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European Radiology

, Volume 29, Issue 3, pp 1348–1354 | Cite as

MR imaging based fractal analysis for differentiating primary CNS lymphoma and glioblastoma

  • Shuai Liu
  • Xing Fan
  • Chuanbao Zhang
  • Zheng Wang
  • Shaowu Li
  • Yinyan Wang
  • Xiaoguang QiuEmail author
  • Tao JiangEmail author
Neuro

Abstract

Objectives

The aim of this study was to differentiate primary central nervous system lymphoma (PCNSL) from glioblastomas (GBM) using the fractal analysis of conventional MRI data.

Materials and methods

Sixty patients with PCNSL and 107 patients with GBM with MRI data available were enrolled. Fractal dimension (FD) and lacunarity values of the tumour region were calculated using fractal analysis. A predictive model combining fractal parameters and anatomical characteristics was built using logistic regression. The role of FD, lacunarity and the predictive model in differential diagnosis was evaluated using receiver-operating characteristic (ROC) curve analysis. The association between fractal parameters and anatomical characteristics of tumours was also investigated.

Results

PCNSL had lower FD values (p < 0.001) and higher lacunarity values (p < 0.001) than GBM. ROC curve analysis revealed that FD, lacunarity, and the predictive model could distinguish PCNSL from GBM (area under the curve: 0.895, 0.776, and 0.969, respectively). The following associations were observed between fractal parameters and anatomical characteristics: multiple lesions were significantly associated with higher lacunarity (p = 0.024), necrosis with higher FD (p = 0.027), corpus callosum involvement with higher lacunarity (p < 0.001) in PCNSL and subventricular zone involvement with higher FD (p < 0.001) in GBM.

Conclusions

The findings of the study indicate that fractal analysis on conventional MRI performs well in distinguishing PCNSL from GBM.

Key Points

• Fractal dimension and lacunarity were capable of differentiating PCNSL from GBM.

• PCNSL and GBM exhibited different anatomical characteristics.

• Fractal parameters were associated with some of these anatomical characteristics.

Keywords

Lymphoma Glioblastoma Diagnosis Magnetic resonance imaging Fractals 

Abbreviations

AUC

Area under the curve

FD

Fractal dimension

GBM

Glioblastoma

MRI

Magnetic resonance imaging

PCNSL

Primary central nervous system lymphoma

ROC

Receiver operating characteristic

ROIs

Regions of interest

Notes

Funding

This study has received funding by the Capital Medical Development Research Fund (2016-2-1073), the National Key Research and Development Plan (No. 2016YFC0902500) and the Beijing Postdoctoral Research Foundation (2017-ZZ-116).

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Tao Jiang.

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

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• Retrospective

• Diagnostic or prognostic study

• Performed at one institution

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Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  1. 1.Department of NeurosurgeryBeijing Tiantan Hospital, Capital Medical UniversityBeijingChina
  2. 2.Beijing Neurosurgical InstituteCapital Medical UniversityBeijingChina
  3. 3.Department of NeuroradiologyBeijing Tiantan Hospital, Capital Medical UniversityBeijingChina
  4. 4.Department of Radiation OncologyBeijing Tiantan Hospital, Capital Medical UniversityBeijingChina
  5. 5.Center of Brain Tumor, Beijing Institute for Brain DisordersBeijingChina

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