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

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-weightedimaging. The 2D and 3D FD and lacunarity parameters from the contrast-enhancing portion of the tumorwere calculated. Reproducibility was assessed with the intraclass correlation coefficient. Multivariablelogistic regression analysis using 2D or 3D fractal features was performed to predict the meningiomagrade. The diagnostic ability of the 2D and 3D fractal models were compared. @*Results@#: The reproducibility between observers was excellent, with intraclass correlation coefficientsof 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) andhigher 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 of0.690 [95% confidence interval (CI) 0.581-0.799], 72.4%, 75.8%, and 64.4%, respectively. The 3Dfractal 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 diagnosticperformance than the 2D fractal model (p<0.001). @*Conclusion@#: The 3D fractal analysis proved superiority in diagnostic performance to 2D fractalanalysis in grading meningioma.