Table 4.
Differential diagnosis in glioma, metastasis, and PCNSL.
| Study (year) (ref) | Tumor type (n) | Average age (year) | Imaging modality (method or model; parameter analysis) | Indexes | Results | Limitations |
|---|---|---|---|---|---|---|
| Law et al. (2002) [116] | HGG (24) MET (12) |
52 | DSC-MRI (leakage effect uncorrected; ROI-based analysis) | rCBV | rCBV in peritumoral region was significantly different between HGG and MET | The peritumoral region was not defined clearly; the threshold value was not provided |
|
| ||||||
| Cha et al. (2007) [117] | GBM (27) MET (16) |
52 | DSC-MRI (alteration of TE and flip angle for leakage correction; ROI-based analysis) | PSR PH |
Significant difference of all parameters between GBM and MET; PSR was the most powerful with 100% specificity | Small sample size; some cases were not confirmed by histopathology |
|
| ||||||
| Mangla et al. (2011) [118] | GBM (22) MET (22) PCNSL (22) |
54 | DSC-MRI (preload for leakage correction; ROI-based analysis) | rCBV PSR |
PSR was better than rCBV for differentiation | Small sample size; impact of steroid treatment on parameter evaluation |
|
| ||||||
| Toh et al. (2013) [119] | GBM (20) PCNSL (15) |
60 | DSC-MRI (algorithm for leakage correction; ROI-based analysis) | rCBV K2 |
Uncorrected rCBV is much better for differentiating | Lack of direct correlation between parameters and histopathologic features |
|
| ||||||
| Xing et al. (2014) [120] | HGG (26) PCNSL (12) |
51 | DSC-MRI (leakage effect uncorrected; ROI-based analysis) | rCBV PSR |
The combination of rCBV with PSR might help in more accurate differentiation | Impact of leakage effect on parameter measurements |
|
| ||||||
| Kickingereder et al. (2014) [121] | GBM (60) PCNSL (11) |
N/A | DCE-MRI (TK model; ROI-based analysis) |
K
trans Ve Kep |
K trans and Kep could identify the two tumors. Ktrans was the optimum parameter | Relative small sample size of PCNSL |
|
| ||||||
| Kickingereder et al. (2014) [122] | GBM (28) PCNSL (19) |
66 | DSC-MRI (preload for leakage correction; ROI-based analysis), DWI, SWI | rCBV ADC ITSS |
Multiparametric MRI allowed differentiation of GBM from PCNSL | Small sample size |
|
| ||||||
| Zhao et al. (2015) [52] | LGG (9) HGG (15) MET (5) |
46 | DCE-MRI (TK model; ROI-based analysis) |
K
trans Ve IAUC |
All parameters were significantly different between LGG, HGG, and MET. IAUC had the most diagnostic power | Small sample size; subjectivity of ROI selection |
|
| ||||||
| Jung et al. (2016) [123] | GBM (26) MET (32) |
N/A | DCE-MRI (ETK model, ROI-based analysis) |
K
trans Vp AUC Washout log slope |
Semiquantitative parameters could differentiate between GBM and hypovascular metastasis | Subjectivity of ROI selection |