, London, United Kingdom, ⁷Department of Radiotherapy, Imperial College Healthcare NHS Trust, London, United Kingdom, ⁸Padova Neuroscience Center, University of Padova, Padova, Italy, ⁹Department of Computer Science, University College London, London, United Kingdom, ¹⁰Lysholm Department of Neuroradiology, University College London Hospitals NHS Foundation Trust, London, United Kingdom

Synopsis

Keywords: Tumors (Pre-Treatment), Tumors, Quantitative Susceptibility Mapping, Radiomics,Data analysis

Motivation: Non-invasive glioma grading is of paramount importance particularly in settings of inability to perform surgical resection or biopsy sampling.

Goal(s): To characterize radiologically graded high- and low-grade gliomas (i.e., HGGs and LGGs respectively) by means of Quantitative Susceptibility Mapping.

Approach: Susceptibility maps were quantified for patients with radiological primary glioma diagnosis. Radiomics features were compared between HGGs and LGGs at whole-lesion level.

Results: A set of eleven radiomics features were successfully able to differentiate between the two groups, highlighting different complexity patterns of susceptibility values across the lesion area, both at local and global levels.

Impact: Radiomic features derived from susceptibility maps of radiologically graded glioma can distinguish between high- and low-grades. This may serve as a non-invasive tool to support clinical decisions when invasive procedures are not an option or watch-and-wait strategies are preferred instead.

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