Radiomics and machine learning may accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging

  • Yae Won Park
  • Jongmin Oh
  • Seng Chan You
  • Kyunghwa Han
  • Sung Soo AhnEmail author
  • Yoon Seong Choi
  • Jong Hee Chang
  • Se Hoon Kim
  • Seung-Koo Lee



Preoperative, noninvasive prediction of the meningioma grade is important because it influences the treatment strategy. The purpose of this study was to evaluate the role of radiomics features of postcontrast T1-weighted images (T1C), apparent diffusion coefficient (ADC), and fractional anisotropy (FA) maps, based on the entire tumor volume, in the differentiation of grades and histological subtypes of meningiomas.


One hundred thirty-six patients with pathologically diagnosed meningiomas (108 low-grade [benign], 28 high-grade [atypical and anaplastic]), who underwent T1C and diffusion tensor imaging, were included in the discovery set. The T1C image, ADC, and FA maps were analyzed to derive volume-based data of the entire tumor. Radiomics features were correlated with meningioma grades and histological subtypes. Various machine learning classifiers were trained to build classification models to predict meningioma grades. We tested the model in a validation set (58 patients; 46 low-grade; 12 high-grade).


The machine learning classifiers showed variable performances depending on the machine learning algorithms. The best classification system for the prediction of meningioma grades had an area under the curve of 0.86 (95% confidence interval [CI], 0.74–0.98) in the validation set. The accuracy, sensitivity, and specificity of the best classifier were 89.7, 75.0, and 93.5% in the validation set, respectively. Various texture parameters differed significantly between fibroblastic and non-fibroblastic subtypes.


Radiomics feature-based machine learning classifiers of T1C images, ADC, and FA maps are useful for differentiating meningioma grades.

Key Points

• Preoperative, noninvasive differentiation of the meningioma grade is important because it influences the treatment strategy.

• Radiomics feature-based machine learning classifiers of T1C images, ADC, and FA maps are useful for differentiating meningioma grades.

• In benign meningiomas, there were significant differences in the various texture parameters between fibroblastic and non-fibroblastic meningioma subtypes.


Diffusion tensor imaging Magnetic resonance imaging Meningioma Radiomics 



Apparent diffusion coefficient


Area under the curve


Diffusion tensor imaging


Fractional anisotropy


Postcontrast T1-weighted image



This research received funding from the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, Information and Communication Technologies, and Future Planning (2017R1D1A1B03030440).

Compliance with ethical standards


The scientific guarantor of this publication is Professor Seung-Koo Lee, MD, PhD, from Yonsei University College of Medicine (

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors has significant statistical expertise (J.M.O., a biostatistician with 5 years of experience in computational biology).

Informed consent

The institutional review board waived the requirement to obtain informed patient consent for this retrospective study.

Ethical approval

Institutional Review Board approval was obtained.


• Retrospective

• Diagnostic or prognostic study

• Performed at one institution

Supplementary material

330_2018_5830_MOESM1_ESM.docx (1.8 mb)
ESM 1 (DOCX 1884 kb)


  1. 1.
    Ostrom QT, Gittleman H, Xu J et al (2016) CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2009–2013. Neuro Oncol 18:v1–v75CrossRefGoogle Scholar
  2. 2.
    Willis J, Smith C, Ironside JW, Erridge S, Whittle IR, Everington D (2005) The accuracy of meningioma grading: a 10-year retrospective audit. Neuropathol Appl Neurobiol 31:141–149CrossRefGoogle Scholar
  3. 3.
    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 17:1166–1173CrossRefGoogle Scholar
  4. 4.
    Modha A, Gutin PH (2005) Diagnosis and treatment of atypical and anaplastic meningiomas: a review. Neurosurgery 57:538–550CrossRefGoogle Scholar
  5. 5.
    Goldbrunner R, Minniti G, Preusser M et al (2016) EANO guidelines for the diagnosis and treatment of meningiomas. Lancet Oncol 17:e383–e391CrossRefGoogle Scholar
  6. 6.
    Balss J, Meyer J, Mueller W, Korshunov A, Hartmann C, von Deimling A (2008) Analysis of the IDH1 codon 132 mutation in brain tumors. Acta Neuropathol 116:597–602CrossRefGoogle Scholar
  7. 7.
    Toh CH, Castillo M, Wong AM et al (2008) Differentiation between classic and atypical meningiomas with use of diffusion tensor imaging. AJNR Am J Neuroradiol 29:1630–1635CrossRefGoogle Scholar
  8. 8.
    Watanabe Y, Yamasaki F, Kajiwara Y et al (2013) Preoperative histological grading of meningiomas using apparent diffusion coefficient at 3T MRI. Eur J Radiol 82:658–663CrossRefGoogle Scholar
  9. 9.
    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 8:517–523CrossRefGoogle Scholar
  10. 10.
    Santelli L, Ramondo G, Della Puppa A et al (2010) Diffusion-weighted imaging does not predict histological grading in meningiomas. Acta Neurochir (Wien) 152:1315–1319CrossRefGoogle Scholar
  11. 11.
    Jolapara M, Kesavadas C, Radhakrishnan VV et al (2010) Role of diffusion tensor imaging in differentiating subtypes of meningiomas. J Neuroradiol 37:277–283CrossRefGoogle Scholar
  12. 12.
    Sanverdi SE, Ozgen B, Oguz KK et al (2012) Is diffusion-weighted imaging useful in grading and differentiating histopathological subtypes of meningiomas? Eur J Radiol 81:2389–2395CrossRefGoogle Scholar
  13. 13.
    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 29:1147–1152CrossRefGoogle Scholar
  14. 14.
    Hashiba T, Hashimoto N, Maruno M et al (2006) Scoring radiologic characteristics to predict proliferative potential in meningiomas. Brain Tumor Pathol 23:49–54CrossRefGoogle Scholar
  15. 15.
    Kawahara Y, Nakada M, Hayashi Y et al (2012) Prediction of high-grade meningioma by preoperative MRI assessment. J Neurooncol 108:147–152CrossRefGoogle Scholar
  16. 16.
    Joo B, Han K, Choi YS et al (2018) Amide proton transfer imaging for differentiation of benign and atypical meningiomas. Eur Radiol 28:331–339CrossRefGoogle Scholar
  17. 17.
    Park YW, Han K, Ahn SS et al (2018) Whole-tumor histogram and texture analyses of DTI for evaluation of IDH1-mutation and 1p/19q-codeletion status in World Health Organization grade II gliomas. AJNR Am J Neuroradiol 39:693–698CrossRefGoogle Scholar
  18. 18.
    Kickingereder P, Bonekamp D, Nowosielski M et al (2016) Radiogenomics of glioblastoma: machine learning–based classification of molecular characteristics by using multiparametric and multiregional MR imaging features. Radiology 281:907–918CrossRefGoogle Scholar
  19. 19.
    Kickingereder P, Burth S, Wick A et al (2016) Radiomic profiling of glioblastoma: identifying an imaging predictor of patient survival with improved performance over established clinical and radiologic risk models. Radiology 280:880–889CrossRefGoogle Scholar
  20. 20.
    Kashimura H, Inoue T, Ogasawara K et al (2007) Prediction of meningioma consistency using fractional anisotropy value measured by magnetic resonance imaging. J Neurosurg 107:784–787CrossRefGoogle Scholar
  21. 21.
    Louis DN, Perry A, Reifenberger G et al (2016) The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol 131:803–820CrossRefGoogle Scholar
  22. 22.
    Tustison NJ, Avants BB, Cook PA et al (2010) N4ITK: improved N3 bias correction. IEEE Trans Med Imaging 29:1310–1320CrossRefGoogle Scholar
  23. 23.
    Shinohara RT, Sweeney EM, Goldsmith J et al (2014) Statistical normalization techniques for magnetic resonance imaging. Neuroimage Clin 6:9–19CrossRefGoogle Scholar
  24. 24.
    Cha J, Kim S, Kim HJ et al (2014) Differentiation of tumor progression from pseudoprogression in patients with posttreatment glioblastoma using multiparametric histogram analysis. AJNR Am J Neuroradiol 35:1309–1317CrossRefGoogle Scholar
  25. 25.
    Tang Y, Dundamadappa SK, Thangasamy S et al (2014) Correlation of apparent diffusion coefficient with Ki-67 proliferation index in grading meningioma. AJR Am J Roentgenol 202:1303–1308CrossRefGoogle Scholar
  26. 26.
    Maes F, Collignon A, Vandermeulen D, Marchal G, Suetens P (1997) Multimodality image registration by maximization of mutual information. IEEE Trans Med Imaging 16:187–198CrossRefGoogle Scholar
  27. 27.
    Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern:610–621CrossRefGoogle Scholar
  28. 28.
    Galloway MM (1975) Texture analysis using gray level run lengths. Comput Gr Image Process 4:172–179CrossRefGoogle Scholar
  29. 29.
    Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:321–357CrossRefGoogle Scholar
  30. 30.
    Provost F (2000) Machine learning from imbalanced data sets 101 proceedings of the AAAI’2000 workshop on imbalanced data sets, pp 1–3Google Scholar
  31. 31.
    Kuhn M (2008) Building predictive models in R using the caret package Caret package. J Stat Softw 28:1–26Google Scholar
  32. 32.
    Lunardon N, Menardi G, Torelli N (2014) ROSE: a package for binary imbalanced learning. R Journal 6(1)Google Scholar
  33. 33.
    Torgo L (2013) Package ‘DMwR’. Comprehensive R Archive NetworkGoogle Scholar
  34. 34.
    van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77:e104–e107CrossRefGoogle Scholar
  35. 35.
    Kollová A, Liscák R, Novotný J Jr, Vladyka V, Simonová G, Janousková L (2007) Gamma Knife surgery for benign meningioma. J Neurosurg 107:325–336CrossRefGoogle Scholar
  36. 36.
    Kaur G, Sayegh ET, Larson A et al (2014) Adjuvant radiotherapy for atypical and malignant meningiomas: a systematic review. Neuro Oncol 16:628–636CrossRefGoogle Scholar
  37. 37.
    Dziuk TW, Woo S, Butler EB et al (1998) Malignant meningioma: an indication for initial aggressive surgery and adjuvant radiotherapy. J Neurooncol 37:177–188CrossRefGoogle Scholar
  38. 38.
    Stafford SL, Pollock BE, Foote RL et al (2001) Meningioma radiosurgery: tumor control, outcomes, and complications among 190 consecutive patients. Neurosurgery 49:1029–1038PubMedGoogle Scholar
  39. 39.
    Maclean J, Fersht N, Short S (2014) Controversies in radiotherapy for meningioma. Clin Oncol (R Coll Radiol) 26:51–64CrossRefGoogle Scholar
  40. 40.
    Yan PF, Yan L, Hu TT et al (2017) The potential value of preoperative MRI texture and shape analysis in grading meningiomas: a preliminary investigation. Transl Oncol 10:570–577CrossRefGoogle Scholar
  41. 41.
    Lin BJ, Chou KN, Kao HW et al (2014) Correlation between magnetic resonance imaging grading and pathological grading in meningioma. J Neurosurg 121:1201–1208CrossRefGoogle Scholar
  42. 42.
    Chen TY, Lai PH, Ho JT et al (2004) Magnetic resonance imaging and diffusion-weighted images of cystic meningioma: correlating with histopathology. Clin Imaging 28:10–19CrossRefGoogle Scholar
  43. 43.
    Hsu CC, Pai CY, Kao HW, Hsueh CJ, Hsu WL, Lo CP (2010) Do aggressive imaging features correlate with advanced histopathological grade in meningiomas? J Clin Neurosci 17:584–587CrossRefGoogle Scholar
  44. 44.
    Nakasu S, Nakasu Y, Nakajima M, Matsuda M, Handa J (1999) Preoperative identification of meningiomas that are highly likely to recur. J Neurosurg 90:455–462CrossRefGoogle Scholar
  45. 45.
    Kang D, Park JE, Kim YH et al (2018) Diffusion radiomics as a diagnostic model for atypical manifestation of primary central nervous system lymphoma: development and multicenter external validation. Neuro Oncol 20:1251–1261CrossRefGoogle Scholar
  46. 46.
    Hwang WL, Marciscano AE, Niemierko A et al (2015) Imaging and extent of surgical resection predict risk of meningioma recurrence better than WHO histopathological grade. Neuro Oncol 18:863–872CrossRefGoogle Scholar
  47. 47.
    New PF, Hesselink JR, O'Carroll CP, Kleinman GM (1982) Malignant meningiomas: CT and histologic criteria, including a new CT sign. AJNR Am J Neuroradiol 3:267–276Google Scholar
  48. 48.
    He H, Garcia EA (2009) Learning from imbalanced data. IEEE Trans Knowl Data Eng 21:1263–1284CrossRefGoogle Scholar
  49. 49.
    Menardi G, Torelli N (2014) Training and assessing classification rules with imbalanced data. Data Min Knowl Disc 28:92–122CrossRefGoogle Scholar
  50. 50.
    Saito T, Rehmsmeier M (2015) The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS One 10:e0118432CrossRefGoogle Scholar
  51. 51.
    Ginat DT, Mangla R, Yeaney G, Wang HZ (2010) Correlation of diffusion and perfusion MRI with Ki-67 in high-grade meningiomas. AJR Am J Roentgenol 195:1391–1395CrossRefGoogle Scholar
  52. 52.
    Jothi JAA, Rajam VMA (2017) A survey on automated cancer diagnosis from histopathology images. Artif Intell Rev 48:31–81CrossRefGoogle Scholar
  53. 53.
    Tropine A, Dellani PD, Glaser M et al (2007) Differentiation of fibroblastic meningiomas from other benign subtypes using diffusion tensor imaging. J Magn Reson Imaging 25:703–708CrossRefGoogle Scholar
  54. 54.
    Kleihues P, Cavenee WK (2000) Pathology and genetics of tumours of the nervous system, vol 1. International Agency for Research on Cancer, LyonGoogle Scholar
  55. 55.
    Wang S, Kim S, Zhang Y et al (2012) Determination of grade and subtype of meningiomas by using histogram analysis of diffusion-tensor imaging metrics. Radiology 262:584–592CrossRefGoogle Scholar
  56. 56.
    Maeda M, Itoh S, Kimura H et al (1994) Vascularity of meningiomas and neuromas: assessment with dynamic susceptibility-contrast MR imaging. AJR Am J Roentgenol 163:181–186CrossRefGoogle Scholar
  57. 57.
    Fatima K, Arooj A, Majeed H (2014) A new texture and shape based technique for improving meningioma classification. Microsc Res Tech 77:862–873CrossRefGoogle Scholar
  58. 58.
    Al-Kadi OS (2010) Texture measures combination for improved meningioma classification of histopathological images. Pattern Recognit 43:2043–2053CrossRefGoogle Scholar
  59. 59.
    Gatenby RA, Grove O, Gillies RJ (2013) Quantitative imaging in cancer evolution and ecology. Radiology 269:8–14CrossRefGoogle Scholar
  60. 60.
    Zhang Z, Yang J, Ho A et al (2018) A predictive model for distinguishing radiation necrosis from tumour progression after gamma knife radiosurgery based on radiomic features from MR images. Eur Radiol 28:2255–2263CrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  1. 1.Department of RadiologyEwha Womans University College of MedicineSeoulSouth Korea
  2. 2.Department of Radiology and Research Institute of Radiological ScienceYonsei University College of MedicineSeoulSouth Korea
  3. 3.Department of Convergence MedicineEwha Womans University College of MedicineSeoulSouth Korea
  4. 4.Department of Biomedical InformaticsAjou University School of MedicineSuwonSouth Korea
  5. 5.Department of NeurosurgeryYonsei University College of MedicineSeoulSouth Korea
  6. 6.Department of PathologyYonsei University College of MedicineSeoulSouth Korea

Personalised recommendations