Background: Lately, radiomic studies featuring the development of a signature to use in prediction models in diagnosis or prognosis outcomes have been increasingly published. While the results are shown to be promising, these studies still have many pitfalls and limitations. One of the main issues of these studies is that radiomic features depend on how the images are preprocessed before their computation. Since, in widely known and used software for radiomic features calculation, it is possible to set these preprocessing parameters before the calculation of the radiomic feature, there are ongoing studies assessing the stability and repeatability of radiomic features to find the most suitable preprocessing parameters for every used imaging modality. Materials and Methods: We performed a comprehensive literature search using four electronic databases: PubMed, Cochrane Library, Embase, and Scopus. Mesh terms and free text were modeled in search strategies for databases. The inclusion criteria were studies where preprocessing parameters’ influence on feature values and model predictions was addressed. Records lacking information on image acquisition parameters were excluded, and any eligible studies with full-text versions were included in the review process, while conference proceedings and monographs were disregarded. We used the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2) tool to investigate the risk of bias. We synthesized our data in a table divided by the imaging modalities subgroups. Results: After applying the inclusion and exclusion criteria, we selected 43 works. This review examines the impact of preprocessing parameters on the reproducibility and reliability of radiomic features extracted from multimodality imaging (CT, MRI, CBCT, and PET/CT). Standardized preprocessing is crucial for consistent radiomic feature extraction. Key preprocessing steps include voxel resampling, normalization, and discretization, which influence feature robustness and reproducibility. In total, 44% of the included works studied the effects of an isotropic voxel resampling, and most studies opted to employ a discretization strategy. From 2021, several studies started selecting the best set of preprocessing parameters based on models’ best performance. As for comparison metrics, ICC was the most used in MRI studies in 58% of the screened works. Conclusions: From our work, we highlighted the need to harmonize the use of preprocessing parameters and their values, especially in light of future studies of prospective studies, which are still lacking in the current literature.
背景：近年来，越来越多关于影像组学特征在诊断或预后预测模型中应用的标志性研究被发表。尽管这些研究结果显示出良好前景，但仍存在诸多缺陷与局限性。其中主要问题之一是影像组学特征的计算结果依赖于图像预处理方式。由于目前广泛使用的影像组学特征计算软件允许在特征计算前设置预处理参数，当前已有研究正在评估影像组学特征的稳定性与可重复性，以寻找适用于不同影像模态的最佳预处理参数。 材料与方法：我们通过PubMed、Cochrane Library、Embase和Scopus四个电子数据库进行了系统性文献检索。检索策略结合了医学主题词与自由词。纳入标准为探讨预处理参数对特征值及模型预测影响的研究。排除缺乏图像采集参数信息的记录，纳入所有符合条件且可获得全文的研究，会议摘要和专著不予考虑。采用QUADAS-2（诊断准确性研究质量评估工具第2版）评估偏倚风险。按影像模态亚组将数据整合成表格进行综合分析。 结果：经纳入排除标准筛选后，共纳入43篇文献。本综述系统分析了预处理参数对多模态影像（CT、MRI、CBCT和PET/CT）提取的影像组学特征可重复性与可靠性的影响。标准化预处理对保证影像组学特征提取的一致性至关重要。体素重采样、标准化和离散化等关键预处理步骤直接影响特征的稳健性与可重复性。总计44%的纳入文献研究了各向同性体素重采样的影响，多数研究采用了离散化策略。自2021年以来，部分研究开始基于模型最佳性能选择最优预处理参数组合。在比较指标方面，58%的MRI研究使用组内相关系数作为主要评估指标。 结论：本研究强调需要规范预处理参数及其取值的标准化应用，特别是在当前文献尚缺乏前瞻性研究的情况下，这对未来前瞻性研究具有重要意义。
Impact of Preprocessing Parameters in Medical Imaging-Based Radiomic Studies: A Systematic Review
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