Xiao Dong-Dong, Yan Peng-Fei, Wang Yu-Xuan, Osman Mohamed Saied, Zhao Hong-Yang
Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
Department of Neurology, Renmin Hospital of Wuhan University, Wuhan 430060, Hubei Province, China.
Clin Neurol Neurosurg. 2018 Oct;173:84-90. doi: 10.1016/j.clineuro.2018.08.004. Epub 2018 Aug 2.
To investigate the diagnostic value of magnetic resonance imaging (MRI)-based 3D texture and shape features in the differentiation of glioblastoma (GBM) and primary central nervous system lymphoma (PCNSL).
A total of eighty-two patients, including sixty patients with GBM and twenty-two patients with PCNSL were followed up retrospectively from January 2012 to September 2017. MRI-based 3D texture and shape analysis were performed to evaluate the detectable differences between the two malignancies. The performance of machine-learning models was assessed. The Mann-Whitney U test and receiver operating characteristic (ROC) analysis were performed, and the corresponding sensitivity, specificity, accuracy, and area under the curve (AUC) were calculated. Ultimately, 60 GBM patients (33 males, 27 females; mean age 51.55 ± 13.58 years, range 8-74 years) and 22 PCNSL patients (14 males, 8 females; mean age 55.18 ± 12.19 years, range 32-78 years) were included in this study. All the PCNSLs were of the diffuse large B-cell type, and all patients were immunocompetent.
The variables Firstorder_Skewness, Firstorder_Kurtosis, and Ngtdm_Busyness, representing features extracted from contrast-enhanced T1-weighted images, showed high discriminatory power. Firstorder_ Skewness was the best selected predictor for classification (AUC = 0.86), followed by Ngtdm_Busyness (AUC = 0.83) and Firstorder_Kurtosis (AUC = 0.80). The sensitivities and specificities ranged from 70.0% to 83.3% and from 71.4% to 90.5%, respectively. Among three classification models, the naive Bayes classifier was superior overall, with a high AUC (0.90) and the best specificity (0.91). The support vector machine models provided the best sensitivity and accuracy (0.92 and 0.88, respectively).
MRI-based 3D texture analysis has potential utility for preoperative discrimination of GBM and PCNSL.
探讨基于磁共振成像（MRI）的三维纹理和形状特征在胶质母细胞瘤（GBM）和原发性中枢神经系统淋巴瘤（PCNSL）鉴别诊断中的价值。
回顾性随访2012年1月至2017年9月期间的82例患者，其中包括60例GBM患者和22例PCNSL患者。采用基于MRI的三维纹理和形状分析来评估这两种恶性肿瘤之间可检测到的差异。评估机器学习模型的性能。进行曼-惠特尼U检验和受试者工作特征（ROC）分析，并计算相应的敏感性、特异性、准确性和曲线下面积（AUC）。最终，本研究纳入了60例GBM患者（男性33例，女性27例；平均年龄51.55±13.58岁，范围8 - 74岁）和22例PCNSL患者（男性14例，女性8例；平均年龄55.18±12.19岁，范围32 - 78岁）。所有PCNSL均为弥漫性大B细胞型，且所有患者免疫功能正常。
代表从对比增强T1加权图像中提取特征的变量一阶偏度、一阶峰度和邻域灰度差矩阵（Ngtdm）忙度显示出较高的鉴别力。一阶偏度是分类的最佳选择预测因子（AUC = 0.86），其次是Ngtdm忙度（AUC = 0.83）和一阶峰度（AUC = 0.80）。敏感性和特异性分别在70.0%至83.3%和71.4%至90.5%之间。在三种分类模型中，朴素贝叶斯分类器总体上表现更优，具有较高的AUC（0.90）和最佳的特异性（0.91）。支持向量机模型提供了最佳的敏感性和准确性（分别为0..92和0.88）。
基于MRI的三维纹理分析在GBM和PCNSL的术前鉴别诊断中具有潜在应用价值。
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