European Radiology

, Volume 28, Issue 9, pp 3832–3839 | Cite as

Primary central nervous system lymphoma and atypical glioblastoma: Differentiation using radiomics approach

  • Hie Bum Suh
  • Yoon Seong Choi
  • Sohi Bae
  • Sung Soo Ahn
  • Jong Hee Chang
  • Seok-Gu Kang
  • Eui Hyun Kim
  • Se Hoon Kim
  • Seung-Koo Lee



To evaluate the diagnostic performance of magnetic resonance (MR) radiomics-based machine-learning algorithms in differentiating primary central nervous system lymphoma (PCNSL) from non-necrotic atypical glioblastoma (GBM).


Seventy-seven patients (54 individuals with PCNSL and 23 with non-necrotic atypical GBM), diagnosed from January 2009 to April 2017, were enrolled in this retrospective study. A total of 6,366 radiomics features, including shape, volume, first-order, texture, and wavelet-transformed features, were extracted from multi-parametric (post-contrast T1- and T2-weighted, and fluid attenuation inversion recovery images) and multiregional (enhanced and non-enhanced) tumour volumes. These features were subjected to recursive feature elimination and random forest (RF) analysis with nested cross-validation. The diagnostic abilities of a radiomics machine-learning classifier, apparent diffusion coefficient (ADC), and three readers, who independently classified the tumours based on conventional MR sequences, were evaluated using receiver operating characteristic (ROC) analysis. Areas under the ROC curves (AUC) of the radiomics classifier, ADC value, and the radiologists were compared.


The mean AUC of the radiomics classifier was 0.921 (95 % CI 0.825–0.990). The AUCs of the three readers and ADC were 0.707 (95 % CI 0.622–0.793), 0.759 (95 %CI 0.656–0.861), 0.695 (95 % CI 0.590–0.800) and 0.684 (95 % CI0.560–0.809), respectively. The AUC of the radiomics-based classifier was significantly higher than those of the three readers and ADC (p< 0.001 for all).


Large-scale radiomics with a machine-learning algorithm can be useful for differentiating PCNSL from atypical GBM, and yields a better diagnostic performance than human radiologists and ADC values.

Key Points

• Machine-learning algorithm radiomics can help to differentiate primary central PCNSL from GBM.

• This approach yields a higher diagnostic accuracy than visual analysis by radiologists.

• Radiomics can strengthen radiologists’ diagnostic decisions whenever conventional MRI sequences are available.


Lymphoma Glioblastoma Machine-learning Magnetic resonance imaging Radiomics 



Apparent diffusion coefficient


Area under the receiver operating characteristic curve


Contrast-enhancing tumours


Contrast-enhanced T1-weighted


Diffusion-tensor imaging


Echo time


Field of view


Fluid-attenuation inversion recovery




Grey level co-occurrence matrix


Grey-level run length matrix


Grey-level size zone matrix


Magnetic resonance imaging


Non-enhancing tumour tissue and oedema


Primary central nervous system lymphoma


Random forest


Relative cerebral blood volume


Repetition time





This study was supported by a faculty research grant of Yonsei University College of Medicine (6 2016-0121).

Compliance with ethical standards


The scientific guarantor of this publication is Yoon Seong Choi.

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.

Ethical approval

Institutional Review Board approval was obtained.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Study subjects or cohorts overlap

Some study subjects or cohorts have been previously reported in European Radiology:

Choi YS, Lee H-J, Ahn SS, et al. Primary central nervous system lymphoma and atypical glioblastoma: differentiation using the initial area under the curve derived from dynamic contrast-enhanced MR and the apparent diffusion coefficient. Eur Radiol. 2017;27(4):1344–1351


• retrospective

• observational

• performed at one institution

Supplementary material

330_2018_5368_MOESM1_ESM.docx (2 mb)
ESM 1 (DOCX 2057 kb)


  1. 1.
    Haldorsen IS, Espeland A, Larsson EM (2011) Central nervous system lymphoma: characteristic findings on traditional and advanced imaging. AJNR Am J Neuroradiol 32:984–992CrossRefPubMedGoogle Scholar
  2. 2.
    Dolecek TA, Propp JM, Stroup NE, Kruchko C (2012) CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2005-2009. Neuro-Oncology 14(Suppl 5):v1–49CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Schlegel U (2009) Primary CNS lymphoma. Ther Adv Neurol Disord 2:93–104CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Stupp R, Mason WP, van den Bent MJ et al (2005) Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 352:987–996CrossRefPubMedGoogle Scholar
  5. 5.
    Kickingereder P, Wiestler B, Sahm F et al (2014) Primary central nervous system lymphoma and atypical glioblastoma: multiparametric differentiation by using diffusion-, perfusion-, and susceptibility-weighted MR imaging. Radiology 272:843–850CrossRefPubMedGoogle Scholar
  6. 6.
    Koeller KK, Smirniotopoulos JG, Jones RV (1997) Primary central nervous system lymphoma: radiologic-pathologic correlation. Radiographics 17:1497–1526CrossRefPubMedGoogle Scholar
  7. 7.
    Rees JH, Smirniotopoulos JG, Jones RV, Wong K (1996) Glioblastoma multiforme: radiologic-pathologic correlation. Radiographics 16:1413–1438 quiz 1462-1413CrossRefPubMedGoogle Scholar
  8. 8.
    Al-Okaili RN, Krejza J, Woo JH et al (2007) Intraaxial brain masses: MR imaging-based diagnostic strategy--initial experience. Radiology 243:539–550CrossRefPubMedGoogle Scholar
  9. 9.
    Buhring U, Herrlinger U, Krings T, Thiex R, Weller M, Kuker W (2001) MRI features of primary central nervous system lymphomas at presentation. Neurology 57:393–396CrossRefPubMedGoogle Scholar
  10. 10.
    Doskaliyev A, Yamasaki F, Ohtaki M et al (2012) Lymphomas and glioblastomas: differences in the apparent diffusion coefficient evaluated with high b-value diffusion-weighted magnetic resonance imaging at 3T. Eur J Radiol 81:339–344CrossRefPubMedGoogle Scholar
  11. 11.
    Toh CH, Wei KC, Chang CN, Ng SH, Wong HF (2013) Differentiation of primary central nervous system lymphomas and glioblastomas: comparisons of diagnostic performance of dynamic susceptibility contrast-enhanced perfusion MR imaging without and with contrast-leakage correction. AJNR Am J Neuroradiol 34:1145–1149CrossRefPubMedGoogle Scholar
  12. 12.
    Liao W, Liu Y, Wang X et al (2009) Differentiation of primary central nervous system lymphoma and high-grade glioma with dynamic susceptibility contrast-enhanced perfusion magnetic resonance imaging. Acta Radiol 50:217–225CrossRefPubMedGoogle Scholar
  13. 13.
    Radbruch A, Wiestler B, Kramp L et al (2013) Differentiation of glioblastoma and primary CNS lymphomas using susceptibility weighted imaging. Eur J Radiol 82:552–556CrossRefPubMedGoogle Scholar
  14. 14.
    Choi YS, Lee H-J, Ahn SS et al (2017) Primary central nervous system lymphoma and atypical glioblastoma: differentiation using the initial area under the curve derived from dynamic contrast-enhanced MR and the apparent diffusion coefficient. Eur Radiol 27:1344–1351CrossRefPubMedGoogle Scholar
  15. 15.
    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 280:880–889CrossRefPubMedGoogle Scholar
  16. 16.
    Prasanna P, Patel J, Partovi S, Madabhushi A, Tiwari P (2017) Radiomic features from the peritumoral brain parenchyma on treatment-naive multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: Preliminary findings. Eur Radiol 27:4188–4197CrossRefPubMedGoogle Scholar
  17. 17.
    Shinohara RT, Sweeney EM, Goldsmith J et al (2014) Statistical normalisation techniques for magnetic resonance imaging. Neuroimage Clin 6:9–19CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational Radiomics System to Decode the Radiographic Phenotype. Cancer Res 77:e104–e107CrossRefPubMedGoogle Scholar
  19. 19.
    Kuhn M (2008) Building Predictive Models in R Using the caret Package. J Stat Softw 28:1–26CrossRefGoogle Scholar
  20. 20.
    DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44:837–845CrossRefPubMedGoogle Scholar
  21. 21.
    Toh CH, Castillo M, Wong AM et al (2008) Primary cerebral lymphoma and glioblastoma multiforme: differences in diffusion characteristics evaluated with diffusion tensor imaging. AJNR Am J Neuroradiol 29:471–475CrossRefPubMedGoogle Scholar
  22. 22.
    Calli C, Kitis O, Yunten N, Yurtseven T, Islekel S, Akalin T (2006) Perfusion and diffusion MR imaging in enhancing malignant cerebral tumors. Eur J Radiol 58:394–403CrossRefPubMedGoogle Scholar
  23. 23.
    Kickingereder P, Sahm F, Wiestler B et al (2014) Evaluation of microvascular permeability with dynamic contrast-enhanced MRI for the differentiation of primary CNS lymphoma and glioblastoma: radiologic-pathologic correlation. AJNR Am J Neuroradiol 35:1503–1508CrossRefPubMedGoogle Scholar
  24. 24.
    Guo AC, Cummings TJ, Dash RC, Provenzale JM (2002) Lymphomas and high-grade astrocytomas: comparison of water diffusibility and histologic characteristics. Radiology 224:177–183CrossRefPubMedGoogle Scholar
  25. 25.
    Yamasaki T, Chen T, Hirai T, Murakami R (2013) Classification of cerebral lymphomas and glioblastomas featuring luminance distribution analysis. Comput Math Methods Med 2013:619658CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Alcaide-Leon P, Dufort P, Geraldo AF et al (2017) Differentiation of Enhancing Glioma and Primary Central Nervous System Lymphoma by Texture-Based Machine Learning. AJNR Am J Neuroradiol 38:1145–1150CrossRefPubMedGoogle Scholar

Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  1. 1.Department of Radiology and Research Institute of Radiological Science, College of MedicineYonsei University College of MedicineSeoulKorea
  2. 2.Department of NeurosurgeryYonsei University College of MedicineSeoulKorea
  3. 3.Department of PathologyYonsei University College of MedicineSeoulKorea

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