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Updated: Feb 5, 2026
Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
Published on: January 7, 2019
Multiparametric imaging-based differentiation of lymphoma and glioblastoma: using T1-perfusion, diffusion, and
J Saini1, P Kumar Gupta2, A Awasthi3
1Neuroimaging & Interventional Radiology, National Institute of Mental Health and Neurosciences, Bangalore, India.
Aim:
To compare the diagnostic performance of T1 perfusion magnetic resonance imaging (MRI), diffusion-weighted imaging (DWI), and susceptibility-weighted imaging (SWI) for differentiating primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM).
Materials And Methods:
This retrospective study comprised a cohort of 70 patients with glioblastoma and 30 patients with PCNSL. T1 perfusion MRI-derived rCBV_corr (leakage corrected relative cerebral blood volume), apparent diffusion coefficient (ADC) derived from DWI, and intratumoural susceptibility signals intensity (ITSS) measured on SWI were evaluated in these 100 patients. The Mann-Whitney U-test was used for pairwise comparison between groups. The diagnostic performance for differentiating PCNSL from glioblastoma was evaluated by using univariate and multivariable logistic regression analyses and receiver operating characteristic (ROC) analysis.
Results:
Minimum ADC, maximum rCBVs_corr, kep (back flux exchange rate), and ITSS scores were significantly lower in patients with PCNSL than in those with glioblastoma (p<0.05). On ROC analysis, ITSS showed the best discrimination ability for differentiation of GBM and PCNSL with an area under the ROC curve (AUC) of 0.80. rCBV_corr and ADC showed AUCs of 0.68 and 0.63, respectively. Multiparametric assessment using ADC, rCBV_corr, kep, and ITSS scores significantly increased the diagnostic ability for differentiating PCNSL from GBM as compared to mean ADC, mean rCBV_corr, and ITSS alone or a combination of these parameters. The multiparametric model could correctly discriminate 84% of tumours with a sensitivity and specificity of 90% and 70% with an AUC of 0.92.
Conclusion:
Multiparametric MRI evaluation using DWI, T1 perfusion MRI, and SWI enabled reliable differentiation of PCNSL and GBM in the majority patients, and these results support an integration of advanced MRI techniques for the diagnostic work-up of patients with these tumours.
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