Skip to main content

Advertisement

SpringerLink
Log in

Imaging score for differentiation of meningioma grade

  • Diagnostic Neuroradiology
  • Published:
Neuroradiology Aims and scope Submit manuscript

Abstract

Purpose

We sought to establish a comprehensive imaging score indicating the likelihood of higher WHO grade meningiomas pre-operatively.

Methods

All surgical intracranial meningioma patients at our institution between 2014 and 2018 underwent retrospective chart review. Preoperative MRI sequences were reviewed, and imaging features were included in the score based on statistical and clinical significance. Point values for each significant feature were assigned based on the beta coefficients obtained from multivariate analysis. The imaging score was calculated by adding up the points, for a total score of 0 to 5. The predictive ability of the score to identify higher-grade meningiomas was evaluated.

Results

Ninety patients, 50% of whom had a postoperative diagnosis of WHO grade II meningioma, were included. The mean age for the population was 59.9 years and 70% were female. Tumor volume ≥ 36.0 cc was assigned 2 points, presence of irregular tumor borders was assigned 2 points, and presence of peritumoral edema was assigned 1 point. The probability of having a WHO grade II meningioma was 0% with a score of 0, 25.0% with a score of 1, 38.5% with a score of 2, 65.4% with a score of 3, and 83.3% with a score of 4 or greater. A threshold of ≥ 3 points achieved a recall of 0.80, precision of 0.73, F1-score of 0.77, accuracy of 0.76, and AUC of 0.82.

Conclusion

The proposed imaging scoring system had good predictive capability for WHO grade II meningiomas with good discrimination and calibration. External validation is needed.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Data Availability

The deidentified data that support the findings of this study are available from the corresponding author, A.F., upon reasonable request.

Abbreviations

WHO:

World Health Organization

MRI:

Magnetic resonance imaging

FLAIR:

Fluid-attenuated inversion recovery

SWI:

Susceptibility weighted imaging

DWI:

Diffusion-weighted imaging

CT:

Computed tomography

ADC:

Apparent diffusion coefficient

ROI:

Region of interest

CSF:

Cerebrospinal fluid

qADC:

Quantitative apparent diffusion coefficient

NADC:

Normalized apparent diffusion coefficient

AUC:

Area under the curve

PIMS:

Preoperative imaging meningioma score

References

  1. Ostrom QT, Patil N, Cioffi G, Waite K, Kruchko C, Barnholtz-Sloan JS (2020) CBTRUS Statistical Report: primary brain and other central nervous system tumors diagnosed in the United States in 2013–2017. Neuro Oncol 22(12 Suppl 2):iv1–iv96. https://doi.org/10.1093/neuonc/noaa200

    Article  Google Scholar 

  2. Alexiou GA, Gogou P, Markoula S, Kyritsis AP (2010) Management of meningiomas. Clin Neurol Neurosurg 112(3):177–182. https://doi.org/10.1016/j.clineuro.2009.12.011

    Article  Google Scholar 

  3. Whittle IR, Smith C, Navoo P, Collie D (2004) Meningiomas. Lancet 363(9420):1535–1543. https://doi.org/10.1016/s0140-6736(04)16153-9

    Article  Google Scholar 

  4. Buerki RA, Horbinski CM, Kruser T, Horowitz PM, James CD, Lukas RV (2018) An overview of meningiomas. Future Oncol 14(21):2161–2177. https://doi.org/10.2217/fon-2018-0006

    Article  CAS  Google Scholar 

  5. Louis DN (2007) WHO classification of tumours of the central nervous system. WHO.

  6. Lin DD, Lin JL, Deng XY et al (2019) Trends in intracranial meningioma incidence in the United States, 2004–2015. Cancer Med 8(14):6458–6467. https://doi.org/10.1002/cam4.2516

    Article  Google Scholar 

  7. Kshettry VR, Ostrom QT, Kruchko C, Al-Mefty O, Barnett GH, Barnholtz-Sloan JS (2015) Descriptive epidemiology of World Health Organization grades II and III intracranial meningiomas in the United States. Neuro Oncol 17(8):1166–1173. https://doi.org/10.1093/neuonc/nov069

    Article  Google Scholar 

  8. Modha A, Gutin PH (2005) Diagnosis and treatment of atypical and anaplastic meningiomas: a review. Neurosurgery 57(3):538–50. https://doi.org/10.1227/01.neu.0000170980.47582.a5. (discussion 538–50)

    Article  Google Scholar 

  9. Marciscano AE, Stemmer-Rachamimov AO, Niemierko A et al (2016) Benign meningiomas (WHO Grade I) with atypical histological features: correlation of histopathological features with clinical outcomes. J Neurosurg 124(1):106–114. https://doi.org/10.3171/2015.1.Jns142228

    Article  Google Scholar 

  10. Ohba S, Kobayashi M, Horiguchi T et al (2011) Long-term surgical outcome and biological prognostic factors in patients with skull base meningiomas. J Neurosurg 114(5):1278–1287. https://doi.org/10.3171/2010.11.Jns10701

    Article  Google Scholar 

  11. Ogasawara C, Philbrick BD, Adamson DC. Meningioma: a review of epidemiology, pathology, diagnosis, treatment, and future directions. Biomedicines. 2021;9(3) https://doi.org/10.3390/biomedicines9030319

  12. Lee EJ, Kim JH, Park ES et al (2017) A novel weighted scoring system for estimating the risk of rapid growth in untreated intracranial meningiomas. J Neurosurg 127(5):971–980. https://doi.org/10.3171/2016.9.Jns161669

    Article  Google Scholar 

  13. Coroller TP, Bi WL, Huynh E et al (2017) Radiographic prediction of meningioma grade by semantic and radiomic features. PLoS One 12(11):e0187908. https://doi.org/10.1371/journal.pone.0187908

    Article  CAS  Google Scholar 

  14. Lin BJ, Chou KN, Kao HW et al (2014) Correlation between magnetic resonance imaging grading and pathological grading in meningioma. J Neurosurg 121(5):1201–1208. https://doi.org/10.3171/2014.7.Jns132359

    Article  Google Scholar 

  15. Spille DC, Sporns PB, Heß K, Stummer W, Brokinkel B (2019) Prediction of high-grade histology and recurrence in meningiomas using routine preoperative magnetic resonance imaging: a systematic review. World Neurosurg 128:174–181. https://doi.org/10.1016/j.wneu.2019.05.017

    Article  Google Scholar 

  16. 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. https://doi.org/10.1016/j.jocn.2017.11.013

    Article  Google Scholar 

  17. Hashiba T, Hashimoto N, Maruno M et al (2006) Scoring radiologic characteristics to predict proliferative potential in meningiomas. Brain Tumor Pathol 23(1):49–54. https://doi.org/10.1007/s10014-006-0199-4

    Article  Google Scholar 

  18. Nakasu S, Nakajima M, Matsumura K, Nakasu Y, Handa J (1995) Meningioma: proliferating potential and clinicoradiological features. Neurosurgery 37(6):1049–1055. https://doi.org/10.1227/00006123-199512000-00003

    Article  CAS  Google Scholar 

  19. Kasuya H, Kubo O, Tanaka M, Amano K, Kato K, Hori T (2006) Clinical and radiological features related to the growth potential of meningioma. Neurosurg Rev 29(4):293–6. https://doi.org/10.1007/s10143-006-0039-3. (discussion 296–7)

    Article  Google Scholar 

  20. Kawahara Y, Nakada M, Hayashi Y et al (2012) Prediction of high-grade meningioma by preoperative MRI assessment. J Neurooncol 108(1):147–152. https://doi.org/10.1007/s11060-012-0809-4

    Article  Google Scholar 

  21. Surov A, Gottschling S, Mawrin C et al (2015) Diffusion-weighted imaging in meningioma: prediction of tumor grade and association with histopathological parameters. Transl Oncol 8(6):517–523. https://doi.org/10.1016/j.tranon.2015.11.012

    Article  Google Scholar 

  22. Nagar VA, Ye JR, Ng WH et al (2008) Diffusion-weighted MR imaging: diagnosing atypical or malignant meningiomas and detecting tumor dedifferentiation. AJNR Am J Neuroradiol 29(6):1147–1152. https://doi.org/10.3174/ajnr.A0996

    Article  CAS  Google Scholar 

  23. Moons KG, Harrell FE, Steyerberg EW (2002) Should scoring rules be based on odds ratios or regression coefficients? J Clin Epidemiol 55(10):1054–1055. https://doi.org/10.1016/s0895-4356(02)00453-5

    Article  Google Scholar 

  24. Shin HK, Park JH, Cho YH et al (2021) Risk factors for high-grade meningioma in brain and spine: systematic review and meta-analysis. World Neurosurg 151:e718–e730. https://doi.org/10.1016/j.wneu.2021.04.138

    Article  Google Scholar 

  25. Meyer HJ, Wienke A, Surov A (2020) ADC values of benign and high grade meningiomas and associations with tumor cellularity and proliferation —a systematic review and meta-analysis. J Neurol Sci 415:116975. https://doi.org/10.1016/j.jns.2020.116975

    Article  Google Scholar 

  26. Bozdağ M, Er A, Ekmekçi S (2021) Association of apparent diffusion coefficient with Ki-67 proliferation index, progesterone-receptor status and various histopathological parameters, and its utility in predicting the high grade in meningiomas. Acta Radiol 62(3):401–413. https://doi.org/10.1177/0284185120922142

    Article  Google Scholar 

  27. Agarwal V, McCutcheon BA, Hughes JD et al (2017) Trends in management of intracranial meningiomas: analysis of 49,921 cases from modern cohort. World Neurosurg 106:145–151. https://doi.org/10.1016/j.wneu.2017.06.127

    Article  Google Scholar 

Download references

Funding

The authors did not receive support from any organization for the submitted work.

Author information

Authors and Affiliations

  1. Albert Einstein College of Medicine, Department of Neurological Surgery, Bronx, NY, 10467, USA

    Abigail Funari, Rafael De la Garza Ramos, Phillip Cezayirli, Yaroslav Gelfand, Michael Longo, Samuel Ahmad, Sadiq Rahman, Andre E. Boyke & Vijay Agarwal

  2. Vanderbilt University Medical Center, Department of Neurosurgery, Nashville, TN, 37232, USA

    Michael Longo

  3. Jacobi Medical Center, Department of Radiology, Bronx, NY, 10461, USA

    Alex Levitt

  4. Montefiore Medical Center, Department of Radiology, Division of Neuroradiology, Bronx, NY, 10467, USA

    Kevin Hsu

Authors
  1. Abigail Funari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rafael De la Garza Ramos
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Phillip Cezayirli
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yaroslav Gelfand
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Michael Longo
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Samuel Ahmad
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Sadiq Rahman
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Andre E. Boyke
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Alex Levitt
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Kevin Hsu
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Vijay Agarwal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abigail Funari.

Ethics declarations

Ethics approval

This research study was conducted retrospectively from data obtained for clinical purposes. We consulted extensively with the IRB of the Albert Einstein College of Medicine, who determined that our study did not need ethical approval. An IRB official waiver of ethical approval was granted from the IRB of the Albert Einstein College of Medicine.

Informed consent

The requirement for informed consent was officially waived by the IRB of Albert Einstein College of Medicine due to the purely retrospective nature of the study. Additionally, no identifying patient information or images are published below.

Competing interests

The authors have no competing interests to declare that are relevant to the content of this article.

Additional information

Publisher's note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Funari, A., De la Garza Ramos, R., Cezayirli, P. et al. Imaging score for differentiation of meningioma grade. Neuroradiology 65, 453–462 (2023). https://doi.org/10.1007/s00234-022-03101-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00234-022-03101-w

Keywords

Search

Navigation