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Magnetic resonance imaging–based 3-dimensional fractal dimension and lacunarity analyses may predict the meningioma grade

  • Magnetic Resonance
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Abstract

Objective

To assess whether 3-dimensional (3D) fractal dimension (FD) and lacunarity features from MRI can predict the meningioma grade.

Methods

This retrospective study included 131 patients with meningiomas (98 low-grade, 33 high-grade) who underwent preoperative MRI with post-contrast T1-weighted imaging. The 3D FD and lacunarity parameters from the enhancing portion of the tumor were extracted by box-counting algorithms. Inter-rater reliability was assessed with the intraclass correlation coefficient (ICC). Additionally, conventional imaging features such as location, heterogeneous enhancement, capsular enhancement, and necrosis were assessed. Independent clinical and imaging risk factors for meningioma grade were investigated using multivariable logistic regression. The discriminative value of the prediction model with and without fractal features was evaluated. The relationship of fractal parameters with the mitosis count and Ki-67 labeling index was also assessed.

Results

The inter-reader reliability was excellent, with ICCs of 0.99 for FD and 0.97 for lacunarity. High-grade meningiomas had higher FD (p < 0.001) and higher lacunarity (p = 0.007) than low-grade meningiomas. In the multivariable logistic regression, the diagnostic performance of the model with clinical and conventional imaging features increased with 3D fractal features for predicting the meningioma grade, with AUCs of 0.78 and 0.84, respectively. The 3D FD showed significant correlations with both mitosis count and Ki-67 labeling index, and lacunarity showed a significant correlation with the Ki-67 labeling index (all p values < 0.05).

Conclusion

The 3D FD and lacunarity are higher in high-grade meningiomas and fractal analysis may be a useful imaging biomarker for predicting the meningioma grade.

Key Points

• Fractal dimension (FD) and lacunarity are the two parameters used in fractal analysis to describe the complexity of a subject and may aid in predicting meningioma grade.

• High-grade meningiomas had a higher fractal dimension and higher lacunarity than low-grade meningiomas, suggesting higher complexity and higher rotational variance.

• The discriminative value of the predictive model using clinical and conventional imaging features improved when combined with 3D fractal features for predicting the meningioma grade.

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Abbreviations

2D:

2-Dimensional

3D:

3-Dimensional

FD:

Fractal dimension

ICC:

Intraclass correlation coefficient

IDI:

Integrated discrimination improvement

NRI:

Net reclassification index

OR:

Odds ratio

TE:

Echo time

TIC:

T1-weighted

TR:

repetition time

WHO:

World Health Organization

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Funding

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 & Future Planning (2017R1D1A1B03030440). This work was supported under the framework of international cooperation program managed by National Research Foundation of Korea (NRF-2018K2A9A2A06020642).

This research received funding from the Korean Society for Neuro-Oncology.

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Author notes

  1. Yae Won Park and Soopil Kim contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology and Research Institute of Radiological Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, South Korea

    Yae Won Park, Sung Soo Ahn, Kyunghwa Han & Seung-Koo Lee

  2. Department of Robotics Engineering, Daegu Gyeongbuk Institute of Science and Technology, 333 Techno Jungang Daero, Hyeonpung-Myeon, Dalseong-Gun, Daegu, 42988, South Korea

    Soopil Kim & Sang Hyun Park

  3. Department of Neurosurgery, Yonsei University College of Medicine, Seoul, South Korea

    Seok-Gu Kang & Jong Hee Chang

  4. Department of Pathology, Yonsei University College of Medicine, Seoul, South Korea

    Se Hoon Kim

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  1. Yae Won Park
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Corresponding authors

Correspondence to Sung Soo Ahn or Sang Hyun Park.

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Guarantor

The scientific guarantor of this publication is Professor Seung-Koo Lee, MD, PhD, from Yonsei University College of Medicine (slee@yuhs.ac).

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 (K.H, a biostatistician with 10 years of experience in biostatistics).

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Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• Retrospective

• Diagnostic or prognostic study

• Performed at one institution

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Park, Y.W., Kim, S., Ahn, S.S. et al. Magnetic resonance imaging–based 3-dimensional fractal dimension and lacunarity analyses may predict the meningioma grade. Eur Radiol 30, 4615–4622 (2020). https://doi.org/10.1007/s00330-020-06788-8

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  • DOI: https://doi.org/10.1007/s00330-020-06788-8

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