Interpretation of METhodological RadiomICs Score (METRICS)_Chinese Journal of Evidence-Based Medicine

Authors：

ZHONG Jingyu ^1,2 , XING Yue ^1,2 , HU Yangfan ^1,2 , DING Defang ^1,2 , LIU Xianwei ¹ , DAI Shun ¹ , LU Junjie ³ , YANG Jiarui ⁴ , CHU Jingshen ⁵ , SONG Yang ⁶ , LU Minda ⁷ , ZHANG Huan ⁸ ,  YANG Guang ^9,10 ,  YAO Weiwu ^1,2 , METRICS working group

1. Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200336, P. R. China;
2. Shanghai Key Laboratory of Flexible Medical Robotics, Tongren Hospital, Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai 200336, P. R. China;
3. Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford 94305, USA;
4. Department of Biomedical Engineering, Boston University, Boston 02215, USA;
5. Editorial Office of Journal of Diagnostics Concepts & Practice, Department of Science and Technology Development, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, P. R. China;
6. MR Research Collaboration, Siemens Healthineers Ltd., Shanghai 200126, P. R. China;
7. MR Application, Siemens Healthineers Ltd., Shanghai 200126, P. R. China;
8. Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, P. R. China;
9. Institution of Magnetic Resonance and Molecular Imaging in Medicine, East China Normal University, Shanghai 200062, P. R. China;
10. Shanghai Key Laboratory of Magnetic Resonance, Department of Physics, East China Normal University, Shanghai 200062, P. R. China;

Corresponding author：

YANG Guang, Email: gyang@phy.ecnu.edu.cn; YAO Weiwu, Email: yaoweiwuhuan@163.com

Keywords：

Radiomics; Artificial intelligence; Quality evaluation; Interpretation

DOI：

10.7507/1672-2531.202504047

Video：

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Radiomics extracts high-throughput quantitative features from medical images and converts into minable data, in order to utilizes the analysis results of these data to support clinical diagnosis and treatment decisions. In recent years, radiomics has emerged as a significant research method in medical imaging field, while their methodological quality varies. To improve the methodological quality of radiomics research, the METhodological RadiomICs Score (METRICS) was developed by the METRICS working group using an expert consensus process. This tool, which was published in January 2024, comprises 30 items and has been endorsed by the European Society for Medical Imaging Informatics (EuSoMII). With authorization from the METRICS working group, this article introduces and interprets the content of this tool, to promote the understanding and application of METRICS among radiomics researchers in China, and to improve the methodological quality of radiomics research.

Citation： ZHONG Jingyu, XING Yue, HU Yangfan, DING Defang, LIU Xianwei, DAI Shun, LU Junjie, YANG Jiarui, CHU Jingshen, SONG Yang, LU Minda, ZHANG Huan, YANG Guang, YAO Weiwu, METRICS working group. Interpretation of METhodological RadiomICs Score (METRICS). Chinese Journal of Evidence-Based Medicine, 2025, 25(8): 943-951. doi: 10.7507/1672-2531.202504047 Copy

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