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核医学中的放射组学:稳健性、可重复性、标准化,以及如何避免数据分析陷阱和再现性危机。

Radiomics in nuclear medicine: robustness, reproducibility, standardization, and how to avoid data analysis traps and replication crisis.

机构信息

OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Helmholtz-Zentrum Dresden - Rossendorf, Technische Universität Dresden, Dresden, Germany.

National Center for Tumor Diseases (NCT), Partner Site Dresden, Dresden, Germany.

出版信息

Eur J Nucl Med Mol Imaging. 2019 Dec;46(13):2638-2655. doi: 10.1007/s00259-019-04391-8. Epub 2019 Jun 25.

Abstract

Radiomics in nuclear medicine is rapidly expanding. Reproducibility of radiomics studies in multicentre settings is an important criterion for clinical translation. We therefore performed a meta-analysis to investigate reproducibility of radiomics biomarkers in PET imaging and to obtain quantitative information regarding their sensitivity to variations in various imaging and radiomics-related factors as well as their inherent sensitivity. Additionally, we identify and describe data analysis pitfalls that affect the reproducibility and generalizability of radiomics studies. After a systematic literature search, 42 studies were included in the qualitative synthesis, and data from 21 were used for the quantitative meta-analysis. Data concerning measurement agreement and reliability were collected for 21 of 38 different factors associated with image acquisition, reconstruction, segmentation and radiomics-specific processing steps. Variations in voxel size, segmentation and several reconstruction parameters strongly affected reproducibility, but the level of evidence remained weak. Based on the meta-analysis, we also assessed inherent sensitivity to variations of 110 PET image biomarkers. SUV and SUV were found to be reliable, whereas image biomarkers based on the neighbourhood grey tone difference matrix and most biomarkers based on the size zone matrix were found to be highly sensitive to variations, and should be used with care in multicentre settings. Lastly, we identify 11 data analysis pitfalls. These pitfalls concern model validation and information leakage during model development, but also relate to reporting and the software used for data analysis. Avoiding such pitfalls is essential for minimizing bias in the results and to enable reproduction and validation of radiomics studies.

摘要

核医学中的放射组学正在迅速发展。在多中心环境中进行放射组学研究的可重复性是临床转化的一个重要标准。因此,我们进行了一项荟萃分析,以调查 PET 成像中放射组学生物标志物的可重复性,并获得有关其对各种成像和放射组学相关因素变化以及固有敏感性的灵敏度的定量信息。此外,我们还确定并描述了影响放射组学研究可重复性和可推广性的数据分析陷阱。在系统的文献搜索后,有 42 项研究被纳入定性综合分析,其中 21 项研究的数据用于定量荟萃分析。与图像采集、重建、分割和放射组学特定处理步骤相关的 38 个不同因素中的 21 个因素收集了有关测量一致性和可靠性的数据。体素大小、分割和几个重建参数的变化强烈影响可重复性,但证据水平仍然较弱。基于荟萃分析,我们还评估了 110 个 PET 图像生物标志物对变化的固有敏感性。SUV 和 SUV 被认为是可靠的,而基于邻域灰度差矩阵的图像生物标志物和基于大小区矩阵的大多数生物标志物被发现对变化非常敏感,因此在多中心环境中应谨慎使用。最后,我们确定了 11 个数据分析陷阱。这些陷阱涉及模型验证和模型开发过程中的信息泄露,还与报告以及用于数据分析的软件有关。避免这些陷阱对于最小化结果中的偏差以及实现放射组学研究的再现和验证至关重要。

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