Department of Radiology, Shahid Beheshti University of Medical Sciences, Iran.
Department of Radiology, University of Illinois College of Medicine, USA.
Neuroradiol J. 2020 Oct;33(5):428-436. doi: 10.1177/1971400920937382. Epub 2020 Jul 6.
The purpose of this study was to differentiate glioblastoma multiforme from primary central nervous system lymphoma using the customised first and second-order histogram features derived from apparent diffusion coefficients. A total of 82 patients (57 with glioblastoma multiforme and 25 with primary central nervous system lymphoma) were included in this study. The axial T1 post-contrast and fluid-attenuated inversion recovery magnetic resonance images were used to delineate regions of interest for the tumour and peritumoral oedema. The regions of interest were then co-registered with the apparent diffusion coefficient maps, and the first and second-order histogram features were extracted and compared between glioblastoma multiforme and primary central nervous system lymphoma groups. Receiver operating characteristic curve analysis was performed to calculate a cut-off value and its sensitivity and specificity to differentiate glioblastoma multiforme from primary central nervous system lymphoma.
Based on the tumour regions of interest, apparent diffusion coefficient mean, maximum, median, uniformity and entropy were higher in the glioblastoma multiforme group than the primary central nervous system lymphoma group ( ≤ 0.001). The most sensitive first and second-order histogram feature to differentiate glioblastoma multiforme from primary central nervous system lymphoma was the maximum of 2.026 or less (95% confidence interval (CI) 75.1-99.9%), and the most specific first and second-order histogram feature was smoothness of 1.28 or greater (84.0% CI 70.9-92.8%). Based on the oedema regions of interest, most of the first and second-order histogram features were higher in the glioblastoma multiforme group compared to the primary central nervous system lymphoma group ( ≤ 0.015). The most sensitive first and second-order histogram feature to differentiate glioblastoma multiforme from primary central nervous system lymphoma was the 25th percentile of 0.675 or less (100% CI 83.2-100%) and the most specific first and second-order histogram feature was the median of 1.28 or less (85.9% CI 66.3-95.8%).
Texture analysis using first and second-order histogram features derived from apparent diffusion coefficient maps may be helpful in differentiating glioblastoma multiforme from primary central nervous system lymphoma.
本研究旨在利用表观扩散系数（ADC）得出的定制一阶和二阶直方图特征来区分多形性胶质母细胞瘤和原发性中枢神经系统淋巴瘤。本研究共纳入 82 例患者（57 例多形性胶质母细胞瘤和 25 例原发性中枢神经系统淋巴瘤）。采用轴位 T1 增强后和液体衰减反转恢复磁共振成像（FLAIR）来勾画肿瘤和瘤周水肿的感兴趣区（ROI）。随后，ROI 与 ADC 图进行配准，提取并比较多形性胶质母细胞瘤和原发性中枢神经系统淋巴瘤组的一阶和二阶直方图特征。通过受试者工作特征曲线（ROC）分析计算鉴别多形性胶质母细胞瘤和原发性中枢神经系统淋巴瘤的截断值及其灵敏度和特异性。
基于肿瘤 ROI，多形性胶质母细胞瘤组的 ADC 值均值、最大值、中位数、均匀性和熵均高于原发性中枢神经系统淋巴瘤组（ ≤ 0.001）。最敏感的鉴别多形性胶质母细胞瘤和原发性中枢神经系统淋巴瘤的一阶和二阶直方图特征是最大值小于等于 2.026（95%置信区间（CI）为 75.1-99.9%），最特异的一阶和二阶直方图特征是平滑度大于等于 1.28（84.0% CI 为 70.9-92.8%）。基于水肿 ROI，多形性胶质母细胞瘤组的大多数一阶和二阶直方图特征均高于原发性中枢神经系统淋巴瘤组（ ≤ 0.015）。最敏感的鉴别多形性胶质母细胞瘤和原发性中枢神经系统淋巴瘤的一阶和二阶直方图特征是 25%分位数小于等于 0.675（100% CI 为 83.2-100%），最特异的一阶和二阶直方图特征是中位数小于等于 1.28（85.9% CI 为 66.3-95.8%）。
利用 ADC 图得出的一阶和二阶直方图特征进行纹理分析，可能有助于鉴别多形性胶质母细胞瘤和原发性中枢神经系统淋巴瘤。
