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METRICS(METRICS-E3)的解释与示例阐述:欧洲医学影像信息学会放射组学审计小组发起的一项倡议

Explanation and Elaboration with Examples for METRICS (METRICS-E3): an initiative from the EuSoMII Radiomics Auditing Group.

作者信息

Kocak Burak, Ammirabile Angela, Ambrosini Ilaria, Akinci D'Antonoli Tugba, Borgheresi Alessandra, Cavallo Armando Ugo, Cannella Roberto, D'Anna Gennaro, Díaz Oliver, Doniselli Fabio M, Fanni Salvatore Claudio, Ghezzo Samuele, Groot Lipman Kevin B W, Klontzas Michail E, Ponsiglione Andrea, Stanzione Arnaldo, Triantafyllou Matthaios, Vernuccio Federica, Cuocolo Renato

机构信息

Department of Radiology, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey.

Department of Biomedical Sciences, Humanitas University, Milan, Italy.

出版信息

Insights Imaging. 2025 Aug 13;16(1):175. doi: 10.1186/s13244-025-02061-y.


DOI:10.1186/s13244-025-02061-y
PMID:40802002
Abstract

Radiomics research has been hindered by inconsistent and often poor methodological quality, limiting its potential for clinical translation. To address this challenge, the METhodological RadiomICs Score (METRICS) was recently introduced as a tool for systematically assessing study rigor. However, its effective application requires clearer guidance. The METRICS-E3 (Explanation and Elaboration with Examples) resource was developed by the European Society of Medical Imaging Informatics-Radiomics Auditing Group in response. This international initiative provides comprehensive support for users by offering detailed rationales, interpretive guidance, scoring recommendations, and illustrative examples for each METRICS item and condition. Each criterion includes positive examples from peer-reviewed, open-access studies and hypothetical negative examples. In total, the finalized METRICS-E3 includes over 200 examples. The complete resource is publicly available through an interactive website. CRITICAL RELEVANCE STATEMENT: METRICS-E3 offers deeper insights into each METRICS item and condition, providing concrete examples with accompanying commentary and recommendations to enhance the evaluation of methodological quality in radiomics research. KEY POINTS: As a complementary initiative to METRICS, METRICS-E3 is intended to support stakeholders in evaluating the methodological aspects of radiomics studies. In METRICS-E3, each METRICS item and condition is supplemented with interpretive guidance, positive literature-based examples, hypothetical negative examples, and scoring recommendations. The complete METRICS-E3 explanation and elaboration resource is accessible at its interactive website.

摘要

放射组学研究一直受到方法学质量不一致且往往较差的阻碍,限制了其临床转化的潜力。为应对这一挑战,最近引入了方法学放射组学评分(METRICS)作为系统评估研究严谨性的工具。然而,其有效应用需要更清晰的指导。作为回应,欧洲医学影像信息学会放射组学审核小组开发了METRICS-E3(示例解释与阐述)资源。这项国际倡议通过为每个METRICS项目和条件提供详细的原理、解释性指导、评分建议和示例,为用户提供全面支持。每个标准都包括同行评审的开放获取研究中的正面示例和假设的负面示例。最终的METRICS-E3总共包含200多个示例。完整资源可通过一个交互式网站公开获取。关键相关性声明:METRICS-E3对每个METRICS项目和条件提供了更深入的见解,提供具体示例并附带评论和建议,以加强放射组学研究中方法学质量的评估。要点:作为METRICS的补充倡议,METRICS-E3旨在支持利益相关者评估放射组学研究的方法学方面。在METRICS-E3中,每个METRICS项目和条件都辅以解释性指导、基于文献的正面示例、假设的负面示例和评分建议。完整的METRICS-E3解释与阐述资源可在其交互式网站上获取。

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本文引用的文献

[1]
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Sci Rep. 2025-4-11

[2]
Machine learning based radiomics approach for outcome prediction of meningioma - a systematic review.

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[3]
Novel deep learning algorithm based MRI radiomics for predicting lymph node metastases in rectal cancer.

Sci Rep. 2025-4-9

[4]
Predicting 1p/19q Codeletion Status in Glioma Using MRI-Derived Radiomics; A Systematic Review and Meta-Analysis of Diagnostic Accuracy.

AJNR Am J Neuroradiol. 2025-4-7

[5]
MRI transformer deep learning and radiomics for predicting IDH wild type TERT promoter mutant gliomas.

NPJ Precis Oncol. 2025-3-27

[6]
Predicting hemorrhagic transformation in acute ischemic stroke: a systematic review, meta-analysis, and methodological quality assessment of CT/MRI-based deep learning and radiomics models.

Emerg Radiol. 2025-3-26

[7]
Artificial intelligence-based radiogenomics reveals the potential immunoregulatory role of COL22A1 in glioma and its induced autoimmune encephalitis.

Front Immunol. 2025-3-6

[8]
Radiomics and machine learning models for diagnosing microvascular invasion in cholangiocarcinoma: a systematic review and meta-analysis of diagnostic test accuracy studies.

Clin Imaging. 2025-5

[9]
A deep learning-based clinical-radiomics model predicting the treatment response of immune checkpoint inhibitors (ICIs)-based conversion therapy in potentially convertible hepatocelluar carcinoma patients: a tumor marker prognostic study.

Int J Surg. 2025-5-1

[10]
Diagnostic Accuracy of Radiomics in the Early Detection of Pancreatic Cancer: A Systematic Review and Qualitative Assessment Using the Methodological Radiomics Score (METRICS).

Cancers (Basel). 2025-2-26

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