Multiparametric differentiation of intracranial central nervous system lymphoma and high-grade glioma using diffusion-, perfusion-, susceptibility-weighted magnetic resonance imaging, and spectroscopy

Aims and Objectives: To observe the characteristics of primary central nervous system lymphoma (PCNSL) and high-grade glioma (HGG) in diffusion-weighted imaging (DWI), perfusion-weighted imaging (PWI), susceptibility-weighted imaging (SWI) and spectroscopy, and differentiate them based on these parameters. Materials and Methods: A total of 45 patients diagnosed with the central nervous system (CNS) neoplasm on magnetic resonance imaging (MRI) using 1.5 Tesla MRI Siemens Magnetom Avanto (Siemens, Germany) and with subsequent histopathological evidence as glioblastoma or CNS lymphoma were included. The study was completed over a period of 2 years. Results: It was found that DWI is effective in the differentiation of HGGs and PCNSLs. A total of 20 (57.1%) HGGs showed diffusion restriction, whereas 9 (90%) of the PCNSLs showed diffusion restriction. The mean apparent diffusion coefficient (ADC) (×10–6 mm2/s) in PCNSLs was 646 whereas, in HGGs, it was found to be 824.3. Thirty-one (88.6%) of the HGGs showed increased perfusion. The mean value of rCBVmean in HGG was found to be 4.06 and the mean value of rCBVmax was 3.63. None of the PCNSLs showed increased perfusion. The mean value of rCBVmean in PCNSLs was 0.097 and rCBVmax was 0.133. 30 (85.7%) of HGGs showed significant areas of blooming on SWI (in the form of intratumoral susceptibility signals [ITSS]). None of the PCNSLs showed blooming. All HGGs and PCNSLs showed increased choline and decreased N acetyl aspartate (NAA) on spectroscopy. Mean Choline/Creatine (Cho/Cr) in HGGs was found to be 3.06, whereas in PCNSLs, it was 1.84. Conclusion: It is important to make a distinction between HGG and PCNSL as the treatment modalities are different for both. Multiparametric evaluation of ADC, ITSS, and rCBVmean allows the differentiation of PCNSLs and solid glioblastoma which supports the integration of advanced MR imaging techniques including DSC-PWI, DWI, and SWI for the routine diagnostic workup of these tumors.

[1]  Christos Davatzikos,et al.  Emerging Applications of Artificial Intelligence in Neuro-Oncology. , 2019, Radiology.

[2]  J. Bladowska,et al.  Differentiation of glioblastoma multiforme, metastases and primary central nervous system lymphomas using multiparametric perfusion and diffusion MR imaging of a tumor core and a peritumoral zone—Searching for a practical approach , 2018, PloS one.

[3]  T. Hirai,et al.  Differentiating Between Primary Central Nervous System Lymphomas and Glioblastomas: Combined Use of Perfusion-Weighted and Diffusion-Weighted Magnetic Resonance Imaging. , 2017, World neurosurgery.

[4]  S. Price,et al.  Multiparametric MR Imaging of Diffusion and Perfusion in Contrast-enhancing and Nonenhancing Components in Patients with Glioblastoma. , 2017, Radiology.

[5]  H. Almassry,et al.  Preoperative glioma grading by MR diffusion and MR spectroscopic imaging , 2016 .

[6]  K. Hoffmann,et al.  Diffusion-Weighted MRI Reflects Proliferative Activity in Primary CNS Lymphoma , 2016, PloS one.

[7]  T. Gupta,et al.  Indian data on central nervous tumors: A summary of published work , 2016, South Asian Journal of Cancer.

[8]  Sumei Wang,et al.  Glioma grading by microvascular permeability parameters derived from dynamic contrast-enhanced MRI and intratumoral susceptibility signal on susceptibility weighted imaging , 2015, Cancer Imaging.

[9]  I. Reda,et al.  The added value of advanced neuro-imaging (MR diffusion, perfusion and proton spectroscopy) in diagnosis of primary CNS lymphoma , 2014 .

[10]  Xinjian Lin,et al.  Differentiation of primary central nervous system lymphoma from high-grade glioma and brain metastases using susceptibility-weighted imaging , 2014, Brain and behavior.

[11]  Alexander Radbruch,et al.  Primary central nervous system lymphoma and atypical glioblastoma: multiparametric differentiation by using diffusion-, perfusion-, and susceptibility-weighted MR imaging. , 2014, Radiology.

[12]  S. Ng,et al.  Differentiation of Primary Central Nervous System Lymphomas and Glioblastomas: Comparisons of Diagnostic Performance of Dynamic Susceptibility Contrast-Enhanced Perfusion MR Imaging without and with Contrast-Leakage Correction , 2013, American Journal of Neuroradiology.

[13]  S. Heiland,et al.  Differentiation of glioblastoma and primary CNS lymphomas using susceptibility weighted imaging. , 2013, European journal of radiology.

[14]  Satoshi O. Suzuki,et al.  Differentiating primary CNS lymphoma from glioblastoma multiforme: assessment using arterial spin labeling, diffusion-weighted imaging, and 18F-fluorodeoxyglucose positron emission tomography , 2013, Neuroradiology.

[15]  D. Zagzag,et al.  Mechanisms of glioma-associated neovascularization. , 2012, The American journal of pathology.

[16]  Aidos Doskaliyev,et al.  Lymphomas and glioblastomas: differences in the apparent diffusion coefficient evaluated with high b-value diffusion-weighted magnetic resonance imaging at 3T. , 2012, European journal of radiology.

[17]  S Ekholm,et al.  Percentage Signal Recovery Derived from MR Dynamic Susceptibility Contrast Imaging Is Useful to Differentiate Common Enhancing Malignant Lesions of the Brain , 2011, American Journal of Neuroradiology.

[18]  E. Melhem,et al.  Differentiation between Glioblastomas, Solitary Brain Metastases, and Primary Cerebral Lymphomas Using Diffusion Tensor and Dynamic Susceptibility Contrast-Enhanced MR Imaging , 2011, American Journal of Neuroradiology.

[19]  P. Barker,et al.  Imaging of brain tumors: MR spectroscopy and metabolic imaging. , 2010, Neuroimaging clinics of North America.

[20]  Elias Melhem,et al.  Proton magnetic resonance spectroscopic imaging to differentiate between nonneoplastic lesions and brain tumors in children , 2006, Journal of magnetic resonance imaging : JMRI.

[21]  M. Hartmann,et al.  Differentiating primary central nervous system lymphoma from glioma in humans using localised proton magnetic resonance spectroscopy , 2003, Neuroscience Letters.

[22]  V. L. Doyle,et al.  Metabolic profiles of human brain tumors using quantitative in vivo 1H magnetic resonance spectroscopy , 2003, Magnetic resonance in medicine.