Filtration-Histogram Based Magnetic Resonance Texture Analysis (MRTA) for the Distinction of Primary Central Nervous System Lymphoma and Glioblastoma
| Autor: | Balaji Ganeshan, Julia E. Markus, Kate Cwynarski, Claire L. MacIver, Stephen J. Wastling, Ashley M. Groves, Martin A. Lewis, John Maynard, Harpreet Hyare, Ayisha Al Busaidi, Sebastian Brandner, Stefanie Thust |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
medicine.diagnostic_test
Receiver operating characteristic business.industry brain Primary central nervous system lymphoma Area under the curve glioblastoma Medicine (miscellaneous) Subgroup analysis Magnetic resonance imaging lymphoma medicine.disease Article Histogram computer-assisted medicine Medicine Effective diffusion coefficient magnetic resonance imaging Nuclear medicine business Glioblastoma |
| Zdroj: | Journal of Personalized Medicine Volume 11 Issue 9 Journal of Personalized Medicine, Vol 11, Iss 876, p 876 (2021) |
| ISSN: | 2075-4426 |
| DOI: | 10.3390/jpm11090876 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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