Radiomic analysis has exponentially increased the amount of quantitative data that can be extracted from a single image. These imaging biomarkers can aid in the generation of prediction models aimed to further personalized medicine. However, the generalizability of the model is dependent on the robustness of these features. The purpose of this study is to review the current literature regarding robustness of radiomic features on magnetic resonance imaging. Additionally, a phantom study is performed to systematically evaluate the behavior of radiomic features under various conditions (signal to noise ratio, region of interest delineation, voxel size change and normalization methods) using intraclass correlation coefficients. The features extracted in this phantom study include first order, shape, gray level cooccurrence matrix and gray level run length matrix. Many features are found to be non-robust to changing parameters. Feature robustness assessment prior to feature selection, especially in the case of combining multi-institutional data, may be warranted. Further investigation is needed in this area of research.

中文翻译：

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磁共振成像中放射学特征的鲁棒性：综述和幻像研究

放射分析已经成倍增加了可以从单个图像中提取的定量数据的数量。这些成像生物标记物可以帮助生成旨在进一步个性化医学的预测模型。但是，模型的一般性取决于这些功能的鲁棒性。这项研究的目的是审查有关磁共振成像放射特征的鲁棒性的当前文献。此外，使用类内相关系数，进行了幻像研究，以系统地评估各种条件（信噪比，感兴趣区域描绘，体素大小变化和归一化方法）下的放射特征的行为。该幻像研究中提取的特征包括一阶，形状，灰度共生矩阵和灰度游程长度矩阵。发现许多功能对于更改参数都不是很可靠。可能需要在特征选择之前进行特征鲁棒性评估，尤其是在结合多机构数据的情况下。该研究领域需要进一步的研究。