Search

The preoperative imaging-based differentiation of primary central nervous system lymphomas (PCNSLs) and glioblastomas (GBs) is of high importance since the therapeutic strategies differ substantially between these tumors. In this study, we investigate whether the gamma distribution (GD) model is useful in this differentiation of PNCSLs and GBs. Twenty-seven patients with PCNSLs and 57 patients with GBs were imaged with diffusion-weighted imaging using 13 b-values ranging from 0 to 1000 sec/mm2. The shape parameter (κ) and scale parameter (θ) were obtained with the GD model. Fractions of three different areas under the probability density function curve (f1, f2, f3) were defined as follows: f1, diffusion coefficient (D) <1.0×10-3 mm2/sec; f2, D >1.0×10-3 and <3.0×10-3 mm2/sec; f3, D >3.0 × 10-3 mm2/sec. The GD model-derived parameters were compared between PCNSLs and GBs. Receiver operating characteristic (ROC) curve analyses were performed to assess diagnostic performance. The correlations with intravoxel incoherent motion (IVIM)-derived parameters were evaluated. The PCNSL group's κ (2.26 ± 1.00) was significantly smaller than the GB group's (3.62 ± 2.01, p = 0.0004). The PCNSL group's f1 (0.542 ± 0.107) was significantly larger than the GB group's (0.348 ± 0.132, p<0.0001). The PCNSL group's f2 (0.372 ± 0.098) was significantly smaller than the GB group's (0.508 ± 0.127, p<0.0001). The PCNSL group's f3 (0.086 ± 0.043) was significantly smaller than the GB group's (0.144 ± 0.062, p<0.0001). The combination of κ, f1, and f3 showed excellent diagnostic performance (area under the curve, 0.909). The f1 had an almost perfect inverse correlation with D. The f2 and f3 had very strong positive correlations with D and f, respectively. The GD model is useful for the differentiation of GBs and PCNSLs., Competing Interests: The authors have declared that no competing interests exist.

Aim: To evaluate the diagnostic feasibility of probabilistic analysis using voxel-based morphometry (VBM) in differentiating primary central nervous system lymphoma (PCNSL) from glioblastoma (GBM)., Materials and Methods: In total, 118 patients with GBM (57 males, 61 females; mean [± standard deviation] age, 56.9±19.3 years; median, 61 years) and 52 patients with PCNSL (37 males, 15 females; mean age, 62±13.3 years, median, 66 years) were studied retrospectively. Each patient underwent preoperative contrast-enhanced T1-weighted imaging (CE-T1WI) using a 1.5 or 3 T magnetic resonance imaging (MRI) system. To assess preferential occurrence sites, images from CE-T1WI were co-registered and spatially normalised using the MNI152 T1 template. Subsequently, a region of interest (ROI) was placed in the centre of the enhancing tumour in normalised images with 1-mm isotropic resolution. The same ROI between normalised and T1 template images was set up using an ROI manager function in ImageJ software. A spherical volume of interest (VOI) with a radius of 10 mm was determined. A probability map was created by overlaying each image with the VOI. Each VOI was removed from T1 template images for VBM analysis. VBM analysis was performed using statistical parametric mapping (SPM) 12 software under default settings., Results: VBM analysis showed significantly higher frequency in the splenium of the corpus callosum among PCNSL patients than among GBM patients (p<0.05; family-wise error correction)., Conclusion: Topographic analysis using VBM provides useful information for differentiating PCNSL from GBM., (Copyright © 2019 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.)

Purpose: The purpose of this study was to identify magnetic resonance imaging (MRI) features that are associated with telomerase reverse transcriptase promoter mutation (TERTm) in glioblastoma., Materials and Methods: A total of 112 patients with glioblastoma who had MRI at 1.5- or 3.0-T were retrospectively included. There were 43 patients with glioblastoma with wild-type TERT (TERTw) (22 men, 21 women; mean age, 47±25 [SD] years; age range: 3-84 years) and 69 patients with glioblastoma with TERTm (34 men, 35 women; mean age 64±11 [SD] years; age range, 41--85 years). The feature vectors consist of 11 input units for two clinical parameters (age and gender) and nine MRI characteristics (tumor location, subventricular extension, cortical extension, multiplicity, enhancing volume, necrosis volume, the percentage of necrosis volume, minimum apparent diffusion coefficient [ADC] and normalized ADC). First, the diagnostic performance using univariate and multivariate logistic regression analyses was evaluated. Second, the cross-validation of the support vector machine (SVM) was performed by using leave-one-out method with 43 TERTw and 69 TERTm to evaluate the diagnostic performance. In addition, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy for the differentiation between TERTw and TERTm were compared between logistic regression analysis and SVM., Results: With multivariate analysis, the percentage of necrosis volume and age were significantly greater in TERTm glioblastoma than in TERTw glioblastoma. SVM allowed discriminating between TERTw glioblastoma and TERTm glioblastoma with sensitivity, specificity, PPV, NPV, and accuracy of 85.7% [60/70; 95% confidence interval (CI): 75.3-92.9%], 54.8% (23/42; 95% CI: 38.7-70.2%), 75.9% (60/79; 95% CI: 69.1-81.7%), 69.7% (23/33; 95% CI: 54.9-81.3%) and 74.1% (83/112; 95% CI: 65.0-81.9%), respectively., Conclusion: The percentage of necrosis volume and age may surrogate for predicting TERT mutation status in glioblastoma., (Copyright © 2019 Société française de radiologie. Published by Elsevier Masson SAS. All rights reserved.)