Department of Radiation Oncology, MAASTRO Clinic, Maastricht, The Netherlands; School for Oncology and Developmental Biology (GROW), Maastricht University, Maastricht, The Netherlands.
Department of Radiation Oncology, MAASTRO Clinic, Maastricht, The Netherlands; School for Oncology and Developmental Biology (GROW), Maastricht University, Maastricht, The Netherlands.
Int J Radiat Oncol Biol Phys. 2018 Nov 15;102(4):1143-1158. doi: 10.1016/j.ijrobp.2018.05.053. Epub 2018 Jun 5.
An ever-growing number of predictive models used to inform clinical decision making have included quantitative, computer-extracted imaging biomarkers, or "radiomic features." Broadly generalizable validity of radiomics-assisted models may be impeded by concerns about reproducibility. We offer a qualitative synthesis of 41 studies that specifically investigated the repeatability and reproducibility of radiomic features, derived from a systematic review of published peer-reviewed literature.
The PubMed electronic database was searched using combinations of the broad Haynes and Ingui filters along with a set of text words specific to cancer, radiomics (including texture analyses), reproducibility, and repeatability. This review has been reported in compliance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. From each full-text article, information was extracted regarding cancer type, class of radiomic feature examined, reporting quality of key processing steps, and statistical metric used to segregate stable features.
Among 624 unique records, 41 full-text articles were subjected to review. The studies primarily addressed non-small cell lung cancer and oropharyngeal cancer. Only 7 studies addressed in detail every methodologic aspect related to image acquisition, preprocessing, and feature extraction. The repeatability and reproducibility of radiomic features are sensitive at various degrees to processing details such as image acquisition settings, image reconstruction algorithm, digital image preprocessing, and software used to extract radiomic features. First-order features were overall more reproducible than shape metrics and textural features. Entropy was consistently reported as one of the most stable first-order features. There was no emergent consensus regarding either shape metrics or textural features; however, coarseness and contrast appeared among the least reproducible.
Investigations of feature repeatability and reproducibility are currently limited to a small number of cancer types. Reporting quality could be improved regarding details of feature extraction software, digital image manipulation (preprocessing), and the cutoff value used to distinguish stable features.
越来越多用于为临床决策提供信息的预测模型都包含了定量的、计算机提取的成像生物标志物，即“放射组学特征”。放射组学辅助模型的广泛适用性可能会受到可重复性问题的阻碍。我们对专门研究放射组学特征重复性和可再现性的 41 项研究进行了定性综合，这些研究来自对已发表同行评议文献的系统综述。
使用广泛的 Haynes 和 Ingui 过滤器的组合以及一组针对癌症、放射组学（包括纹理分析）、可重复性和可再现性的特定文本词，对 PubMed 电子数据库进行了搜索。本综述是根据系统评价和荟萃分析的首选报告项目进行报告的。从每篇全文文章中提取有关癌症类型、所检查的放射组学特征类别、关键处理步骤的报告质量以及用于分离稳定特征的统计指标的信息。
在 624 个独特记录中，有 41 篇全文文章进行了审查。这些研究主要针对非小细胞肺癌和口咽癌。只有 7 项研究详细探讨了与图像采集、预处理和特征提取相关的所有方法学方面。放射组学特征的可重复性和可再现性在不同程度上对处理细节敏感，例如图像采集设置、图像重建算法、数字图像预处理以及用于提取放射组学特征的软件。一阶特征总体上比形状度量和纹理特征更具可重复性。熵一直被报道为最稳定的一阶特征之一。在形状度量和纹理特征方面，目前还没有出现共识；然而，粗糙度和对比度似乎是最不可重复的特征之一。
目前对特征可重复性和可再现性的研究仅限于少数几种癌症类型。关于特征提取软件、数字图像处理（预处理）和用于区分稳定特征的截止值的详细信息，报告质量可以提高。
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