目的：本研究的主要目的是通过检查肿瘤成分和非肿瘤灰质（GM）和白质（WM）区域，全面调查分形维度（FD）测量在将脑胶质瘤区分为低级别脑胶质瘤（LGG）和高级别脑胶质瘤（HGG）方面的潜力。 方法：本研究使用42名脑胶质瘤患者（LGG，n = 27；HGG，n = 15）的回顾性磁共振成像（MRI）数据。使用MRI，我们根据肿瘤和非肿瘤脑GM和WM区域的一般结构、边界和骨架方面计算了不同的FD测量。还测量了肿瘤和非肿瘤区域的纹理特征，即角二阶矩、对比度、逆差异矩、相关性和熵。通过将FD特征与纹理特征进行比较来评估FD特征的有效性。对上述测量进行统计推断和机器学习方法，以区分LGG和HGG患者。 结果：肿瘤和非肿瘤区域的FD测量能够区分LGG和HGG患者。在15种不同的FD测量中，增强肿瘤区域的一般结构FD值的准确度（93%）、灵敏度（97%）、特异度（98%）和受试者工作特征曲线下面积（AUC）得分（98%）较高。非肿瘤GM骨架FD值在分类肿瘤分级方面也具有良好的准确度（83.3%）、灵敏度（100%）、特异度（60%）和AUC得分（80%）。这些测量结果在LGG和HGG患者之间也有显著差异（p < 0.05）。另一方面，25个纹理特征中，增强肿瘤区域的对比度、相关性和熵等特征在LGG和HGG之间显示出显著差异。在机器学习中，增强肿瘤区域的纹理特征表现出较高的准确度、灵敏度、特异度和AUC得分。 结论：纹理和FD特征之间的比较表明，在不同方面对肿瘤和非肿瘤成分进行FD分析不仅具有高度统计学显著性和分类准确性，而且为脑胶质瘤分级提供了更好的洞察力。因此，FD特征可以作为潜在的脑胶质瘤神经影像标志物。 版权所有 © 2023 Battalapalli, Vidyadharan, Prabhakar Rao, Yogeeswari, Kesavadas and Rajagopalan.

Purpose: The main purpose of this study was to comprehensively investigate the potential of fractal dimension (FD) measures in discriminating brain gliomas into low-grade glioma (LGG) and high-grade glioma (HGG) by examining tumor constituents and non-tumorous gray matter (GM) and white matter (WM) regions. Methods: Retrospective magnetic resonance imaging (MRI) data of 42 glioma patients (LGG, n = 27 and HGG, n = 15) were used in this study. Using MRI, we calculated different FD measures based on the general structure, boundary, and skeleton aspects of the tumorous and non-tumorous brain GM and WM regions. Texture features, namely, angular second moment, contrast, inverse difference moment, correlation, and entropy, were also measured in the tumorous and non-tumorous regions. The efficacy of FD features was assessed by comparing them with texture features. Statistical inference and machine learning approaches were used on the aforementioned measures to distinguish LGG and HGG patients. Results: FD measures from tumorous and non-tumorous regions were able to distinguish LGG and HGG patients. Among the 15 different FD measures, the general structure FD values of enhanced tumor regions yielded high accuracy (93%), sensitivity (97%), specificity (98%), and area under the receiver operating characteristic curve (AUC) score (98%). Non-tumorous GM skeleton FD values also yielded good accuracy (83.3%), sensitivity (100%), specificity (60%), and AUC score (80%) in classifying the tumor grades. These measures were also found to be significantly (p < 0.05) different between LGG and HGG patients. On the other hand, among the 25 texture features, enhanced tumor region features, namely, contrast, correlation, and entropy, revealed significant differences between LGG and HGG. In machine learning, the enhanced tumor region texture features yielded high accuracy, sensitivity, specificity, and AUC score. Conclusion: A comparison between texture and FD features revealed that FD analysis on different aspects of the tumorous and non-tumorous components not only distinguished LGG and HGG patients with high statistical significance and classification accuracy but also provided better insights into glioma grade classification. Therefore, FD features can serve as potential neuroimaging biomarkers for glioma.Copyright © 2023 Battalapalli, Vidyadharan, Prabhakar Rao, Yogeeswari, Kesavadas and Rajagopalan.