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18F-FDG-PET/CT在原发性中枢神经系统淋巴瘤和高级别胶质瘤鉴别诊断中的应用:一项荟萃分析。

Use of 18F-FDG-PET/CT in differential diagnosis of primary central nervous system lymphoma and high-grade gliomas: A meta-analysis.

作者信息

Zhang Guisheng, Li Jiuhong, Hui Xuhui

机构信息

Department of Neurosurgery of West China Hospital, Sichuan University, Chengdu, China.

West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China.

出版信息

Front Neurol. 2022 Aug 17;13:935459. doi: 10.3389/fneur.2022.935459. eCollection 2022.

DOI:10.3389/fneur.2022.935459
PMID:36061992
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9428250/
Abstract

BACKGROUND

Primary central nervous system lymphoma (PCNSL) and high-grade glioma (HGG) appear similar under imaging. However, since the two tumors vary in their treatment methods, their differential diagnosis is crucial. The use of 18F-fluorodeoxyglucose positron emission tomography computed tomography (18F-FDG-PET/CT) imaging to effectively distinguish between the two tumors is not clear; therefore, a meta-analysis was carried out to determine its effectiveness.

MATERIALS AND METHODS

The databases PubMed, EMBASE, Cochrane, Web of Science, China National Knowledge Infrastructure (CNKI), Wanfang, China Science, and Technology Journal Database (CQVIP) were exhaustively searched using stringent inclusion and exclusion criteria to select high-quality literature. The Quality Assessment Tool for Diagnostic Accuracy Studies (QUADAS-2) was used for the qualitative assessment of the included literature. The bivariate effect model was used to combine statistics such as sensitivity (SEN) and specificity (SPE), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) [95% confidence intervals (CI)], plot summary receiver operating characteristic (SROC) curve, and calculate the area under the curve (AUC) value. Sensitivity analysis was used to evaluate the stability of the results, and Deek's test was used to assess publication bias. Meta-regression and subgroup analysis was used to determine the sources of heterogeneity.

RESULTS

A total of nine studies were included in this study. For differential diagnosis of PCNSL and HGG, the combined SEN was 0.91 (95% CI: 0.80-0.96; I = 46.73%), combined SPE was 0.88 (95% CI: 0.82-0.93; I = 56.30%), the combined PLR was 7.83 (95% CI: 4.96-12.37; I = 15.57%), combined NLR was 0.10 (95% CI: 0.05-0.23; I = 31.99%), combined DOR was 77.36 (95% CI: 32.74-182.77; I = 70.70%). The AUC of SROC was 0.95 (95% CI: 0.93-0.97). No publication bias was found and the sample size and different parameters were the primary reason for heterogeneity.

CONCLUSION

The 18F-FDG-PET/CT imaging technique has a high diagnostic accuracy in the differential diagnosis of PNCSL and HGG. Patients suspected to have the above two tumors are suggested to be examined by 18F-FDG-PET / CT to help in the clinical distinction and further treatment modalities.

摘要

背景

原发性中枢神经系统淋巴瘤(PCNSL)和高级别胶质瘤(HGG)在影像学上表现相似。然而,由于这两种肿瘤的治疗方法不同,其鉴别诊断至关重要。18F-氟脱氧葡萄糖正电子发射断层扫描计算机断层扫描(18F-FDG-PET/CT)成像能否有效区分这两种肿瘤尚不清楚;因此,进行了一项荟萃分析以确定其有效性。

材料与方法

使用严格的纳入和排除标准,全面检索了PubMed、EMBASE、Cochrane、Web of Science、中国知网(CNKI)、万方、中国科学和科技期刊数据库(CQVIP)等数据库,以选择高质量文献。使用诊断准确性研究质量评估工具(QUADAS-2)对纳入文献进行定性评估。采用双变量效应模型合并敏感性(SEN)、特异性(SPE)、阳性似然比(PLR)、阴性似然比(NLR)和诊断比值比(DOR)[95%置信区间(CI)]等统计量,绘制汇总受试者工作特征(SROC)曲线,并计算曲线下面积(AUC)值。采用敏感性分析评估结果的稳定性,使用Deek检验评估发表偏倚。采用Meta回归和亚组分析确定异质性来源。

结果

本研究共纳入9项研究。对于PCNSL和HGG的鉴别诊断,合并SEN为0.91(95%CI:0.80-0.96;I=46.73%),合并SPE为0.88(95%CI:0.82-0.93;I=56.30%),合并PLR为7.83(95%CI:4.96-12.37;I=15.57%),合并NLR为0.10(95%CI:0.05-0.23;I=31.99%),合并DOR为77.36(95%CI:32.74-182.77;I=70.70%)。SROC的AUC为0.95(95%CI:0.93-0.97)。未发现发表偏倚,样本量和不同参数是异质性的主要原因。

结论

18F-FDG-PET/CT成像技术在PCNSL和HGG的鉴别诊断中具有较高的诊断准确性。建议疑似患有上述两种肿瘤的患者进行18F-FDG-PET/CT检查,以帮助临床鉴别和进一步确定治疗方式。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/27bc/9428250/e48de16d192a/fneur-13-935459-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/27bc/9428250/624db2b7aea2/fneur-13-935459-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/27bc/9428250/4e9d58bab3e4/fneur-13-935459-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/27bc/9428250/ed1dd8cb2572/fneur-13-935459-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/27bc/9428250/8f7daae2b9ee/fneur-13-935459-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/27bc/9428250/e48de16d192a/fneur-13-935459-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/27bc/9428250/624db2b7aea2/fneur-13-935459-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/27bc/9428250/4e9d58bab3e4/fneur-13-935459-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/27bc/9428250/ed1dd8cb2572/fneur-13-935459-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/27bc/9428250/8f7daae2b9ee/fneur-13-935459-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/27bc/9428250/e48de16d192a/fneur-13-935459-g0005.jpg

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