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通过组内相关系数评估的影像组学特征可靠性:一项系统评价

Radiomics feature reliability assessed by intraclass correlation coefficient: a systematic review.

作者信息

Xue Cindy, Yuan Jing, Lo Gladys G, Chang Amy T Y, Poon Darren M C, Wong Oi Lei, Zhou Yihang, Chu Winnie C W

机构信息

Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China.

Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China.

出版信息

Quant Imaging Med Surg. 2021 Oct;11(10):4431-4460. doi: 10.21037/qims-21-86.


DOI:10.21037/qims-21-86
PMID:34603997
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8408801/
Abstract

Radiomics research is rapidly growing in recent years, but more concerns on radiomics reliability are also raised. This review attempts to update and overview the current status of radiomics reliability research in the ever expanding medical literature from the perspective of a single reliability metric of intraclass correlation coefficient (ICC). To conduct this systematic review, Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. After literature search and selection, a total of 481 radiomics studies using CT, PET, or MRI, covering a wide range of subject and disease types, were included for review. In these highly heterogeneous studies, feature reliability to image segmentation was much more investigated than reliability to other factors, such as image acquisition, reconstruction, post-processing, and feature quantification. The reported ICCs also suggested high radiomics feature reliability to image segmentation. Image acquisition was found to introduce much more feature variability than image segmentation, in particular for MRI, based on the reported ICC values. Image post-processing and feature quantification yielded different levels of radiomics reliability and might be used to mitigate image acquisition-induced variability. Some common flaws and pitfalls in ICC use were identified, and suggestions on better ICC use were given. Due to the extremely high study heterogeneities and possible risks of bias, the degree of radiomics feature reliability that has been achieved could not yet be safely synthesized or derived in this review. More future researches on radiomics reliability are warranted.

摘要

近年来,放射组学研究发展迅速,但人们也对放射组学的可靠性提出了更多关注。本综述试图从组内相关系数(ICC)这一单一可靠性指标的角度,更新并概述不断扩展的医学文献中放射组学可靠性研究的现状。为进行这项系统综述,我们遵循了系统综述和Meta分析的首选报告项目(PRISMA)指南。经过文献检索和筛选,共纳入481项使用CT、PET或MRI的放射组学研究,涵盖了广泛的研究对象和疾病类型进行综述。在这些高度异质性的研究中,与图像分割的特征可靠性相比,对其他因素(如图像采集、重建、后处理和特征量化)的可靠性研究要少得多。报告的ICC值也表明放射组学特征对图像分割具有较高的可靠性。基于报告的ICC值发现,图像采集比图像分割引入的特征变异性要大得多,尤其是对于MRI。图像后处理和特征量化产生了不同程度的放射组学可靠性,并且可能用于减轻图像采集引起的变异性。我们确定了ICC使用中的一些常见缺陷和陷阱,并给出了更好使用ICC的建议。由于研究的异质性极高以及可能存在的偏倚风险,本综述尚无法安全地综合或推导已实现的放射组学特征可靠性程度。未来需要更多关于放射组学可靠性的研究。

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本文引用的文献

[1]
Distinguishing brain inflammation from grade II glioma in population without contrast enhancement: a radiomics analysis based on conventional MRI.

Eur J Radiol. 2021-1

[2]
Diffusion and perfusion MRI radiomics obtained from deep learning segmentation provides reproducible and comparable diagnostic model to human in post-treatment glioblastoma.

Eur Radiol. 2021-5

[3]
Reproducibility analysis of multi-institutional paired expert annotations and radiomic features of the Ivy Glioblastoma Atlas Project (Ivy GAP) dataset.

Med Phys. 2020-12

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Eur Radiol. 2021-5

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Annu Int Conf IEEE Eng Med Biol Soc. 2020-7

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A multicenter study on radiomic features from T -weighted images of a customized MR pelvic phantom setting the basis for robust radiomic models in clinics.

Magn Reson Med. 2021-3

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Pancreas adenocarcinoma CT texture analysis: comparison of 3D and 2D tumor segmentation techniques.

Abdom Radiol (NY). 2021-3

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Effect of phase of enhancement on texture analysis in renal masses evaluated with non-contrast-enhanced, corticomedullary, and nephrographic phase-enhanced CT images.

Eur Radiol. 2021-3

[9]
Minimizing acquisition-related radiomics variability by image resampling and batch effect correction to allow for large-scale data analysis.

Eur Radiol. 2021-3

[10]
Variability of computed tomography radiomics features of fibrosing interstitial lung disease: A test-retest study.

Methods. 2021-4

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