Radiomics in nuclear medicine is rapidly expanding. Reproducibility of radiomics studies in multicentre settings is an important criterion for clinical translation. We therefore performed a meta-analysis to investigate reproducibility of radiomics biomarkers in PET imaging and to obtain quantitative information regarding their sensitivity to variations in various imaging and radiomics-related factors as well as their inherent sensitivity. Additionally, we identify and describe data analysis pitfalls that affect the reproducibility and generalizability of radiomics studies. After a systematic literature search, 42 studies were included in the qualitative synthesis, and data from 21 were used for the quantitative meta-analysis. Data concerning measurement agreement and reliability were collected for 21 of 38 different factors associated with image acquisition, reconstruction, segmentation and radiomics-specific processing steps. Variations in voxel size, segmentation and several reconstruction parameters strongly affected reproducibility, but the level of evidence remained weak. Based on the meta-analysis, we also assessed inherent sensitivity to variations of 110 PET image biomarkers. SUVmean and SUVmax were found to be reliable, whereas image biomarkers based on the neighbourhood grey tone difference matrix and most biomarkers based on the size zone matrix were found to be highly sensitive to variations, and should be used with care in multicentre settings. Lastly, we identify 11 data analysis pitfalls. These pitfalls concern model validation and information leakage during model development, but also relate to reporting and the software used for data analysis. Avoiding such pitfalls is essential for minimizing bias in the results and to enable reproduction and validation of radiomics studies.

中文翻译：

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核医学中的放射学：稳健性，可重复性，标准化以及如何避免数据分析陷阱和复制危机。

核医学中的放射学正在迅速扩展。在多中心环境中进行放射学研究的可重复性是临床翻译的重要标准。因此，我们进行了一项荟萃分析，以研究放射成像生物标志物在PET成像中的可再现性，并获得定量信息，以了解其对各种成像和放射学相关因素变化的敏感性及其固有敏感性。此外，我们确定并描述了影响放射线学研究的可重复性和通用性的数据分析陷阱。经过系统的文献检索，定性合成中包括42项研究，来自21项的数据用于定量荟萃分析。收集了与38个不同因素中的21个有关测量一致性和可靠性的数据，这些因素与图像采集，重建，分割和放射线学特定的处理步骤有关。体素大小，分割和几个重建参数的变化强烈影响了可重复性，但是证据水平仍然很弱。基于荟萃分析，我们还评估了对110种PET图像生物标志物变异的内在敏感性。SUVmean和SUVmax被认为是可靠的，而基于邻域灰度差矩阵的图像生物标志物和基于大小带矩阵的大多数生物标志物对变化高度敏感，因此在多中心环境中应谨慎使用。最后，我们确定了11个数据分析陷阱。这些陷阱涉及模型开发期间的模型验证和信息泄漏，但也涉及报告和用于数据分析的软件。避免此类陷阱对于将结果的偏倚降至最低，并使放射线学研究能够重现和验证是必不可少的。