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. 2020 Apr;8(1):36-42.
doi: 10.14791/btrt.2020.8.e3.

Comparison of Diagnostic Performance of Two-Dimensional and Three-Dimensional Fractal Dimension and Lacunarity Analyses for Predicting the Meningioma Grade

Soopil Kim  1 , Yae Won Park  2 , Sang Hyun Park  1 , Sung Soo Ahn  3 , Jong Hee Chang  4 , Se Hoon Kim  5 , Seung Koo Lee  3
Affiliations

Comparison of Diagnostic Performance of Two-Dimensional and Three-Dimensional Fractal Dimension and Lacunarity Analyses for Predicting the Meningioma Grade

Soopil Kim et al. Brain Tumor Res Treat. 2020 Apr.

Abstract

Background: To compare the diagnostic performance of two-dimensional (2D) and three-dimensional (3D) fractal dimension (FD) and lacunarity features from MRI for predicting the meningioma grade.

Methods: This retrospective study included 123 meningioma patients [90 World Health Organization (WHO) grade I, 33 WHO grade II/III] with preoperative MRI including post-contrast T1-weighted imaging. The 2D and 3D FD and lacunarity parameters from the contrast-enhancing portion of the tumor were calculated. Reproducibility was assessed with the intraclass correlation coefficient. Multivariable logistic regression analysis using 2D or 3D fractal features was performed to predict the meningioma grade. The diagnostic ability of the 2D and 3D fractal models were compared.

Results: The reproducibility between observers was excellent, with intraclass correlation coefficients of 0.97, 0.95, 0.98, and 0.96 for 2D FD, 2D lacunarity, 3D FD, and 3D lacunarity, respectively. WHO grade II/III meningiomas had a higher 2D and 3D FD (p=0.003 and p<0.001, respectively) and higher 2D and 3D lacunarity (p=0.002 and p=0.006, respectively) than WHO grade I meningiomas. The 2D fractal model showed an area under the curve (AUC), accuracy, sensitivity, and specificity of 0.690 [95% confidence interval (CI) 0.581-0.799], 72.4%, 75.8%, and 64.4%, respectively. The 3D fractal model showed an AUC, accuracy, sensitivity, and specificity of 0.813 (95% CI 0.733-0.878), 82.9%, 81.8%, and 70.0%, respectively. The 3D fractal model exhibited significantly better diagnostic performance than the 2D fractal model (p<0.001).

Conclusion: The 3D fractal analysis proved superiority in diagnostic performance to 2D fractal analysis in grading meningioma.

Keywords: Fractals; Magnetic resonance imaging; Meningioma.

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Conflict of interest statement

The authors have no potential conflicts of interest.

Figures

Fig. 1
Fig. 1. The schematic of segmentation and fractal analysis in our study. A: A post-contrast T1-weighted imaging of a representative case with meningioma. B: After segementation of the enhancing portion of the tumor, two-dimensional and three-dimensional fractal analysis were performed by using box-counting methods.
Fig. 2
Fig. 2. Boxplot representation of the FD (A,C) and lacunarity (B, D) according to different meningioma grades. FD, fractal dimension; 2D, two-dimensional; 3D, three-dimensional.
Fig. 3
Fig. 3. Receiver operating characteristic curves of 2D fractal models and 3D fractal models for predicting meningioma grades. 2D, two-dimensional; 3D, three-dimensional.
See this image and copyright information in PMC

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