Alcaide-Leon P, Dufort P, Geraldo A F, Alshafai L, Maralani P J, Spears J, Bharatha A
From the Departments of Medical Imaging (P.A.-L., A.B.)
Department of Medical Imaging (P.D., A.F.G.) Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada.
AJNR Am J Neuroradiol. 2017 Jun;38(6):1145-1150. doi: 10.3174/ajnr.A5173. Epub 2017 Apr 27.
Accurate preoperative differentiation of primary central nervous system lymphoma and enhancing glioma is essential to avoid unnecessary neurosurgical resection in patients with primary central nervous system lymphoma. The purpose of the study was to evaluate the diagnostic performance of a machine-learning algorithm by using texture analysis of contrast-enhanced T1-weighted images for differentiation of primary central nervous system lymphoma and enhancing glioma.
Seventy-one adult patients with enhancing gliomas and 35 adult patients with primary central nervous system lymphomas were included. The tumors were manually contoured on contrast-enhanced T1WI, and the resulting volumes of interest were mined for textural features and subjected to a support vector machine-based machine-learning protocol. Three readers classified the tumors independently on contrast-enhanced T1WI. Areas under the receiver operating characteristic curves were estimated for each reader and for the support vector machine classifier. A noninferiority test for diagnostic accuracy based on paired areas under the receiver operating characteristic curve was performed with a noninferiority margin of 0.15.
The mean areas under the receiver operating characteristic curve were 0.877 (95% CI, 0.798-0.955) for the support vector machine classifier; 0.878 (95% CI, 0.807-0.949) for reader 1; 0.899 (95% CI, 0.833-0.966) for reader 2; and 0.845 (95% CI, 0.757-0.933) for reader 3. The mean area under the receiver operating characteristic curve of the support vector machine classifier was significantly noninferior to the mean area under the curve of reader 1 ( = .021), reader 2 ( = .035), and reader 3 ( = .007).
Support vector machine classification based on textural features of contrast-enhanced T1WI is noninferior to expert human evaluation in the differentiation of primary central nervous system lymphoma and enhancing glioma.
准确术前鉴别原发性中枢神经系统淋巴瘤和强化型胶质瘤对于避免原发性中枢神经系统淋巴瘤患者进行不必要的神经外科手术切除至关重要。本研究的目的是通过使用对比增强T1加权图像的纹理分析来评估机器学习算法对原发性中枢神经系统淋巴瘤和强化型胶质瘤的鉴别诊断性能。
纳入71例患有强化型胶质瘤的成年患者和35例患有原发性中枢神经系统淋巴瘤的成年患者。在对比增强T1WI上手动勾勒肿瘤轮廓，对得到的感兴趣体积进行纹理特征提取，并采用基于支持向量机的机器学习方案。三名阅片者在对比增强T1WI上独立对肿瘤进行分类。估计每位阅片者以及支持向量机分类器的受试者操作特征曲线下面积。基于配对的受试者操作特征曲线下面积进行诊断准确性的非劣效性检验，非劣效性界值为0.15。
支持向量机分类器的受试者操作特征曲线下面积均值为0.877（95%CI，0.798 - 0.955）；阅片者1为0.878（95%CI，0.807 - 0.949）；阅片者2为0.899（95%CI，0.833 - 0.966）；阅片者3为0.845（95%CI，0.757 - 0.933）。支持向量机分类器的受试者操作特征曲线下面积均值显著不劣于阅片者1（P = 0.021）、阅片者2（P = 0.035）和阅片者3（P = 0.007）的曲线下面积均值。
基于对比增强T1WI纹理特征的支持向量机分类在原发性中枢神经系统淋巴瘤和强化型胶质瘤的鉴别诊断中不劣于专家人工评估。
