Filtration-Histogram Based Magnetic Resonance Texture Analysis (MRTA) for the Distinction of Primary Central Nervous System Lymphoma and Glioblastoma

Claire L MacIver; Ayisha Al Busaidi; Balaji Ganeshan; John A Maynard; Stephen Wastling; Harpreet Hyare; Sebastian Brandner; Julia E Markus; Martin A Lewis; Ashley M Groves; Kate Cwynarski; Stefanie C Thust

doi:10.3390/jpm11090876

Filtration-Histogram Based Magnetic Resonance Texture Analysis (MRTA) for the Distinction of Primary Central Nervous System Lymphoma and Glioblastoma

J Pers Med. 2021 Aug 31;11(9):876. doi: 10.3390/jpm11090876.

Authors

Claire L MacIver¹, Ayisha Al Busaidi², Balaji Ganeshan³, John A Maynard^{1

2}, Stephen Wastling^{1

2}, Harpreet Hyare⁴, Sebastian Brandner⁵, Julia E Markus⁴, Martin A Lewis¹, Ashley M Groves³, Kate Cwynarski⁶, Stefanie C Thust^{1

2

4}

Affiliations

¹ Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, London WC1N 3BG, UK.
² Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK.
³ Institute of Nuclear Medicine, University College London Hospitals NHS Foundation Trust, London NW1 2BU, UK.
⁴ Imaging Department, University College London Hospitals NHS Foundation Trust, London NW1 3BG, UK.
⁵ Department of Neurodegenerative Disease, UCL Institute of Neurology and Division of Neuropathology, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK.
⁶ Haematology and Oncology Department, University College London Hospitals NHS Foundation Trust, London NW1 3BG, UK.

Abstract

Primary central nervous system lymphoma (PCNSL) has variable imaging appearances, which overlap with those of glioblastoma (GBM), thereby necessitating invasive tissue diagnosis. We aimed to investigate whether a rapid filtration histogram analysis of clinical MRI data supports the distinction of PCNSL from GBM. Ninety tumours (PCNSL n = 48, GBM n = 42) were analysed using pre-treatment MRI sequences (T₁-weighted contrast-enhanced (T₁CE), T₂-weighted (T₂), and apparent diffusion coefficient maps (ADC)). The segmentations were completed with proprietary texture analysis software (TexRAD version 3.3). Filtered (five filter sizes SSF = 2-6 mm) and unfiltered (SSF = 0) histogram parameters were compared using Mann-Whitney U non-parametric testing, with receiver operating characteristic (ROC) derived area under the curve (AUC) analysis for significant results. Across all (n = 90) tumours, the optimal algorithm performance was achieved using an unfiltered ADC mean and the mean of positive pixels (MPP), with a sensitivity of 83.8%, specificity of 8.9%, and AUC of 0.88. For subgroup analysis with >1/3 necrosis masses, ADC permitted the identification of PCNSL with a sensitivity of 96.9% and specificity of 100%. For T₁CE-derived regions, the distinction was less accurate, with a sensitivity of 71.4%, specificity of 77.1%, and AUC of 0.779. A role may exist for cross-sectional texture analysis without complex machine learning models to differentiate PCNSL from GBM. ADC appears the most suitable sequence, especially for necrotic lesion distinction.

Keywords: brain; computer-assisted; glioblastoma; lymphoma; magnetic resonance imaging.