Multi-parametric qualitative and quantitative MRI assessment as predictor of histological grading in previously treated meningiomas



Meningiomas are mainly benign tumors, though a considerable proportion shows aggressive behaviors histologically consistent with atypia/anaplasia. Histopathological grading is usually assessed through invasive procedures, which is not always feasible due to the inaccessibility of the lesion or to treatment contraindications. Therefore, we propose a multi-parametric MRI assessment as a predictor of meningioma histopathological grading.


Seventy-three patients with 74 histologically proven and previously treated meningiomas were retrospectively enrolled (42 WHO I, 24 WHO II, 8 WHO III) and studied with MRI including T2 TSE, FLAIR, Gradient Echo, DWI, and pre- and post-contrast T1 sequences. Lesion masks were segmented on post-contrast T1 sequences and rigidly registered to ADC maps to extract quantitative parameters from conventional DWI and intravoxel incoherent motion model assessing tumor perfusion. Two expert neuroradiologists assessed morphological features of meningiomas with semi-quantitative scores.


Univariate analysis showed different distributions (p < 0.05) of quantitative diffusion parameters (Wilcoxon rank-sum test) and morphological features (Pearson’s chi-square; Fisher’s exact test) among meningiomas grouped in low-grade (WHO I) and higher grade forms (WHO II/III); the only exception consisted of the tumor-brain interface. A multivariate logistic regression, combining all parameters showing statistical significance in the univariate analysis, allowed discrimination between the groups of meningiomas with high sensitivity (0.968) and specificity (0.925). Heterogeneous contrast enhancement and low ADC were the best independent predictors of atypia and anaplasia.


Our multi-parametric MRI assessment showed high sensitivity and specificity in predicting histological grading of meningiomas. Such an assessment may be clinically useful in characterizing lesions without histological diagnosis.

Key points
When surgery and biopsy are not feasible, parameters obtained from both conventional and diffusion-weighted MRI can predict atypia and anaplasia in meningiomas with high sensitivity and specificity.
Low ADC values and heterogeneous contrast enhancement are the best predictors of higher grade meningioma

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2



Contrast enhancement


High-grade meningioma


Low-grade meningioma


Intravoxel incoherent motion


Volumetric interpolated breath-hold examination


Peritumoral edema


Capsular enhancement


Tumor-brain interface


Area under the curve


  1. 1.

    Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella-Branger D, Cavenee WK, Ohgaki H, Wiestler OD, Kleihues P, Ellison DW (2016) The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol 131(6):803–820

    Article  Google Scholar 

  2. 2.

    Perry A, Louis DN, Scheithauer BW, Budka H, von Deimling A. Meningiomas. In: David N Louis, et al, editors. (2000) WHO classification of tumors of the central nervous system. Lyon: IARC p. 164–172

    Google Scholar 

  3. 3.

    Buetow MP, Buetow PC, Smirniotopoulos JG (1991) Typical, atypical, and misleading features in meningioma. Radiographics. 11(6):1087–1106

    CAS  Article  Google Scholar 

  4. 4.

    Moliterno J, Cope WP, Vartanian ED, Reiner AS, Kellen R, Ogilvie SQ, Huse JT, Gutin PH (2015) Survival in patients treated for anaplastic meningioma. J Neurosurg 123(1):23–30

    Article  Google Scholar 

  5. 5.

    Wen PY, Quant E, Drappatz J, Beroukhim R, Norden AD (2010) Medical therapies for meningiomas. J Neuro-Oncol 99(3):365–378

    CAS  Article  Google Scholar 

  6. 6.

    Louis DN, Ohgaki H, Wiestler OD, Cavenee WK, Burger PC, Jouvet A, Scheithauer BW, Kleihues P (2007) The 2007 WHO classification of tumors of the central nervous system. Acta Neuropathol 114(2):97–109

    Article  Google Scholar 

  7. 7.

    Chen CC, Hsu PW, Erich Wu TW et al (2009) Stereotactic brain biopsy: single center retrospective analysis of complications. Clin Neurol Neurosurg 111(10):835–839

    Article  Google Scholar 

  8. 8.

    Schipmann S, Brix T, Varghese J, Warneke N, Schwake M, Brokinkel B, Ewelt C, Dugas M, Stummer W (2019) Adverse events in brain tumor surgery: incidence, type, and impact on current quality metrics. Acta Neurochir 161(2):287–306

    Article  Google Scholar 

  9. 9.

    Kawahara Y, Nakada M, Hayashi Y, Kai Y, Hayashi Y, Uchiyama N, Nakamura H, Kuratsu JI, Hamada JI (2012) Prediction of high-grade meningioma by preoperative MRI assessment. J Neuro-Oncol 108(1):147–152

    Article  Google Scholar 

  10. 10.

    Aslan K, Gunbey HP, Tomak L, Incesu L (2018) The diagnostic value of using combined MR diffusion tensor imaging parameters to differentiate between low- and high-grade meningioma. Br J Radiol 91(1088):20180088

    Article  Google Scholar 

  11. 11.

    Yiping L, Kawai S, Jianbo W, Li L, Daoying G, Bo Y (2017) Evaluation parameters between intra-voxel incoherent motion and diffusion-weighted imaging in grading and differentiating histological subtypes of meningioma: a prospective pilot study. J Neurol Sci 372:60–69

    Article  Google Scholar 

  12. 12.

    Bohara M, Nakajo M, Kamimura K, Yoneyama T, Fukukura Y, Kiyao Y, Yonezawa H, Higa N, Kirishima M, Yoshiura T (2020) Histological grade of meningioma: prediction by Intravoxel incoherent motion histogram parameters. Acad Radiol 27(3):342–353

    Article  Google Scholar 

  13. 13.

    Lin BJ, Chou KN, Kao HW, Lin C, Tsai WC, Feng SW, Lee MS, Hueng DY (2014) Correlation between magnetic resonance imaging grading and pathological grading in meningioma. J Neurosurg 121(5):1201–1208

    Article  Google Scholar 

  14. 14.

    Zhang S, Chiang GC, Knapp JM et al (2019) Grading meningiomas utilizing multiparametric MRI with inclusion of susceptibility weighted imaging and quantitative susceptibility mapping. J Neuroradiol S0150-9861(19):30162–30162

    Google Scholar 

  15. 15.

    Abdel-Kerim A, Shehata M, El Sabaa B, Fadel S, Heikal A, Mazloum Y (2018) Differentiation between benign and atypical cranial meningiomas. Can ADC measurement help? MRI findings with hystopathologial correlation. Egypt J Radiol Nucl Med 49(1):172–175

    Article  Google Scholar 

  16. 16.

    Surov A, Ginat DT, Sanverdi E, Lim CCT, Hakyemez B, Yogi A, Cabada T, Wienke A (2016) Use of diffusion weighted imaging in differentiating between maligant and benign meningiomas. A Multicenter Analysis. World Neurosurg 88:598–602

    Article  Google Scholar 

  17. 17.

    Le bihan D (2019) What can we see with IVIM MRI? Neuroimage 187:56–67

    Article  Google Scholar 

  18. 18.

    Tini P, Nardone V, Pastina P, Battaglia G, Vinciguerra C, Carfagno T, Rubino G, Carbone SF, Sebaste L, Cerase A, Federico A, Pirtoli L (2017) Perilesional edema in brain metastasis from non-small cell lung cancer (NSCLC) as predictor of response to radiosurgery (SRS). Neurol Sci 38(6):975–982

    Article  Google Scholar 

  19. 19.

    Coroller TP, Bi WL, Huynh E, Abedalthagafi M, Aizer AA, Greenwald NF, Parmar C, Narayan V, Wu WW, Miranda de Moura S, Gupta S, Beroukhim R, Wen PY, al-Mefty O, Dunn IF, Santagata S, Alexander BM, Huang RY, Aerts HJWL (2017) Radiographic prediction of meningioma grade by semantic and radiomic features. PLoS One 12(11):e0187908

    Article  Google Scholar 

  20. 20.

    Watanabe Y, Yamasaki F, Kajiwara Y, Takayasu T, Nosaka R, Akiyama Y, Sugiyama K, Kurisu K (2013) Preoperative histological grading of meningiomas using apparent diffusion coefficient at 3T MRI. Eur J Radiol 82(4):658–663

    Article  Google Scholar 

  21. 21.

    Surov A, Hamerla G, Meyer HJ, Winter K, Schob S, Fiedler E (2018) Whole lesion histogram analysis of meningiomas derived from ADC values. Correlation with several cellularity parameters, proliferation index KI 67, nucleic content, and membrane permeability. Magn Reson Imaging 51(March):158–162

    CAS  Article  Google Scholar 

  22. 22.

    Hale AT, Wang L, Strother MK, Chambless LB (2018) Differentiating meningioma grade by imaging features on magnetic resonance imaging. J Clin Neurosci 48:71–75

    Article  Google Scholar 

  23. 23.

    Palaniandy K, Haspani MSM, Zain NRM (2017) Prediction of histological grade and completeness of resection of intracranial meningiomas: role of peritumoural brain edema. Malays J Med Sci 24(3):33–43

    PubMed  PubMed Central  Google Scholar 

  24. 24.

    Gawlitza M, Fiedler E, Schob S, Hoffmann KT, Surov A (2017) Peritumoral brain edema in meningiomas depends on Aquaporin-4 expression and not on tumor grade, tumor volume, cell count, or Ki-67 labeling index. Mol Imaging Biol 19(2):298–304

    CAS  Article  Google Scholar 

  25. 25.

    Ng WH, Hy JW, Tan WL, Liew D, Lim T, Ang BT, Ng I (2009) Aquaporin-4 expression is increased in edematous meningiomas. J Clin Neurosci 16(3):441–443

    CAS  Article  Google Scholar 

  26. 26.

    Cornelius JF, Slotty PJ, Steiger HJ, Hänggi D, Polivka M, George B (2013) Malignant potential of skull base versus non-skull base meningiomas: clinical series of 1,663 cases. Acta Neurochir 155(3):407–413

    Article  Google Scholar 

  27. 27.

    Adeli A, Hess K, Mawrin C, Streckert EMS, Stummer W, Paulus W, Kemmling A, Holling M, Heindel W, Schmidt R, Spille DC, Sporns PB, Brokinkel B (2018) Prediction of brain invasion in patients with meningiomas using preoperative magnetic resonance imaging. Oncotarget. 9(89):35974–35982

    Article  Google Scholar 

  28. 28.

    Kalamarides M, Stemmer-rachamimov AO, Niwa-kawakita M et al (2011) Identification of a progenitor cell of origin capable of generating diverse meningioma histological subtypes. Oncogene. 30(20):2333–2344

    CAS  Article  Google Scholar 

  29. 29.

    Hoover JM, Morris JM, Meyer FB (2011) Use of preoperative magnetic resonance imaging T1 and T2 sequences to determine intraoperative meningioma consistency. Surg Neurol Int 2:142

    Article  Google Scholar 

  30. 30.

    Li H, Zhao M, Jiao Y, Ge P, Li Z, Ma J, Wang S, Cao Y, Zhao J (2016) Prediction of high-grade pediatric meningiomas: magnetic resonance imaging features based on T1-weighted, T2-weighted, and contrast-enhanced T1-weighted images. World Neurosurg 91:89–95

    Article  Google Scholar 

  31. 31.

    Zhang H, Rödiger LA, Shen T, Miao J, Oudkerk M (2008) Perfusion MR imaging for differentiation of benign and malignant meningiomas. Neuroradiology. 50(6):525–530

    Article  Google Scholar 

  32. 32.

    Zampini MA, Buizza G, Paganelli C, Fontana G, D’Ippolito E, Valvo F, Preda L, Baroni G (2020) Perfusion and diffusion in meningioma tumors: a preliminary multiparametric analysis with dynamic susceptibility contrast and IntraVoxel incoherent motion MRI. Magn Reson Imaging 67:69–78

    Article  Google Scholar 

  33. 33.

    Essig M, Shiroishi MS, Nguyen TB, Saake M, Provenzale JM, Enterline D, Anzalone N, Dörfler A, Rovira À, Wintermark M, Law M (2013) Perfusion MRI: the five most frequently asked technical questions. AJR Am J Roentgenol 200(1):24–34

    Article  Google Scholar 

  34. 34.

    Qiao XJ, Kim HG, Wang DJJ, Salamon N, Linetsky M, Sepahdari A, Ellingson BM, Pope WB (2017) Application of arterial spin labeling perfusion MRI to differentiate benign from malignant intracranial meningiomas. Eur J Radiol 97:31–36

    Article  Google Scholar 

  35. 35.

    Juratli TA, Thiede C, Koerner MVA, Tummala SS, Daubner D, Shankar GM, Williams EA, Martinez-Lage M, Soucek S, Robel K, Penson T, Krause M, Appold S, Meinhardt M, Pinzer T, Miller JJ, Krex D, Ely HA, Silverman IM, Christiansen J, Schackert G, Wakimoto H, Kirsch M, Brastianos PK, Cahill DP (2017) Intratumoral heterogeneity and promoter mutations in progressive/higher-grade meningiomas. Oncotarget. 8(65):109228–109237

    Article  Google Scholar 

Download references


The authors would like to thank our patients and their families for participating in our research.


No funding was received for this study.

Author information



Corresponding author

Correspondence to Lorenzo Preda.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in the studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Sacco, S., Ballati, F., Gaetani, C. et al. Multi-parametric qualitative and quantitative MRI assessment as predictor of histological grading in previously treated meningiomas. Neuroradiology (2020).

Download citation


  • Meningioma
  • Brain neoplasms
  • Multi-parametric magnetic resonance imaging
  • Diffusion imaging