放射组学模型诊断骨质疏松的质量和准确性:一项系统综述和荟萃分析。

IF 3.8 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Academic Radiology Pub Date : 2024-12-18 DOI:10.1016/j.acra.2024.11.065
Jianan Chen, Song Liu, Youxi Lin, Wenjun Hu, Huihong Shi, Nianchun Liao, Miaomiao Zhou, Wenjie Gao, Yanbo Chen, Peijie Shi
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引用次数: 0

摘要

基本原理和目的:本研究的目的是进行一项荟萃分析,以评估当前放射组学模型诊断骨质疏松症的性能,以及评估这些放射组学研究的方法和报告质量。方法:根据PRISMA指南,系统检索MEDLINE、Web of Science、Embase、Cochrane Library 4个数据库,选取2024年7月18日前发表的相关研究。使用放射组学模型诊断骨质疏松症的文章被认为是合格的。使用诊断准确性研究质量评估2 (QUADAS-2)工具和放射组学质量评分(RQS)来评估纳入研究的质量。通过计算合并诊断优势比(DOR)、敏感性、特异性、综合受试者操作者特征曲线下面积(AUC)来估计合并模型的诊断效率。结果:共纳入25项研究,其中24项提供可用数据用于meta分析,其中骨质疏松症患者1553例,非骨质疏松症患者2200例。纳入研究的RQS平均评分为11.48±4.92,依从率为31.89%。模型诊断骨质疏松症的DOR、敏感性和特异性分别为81.72 (95% CI: 51.08 ~ 130.73)、0.90 (95% CI: 0.87 ~ 0.93)和0.90 (95% CI: 0.87 ~ 0.93)。AUC为0.96,诊断能力强。亚组分析显示,使用不同的成像方式来构建放射组学模型可能是异质性的一个来源。使用CT图像和深度学习算法建立的放射组学模型对骨质疏松症的诊断具有更高的准确性。结论:放射组学模型诊断骨质疏松具有较高的诊断效能。未来,骨质疏松的放射组学诊断模型将成为辅助临床医生筛查骨质疏松患者的有效工具。然而,为了提高放射组学研究的质量,应严格遵循相关指南。
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The quality and accuracy of radiomics model in diagnosing osteoporosis: a systematic review and meta-analysis.

Rationale and objectives: The purpose of this study is to conduct a meta-analysis to evaluate the diagnostic performance of current radiomics models for diagnosing osteoporosis, as well as to assess the methodology and reporting quality of these radiomics studies.

Methods: According to PRISMA guidelines, four databases including MEDLINE, Web of Science, Embase and the Cochrane Library were searched systematically to select relevant studies published before July 18, 2024. The articles that used radiomics models for diagnosing osteoporosis were considered eligible. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool and radiomics quality score (RQS) were used to assess the quality of included studies. The pooled diagnostic odds ratio (DOR), sensitivity, specificity, area under the summary receiver operator characteristic curve (AUC) were calculated to estimated diagnostic efficiency of pooled model.

Results: A total of 25 studies were included, of which 24 provided usable data that were utilized for the meta-analysis, including 1553 patients with osteoporosis and 2200 patients without osteoporosis. The mean RQS score of included studies was 11.48 ± 4.92, with an adherence rate of 31.89%. The pooled DOR, sensitivity and specificity for model to diagnose osteoporosis were 81.72 (95% CI: 51.08 - 130.73), 0.90 (95% CI: 0.87-0.93) and 0.90 (95% CI: 0.87-0.93), respectively. The AUC was 0.96, indicating a high diagnostic capability. Subgroup analysis revealed that the use of different imaging modalities to construct radiomics models might be one source of heterogeneity. Radiomics models built using CT images and deep learning algorithms demonstrated higher diagnostic accuracy for osteoporosis.

Conclusion: Radiomics models for the diagnosis of osteoporosis have high diagnostic efficacy. In the future, radiomics models for diagnosing osteoporosis will be an efficient instrument to assist clinical doctors in screening osteoporosis patients. However, relevant guidelines should be followed strictly to improve the quality of radiomics studies.

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来源期刊
Academic Radiology
Academic Radiology 医学-核医学
CiteScore
7.60
自引率
10.40%
发文量
432
审稿时长
18 days
期刊介绍: Academic Radiology publishes original reports of clinical and laboratory investigations in diagnostic imaging, the diagnostic use of radioactive isotopes, computed tomography, positron emission tomography, magnetic resonance imaging, ultrasound, digital subtraction angiography, image-guided interventions and related techniques. It also includes brief technical reports describing original observations, techniques, and instrumental developments; state-of-the-art reports on clinical issues, new technology and other topics of current medical importance; meta-analyses; scientific studies and opinions on radiologic education; and letters to the Editor.
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