Magnetic resonance texture analysis utility in differentiating intraparenchymal neurosarcoidosis from primary central nervous system lymphoma: a preliminary analysis

• Published in: The neuroradiology journal Vol. 32; no. 3; pp. 203 - 209
• Main Authors: Bathla, Girish, Soni, Neetu, Endozo, Raymondo, Ganeshan, Balaji
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.06.2019
• Subjects: Adult; Central Nervous System Diseases - diagnostic imaging; Central Nervous System Neoplasms - diagnostic imaging; Contrast Media; Diagnosis, Differential; Female; Humans; Lymphoma - diagnostic imaging; Magnetic Resonance Imaging - methods; Male; Middle Aged; Neoplastic Diseases; Sarcoidosis - diagnostic imaging; neurosarcoidosis; histogram; Texture analysis; MRI; primary central nervous system lymphoma
• ISSN: 1971-4009; 2385-1996
• DOI: 10.1177/1971400919830173

Purpose Neurosarcoidosis and primary central nervous system lymphomas, although distinct disease entities, can both have overlapping neuroimaging findings. The purpose of our preliminary study was to assess if magnetic resonance texture analysis can differentiate parenchymal mass-like neurosarcoidosis granulomas from primary central nervous system lymphomas. Methods A total of nine patients was evaluated, four with parenchymal neurosarcoidosis granulomas and five with primary central nervous system lymphomas. Magnetic resonance texture analysis was performed with commercial software using a filtration histogram technique. Texture features of different sizes and variations in signal intensity were extracted at six different spatial scale filters, followed by feature quantification using statistical and histogram parameters and 36 features were analysed for each sequence (T1-weighted, T2-weighted, fluid-attenuated inversion recovery, diffusion-weighted, apparent diffusion coefficient, T1-post contrast). The non-parametric Mann–Whitney test was used to evaluate the differences between different texture parameters. Results The differences in distribution of entropy on T2-weighted imaging, apparent diffusion coefficient and T1-weighted post-contrast images were statistically significant on all spatial scale filters. Magnetic resonance texture analysis using medium and coarse spatial scale filters was especially useful in discriminating neurosarcoidosis from primary central nervous system lymphomas for mean, mean positive pixels, kurtosis, and skewness on diffusion-weighted imaging (P < 0.004–0.030). At spatial scale filter 5, entropy on T2-weighted imaging (P = 0.001) was the most useful discriminator with a cut-off value of 6.12 (P = 0.001, area under the curve (AUC)-1, sensitivity (Sn)-100%, specificity (Sp)-100%), followed by kurtosis and skewness on diffusion-weighted imaging with a cut-off value of −0.565 (P = 0.011, AUC-0.97, Sn-100%, Sp-83%) and–0.365 (P = 0.008, AUC-0.98, Sn-100%, Sp-100%) respectively. Conclusion Filtration histogram-based magnetic resonance texture analysis appears to be a promising modality to distinguish parenchymal neurosarcoidosis granulomas from primary central nervous system lymphomas.