Teng Xinzhi, Wang Yongqiang, Nicol Alexander James, Ching Jerry Chi Fung, Wong Edwin Ka Yiu, Lam Kenneth Tsz Chun, Zhang Jiang, Lee Shara Wee-Yee, Cai Jing
Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China.
Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen 518057, China.
Diagnostics (Basel). 2024 Aug 22;14(16):1835. doi: 10.3390/diagnostics14161835.
Radiomics, which integrates the comprehensive characterization of imaging phenotypes with machine learning algorithms, is increasingly recognized for its potential in the diagnosis and prognosis of oncological conditions. However, the repeatability and reproducibility of radiomic features are critical challenges that hinder their widespread clinical adoption. This review aims to address the paucity of discussion regarding the factors that influence the reproducibility and repeatability of radiomic features and their subsequent impact on the application of radiomic models. We provide a synthesis of the literature on the repeatability and reproducibility of CT/MR-based radiomic features, examining sources of variation, the number of reproducible features, and the availability of individual feature repeatability indices. We differentiate sources of variation into random effects, which are challenging to control but can be quantified through simulation methods such as perturbation, and biases, which arise from scanner variability and inter-reader differences and can significantly affect the generalizability of radiomic model performance in diverse settings. Four suggestions for repeatability and reproducibility studies are suggested: (1) detailed reporting of variation sources, (2) transparent disclosure of calculation parameters, (3) careful selection of suitable reliability indices, and (4) comprehensive reporting of reliability metrics. This review underscores the importance of random effects in feature selection and harmonizing biases between development and clinical application settings to facilitate the successful translation of radiomic models from research to clinical practice.
放射组学将成像表型的综合特征与机器学习算法相结合，其在肿瘤疾病诊断和预后方面的潜力日益受到认可。然而，放射组学特征的可重复性和再现性是阻碍其在临床广泛应用的关键挑战。本综述旨在解决目前关于影响放射组学特征可重复性和再现性的因素及其对放射组学模型应用后续影响的讨论匮乏的问题。我们综合了基于CT/MR的放射组学特征的可重复性和再现性的文献，研究了变异来源、可重复特征的数量以及个体特征可重复性指数的可用性。我们将变异来源分为随机效应（难以控制，但可通过诸如扰动等模拟方法进行量化）和偏差（由扫描仪变异性和阅片者间差异引起，可显著影响放射组学模型性能在不同环境中的通用性）。针对可重复性和再现性研究提出了四条建议：（1）详细报告变异来源；（2）透明披露计算参数；（3）谨慎选择合适的可靠性指数；（4）全面报告可靠性指标。本综述强调了随机效应在特征选择中的重要性，以及协调开发和临床应用环境之间偏差的重要性，以促进放射组学模型从研究成功转化为临床实践。
