Rationale and Objectives
This study aimed to collect the studies on the role of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and dynamic susceptibility contrast MRI (DSC-MRI) in differentiating the grades of gliomas, and evaluate the diagnostic performances of relevant quantitative parameters in glioma grading.
Materials and Methods
We systematically searched studies on the diagnosis of gliomas with DCE-MRI or DSC-MRI in Medline, PubMed, China National Knowledge Infrastructure database, Cochrane Library, and Embase published between January 2005 and December 2016. Standardized mean differences and 95% confidence intervals were calculated for volume transfer coefficient (K trans ), volume fraction of extravascular extracellular space (V e ), rate constant of backflux (K ep ), relative cerebral blood volume (rCBV), and relative cerebral blood flow (rCBF) using Review Manager 5.2 software. Sensitivity, specificity, area under the curve (AUC), and Begg test were calculated by Stata 12.0.
Results
Twenty-two studies with available outcome data were included in the analysis. The standardized mean difference of K trans values between high-grade glioma and low-grade glioma were 1.18 (0.91, 1.45); V e values were 1.43 (1.06, 1.80); K ep values were 0.65 (−0.05, 1.36); rCBV values were 1.44 (1.08, 1.81); and rCBF values were 1.17 (0.68, 1.67), respectively. The results were all significant statistically ( P < .05) except K ep values ( P = .07), and high-grade glioma had higher K trans , V e , rCBV, and rCBF values than low-grade glioma. AUC values of K trans , V e , rCBV, and rCBF were 0.90, 0.88, 0.93, and 0.73, respectively; rCBV had the largest AUC among the four parameters ( P < .05).
Conclusion
Both DCE-MRI and DSC-MRI are reliable techniques in differentiating the grades of gliomas, and rCBV was found to be the most sensitive one.
Introduction
Gliomas are the most common primary malignant tumors of the central nervous system. According to the 2016 World Health Organization (WHO) classification of tumors of the central nervous system , gliomas are divided into four grades based on their histology and molecular features. Accurate grading of gliomas is critical to the determination of surgery scheme, treatment response, and prognostic evaluation. On pathology, low-grade gliomas (LGGs) are slowly proliferating tumors that display cytological atypia but no signs of anaplasia, endothelial cell proliferation, or brisk mitotic activity . However, in high-grade gliomas (HGGs), substantial hyperplasia of anomalous cells can be observed, resulting in neovascularization and incomplete basement membrane of tumor neovasculature, which in turns leads to augmentation of microvascular permeability, a histologic marker of HGG . Furthermore, the abnormal vessels of tumors are usually tortuous and disorganized. The resultant disordered cerebral hemodynamics alter blood volume and blood flow directly.
Conventional morphologic magnetic resonance imaging (MRI) can estimate benign and malignant tumors based grossly on the range of cytotoxic edema, hemorrhage, necrosis, signal intensity heterogeneity, and degree of enhancement. However, it has been reported that 9.5% HGG showed no enhancement, whereas 22.72% of LGG enhanced after contrast administration . Therefore, quantitative and reliable imaging methods are needed. Dynamic contrast-enhanced MRI (DCE-MRI) is a noninvasive technology that provides information about the microcirculation of tumors. It assesses several valuable parameters including volume transfer coefficient (K trans ), volume fraction of extravascular extracellular space (V e ), and rate constant of backflux (K ep ), all of which can reflect the permeability of new vessels and are indicative of malignant grade of tumors . Dynamic susceptibility contrast MRI (DSC-MRI) is another advanced technique that provides perfusion information with parameters such as cerebral blood volume (CBV) and cerebral blood flow (CBF). Increased tumor vascularity and tumor grade correlate credibly with relative CBV (rCBV) and relative CBF (rCBF) .
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Materials and Methods
Data Sources
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Studies Selection
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Data Abstraction and Quality Assessment
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Data Synthesis
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Results
Literature Search and Selection of Studies
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TABLE 1
Characteristics of Studies Included in the Meta-analysis
Author Year Machine Type Country Age (y) Type (Count) Journal Quality Assessment Awasthi et al. 2012 1.5T GE India 16–65 I + II (21), III +IV (55) Neuroradiology 12 Falk et al. 2014 3T Philips Sweden 22–79 II(18), III(7) Neuroradiology 10 Tietze et al. 2015 3T Philips Denmark NA II(10), IV(23) Neuroradiology 13 Choi et al. 2013 3T Philips Korea 51.79 ± 18.34 I (1), II(9), III(8), IV(15) Korean J Radiol 10 Roy et al. 2013 3T GE India 43 I(3), II(23),III (9), IV(29) J Comput Assist Tomogr 13 Sahoo et al. 2013 1.5T GE India 21–63 I + II (45), III + IV (102) J Magn Reson Imaging 13 Jia et al. 2015 3T Siemens China 46 ± 12 II (24), III (7), IV (26) Eur J Radiol 14 Nguyen et al. 2012 1.5T Siemens Canada NA II (8), III (4), IV (19) Am J Neuroradiol 12 Arevalo-Perez et al. 2015 1.5T GE USA 54.3 II (20), III (10), IV (33) J Neuroimaging 11 Li et al. 2015 3T Siemens China 42.6 ± 14.3 I + II (15), III (8), IV (9) Cancer Imaging 10 Zhang et al. 2012 1.5T Siemens China 47.11 ± 14.18 I (8), II (6), III (6), IV (8) J Magn Reson Imaging 13 Sun et al. 2015 3T GE China 45.7(14–73) I (2), II (10), III (7), IV (9) Acta acad Med sin 12 Wang et al. 2015 3T Siemens China 12.7 ± 4.6 I + II (11), III + IV (5) Chin J Med Imaging Technol 9 Huang et al. 2015 3T Siemens China 45(17–72) I (2), II (34), III (21), IV (28) J Third Mil Med Univ 9 Server et al. 2011 3T GE Norway 57.73 ± 12.95 II (18), III (14), IV (47) Neuroradiology 14 de Fatima Vasco Aragao et al. 2014 1.5T GE Brazil 36.23 ± 16.95 I + II (9), III + IV (20) Am J Neuroradiol 13 Direksunthorn et al. 2013 3T Philips Thailand 45.9(12–74) I + II (18), III + IV (26) J Med Assoc Thai 9 Law et al. 2006 1.5T Siemens USA 42(4–85) II (31), III (16), IV (26) Am J Neuroradiol 11 Santarosa et al. 2016 3T Philips Italy 55.4(22–79) II (9), III (4), IV (13) Eur J Radiol 13 Kim et al. 2013 3T Siemens Korea NA II (9), III (16), IV (38) PLOS ONE 9 Patankar et al. 2005 1.5T Philips UK 52.9(31–77) II (10), III (6), IV (23) Am J Neuroradiol 10 Boxerman et al. 2006 1.5T GE USA 52(19–80) II (11), III (9), IV (23) Am J Neuroradiol 12
NA, not available.
TABLE 2
The Specific Parameters of DCE- and DSC-MRI of the Included Studies
Study DCE Sequence Model Leakage Correction of rCBV DSC Sequence Contrast Type Flow rate Dose OA/OG Post-Processing Software Awasthi et al. 3D-SPGR Pharmacokinetic model Corrected NA Omniscan 5 mL/s 0.2 mmol/kg NA NA Falk et al. T1W SPGR Tikhonov method NA T2*W SS-GE-EPI Gadovist 3 mL/s 0.05 mmol/kg 7/8 MATLAB Tietze et al. Turbo FLASH extended two compartment exchange model Corrected NA Gadovist 2.5 mL/s 0.05 mmol/kg 0/6 SPM8 and MATLAB Choi et al. NA classic Tofs-Kermode model NA NA Gadovist 2 mL/s 0.1 mmol/kg 3/2 PRIDE tools(Philips Medical Systems) Roy et al. 3D-SPGR Pharmacokinetic model Corrected NA Omniscan 5 mL/s NA NA NA Sahoo et al. 3D-FSPGR leaky tracer kinetic model NA NA Gd-DTPA 5 mL/s 0.2 mmol/kg NA in-house-developed software Jia et al. NA classic Tofs-Kermode model NA NA Omniscan 4 mL/s 0.1 mmol/kg 5/8 Tissue-4D software(Siemens Syngo) Nguyen et al. 2D FLASH Phase-Derived VIF with Bookend T1 Correction NA NA Magnevist 4 mL/s 0.1 mmol/kg 3/1 NordicICE software Arevalo-Perez et al. 3D-SPGR classic Tofs-Kermode model NA NA Magnevist 2–3 mL/s 0.2 mmol/kg 4/6 NordicICE software Li et al. T1-twist classic Tofs-Kermode model NA NA Omniscan 4 mL/s 0.1 mmol/kg 2/6 Tissue-4D software(Siemens Syngo) Zhang et al. 3D Turbo FLASH modified Tofts’ two compartment model NA NA Gd-DTPA 4 mL/s 0.1 mmol/kg NA MATLAB Sun et al. NA extended Tofts Linear model NA NA Omniscan 2 mL/s 0.1 mmol/kg 6/1 Omni Kinetics(GE) Wang et al. T1-twist classic Tofs-Kermode model NA NA Gd-DTPA 2–3 mL/s 0.1 mmol/kg 0/0 Tissue-4D software(Siemens Syngo) Huang et al. T1-twist classic Tofs-Kermode model NA T2W SE-EPI Gd-DTPA 4 mL/s 0.1 mmol/kg NA Tissue-4D software(Siemens Syngo) Server et al. NA NA NA T2*W SS-GE-EPI Magnevist 5 mL/s 18 ml 3/2 NordicICE software de Fatima Vasco Aragao et al. SE NA Corrected NA Magnevist 4 mL/s 0.1 mmol/kg 0/0 FuncTool (GE AW4.2 workstation) Direksunthorn et al. NA NA NA 3D-PRESTO Gadovist 4–5 mL/s NA NA proprietary analytic software((Philip) Law et al. NA classic Tofs-Kermode model Corrected GE-EPI Magnevist 5 mL/s 0.1 mmol/kg 0/10 in-house-developed software Santarosa et al. GE classic Tofs-Kermode model Corrected T2*W GE-EPI Gadovist 2–5 mL/s 10 ml 4/3 NordicICE software Kim et al. NA NA Corrected SS-GE-EPI Gadovist 4 mL/s 0.1 mmol/kg 0/0 NordicICE software Patankar et al. 3D T1 FFE biexponential model of PCCF Corrected NA Omniscan NA 0.1 mmol/kg NA in-house-developed software Boxerman et al. NA NA Corrected SS-GE-EPI Omniscan 3–5 mL/s 0.15–0.25 mmol/kg 2/5 NA
DCE, dynamic contrast-enhanced; DSC, dynamic susceptibility contrast; EPI, echo planar imaging; FFE, fast-field echo sequence; FLASH, fast low angle shot; FSPGR, fast spoiled gradient echo images; GE, gradient echo; NA, Not available; OA, oligoastrocytomas; OG, oligodendrogliomas; SS, single shot.
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Quantitative Analysis
K trans
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V e
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K ep
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rCBV
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rCBF
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Diagnosis Values
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TABLE 3
Diagnosis Values of K trans , V e , rCBV, and rCBF
Index Combined Studies Sensitivity (95% CI) Specificity (95% CI) PLR (95% CI) NLR (95% CI) DOR (95% CI) AUC (95% CI) I 2 Sensitivity Specificity K trans 13 0.88(0.81,0.93) 0.80(0.72,0.86) 4.3(3.0,6.2) 0.15(0.09,0.25) 28(14,58) 0.90(0.87,0.92) 63.80% 39.88% V e 6 0.85(0.73,0.92) 0.84(0.75,0.91) 5.5(3.3,9.2) 0.18(0.09,0.34) 31(12,78) 0.88(0.85,0.91) 44.87% 0 rCBV 8 0.91(0.83,0.95) 0.82(0.71,0.90) 5.1(3.1,8.6) 0.11(0.06,0.20) 46(23,94) 0.93(0.90,0.95) 61.22% 42.05% rCBF 5 0.88(0.77,0.94) 0.68(0.56,0.77) 2.7(1.9,3.9) 0.18(0.09,0.36) 15(6,40) 0.73(0.69,0.77) 57.33% 36.16%
AUC, area under the curve; CI, confidence interval; DOR, diagnostic odds ratio; K trans , volume transfer coefficient; NLR, negative likelihood ratio; PLR, positive likelihood ratio; rCBF, relative cerebral blood flow; rCBV, relative cerebral blood volume; V e , volume fraction of extravascular extracellular space.
K trans ; V e ; rCBV ; rCBF . K ep was not listed because no statistic difference was seen in SMD.
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Sensitivity Analysis
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Discussion
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Limitations
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Conclusions
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