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

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

Claire L MacIver  1 , Ayisha Al Busaidi  2 , Balaji Ganeshan  3 , John A Maynard  1   2 , Stephen Wastling  1   2 , Harpreet Hyare  4 , Sebastian Brandner  5 , Julia E Markus  4 , Martin A Lewis  1 , Ashley M Groves  3 , Kate Cwynarski  6 , Stefanie C Thust  1   2   4
Affiliations

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

Claire L MacIver et al. J Pers Med. .

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.

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Conflict of interest statement

C.L.M., A.A.B., J.A.M., S.W., H.H., S.B., J.E.M., M.A.L., A.G.M., K.C., and S.C.T. declare no conflicts of interest. B.G. (who was not a data controller for this study) is the co-founder/co-inventor of TexRAD texture analysis software used in this study and a shareholder (not an employee) of Feedback Plc., a UK-based company which owns, develops, and markets the TexRAD texture analysis software.

Figures

Figure 1
Figure 1
Flow diagram showing patient exclusion and inclusion criteria.
Figure 2
Figure 2
Example segmentations. T2, ADC maps, and T1CE images in two patients with PCNSL (A–C) and glioblastoma (D–F) demonstrating MRTA regions of interest (blue outlines). In the glioblastoma patient, the segmentation, excluding necrosis, is additionally shown (blue and orange outlines). ADC = apparent diffusion coefficient; PCNSL = primary central nervous system lymphoma; MRTA = magnetic resonance texture analysis.
Figure 3
Figure 3
Example of the MRTA filtration process applied to a T1CE image (A) in a patient with PCNSL using fine (B), medium (C), and coarse filtration (D).
See this image and copyright information in PMC

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