Committee on Medical Physics, Department of Radiology, University of Chicago, MC 2026, 5841 South Maryland Avenue, Chicago, IL, 60637, USA.
Med Phys. 2019 May;46(5):2145-2156. doi: 10.1002/mp.13455. Epub 2019 Mar 12.
Radiomic texture analysis is typically performed on images acquired under specific, homogeneous imaging conditions. These controlled conditions may not be representative of the range of imaging conditions implemented clinically. We aim to develop a two-stage method of radiomic texture analysis that incorporates the reproducibility of individual texture features across imaging conditions to guide the development of texture signatures which are robust across mammography unit vendors.
Full-field digital mammograms were retrospectively collected for women who underwent screening mammography on both a Hologic Lorad Selenia and GE Senographe 2000D system. Radiomic features were calculated on manually placed regions of interest in each image. In stage one (robustness assessment), we identified a set of nonredundant features that were reproducible across the two different vendors. This was achieved through hierarchical clustering and application of robustness metrics. In stage two (classification evaluation), we performed stepwise feature selection and leave-one-out quadratic discriminant analysis (QDA) to construct radiomic signatures. We refer to this two-state method as robustness assessment, classification evaluation (RACE). These radiomic signatures were used to classify the risk of breast cancer through receiver operator characteristic (ROC) analysis, using the area under the ROC curve as a figure of merit in the task of distinguishing between women with and without high-risk factors present. Generalizability was investigated by comparing the classification performance of a feature set on the images from which they were selected (intravendor) to the classification performance on images from the vendor on which it was not selected (intervendor). Intervendor and intravendor performances were also compared to the performance obtained by implementing ComBat, a feature-level harmonization method and to the performance by implementing ComBat followed by RACE.
Generalizability, defined as the difference between intervendor and intravendor classification performance, was shown to monotonically decrease as the number of clusters used in stage one increased (Mann-Kendall P < 0.001). Intravendor performance was not shown to be statistically different from ComBat harmonization while intervendor performance was significantly higher than ComBat. No significant difference was observed between either of the single methods and the use of ComBat followed by RACE.
A two-stage method for robust radiomic signature construction is proposed and demonstrated in the task of breast cancer risk assessment. The proposed method was used to assess generalizability of radiomic texture signatures at varying levels of feature robustness criteria. The results suggest that generalizability of feature sets monotonically decreases as reproducibility of features decreases. This trend suggests that considerations of feature robustness in feature selection methodology could improve classifier generalizability in multifarious full-field digital mammography datasets collected on various vendor units. Additionally, harmonization methods such as ComBat may hold utility in classification schemes and should continue to be investigated.
放射组学纹理分析通常是在特定的、同质的成像条件下对图像进行的。这些控制条件可能无法代表临床实施的成像条件范围。我们旨在开发一种放射组学纹理分析的两阶段方法，该方法结合了各个纹理特征在成像条件下的可重复性，以指导开发在不同乳腺摄影设备供应商中稳健的纹理特征。
回顾性收集了在 Hologic Lorad Selenia 和 GE Senographe 2000D 系统上进行筛查性乳房 X 光摄影的女性的全视野数字乳房 X 光片。在每个图像中的手动放置的感兴趣区域上计算放射组学特征。在第一阶段（稳健性评估）中，我们确定了一组在两个不同供应商之间具有可重复性的非冗余特征。这是通过层次聚类和稳健性度量的应用来实现的。在第二阶段（分类评估）中，我们进行了逐步特征选择和留一法二次判别分析（QDA），以构建放射组学特征。我们将这种两状态方法称为稳健性评估，分类评估（RACE）。使用接收器操作特征（ROC）分析，通过 ROC 曲线下的面积作为区分有和无高危因素的女性的优劣标准，这些放射组学特征用于通过 ROC 分析对乳腺癌的风险进行分类。通过比较从选择它们的图像（内部供应商）获得的特征集的分类性能与从未选择它们的供应商的图像（外部供应商）获得的分类性能来研究可推广性。内部供应商和外部供应商的性能也与 ComBat（一种特征级别的调和方法）的性能以及 ComBat 之后的 RACE 性能进行了比较。
定义为外部供应商和内部供应商分类性能之间的差异的可推广性随着第一阶段中使用的聚类数量的增加而单调下降（Mann-Kendall P<0.001）。内部供应商的性能与 ComBat 调和没有统计学差异，而外部供应商的性能明显高于 ComBat。没有观察到任何一种单一方法与使用 ComBat 后进行 RACE 之间的显著差异。
提出并演示了一种用于稳健的放射组学特征构建的两阶段方法，用于乳腺癌风险评估任务。所提出的方法用于评估不同特征稳健性标准下放射组学纹理特征的可推广性。结果表明，特征的可重复性越低，特征集的可推广性越低。这种趋势表明，在特征选择方法中考虑特征稳健性可以提高在各种供应商设备上收集的各种全视野数字乳房 X 光摄影数据集的分类器的可推广性。此外，ComBat 等调和方法在分类方案中可能具有实用性，应继续进行研究。
