Chen Jianan, Liu Song, Lin Youxi, Hu Wenjun, Shi Huihong, Liao Nianchun, Zhou Miaomiao, Gao Wenjie, Chen Yanbo, Shi Peijie
Department of Orthopedics, Sun Yat-Sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, PR China (J.C., S.L., Y.L., W.H., H.S., N.L., W.G., Y.C.).
Department of Endocrinology, People's Hospital of Dingbian, Dingbian, Shanxi, PR China (M.Z.).
Acad Radiol. 2025 May;32(5):2863-2875. doi: 10.1016/j.acra.2024.11.065. Epub 2024 Dec 18.
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.
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.
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.
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.
本研究的目的是进行一项荟萃分析,以评估当前用于诊断骨质疏松症的放射组学模型的诊断性能,并评估这些放射组学研究的方法和报告质量。
根据PRISMA指南,系统检索了包括MEDLINE、Web of Science、Embase和Cochrane图书馆在内的四个数据库,以选择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视为合格。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。计算合并诊断比值比(DOR)、敏感性、特异性、汇总接受者操作特征曲线下面积(AUC),以估计合并模型的诊断效率。
共纳入25项研究,其中24项提供了可用于荟萃分析的数据,包括1553例骨质疏松症患者和2200例非骨质疏松症患者。纳入研究的平均RQS评分为11.48±4.92,依从率为31.89%。模型诊断骨质疏松症的合并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,表明诊断能力较高。亚组分析显示,使用不同的成像方式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构建的放射组学模型对骨质疏松症具有更高的诊断准确性。
用于诊断骨质疏松症的放射组学模型具有较高的诊断效能。未来,用于诊断骨质疏松症的放射组学模型将成为协助临床医生筛查骨质疏松症患者的有效工具。然而,应严格遵循相关指南,以提高放射组学研究的质量。