Conducting side experiments termed robustness experiments, to identify features that are stable with respect to rescans, annotation, or other confounding effects is an important element in radiomics research. However, the matter of how to include the finding of these experiments into the model building process still needs to be explored. Three different methods for incorporating prior knowledge into a radiomics modelling process were evaluated: the naïve approach (ignoring feature quality), the most common approach consisting of removing unstable features, and a novel approach using data augmentation for information transfer (DAFIT). Multiple experiments were conducted using both synthetic and publicly available real lung imaging patient data. Ignoring additional information from side experiments resulted in significantly overestimated model performances meaning the estimated mean area under the curve achieved with a model was increased. Removing unstable features improved the performance estimation, while slightly decreasing the model performance, i.e. decreasing the area under curve achieved with the model. The proposed approach was superior both in terms of the estimation of the model performance and the actual model performance. Our experiments show that data augmentation can prevent biases in performance estimation and has several advantages over the plain omission of the unstable feature. The actual gain that can be obtained depends on the quality and applicability of the prior information on the features in the given domain. This will be an important topic of future research.

中文翻译：

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将独立特征稳定性信息最佳统计纳入放射学研究中。

进行称为稳健性实验的辅助实验，以识别相对于重新扫描，注释或其他混杂效应稳定的特征，这是放射学研究的重要内容。但是，如何将这些实验的发现纳入模型构建过程的问题仍然需要探索。评估了三种将先验知识整合到放射线学建模过程中的不同方法：朴素的方法（忽略特征质量），最常见的方法（包括消除不稳定的特征）以及使用数据增强信息传输（DAFIT）的新颖方法。使用合成的和可公开获得的真实肺部成像患者数据进行了多次实验。忽略来自辅助实验的其他信息会导致模型性能明显高估，这意味着使用模型获得的曲线下的估计平均面积增加了。消除不稳定的特征可以改善性能估计，同时会稍微降低模型的性能，即减少使用模型获得的曲线下面积。所提出的方法在模型性能的估计和实际模型性能方面均优于。我们的实验表明，数据增强可以防止性能估计方面的偏差，并且相对于简单地省略不稳定特征而言，它具有许多优势。可以获取的实际增益取决于给定域中有关特征的现有信息的质量和适用性。这将是未来研究的重要课题。