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Updated: Apr 30, 2026
Isolation and Flow Cytometric Analysis of Glioma-infiltrating Peripheral Blood Mononuclear Cells
Published on: November 28, 2015
Primary central nervous system lymphoma and atypical glioblastoma: multiparametric differentiation by using
Philipp Kickingereder1, Benedikt Wiestler, Felix Sahm
1From the Departments of Neuroradiology (P.K., S.H., M.B., A.R.), Neuro-oncology (B.W., W.W.), and Neuropathology (F.S.), University of Heidelberg Medical Center, Heidelberg, Germany; German Cancer Consortium (DKTK), Clinical Cooperation Unit Neuro-oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (B.W., W.W.); DKTK, Clinical Cooperation Unit Neuropathology, DKFZ, Heidelberg, Germany (F.S.); Department of Radiology, DKFZ, Heidelberg, Germany (M.R., H.P.S.); and Department of Neuro-oncologic Imaging (E012), Department of Radiology, DKFZ, Heidelberg, Germany (A.R.).
Purpose:
To compare multiparametric diagnostic performance with diffusion-weighted, dynamic susceptibility-weighted contrast material-enhanced perfusion-weighted, and susceptibility-weighted magnetic resonance (MR) imaging for differentiating primary central nervous system lymphoma (PCNSL) and atypical glioblastoma.
Materials And Methods:
This retrospective study was institutional review board-approved and informed consent was waived. Pretreatment MR imaging was performed in 314 patients with glioblastoma, and a subset of 28 patients with glioblastoma of atypical appearance (solid enhancement with no visible necrosis) was selected. Parameters of diffusion-weighted (apparent diffusion coefficient [ADC]), susceptibility-weighted (intratumoral susceptibility signals [ITSS]), and dynamic susceptibility-weighted contrast-enhanced perfusion-weighted (relative cerebral blood volume [rCBV]) imaging were evaluated in these 28 patients with glioblastoma and 19 immunocompetent patients with PCNSL. A two-sample t test and χ(2) test were used to compare parameters.The diagnostic performance for differentiating PCNSL from glioblastoma was evaluated by using logistic regression analyses with leave-one-out cross validation.
Results:
Minimum, maximum, and mean ADCs and maximum and mean rCBVs were significantly lower in patients with PCNSL than in those with glioblastoma (P < .01, respectively), whereas mean ADCs and mean rCBVs allowed the best diagnostic performance. Presence of ITSS was significantly lower in patients with PCNSL (32% [six of 19]) than in those with glioblastoma (82% [23 of 28]) (P < .01). Multiparametric assessment of mean ADC, mean rCBV, and presence of ITSS significantly increased the probability for differentiating PCNSL and atypical glioblastoma compared with the evaluation of one or two imaging parameters (P < .01), thereby correctly predicting histologic results in 95% (18 of 19) of patients with PCNSL and 96% (27 of 28) of patients with atypical glioblastoma.
Conclusion:
Combined evaluation of mean ADC, mean rCBV, and presence of ITSS allowed reliable differentiation of PCNSL and atypical glioblastoma in most patients, and these results support an integration of advanced MR imaging techniques for the routine diagnostic workup of patients with these tumors.
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