Prediction of consistency of intracranial meningiomas based on conventional MRI examination

Czech version

Authors: A. Lysak ¹; V. Belan ²; M. Jezberová ²; M. Fabian ²; A. Šteňo ¹
Authors‘ workplace: Neurochirurgická klinika LF UK, a Univerzitnej nemocnice Bratislava, Slovensko ¹; Dr. Magnet s. r. o., pracovisko Kramáre, Bratislava, Slovensko ²
Published in: Cesk Slov Neurol N 2024; 87(3): 174-180
Category: Review Article
doi: https://doi.org/10.48095/csbb2024174

Overview

One of the main factors affecting the resectability of meningiomas is their consistency. Preoperative prediction of the consistency of meningiomas can bring helpful information when planning the operation (e. g., estimating its duration), and, in some cases, it can even be beneficial when deciding on the therapeutic procedure itself. A reliable prediction of consistency could help during the treatment management planning process – e. g., to choose whether surgical treatment or non-surgical procedures (radiosurgery or observation using MRI) should be performed. A reliable prediction could be especially important in cases of extremely hard tumors growing in surgically challenging locations and/ or in elderly polymorbid patients. Unfortunately, this topic has not been sufficiently investigated and a generally accepted method allowing simple, fast and reliable prediction of the meningioma consistency using preoperative MRI is still lacking. Our work aims to provide readers with a brief overview of current knowledge about the possibilities of preoperative prediction of the consistency of meningiomas based on MRI.

Keywords:

Meningioma – prediction – consistency – resectability

Sources

1. Pothula V, Kumar A, Vyas S et al. Preoperative assessment and prediction of consistency of intracranial meningioma utilizing the apparent diffusion coefficient values. [online]. Dostupné z: https:/ / www.thieme-connect.com/ products/ ejournals/ pdf/ 10.1055/ s-0042-1750357.pdf.

2. ElBeheiry AA, Fayed AA, Alkassas AH et al. Can magnetic resonance imaging predict preoperative consistency and vascularity of intracranial meningioma? Egypt J Radiol Nucl Med 2022; 53(1): 1–15. doi: 10.1186/ s43055-022-00706-2.

3. Sitthinamsuwan B, Khampalikit I, Nunta-aree S et al. Predictors of meningioma consistency: a study in 243 consecutive cases. Acta Neurochir (Wien) 2012; 154(8): 1383–1389. doi: 10.1007/ s00701-012-1427-9.

4. Soleman J, Fathi AR, Marbacher S et al. The role of intraoperative magnetic resonance imaging in complex meningioma surgery. Magn Reson Imaging 2013; 31(6): 923–929. doi: 10.1016/ j.mri. 2012.12.005.

5. Jolapara M, Kesavadas C, Radhakrishnan VV et al. Role of diffusion tensor imaging in differentiating subtypes of meningiomas. J Neuroradiol 2010; 37(5): 277–283. doi: 10.1016/ j.neurad.2010.03.001.

6. Phuttharak W, Boonrod A, Thammaroj J et al. Preoperative MRI evaluation of meningioma consistency: a focus on detailed architectures. Clin Neurol Neurosurg 2018; 169: 178–184. doi: 10.1016/ j.clineuro.2018.04.
025.

7. Alyamany M, Alshardan MM, Jamea AA e al. Meningioma consistency: correlation between magnetic resonance imaging characteristics, operative findings, and histopathological features. Asian J Neurosurg 2018; 13(2): 324–328. doi: 10.4103/ 1793-5482.228515.

8. 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(8): 4068–4076. doi: 10.1007/ s00330-018-5830-3.

9. Moliterno J, Cope WP, Vartanian ED et al. Survival in patients treated for anaplastic meningioma. J Neurosurg 2015; 123(1): 23–30. doi: 10.3171/ 2014.10.JNS14502.

10. Rydzewski NR, Lesniak MS, Chandler JP et al. Gross total resection and adjuvant radiotherapy most significant predictors of improved survival in patients with atypical meningioma. Cancer 2018; 124(4): 734–742. doi: 10.1002/ cncr.31088.

11. 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(6): 1691–1698. doi: 10.1016/ j.wneu.2015.07.018.

12. Chartrain AG, Kurt M, Yao A et al. Utility of preoperative meningioma consistency measurement with magnetic resonance elastography (MRE): a review. Neurosurg Rev 2019; 42(1): 1–7. doi: 10.1007/ s10143-017-0862-8.

13. Smith KA, Leever JD, Hylton PD et al. Meningioma consistency prediction utilizing tumor to cerebellar peduncle intensity on T2-weighted magnetic resonance imaging sequences: TCTI ratio. J Neurosurg 2017; 126(1): 242–248. doi: 10.3171/ 2016.1.JNS152329.

14. Zhai Y, Song D, Yang F et al. Preoperative prediction of eningioma consistency via machine learning--based radiomics. Front Oncol 2021; 11: 657288. doi: 10.3389/ fonc.2021.657288.

15. Shi Y, Huo Y, Pan Cet al. Use of magnetic resonance elastography to gauge meningioma intratumoral consistency and histotype. Neuroimage Clin 2022; 36: 103173. doi: 10.1016/ j.nicl.2022.103173.

16. 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(10): 1837–1845. doi: 10.1007/ s00701-014-21
49-y.

17. Krivoshapkin AL, Sergeev GS, Kalneus LE et al. New software for preoperative diagnostics of meningeal tumor histologic types. World Neurosurg 2016; 90:
123–132. doi: 10.1016/ j.wneu.2016.02.084.

18. Hoover JM, Morris JM, Meyer FB. Use of preoperative magnetic resonance imaging T1 and T2 sequences to determine intraoperative meningioma consistency. Surg Neurol Int 2011; 2: 142. doi: 10.4103/ 2152-7806.85983.

19. Yao A, Pain M, Balchandani P et al. Can MRI predict meningioma consistency? A correlation with tumor pathology and systematic review. Neurosurg Rev 2018; 41(3): 745–753. doi: 10.1007/ s10143-016-0801-0.

20. Shiroishi MS, Cen SY, Tamrazi B et al. Predicting meningioma consistency on preoperative neuroimaging studies. Neurosurg Clin N Am 2016; 27(2): 145–154. doi: 10.1016/ j.nec.2015.11.007.

21. Watanabe K, Kakeda S, Yamamoto J et al. Prediction of hard meningiomas: quantitative evaluation based on the magnetic resonance signal intensity. Acta Radiol 2016; 57(3): 333–340. doi: 10.1177/ 0284185115578323.

22. Yamada H, Tanikawa M, Sakata T et al. Usefulness of T2 relaxation time for quantitative prediction of meningioma consistency. World Neurosurg 2022; 157:
e484–e491. doi: 10.1016/ j.wneu.2021.10.135.

23. Yogi A, Koga T, Azama K et al. Usefulness of the apparent diffusion coefficient (ADC) for predicting the consistency of intracranial meningiomas. Clin Imaging 2014; 38(6): 802–807. doi: 10.1016/ j.clinimag.2014.06.016.

24. Miyoshi K, Wada T, Uwano Iet al. Predicting the consistency of intracranial meningiomas using apparent diffusion coefficient maps derived from preoperative diffusion-weighted imaging. J Neurosurg 2020; 135(3): 969–976. doi: 10.3171/ 2020.6.JNS20740.

25. 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(4): 784–787. doi: 10.3171/ JNS-07/ 10/ 0784.

26. Brabec J, Szczepankiewicz F, Lennartsson F et al. Histogram analysis of tensor-valued diffusion MRI in meningiomas: relation to consistency, histological grade and type. Neuroimage Clin 2022; 33: 102912. doi: 10.1016/
j.nicl.2021.102912.

27. Cepeda S, Arrese I, García-García S et al. Meningioma consistency can be defined by combining the radiomic features of magnetic resonance imaging and ultrasound elastography. A pilot study using machine learning classifiers. World Neurosurg 2021; 146: e1147–e1159. doi: 10.1016/ j.wneu.2020.11.113.

28. Zada G, Yashar P, Robison A et al. A proposed grading system for standardizing tumor consistency of intracranial meningiomas. Neurosurg Focus 2013; 35(6): E1. doi: 10.3171/ 2013.8.FOCUS13274.

29. Murphy MC, Huston J 3rd, Glaser KJ e al. Preoperative assessment of meningioma stiffness using magnetic resonance elastography. J Neurosurg 2013; 118(3): 643–648. doi: 10.3171/ 2012.9.JNS12519.

30. Smith KA, Leever JD, Chamoun RB. Predicting consistency of meningioma by magnetic resonance imaging. J Neurol Surg B Skull Base 2015; 76(3): 225–229. doi: 10.1055/ s-0034-1543965.

31. Maiuri F, Iaconetta G, de Divitiis O et al. Intracranial meningiomas: correlations between MR imaging and histology. Eur J Radiol 1999; 31(1): 69–75. doi: 10.1016/ s0720-048x(98)00083-7. PMID: 10477102.

32. Suzuki Y, Sugimoto T, Shibuya M et al. Meningiomas: correlation between MRI characteristics and operative findings including consistency. Acta Neurochir (Wien) 1994; 129(1–2): 39–46. doi: 10.1007/ BF01400871.

33. Carpeggiani P, Crisi G, Trevisan C. MRI of intracranial meningiomas: correlations with histology and physical consistency. Neuroradiology 1993; 35(7): 532–536. doi: 10.1007/ BF00588715.

34. Banzato T, Causin F, Della Puppa A et al. Accuracy of deep learning to differentiate the histopathological grading of meningiomas on MR images: a preliminary study. J Magn Reson Imaging 2019; 50(4): 1152–1159. doi: 10.1002/ jmri.26723.

35. Hamerla G, Meyer HJ, Schob S et al. Comparison of machine learning classifiers for differentiation of grade 1 from higher gradings in meningioma: a multicenter radiomics study. Magn Reson Imaging 2019; 63: 244–249. doi: 10.1016/ j.mri.2019.08.011.

36. Al Abdulsalam HK, Aldahish AK, Albakr A et al. Endoscopic transnasal resection of midline skull base meningiomas: tumor consistency and surgical outcomes. J Neurol Surg B Skull Base 2021; 82(5): 500–505. doi: 10.1055/ s-0040-1714111.

37. Hughes JD, Fattahi N, Van Gompel J et al. Higher-resolution magnetic resonance elastography in meningiomas to determine intratumoral consistency. Neurosurgery 2015; 77(4): 653–658. doi: 10.1227/ NEU.000000 0000000892.

38. Yamaguchi N, Kawase T, Sagoh M et al. Prediction of consistency of meningiomas with preoperative magnetic resonance imaging. Surg Neurol 1997; 48(6):
579–583. doi: 10.1016/ s0090-3019(96)00439-9.

39. Kanazawa T, Minami Y, Jinzaki M et al. Preoperative prediction of solitary fibrous tumor/ hemangiopericytoma and angiomatous meningioma using magnetic resonance imaging texture analysis. World Neurosurg 2018; 120: e1208–e1216. doi: 10.1016/ j.wneu.2018.09.044.

40. Mori N, Mugikura S, Endo T et al. Principal component analysis of texture features for grading of meningioma: not effective from the peritumoral area but effective from the tumor area. Neuroradiology 2023; 65(2): 257–274. doi: 10.1007/ s00234-022-03045-1.