REFERENCES
1. Goutagny S, Bah AB, Henin D, et al. Long-term follow-up of 287 meningiomas in neurofibromatosis type 2 patients: clinical, radiological, and molecular features. Neuro Oncol 2012;14:1090-6.
2. Buerki RA, Horbinski CM, Kruser T, Horowitz PM, James CD, Lukas RV. An overview of meningiomas. Future Oncol 2018;14:2161-77.
3. Mariniello G, Briganti F, De Caro ML, Maiuri F. Cervical extradural “en-plaque” meningioma. J Neurol Surg A Cent Eur Neurosurg 2012;73:330-3.
4. Hashiba T, Hashimoto N, Izumoto S, et al. Serial volumetric assessment of the natural history and growth pattern of incidentally discovered meningiomas. J Neurosurg 2009;110:675-84.
5. Louis DN, Perry A, Reifenberger G, et al. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta Neuropathol 2016;131:803-20.
6. Alghamdi M, Li H, Olivotto I, et al. Atypical meningioma: referral patterns, treatment and adherence to guidelines. Can J Neurol Sci 2017;44:283-7.
7. van Alkemade H, de Leau M, Dieleman EM, et al. Impaired survival and long-term neurological problems in benign meningioma. Neuro Oncol 2012;14:658-66.
9. Jääskeläinen J. Seemingly complete removal of histologically benign intracranial meningioma: late recurrence rate and factors predicting recurrence in 657 patients. A multivariate analysis. Surgical Neurology 1986;26:461-9.
10. Islam OH, Grayson, Coombs B, et al. Imaging in brain meningioma. 2014. Available from: http://emedicine.medscape.com/article/341624-overview.2015. [Last accessed on 9 Nov 2020].
11. Watts J, Box G, Galvin A, Brotchie P, Trost N, Sutherland T. Magnetic resonance imaging of meningiomas: a pictorial review. Insights Imaging 2014;5:113-22.
12. Huang RY, Bi WL, Griffith B, et al; International Consortium on Meningiomas. Imaging and diagnostic advances for intracranial meningiomas. Neuro Oncol 2019;21:i44-61.
13. Ly KI, Wen PY, Huang RY. Imaging of Central Nervous System Tumors Based on the 2016 World Health Organization Classification. Neurol Clin 2020;38:95-113.
14. Buetow MP, Buetow PC, Smirniotopoulos JG. Typical, atypical, and misleading features in meningioma. Radiographics 1991;11:1087-106.
15. Sade B, Lee JH. High incidence of optic canal involvement in clinoidal meningiomas: rationale for aggressive skull base approach. Acta Neurochir (Wien) 2008;150:1127-32. discussion 1132
16. Tamiya T, Ono Y, Matsumoto K, Ohmoto T. Peritumoral brain edema in intracranial meningiomas: effects of radiological and histological factors. Neurosurgery 2001;49:1046-51. discussion 1051-2
17. Todua FI, Chedia SV, Nuralidze KI. Computed tomography and magnetic resonance angiography of brain meningiomas. Georgian Med News 2013:21-7.
18. Raza SM, Gallia GL, Brem H, Weingart JD, Long DM, Olivi A. Perioperative and long-term outcomes from the management of parasagittal meningiomas invading the superior sagittal sinus. Neurosurgery 2010;67:885-93. discussion 893
19. Mariniello G, Napoli M, Russo C, et al. MRI features of spinal solitary fibrous tumors. A report of two cases and literature review. Neuroradiol J 2012;25:610-6.
20. Hakyemez B, Yildirim N, Erdoğan C, Kocaeli H, Korfali E, Parlak M. Meningiomas with conventional MRI findings resembling intraaxial tumors: can perfusion-weighted MRI be helpful in differentiation? Neuroradiology 2006;48:695-702.
21. Watanabe Y, Yamasaki F, Kajiwara Y, et al. Preoperative histological grading of meningiomas using apparent diffusion coefficient at 3T MRI. Eur J Radiol 2013;82:658-63.
22. Tang Y, Dundamadappa SK, Thangasamy S, et al. Correlation of apparent diffusion coefficient with Ki-67 proliferation index in grading meningioma. AJR Am J Roentgenol 2014;202:1303-8.
23. Sanverdi SE, Ozgen B, Oguz KK, et al. Is diffusion-weighted imaging useful in grading and differentiating histopathological subtypes of meningiomas? Eur J Radiol 2012;81:2389-95.
24. Lee EJ, Kim JH, Park ES, et al. A novel weighted scoring system for estimating the risk of rapid growth in untreated intracranial meningiomas. J Neurosurg 2017;127:971-80.
25. O’Leary S, Adams WM, Parrish RW, Mukonoweshuro W. Atypical imaging appearances of intracranial meningiomas. Clin Radiol 2007;62:10-7.
26. Mariniello G, Malacario F, Dones F, et al. Sudden post-traumatic sciatica caused by a thoracic spinal meningioma. Neuroradiol J 2016;29:390-2.
27. Sung CW, Hsieh KL, Kuo YJ. A primary meningioma of the lumbar spine with neck metastasis. J Spinal Cord Med 2019:1-4.
28. De Verdelhan O, Haegelen C, Carsin-nicol B, et al. MR imaging features of spinal schwannomas and meningiomas. J Neuroradiol 2005;32:42-9.
29. Yeo Y, Park C, Lee JW, et al. Magnetic resonance imaging spectrum of spinal meningioma. Clin Imaging 2019;55:100-6.
30. Zhang LH, Yuan HS. Imaging appearances and pathologic characteristics of spinal epidural meningioma. AJNR Am J Neuroradiol 2018;39:199-204.
31. Liu WC, Choi G, Lee SH, et al. Radiological findings of spinal schwannomas and meningiomas: focus on discrimination of two disease entities. Eur Radiol 2009;19:2707-15.
32. Tamrazi B, Shiroishi MS, Liu CS. Advanced imaging of intracranial meningiomas. Neurosurg Clin N Am 2016;27:137-43.
33. Shiroishi MS, Cen SY, Tamrazi B, et al. Predicting meningioma consistency on preoperative neuroimaging studies. Neurosurg Clin N Am 2016;27:145-54.
34. Demir MK, Iplikcioglu AC, Dincer A, Arslan M, Sav A. Single voxel proton MR spectroscopy findings of typical and atypical intracranial meningiomas. Eur J Radiol 2006;60:48-55.
35. Gajjar K, Heppenstall LD, Pang W, et al. Diagnostic segregation of human brain tumours using Fourier-transform infrared and/or Raman spectroscopy coupled with discriminant analysis. Anal Methods 2012;5:89-102.
36. Zakhari N, Torres C, Castillo M, Nguyen TB. Uncommon cranial meningioma: key imaging features on conventional and advanced imaging. Clin Neuroradiol 2017;27:135-44.
37. Newton AT, Pruthi S, Stokes AM, Skinner JT, Quarles CC. Improving perfusion measurement in DSC-MR imaging with multiecho information for arterial input function determination. AJNR Am J Neuroradiol 2016;37:1237-43.
38. Kremer S, Grand S, Rémy C, et al. Contribution of dynamic contrast MR imaging to the differentiation between dural metastasis and meningioma. Neuroradiology 2004;46:642-8.
39. Kimura H, Takeuchi H, Koshimoto Y, et al. Perfusion imaging of meningioma by using continuous arterial spin-labeling: comparison with dynamic susceptibility-weighted contrast-enhanced MR images and histopathologic features. AJNR Am J Neuroradiol 2006;27:85-93.
40. Hakyemez B, Erdogan C, Bolca N, Yildirim N, Gokalp G, Parlak M. Evaluation of different cerebral mass lesions by perfusion-weighted MR imaging. J Magn Reson Imaging 2006;24:817-24.
41. Cha S, Knopp EA, Johnson G, Wetzel SG, Litt AW, Zagzag D. Intracranial mass lesions: dynamic contrast-enhanced susceptibility-weighted echo-planar perfusion MR imaging. Radiology 2002;223:11-29.
42. Ginat DT, Mangla R, Yeaney G, Schaefer PW, Wang H. Correlation between dynamic contrast-enhanced perfusion MRI relative cerebral blood volume and vascular endothelial growth factor expression in meningiomas. Acad Radiol 2012;19:986-90.
43. Ginat DT, Mangla R, Yeaney G, Wang HZ. Correlation of diffusion and perfusion MRI with Ki-67 in high-grade meningiomas. AJR Am J Roentgenol 2010;195:1391-5.
44. Bitzer M, Klose U, Geist-Barth B, et al. Alterations in diffusion and perfusion in the pathogenesis of peritumoral brain edema in meningiomas. Eur Radiol 2002;12:2062-76.
45. Zhang Q, Jia GJ, Zhang GB, et al. A logistic regression model for detecting the presence of malignant progression in atypical meningiomas. World Neurosurg 2019;126:e392-401.
46. Sergides I, Hussain Z, Naik S, Good C, Miles K, Critchley G. Utilization of dynamic CT perfusion in the study of intracranial meningiomas and their surrounding tissue. Neurol Res 2009;31:84-9.
47. Qiao XJ, Kim HG, Wang DJJ, et al. Application of arterial spin labeling perfusion MRI to differentiate benign from malignant intracranial meningiomas. Eur J Radiol 2017;97:31-6.
48. Yang S, Law M, Zagzag D, et al. Dynamic contrast-enhanced perfusion MR imaging measurements of endothelial permeability: differentiation between atypical and typical meningiomas. AJNR Am J Neuroradiol 2003;24:1554-9.
49. Nagar VA, Ye JR, Ng WH, et al. Diffusion-weighted MR imaging: diagnosing atypical or malignant meningiomas and detecting tumor dedifferentiation. AJNR Am J Neuroradiol 2008;29:1147-52.
50. Santelli L, Ramondo G, Della Puppa A, et al. Diffusion-weighted imaging does not predict histological grading in meningiomas. Acta Neurochir (Wien) 2010;152:1315-9. discussion 1319
51. Wang S, Kim S, Zhang Y, et al. Determination of grade and subtype of meningiomas by using histogram analysis of diffusion-tensor imaging metrics. Radiology 2012;262:584-92.
52. Tropine A, Dellani PD, Glaser M, et al. Differentiation of fibroblastic meningiomas from other benign subtypes using diffusion tensor imaging. J Magn Reson Imaging 2007;25:703-8.
53. Kashimura H, Inoue T, Ogasawara K, et al. Prediction of meningioma consistency using fractional anisotropy value measured by magnetic resonance imaging. J Neurosurg 2007;107:784-7.
54. Ortega-Porcayo LA, Ballesteros-Zebadúa P, Marrufo-Meléndez OR, et al. Prediction of mechanical properties and subjective consistency of meningiomas using T1-T2 assessment versus fractional anisotropy. World Neurosurg 2015;84:1691-8.
55. Romani R, Tang WJ, Mao Y, et al. Diffusion tensor magnetic resonance imaging for predicting the consistency of intracranial meningiomas. Acta Neurochir (Wien) 2014;156:1837-45.
56. Yin Z, Hughes JD, Trzasko JD, et al. Slip interface imaging based on MR-elastography preoperatively predicts meningioma-brain adhesion. J Magn Reson Imaging 2017;46:1007-16.
59. Afshar-Oromieh A, Giesel FL, Linhart HG, et al. Detection of cranial meningiomas: comparison of 68Ga-DOTATOC PET/CT and contrast-enhanced MRI. Eur J Nucl Med Mol Imaging 2012;39:1409-15.
60. Klingenstein A, Haug AR, Miller C, Hintschich C. Ga-68-DOTA-TATE PET/CT for discrimination of tumors of the optic pathway. Orbit 2015;34:16-22.
61. Nowosielski M, Galldiks N, Iglseder S, et al. Diagnostic challenges in meningioma. Neuro Oncol 2017;19:1588-98.
62. Sommerauer M, Burkhardt JK, Frontzek K, et al. 68Gallium-DOTATATE PET in meningioma: a reliable predictor of tumor growth rate? Neuro Oncol 2016;18:1021-7.
63. Slotty PJ, Behrendt FF, Langen KJ, Cornelius JF. (68)Ga-DOTATATE-positron emission tomography imaging in spinal meningioma. J Craniovertebr Junction Spine 2014;5:44-6.
64. Rachinger W, Stoecklein VM, Terpolilli NA, et al. Increased 68Ga-DOTATATE uptake in PET imaging discriminates meningioma and tumor-free tissue. J Nucl Med 2015;56:347-53.
65. Galldiks N, Albert NL, Sommerauer M, et al. PET imaging in patients with meningioma-report of the RANO/PET Group. Neuro Oncol 2017;19:1576-87.
66. Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology 2016;278:563-77.
67. Koçak B, Durmaz EŞ, Ateş E, Kılıçkesmez Ö. Radiomics with artificial intelligence: a practical guide for beginners. Diagn Interv Radiol 2019;25:485-95.
68. Park YW, Oh J, You SC, et al. Radiomics and machine learning may accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging. Eur Radiol 2019;29:4068-76.
69. Laukamp KR, Thiele F, Shakirin G, et al. Fully automated detection and segmentation of meningiomas using deep learning on routine multiparametric MRI. Eur Radiol 2019;29:124-32.
70. Morin O, Chen WC, Nassiri F, et al. Integrated models incorporating radiologic and radiomic features predict meningioma grade, local failure, and overall survival. Neurooncol Adv 2019;1:vdz011.
71. Zhang Y, Chen JH, Chen TY, et al. Radiomics approach for prediction of recurrence in skull base meningiomas. Neuroradiology 2019;61:1355-64.
