Brigham and Women's Hospital, Boston, MA, USA.
Harvard Medical School, Boston, MA, USA.
Sci Rep. 2019 Jul 1;9(1):9441. doi: 10.1038/s41598-019-45766-z.
In this study we assessed the repeatability of radiomics features on small prostate tumors using test-retest Multiparametric Magnetic Resonance Imaging (mpMRI). The premise of radiomics is that quantitative image-based features can serve as biomarkers for detecting and characterizing disease. For such biomarkers to be useful, repeatability is a basic requirement, meaning its value must remain stable between two scans, if the conditions remain stable. We investigated repeatability of radiomics features under various preprocessing and extraction configurations including various image normalization schemes, different image pre-filtering, and different bin widths for image discretization. Although we found many radiomics features and preprocessing combinations with high repeatability (Intraclass Correlation Coefficient > 0.85), our results indicate that overall the repeatability is highly sensitive to the processing parameters. Neither image normalization, using a variety of approaches, nor the use of pre-filtering options resulted in consistent improvements in repeatability. We urge caution when interpreting radiomics features and advise paying close attention to the processing configuration details of reported results. Furthermore, we advocate reporting all processing details in radiomics studies and strongly recommend the use of open source implementations.
在这项研究中,我们使用测试-重测多参数磁共振成像(mpMRI)评估了小前列腺肿瘤的放射组学特征的可重复性。放射组学的前提是,定量基于图像的特征可以作为检测和表征疾病的生物标志物。为了使这些生物标志物有用,可重复性是一个基本要求,这意味着如果条件保持稳定,其值必须在两次扫描之间保持稳定。我们研究了各种预处理和提取配置下的放射组学特征的可重复性,包括各种图像归一化方案、不同的图像预滤波以及图像离散化的不同分箱宽度。尽管我们发现了许多具有高可重复性(组内相关系数>0.85)的放射组学特征和预处理组合,但我们的结果表明,总体而言,可重复性对处理参数非常敏感。图像归一化(使用多种方法)和使用预滤波选项都没有导致可重复性的一致提高。在解释放射组学特征时应谨慎,并建议密切关注报告结果的处理配置细节。此外,我们主张在放射组学研究中报告所有处理细节,并强烈建议使用开源实现。