转移瘤是成人脑部最常见的肿瘤。为了开始治疗，通常需要进行广泛的诊断工作。放射组学是一门旨在将放射学图像中的视觉数据转化为可靠的诊断信息的学科。我们的目的是检查深度学习方法对脑部磁共振成像中转移性病变的来源进行分类的能力，并将深度卷积神经网络（CNN）方法与基于图像纹理特征进行比较。 共有143例患者的157个转移性脑瘤被纳入研究。在手动分割过程中从转移性瘤中提取了统计和基于纹理的图像特征。在2D和3D肿瘤图像上使用了三个强大的预训练CNN架构和基于纹理的特征来区分肺部和乳腺转移病灶。使用十折交叉验证进行评估。计算准确度、精确度、召回率和曲线下面积（AUC）指标以分析诊断性能。基于3D体积的基于纹理的图像特征实现了比2D图像特征更好的区分结果。 CNN架构以3D输入为基础的整体性能优于基于纹理的特征。使用3D体积作为输入的Xception架构在准确度（0.85）方面表现最好，而AUC值为0.84。 VGG19和InceptionV3架构的AUC值分别为0.82和0.81。与基于纹理的图像特征相比，CNN在区分脑部转移病灶和肺部、乳腺恶性肿瘤方面实现了更优异的诊断性能。使用3D体积作为输入的区分在成功率上高于2D矢状图像。©2023年Wiley Periodicals LLC.

Metastases are the most common neoplasm in the adult brain. In order to initiate the treatment, an extensive diagnostic workup is usually required. Radiomics is a discipline aimed at transforming visual data in radiological images into reliable diagnostic information. We aimed to examine the capability of deep learning methods to classify the origin of metastatic lesions in brain MRIs and compare the deep Convolutional Neural Network (CNN) methods with image texture based features.One hundred forty three patients with 157 metastatic brain tumors were included in the study. The statistical and texture based image features were extracted from metastatic tumors after manual segmentation process. Three powerful pre-trained CNN architectures and the texture-based features on both 2D and 3D tumor images were used to differentiate lung and breast metastases. Ten-fold cross-validation was used for evaluation. Accuracy, precision, recall, and area under curve (AUC) metrics were calculated to analyze the diagnostic performance.The texture-based image features on 3D volumes achieved better discrimination results than 2D image features. The overall performance of CNN architectures with 3D inputs was higher than the texture-based features. Xception architecture, with 3D volumes as input, yielded the highest accuracy (0.85) while the AUC value was 0.84. The AUC values of VGG19 and the InceptionV3 architectures were 0.82 and 0.81, respectively.CNNs achieved superior diagnostic performance in differentiating brain metastases from lung and breast malignancies than texture-based image features. Differentiation using 3D volumes as input exhibited a higher success rate than 2D sagittal images.© 2023 Wiley Periodicals LLC.