Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada.
Joint Department of Medical Imaging, University of Toronto, Toronto, Canada.
Comput Biol Med. 2021 Jun;133:104400. doi: 10.1016/j.compbiomed.2021.104400. Epub 2021 Apr 16.
The field of radiomics is at the forefront of personalized medicine. However, there is concern that high variation in imaging parameters will impact robustness of radiomic features and subsequently the performance of the predictive models built upon them. Therefore, our review aims to evaluate the impact of imaging parameters on the robustness of radiomic features. We also provide insights into the validity and discrepancy of different methodologies applied to investigate the robustness of radiomic features. We selected 47 papers based on our predefined inclusion criteria and grouped these papers by the imaging parameter under investigation: (i) scanner parameters, (ii) acquisition parameters and (iii) reconstruction parameters. Our review highlighted that most of the imaging parameters are disruptive parameters, and shape along with First order statistics were reported as the most robust radiomic features against variation in imaging parameters. This review identified inconsistencies related to the methodology of the reviewed studies such as the metrics used for robustness, the feature extraction techniques, the reporting style, and their outcome inclusion. We hope this review will aid the scientific community in conducting research in a way that is more reproducible and avoids the pitfalls of previous analyses.
放射组学领域处于个性化医疗的前沿。然而，人们担心成像参数的高度变化会影响放射组学特征的稳健性，从而影响基于这些特征构建的预测模型的性能。因此，我们的综述旨在评估成像参数对放射组学特征稳健性的影响。我们还深入探讨了应用于研究放射组学特征稳健性的不同方法的有效性和差异。我们根据预先设定的纳入标准选择了 47 篇论文，并根据研究中的成像参数对这些论文进行了分组：(i) 扫描仪参数，(ii) 采集参数和 (iii) 重建参数。我们的综述强调，大多数成像参数都是干扰参数，形状和一阶统计被报道为最稳健的放射组学特征，可以抵抗成像参数的变化。本综述确定了与综述研究方法相关的不一致性，例如用于稳健性的指标、特征提取技术、报告方式以及它们的结果纳入。我们希望本综述能够帮助科学界以更具可重复性的方式开展研究，避免之前分析的陷阱。
