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肾肿瘤影像组学质量评分的系统评价:当前文献研究。

Radiomics quality score in renal masses: a systematic assessment on current literature.

机构信息

USC Radiomics Laboratory, Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, United States.

出版信息

Br J Radiol. 2022 Sep 1;95(1137):20211211. doi: 10.1259/bjr.20211211. Epub 2022 Jun 15.

DOI:10.1259/bjr.20211211
PMID:35671097
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10996962/
Abstract

OBJECTIVE

To perform a systematic assessment and analyze the quality of radiomics methodology in current literature in the evaluation of renal masses using the Radiomics Quality Score (RQS) approach.

METHODS

We systematically reviewed recent radiomics literature in renal masses published in PubMed, EMBASE, Elsevier, and Web of Science. Two reviewers blinded by each other's scores evaluated the quality of radiomics methodology in studies published from 2015 to August 2021 using the RQS approach. Owing to the diversity in the imaging modalities and radiomics applications, a meta-analysis could not be performed.

RESULTS

Based on our inclusion/exclusion criteria, a total of 87 published studies were included in our study. The highest RQS was noted in three categories: reporting of clinical utility, gold standard, and feature reduction. The average RQS of the two reviewers ranged from 5 ≤ RQS≤19, with the maximum attainable RQS being 36. Very few (7/87 8%) studies received an average RQS that ranged from 17 < RQS≤19, which represents studies with the highest RQS in our study. Many (39/87 45%) studies received an average RQS that ranged from 13 < RQS≤15. No significant interreviewer scoring differences were observed.

CONCLUSIONS

We report that the overall scientific quality and reporting of radiomics studies in renal masses is suboptimal, and subsequent studies should bolster current deficiencies to improve reporting of radiomics methodologies.

ADVANCES IN KNOWLEDGE

The RQS approach is a meaningful quantitative scoring system to assess radiomics methodology quality and supports a comprehensive evaluation of the radiomics approach before its incorporation into clinical practice.

摘要

目的

使用放射组学质量评分(RQS)方法对当前评估肾肿块的放射组学方法文献进行系统评估和分析。

方法

我们系统地回顾了 2015 年至 2021 年 8 月期间在 PubMed、EMBASE、Elsevier 和 Web of Science 上发表的关于肾肿块放射组学的最新文献。两名审阅者相互盲法评分,使用 RQS 方法评估发表研究的放射组学方法质量。由于成像方式和放射组学应用的多样性,无法进行荟萃分析。

结果

根据我们的纳入/排除标准,共有 87 项已发表的研究纳入了我们的研究。报告临床效用、金标准和特征减少三个类别的 RQS 最高。两名审阅者的平均 RQS 范围为 5≤RQS≤19,最大可达 36。很少有(7/87 8%)研究获得的平均 RQS 范围为 17<RQS≤19,这代表了我们研究中 RQS 最高的研究。许多(39/87 45%)研究获得的平均 RQS 范围为 13<RQS≤15。没有观察到审阅者之间评分差异有统计学意义。

结论

我们报告称,肾肿块放射组学研究的整体科学质量和报告情况并不理想,后续研究应加强当前的不足,以提高放射组学方法的报告。

知识进展

RQS 方法是一种有意义的定量评分系统,可用于评估放射组学方法的质量,并支持在将放射组学方法纳入临床实践之前对其进行全面评估。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95d5/10996962/370ae5dbbd48/bjr.20211211.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95d5/10996962/f9cc329cf669/bjr.20211211.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95d5/10996962/820e4319c17f/bjr.20211211.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95d5/10996962/3df8c1047f8d/bjr.20211211.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95d5/10996962/be506c849dc1/bjr.20211211.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95d5/10996962/370ae5dbbd48/bjr.20211211.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95d5/10996962/f9cc329cf669/bjr.20211211.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95d5/10996962/820e4319c17f/bjr.20211211.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95d5/10996962/3df8c1047f8d/bjr.20211211.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95d5/10996962/be506c849dc1/bjr.20211211.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95d5/10996962/370ae5dbbd48/bjr.20211211.g005.jpg

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本文引用的文献

1
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Korean J Radiol. 2022 Jan;23(1):77-88. doi: 10.3348/kjr.2021.0421.
3
Radiomics Models for Predicting Microvascular Invasion in Hepatocellular Carcinoma: A Systematic Review and Radiomics Quality Score Assessment.预测肝细胞癌微血管侵犯的放射组学模型:系统评价与放射组学质量评分评估
使用体模和放射科医生观察者间选择评估用于博斯尼亚克囊肿分类的最佳影像组学特征的初步研究。
Diagnostics (Basel). 2023 Apr 10;13(8):1384. doi: 10.3390/diagnostics13081384.
Cancers (Basel). 2021 Nov 22;13(22):5864. doi: 10.3390/cancers13225864.
4
Artificial intelligence-based radiomics models in endometrial cancer: A systematic review.基于人工智能的子宫内膜癌影像组学模型:一项系统综述。
Eur J Surg Oncol. 2021 Nov;47(11):2734-2741. doi: 10.1016/j.ejso.2021.06.023. Epub 2021 Jun 24.
5
Radiomics in Renal Cell Carcinoma-A Systematic Review and Meta-Analysis.肾细胞癌中的放射组学——一项系统综述与荟萃分析
Cancers (Basel). 2021 Mar 17;13(6):1348. doi: 10.3390/cancers13061348.
6
Artificial intelligence: Deep learning in oncological radiomics and challenges of interpretability and data harmonization.人工智能:肿瘤放射组学中的深度学习及其可解释性和数据协调的挑战。
Phys Med. 2021 Mar;83:108-121. doi: 10.1016/j.ejmp.2021.03.009. Epub 2021 Mar 22.
7
Exploring Uncertainty Measures in Bayesian Deep Attentive Neural Networks for Prostate Zonal Segmentation.探索用于前列腺分区分割的贝叶斯深度注意力神经网络中的不确定性度量
IEEE Access. 2020;8:151817-151828. doi: 10.1109/ACCESS.2020.3017168. Epub 2020 Aug 17.
8
MRI texture feature repeatability and image acquisition factor robustness, a phantom study and in silico study.MRI 纹理特征重复性和图像采集因素稳健性:一项体模研究和计算机模拟研究。
Eur Radiol Exp. 2021 Jan 19;5(1):2. doi: 10.1186/s41747-020-00199-6.
9
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10
CT-based radiomics for differentiating renal tumours: a systematic review.基于 CT 的放射组学在肾脏肿瘤鉴别中的应用:一项系统综述。
Abdom Radiol (NY). 2021 May;46(5):2052-2063. doi: 10.1007/s00261-020-02832-9. Epub 2020 Nov 2.