1. Osborn, AG. Osborn’s Brain: Imaging, pathology and anatomy. Salt Lake City: Amirsys Inc; 2013. p.445.
Google Scholar2. Law, M, Cha, S, Knopp, EA, et al. High-grade gliomas and solitary metastases: Differentiation by using perfusion and proton spectroscopic MR imaging. Radiology 2002; 222: 715–21.
Google Scholar | Crossref | Medline | ISI3. Law, M, Yang, S, Wang, H, et al. Glioma grading: Specificity and predictive values of perfusion MR imaging and proton MR spectroscopic imaging compared with conventional MR imaging. Am J Neuroradiol 2003; 24: 1989–98.
Google Scholar | Medline | ISI4. Ellika, SK, Jain, R, Patel, SC, et al. Role of perfusion CT in glioma grading and comparison with conventional MR imaging features. Am J Neuroradiol 2007; 28: 1981–87.
Google Scholar | Crossref | Medline5. Teng, K, Zhang, ZH, Jiang, LL. The application of apparent diffusion coefficient in preoperative grading of gliomas. J Med Imaging 2007; 17: 1250–51.
Google Scholar6. Saito, M. Potential of dual-energy subtraction for converting CT numbers to electron density based on a single linear relationship. Med Phys 2012; 39: 2021–30.
Google Scholar | Crossref | Medline7. Louis, DN. WHO classification and grading of tumours of the central nervous system. In: Louis, DN, Ohgaki, H, Wiestier, OD, et al. (eds) WHO classification of tumours of the central nervous system. 4th ed. Lyon: International Agency for Research on Cancer, 2016, pp.12–13.
Google Scholar8. Bailey, P, Cushing, H. A classification of the tumours of the glioma group on a histogenetic basis with a correlated study of prognosis. Philadelphia: J. B. Lippincott Co, 1926.
Google Scholar9. Komori, T, Muragaki, Y, Chernov, MF. Pathology and genetics of gliomas. Prog Neurol Surg 2018; 31: 1–37.
Google Scholar | Crossref | Medline10. Jain, R, Ellika, SK, Scarpace, L, et al. Quantitative estimation of permeability surface-area product in astroglial brain tumors using perfusion CT and correlation with histopathologic grade. Am J Neuroradiol 2008; 29: 694–700.
Google Scholar | Crossref | Medline | ISI11. Bian, W, Khayal, IS, Lupo, JM, et al. Multiparametric characterization of grade 2 glioma subtypes using magnetic resonance spectroscopic, perfusion, and diffusion imaging. Transl Oncol 2009; 2: 271– 80.
Google Scholar | Crossref | Medline12. Sui, Y, Xiong, Y, Jiang, J, et al. Differentiation of low- and high-grade gliomas using High b-value diffusion imaging with a non-Gaussian diffusion model. Am J Neuroradiol 2016; 37: 1643–49.
Google Scholar | Crossref | Medline13. Shim, WH, Kim, HS, Choi, CG, et al. Comparison of apparent diffusion coefficient and intravoxel incoherent motion for differentiating among glioblastoma, metastasis, and lymphoma focusing on diffusion-related parameter. PLoS One 2015; 10: e0134761.
Google Scholar | Crossref | Medline14. Hünemohr, N, Krauss, B, Tremmel, C, et al. Experimental verification of ion stopping power prediction from dual energy CT data in tissue surrogates. Phys Med Biol 2014; 59: 83–96.
Google Scholar | Crossref | Medline | ISI15. Kaichi, Y, Tatsugami, F, Nakamura, Y, et al. Improved differentiation between high- and low-grade gliomas by combining dual-energy CT analysis and perfusion CT. Medicine (Baltimore) 2018; 97: e11670.
Google Scholar | Crossref | Medline16. ICRP Publication 105 . Radiation protection in medicine. Ann ICRP 2007; 37: 1–63.
Google Scholar | SAGE Journals