目的  应用临床常规3T磁共振T1、T2和液体衰减反转恢复(FLAIR)成像分析胶质瘤和单发性脑转移瘤的影像组学特征差异，探讨肿瘤区域不同方向以不同角度构建的纹理特征对区别两种肿瘤的意义，寻找一种可行的胶质瘤和单发性脑转移瘤高精度分类方法。   方法  43例胶质瘤患者和年龄、性别匹配的45例单发性脑转移瘤患者，从肿瘤区域轴状面、冠状面和矢状面方向的每1层构建不同角度的影像组学灰度共生矩阵，计算相应的纹理空间关系特征（包括对比度、相关性、能量和同质性）；使用Wilcoxon秩和检验选择特征并降低冗余；所选特征经SVM线性核分类器分类，实现两种肿瘤的诊断。   结果  在分类胶质瘤和单发性脑转移瘤时，多模态多方向组合特征的精确性、召回率、F1分值和准确性分别是0.8857、0.9114、0.8944和0.8922；该组合特征在SVM线性核分类器下的受试者工作特征曲线下面积为0.9602；并将45例单发性脑转移瘤患者中的40例正确分类；43例胶质瘤患者中的39例正确分类。   结论  肿瘤区域的多模态多方向组合特征经SVM线性核分类器分类，可以鉴别胶质瘤和单发性脑转移瘤，这可作为第2意见，有效协助医生做出诊断。

Objective  To analyze the difference of radiomics features between solitary brain metastasis and glioma using routine 3T T1, T2 and fluid attenuation inversion recovery (FLAIR) magnetic resonance imaging, to explore the significance of texture features constructed in different directions and angles in tumor regions in distinguishing the two kinds of tumors, and to explore a feasible method  for high-precision classification of solitary brain metastases and gliomas.    Methods  Given the multimodal images of 43 patients with glioma and 45 age- and sex- matched patients with solitary brain metastasis, the gray level co-occurrence matrices of different angles of each slice were constructed from the transverse, coronal and sagittal directions of the tumor regions of these images, and the texture spatial relationship features (including contrast, correlation, energy and homogeneity) were calculated. Wilcoxon rank sum test was used to eliminate redundant features and select features with strong distinguishing ability. Finally, SVM linear kernel classifier was used to classify the selected features to achieve the identification of the two kinds of tumors.    Results  When classifying glioma and solitary brain metastasis, the precision, recall, F1 score and accuracy of multimodal and multidirectional combination features were 0.8857, 0.9114, 0.8944 and 0.8922, respectively. The area under the receiver operating characteristic curve obtained by linear kernel SVM classifier was 0.9602. Totally 40 of the 45 patients with solitary brain metastases were correctly classified, and 39 of the 43 gliomas were correctly classified.   Conclusion  The multimodal and multi-directional combination features of tumor areas can be classified by linear kernel SVM classifier to distinguish gliomas from solitary brain metastases, which can be used as a second opinion to effectively assist doctors in making diagnosis.