背景:最近,放射学研究越来越多地发表了以开发用于诊断或预后结果的预测模型的特征。虽然结果被证明是有希望的,这些研究仍然存在许多缺陷和局限性。这些研究的主要问题之一是放射学特征取决于图像在计算之前如何进行预处理。因为,在广为人知和使用的放射学特征计算软件中,可以在计算放射学特征之前设置这些预处理参数,目前正在进行的研究评估影像组学特征的稳定性和可重复性,以便为每种使用的成像模式找到最合适的预处理参数.
方法:我们使用四个电子数据库进行了全面的文献检索:PubMed,科克伦图书馆,Embase,还有Scopus.在数据库的搜索策略中对网格术语和自由文本进行了建模。纳入标准是研究预处理参数对特征值和模型预测的影响。排除了缺乏图像采集参数信息的记录,和任何符合条件的研究与全文版本被纳入审查过程中,而会议记录和专著被忽略。我们使用QUADAS-2(诊断准确性研究2的质量评估)工具来调查偏倚的风险。我们将我们的数据合成在一个由成像模式亚组划分的表格中。
结果:应用纳入和排除标准后,我们选择了43个作品。这篇综述研究了预处理参数对从多模态成像(CT,MRI,CBCT,和PET/CT)。标准化预处理对于一致的放射学特征提取至关重要。关键预处理步骤包括体素重采样,归一化,和离散化,影响特征的鲁棒性和再现性。总的来说,44%的作品研究了各向同性体素重采样的影响,大多数研究选择采用离散化策略。从2021年开始,一些研究开始根据模型的最佳性能选择最佳的预处理参数集。至于比较指标,在58%的筛查工作中,ICC在MRI研究中使用最多。
结论:从我们的工作来看,我们强调需要协调预处理参数及其值的使用,特别是考虑到未来的前瞻性研究,这在当前的文献中仍然缺乏。
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.
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.