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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
Neuro

Abstract

Objectives

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).

Methods

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.

Results

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).

Conclusions

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.

Keywords

Lymphoma Glioblastoma Machine-learning Magnetic resonance imaging Radiomics 

Abbreviations

ADC

Apparent diffusion coefficient

AUC

Area under the receiver operating characteristic curve

CETs

Contrast-enhancing tumours

T1C

Contrast-enhanced T1-weighted

DTI

Diffusion-tensor imaging

TE

Echo time

FOV

Field of view

FLAIR

Fluid-attenuation inversion recovery

GBM

Glioblastoma

GLCM

Grey level co-occurrence matrix

GLRM

Grey-level run length matrix

GLSZM

Grey-level size zone matrix

MRI

Magnetic resonance imaging

NET

Non-enhancing tumour tissue and oedema

PCNSL

Primary central nervous system lymphoma

RF

Random forest

rCBV

Relative cerebral blood volume

TR

Repetition time

T2

T2-weighted

Notes

Funding

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

Compliance with ethical standards

Guarantor

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

Methodology

• retrospective

• observational

• performed at one institution

Supplementary material

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

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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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