参考文献/References:
[1] Louis DN, Perry A, Reifenberger G, et al. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary [J]. Acta Neuropathol, 2016,131(6): 803-820.
[2] 任 彦，李安宁，吴 越，等. 从影像医学角度解读2016 WHO中枢神经系统肿瘤分类[J]. 中华放射学杂志，2016，50（11）：811-816.
[3] Wang Q, Zhang H, Zhang J, et al. The diagnostic perform-ance of magnetic resonance spectroscopy in differentiating high-from low-grade gliomas: a systematic review andmeta-analysis [J]. Eur Radiol, 2016, 26(8): 2670-2684.
[4] Lin Q, Tang L, Lin Z. Application value of 3.0-T multivoxel H-MR spectroscopy in the peritumoral tissue of brain astrocytic tumors [J]. Neurol India, 2020, 68(2): 389-393.
[5] Phuttharak W, Thammaroj J, Wara-Asawapati S, et al. Grading gliomas capability: comparison between visual assessment and apparent diffusion coefficient (ADC) value measurement on diffusion-weighted imaging (DWI) [J]. Asian Pac J Cancer Prev, 2020, 21(2): 385-390.
[6] Yamasaki F, Kurisu K, Satoh K, et al. Apparent diffusioncoefficient of human brain tumors at MR imaging [J]. Radiology, 2005, 235(3): 985-991.
[7] Qin JB, Zhang H, Wang XC, et al. Combination value ofdiffusion-weighted imaging and dynamic susceptibility contrast-enhanced MRI in astrocytoma grading and correlation with GFAP, topoisomerase IIα and MGMT [J]. Oncol Lett, 2019, 18(3): 2763-2770.
[8] Abdel Razek AAK, El-Serougy L, Abdelsalam M, et al. Dif-ferentiation of primary central nervous system lymphoma from glioblastoma: quantitative analysis using arterial spin labeling and diffusion tensor imaging [J]. World Neurosurg,2019, 123: e303-e309.
[9] Raja R, Sinha N, Saini J, et al. Assessment of tissue hetero-geneity using diffusion tensor and diffusion kurtosis imaging for grading gliomas [J]. Neuroradiology, 2016, 58(12): 1217-1231.
[10] Jolapara M, Patro SN, Kesavadas C, et al. Can diffusiontensor metrics help in preoperative grading of diffusely infiltrating astrocytomas: a retrospective study of 36 cases[J]. Neuroradiology, 2011, 53(1): 63-68.
[11] Bae J, Zhang J, Wadghiri YZ, et al. Measurement of blood-brain barrier permeability using dynamic contrast-enhancedmagnetic resonance imaging with reduced scan time [J]. Magn Reson Med, 2018, 80(4): 1686-1696.
[12] Zhao M, Guo LL, Huang N, et al. Quantitative analysis of permeability for glioma grading using dynamic contrast-enhanced magnetic resonance imaging [J]. Oncol Lett, 2017,14(5): 5418-5426.
[13] Jia ZZ, Geng DY, Liu Y, et al. Microvascular permeability of brain astrocytoma with contrast-enhanced magnetic resonance imaging: correlation analysis with histopathologic grade [J]. Chin Med J (Engl), 2013, 126(10): 1953-1956.
[14] 章 瑜，靳二虎. 低级别胶质瘤:动态磁敏感对比增强磁共振灌注成像预测肿瘤恶变和病人临床反应[J]. 临床和实验医学杂志，2008，7（1）：65-66.
[15] Mangla R, Ginat DT, Kamalian S, et al. Correlation between progression free survival and dynamic susceptibility con-trast MRI perfusion in WHO grade III glioma subtypes [J]. J Neuro-Oncol, 2014, 116(2): 325-331.
[16] 李 勇，乔 飞，孔祥泉，等. 3D-ASL全脑灌注成像在脑肿瘤术前诊断与分级中的应用价值[J]. 临床放射学杂志，2015，34（6）：871-875.
[17] Morana G, Tortora D, Staglianò S, et al. Pediatric astrocytic tumor grading: comparison between arterial spin labeling and dynamic susceptibility contrast MRI perfusion [J]. Neuroradiology, 2018, 60(4): 437-446.
[18] Khashbat D, Harada M, Abe T, et al. Diagnostic perform-ance of arterial spin labeling for grading nonenhancing astrocytic tumors [J]. Magn Reson Med Sci, 2018, 17(4):277-282.