PURPOSE A highly accurate and robust computer-aided system based on quantitative high-throughput Breast Imaging Reporting and Data System (BI-RADS) features from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can drive the success of radiomic applications in breast cancer diagnosis. We aim to build a stable system with highly reproducible radiomics features, which can make diagnostic performance independent of datasets bias and segmentation methods. METHOD We applied a dataset of 267 patients including 136 malignant and 131 benign tumors from two MRI manufacturers, where 211 cases from a Philips system and 55 cases from a GE system. First, manual annotations, 3D-Unet and 2D-Unet were applied as different segmentation methods. Second, we designed and extracted 3172 features from six modalities of DCE-MRI based on BI-RADS. Third, the feature selection was conducted. Between-class distance was utilized to eliminate the effect of dataset bias caused by two machines. Concordance correlation coefficient, intraclass correlation coefficient and deviation were employed to evaluate the influence of three segmentation methods. We further eliminated features redundancy using genetic algorithm. Finally, three classifiers including support vector machine (SVM), the bagged trees and K-Nearest Neighbor were evaluated by their performance for diagnosing malignant and benign tumors. RESULTS A total of 246 features were preserved to have high stability and reproducibility. The final feature set showed the robust performance under these factors and achieved the area under curve of 0.88, the accuracy of 0.824, the sensitivity of 0.844, the specificity of 0.807 in differentiating benign and malignant tumors with the SVM classifier using manually segmentation results. CONCLUSION The final selected 246 features are reproducible and show little dependence on segmentation methods and data perturbation. The high stability and effectiveness of diagnosis across these factors illustrate that the preserved features can be used for prognostic analysis and help radiologists in the diagnosis of breast cancer.

中文翻译：

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乳腺DCE-MRI放射学：基于可重现的BI-RADS功能的强大计算机辅助系统，可克服数据集偏差和分割方法的影响。

目的基于来自动态对比增强磁共振成像（DCE-MRI）的定量高通量乳房成像报告和数据系统（BI-RADS）功能的高精度，强大的计算机辅助系统，可以推动乳房放射学应用的成功癌症诊断。我们的目标是建立一个具有高度可重复的放射学特征的稳定系统，使诊断性能独立于数据集的偏倚和分割方法。方法我们应用了来自两个MRI制造商的267例患者的数据集，包括136例恶性肿瘤和131例良性肿瘤，其中211例来自Philips系统，55例来自GE系统。首先，将手动注释，3D-Unet和2D-Unet用作不同的分割方法。其次，我们基于BI-RADS从6种DCE-MRI模式中设计并提取了3172个特征。第三，进行特征选择。使用类间距离来消除由两台机器引起的数据集偏差的影响。采用一致性相关系数，组内相关系数和偏差来评估三种分割方法的影响。我们使用遗传算法进一步消除了特征冗余。最后，通过它们对恶性和良性肿瘤的诊断性能，评估了三个分类器，包括支持向量机（SVM），袋装树和K近邻。结果总共保留了246个特征，以具有较高的稳定性和可重复性。最终特征集显示了在这些因素下的鲁棒性能，并获得了0.88的曲线下面积，0.824的准确度，0.844的灵敏度，0的特异性。807使用手动分割结果通过SVM分类器区分良性和恶性肿瘤。结论最终选择的246个特征是可重现的，并且几乎不依赖于分割方法和数据扰动。跨这些因素的诊断具有很高的稳定性和有效性，表明保留的特征可用于预后分析，并帮助放射科医生诊断乳腺癌。