图像生物标志物标准化倡议已经帮助提高了MRI放射医学特征的计算可重复性。然而，MRI序列和具有高成像再现性的特征尚未建立。本研究旨在确定经过测试重测、多扫描仪和计算可重复性比较后具有可重复性的多参数MRI放射医学特征，并评估其在脑肿瘤诊断中的临床价值。为了评估可重复性，使用标准化模型场景从三台3T MRI扫描仪中获取了T1加权成像(T1WI)、T2加权成像(T2WI)和扩散加权成像(DWI)图像，并使用两种计算算法提取了放射医学特征。当协方差相关系数的值在多个会话、扫描仪和计算算法之间均大于0.9时，选择可重复的放射医学特征。随机森林分类器使用可重复的特征(n=117)训练，并在临床队列(n=50)中进行验证，以评估具有高可重复性的特征是否改善了胶质母细胞瘤与原发性中枢神经系统淋巴瘤的区分度。T2WI的放射医学特征表现出比DWI (38-48%)或T1WI (2-92%)更高的重复性(65-94%)。在测试重测、多扫描仪和计算比较中，T2WI提供了41个可重复的特征，DWI提供了6个，T1WI提供了2个。使用可重复特征的分类模型在训练集 (AUC, 0.916 vs. 0.877)和验证集 (AUC, 0.957 vs. 0.869) 中的性能均高于使用不可重复特征的模型。通过识别在多个会话、扫描仪和计算算法之间具有高可重复性的放射医学特征，我们发现它们在胶质母细胞瘤和原发性中枢神经系统淋巴瘤的鉴别性能方面优于不可重复的放射医学特征。通过确定显示更高机器间重复性的放射医学特征，我们的研究结果表明在胶质母细胞瘤和原发性中枢神经系统淋巴瘤的鉴别方面具有更高放射医学诊断性能，为神经肿瘤领域的进一步研究设计和临床应用铺平了道路。• 使用模型场景从幻影中获取的高度可重复的放射医学特征应用于临床诊断。• T2加权成像的放射医学特征比T1加权和扩散加权成像的特征更具重复性。• 具有良好重复性的放射医学特征在脑肿瘤的诊断性能上优于可重复性差的特征。© 2023作者（们）。根据欧洲放射学会的独家许可。

The Image Biomarker Standardization Initiative has helped improve the computational reproducibility of MRI radiomics features. Nonetheless, the MRI sequences and features with high imaging reproducibility are yet to be established. To determine reproducible multiparametric MRI radiomics features across test-retest, multi-scanner, and computational reproducibility comparisons, and to evaluate their clinical value in brain tumor diagnosis.To assess reproducibility, T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI) were acquired from three 3-T MRI scanners using standardized phantom, and radiomics features were extracted using two computational algorithms. Reproducible radiomics features were selected when the concordance correlation coefficient value above 0.9 across multiple sessions, scanners, and computational algorithms. Random forest classifiers were trained with reproducible features (n = 117) and validated in a clinical cohort (n = 50) to evaluate whether features with high reproducibility improved the differentiation of glioblastoma from primary central nervous system lymphomas (PCNSLs).Radiomics features from T2WI demonstrated higher repeatability (65-94%) than those from DWI (38-48%) or T1WI (2-92%). Across test-retest, multi-scanner, and computational comparisons, T2WI provided 41 reproducible features, DWI provided six, and T1WI provided two. The performance of the classification model with reproducible features was higher than that using non-reproducible features in both training set (AUC, 0.916 vs. 0.877) and validation set (AUC, 0.957 vs. 0.869).Radiomics features with high reproducibility across multiple sessions, scanners, and computational algorithms were identified, and they showed higher diagnostic performance than non-reproducible radiomics features in the differentiation of glioblastoma from PCNSL.By identifying the radiomics features showing higher multi-machine reproducibility, our results also demonstrated higher radiomics diagnostic performance in the differentiation of glioblastoma from PCNSL, paving the way for further research designs and clinical application in neuro-oncology.• Highly reproducible radiomics features across multiple sessions, scanners, and computational algorithms were identified using phantom and applied to clinical diagnosis. • Radiomics features from T2-weighted imaging were more reproducible than those from T1-weighted and diffusion-weighted imaging. • Radiomics features with good reproducibility had better diagnostic performance for brain tumors than features with poor reproducibility.© 2023. The Author(s), under exclusive licence to European Society of Radiology.