Magnetic resonance imaging-based 3-dimensional fractal dimension and lacunarity analyses may predict the meningioma grade
Magnetic resonance imaging-based 3-dimensional fractal dimension and lacunarity analyses may predict the meningioma grade
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DOI：
10.1007/s00330-020-06788-8
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发表时间：
2020-08-01
影响因子：
5.9
通讯作者：
Park, Sang Hyun
中科院分区：
文献类型：
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作者：
Park, Yae Won;Kim, Soopil;Park, Sang Hyun
目的探讨MRI三维分形维数（FD）和腔隙特征对脑膜瘤分级的预测价值。方法回顾性研究131例脑膜瘤患者（98例低级别，33例高级别），术前行MRI对比后t1加权成像。通过盒计数算法提取肿瘤增强部分的三维FD和空隙度参数。用类内相关系数（ICC）评估组间信度。此外，常规影像学特征如定位、非均匀增强、囊膜增强和坏死进行评估。采用多变量logistic回归分析脑膜瘤分级的独立临床和影像学危险因素。评价了含分形特征和不含分形特征的预测模型的判别值。分形参数与有丝分裂数和Ki-67标记指数的关系也进行了评价。结果阅读器间信度良好，FD的ICCs为0.99，空隙度的ICCs为0.97。高级别脑膜瘤比低级别脑膜瘤有更高的FD （p < 0.001）和更高的腔隙（p = 0.007）。在多变量logistic回归分析中，三维分形特征对脑膜瘤分级的预测，具有临床和常规影像学特征的模型的诊断效能提高，auc分别为0.78和0.84。三维FD与有丝分裂计数和Ki-67标记指数均有显著相关性，空隙度与Ki-67标记指数有显著相关性（均值< 0.05）。结论高级别脑膜瘤的三维FD和腔隙度较高，分形分析可作为预测脑膜瘤分级的有效影像生物标志物。
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 (allpvalues < 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.