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结合临床信息、超声和生化标志物的卵巢癌诊断模型:Cochrane系统评价与Meta分析

Diagnostic Models Combining Clinical Information, Ultrasound and Biochemical Markers for Ovarian Cancer: Cochrane Systematic Review and Meta-Analysis.

作者信息

Davenport Clare F, Rai Nirmala, Sharma Pawana, Deeks Jon, Berhane Sarah, Mallett Sue, Saha Pratyusha, Solanki Rita, Bayliss Susan, Snell Kym, Sundar Sudha

机构信息

Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham B15 2TT, UK.

NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust, University of Birmingham, Birmingham B15 2TT, UK.

出版信息

Cancers (Basel). 2022 Jul 26;14(15):3621. doi: 10.3390/cancers14153621.

DOI:10.3390/cancers14153621
PMID:35892881
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9332683/
Abstract

Background: Ovarian cancer (OC) is a diagnostic challenge, with the majority diagnosed at late stages. Existing systematic reviews of diagnostic models either use inappropriate meta-analytic methods or do not conduct statistical comparisons of models or stratify test performance by menopausal status. Methods: We searched CENTRAL, MEDLINE, EMBASE, CINAHL, CDSR, DARE, Health Technology Assessment Database and SCI Science Citation Index, trials registers, conference proceedings from 1991 to June 2019. Cochrane collaboration review methods included QUADAS-2 quality assessment and meta-analysis using hierarchical modelling. RMI, ROMA or ADNEX at any test positivity threshold were investigated. Histology or clinical follow-up was the reference standard. We excluded screening studies, studies restricted to pregnancy, recurrent or metastatic OC. 2 × 2 diagnostic tables were extracted separately for pre- and post-menopausal women. Results: We included 58 studies (30,121 patients, 9061 cases of ovarian cancer). Prevalence of OC ranged from 16 to 55% in studies. For premenopausal women, ROMA at a threshold of 13.1 (+/−2) and ADNEX at a threshold of 10% demonstrated significantly higher sensitivity compared to RMI I at 200 (p < 0.0001) 77.8 (72.5, 82.4), 94.9 (92.5, 96.6), and 57.1% (50.6 to 63.4) but lower specificity (p < 0.002), 92.5 (90.0, 94.4), 84.3 (81.3, 86.8), and 78.2 (75.8, 80.4). For postmenopausal women, ROMA at a threshold of 27.7 (+/−2) and AdNEX at a threshold of 10% demonstrated significantly higher sensitivity compared to RMI I at a threshold of 200 (p < 0.001) 90.4 (87.4, 92.7), 97.6 (96.2, 98.5), and 78.7 (74.3, 82.5), specificity of ROMA was comparable, whilst ADneX was lower, 85.5 (81.3, 88.9), 81.3 (76.9, 85.0) (p = 0.155), compared to RMI 55.2 (51.2, 59.1) (p < 0.001). Conclusions: In pre-menopausal women, ROMA and ADNEX offer significantly higher sensitivity but significantly decreased specificity. In post-menopausal women, ROMA demonstrates significantly higher sensitivity and comparable specificity to RMI I, ADNEX has the highest sensitivity of all models, but with significantly reduced specificity. RMI I has poor sensitivity compared to ROMA or ADNEX. Choice between ROMA and ADNEX as a replacement test will depend on cost effectiveness and resource implications.

摘要

背景

卵巢癌(OC)的诊断颇具挑战,大多数患者在晚期才被确诊。现有的诊断模型系统评价要么使用了不恰当的荟萃分析方法,要么未对模型进行统计比较,也未按绝经状态对检测性能进行分层。方法:我们检索了CENTRAL、MEDLINE、EMBASE、CINAHL、CDSR、DARE、卫生技术评估数据库以及SCI科学引文索引、试验注册库、1991年至2019年6月的会议论文集。Cochrane协作网的综述方法包括QUADAS - 2质量评估以及使用分层模型的荟萃分析。研究了任何检测阳性阈值下的RMI、ROMA或ADNEX。组织学或临床随访作为参考标准。我们排除了筛查研究、仅限于妊娠、复发性或转移性OC的研究。分别为绝经前和绝经后女性提取2×2诊断表。结果:我们纳入了58项研究(30121例患者,9061例卵巢癌病例)。研究中OC的患病率在16%至55%之间。对于绝经前女性,阈值为13.1(±2)时的ROMA和阈值为10%时的ADNEX与阈值为200时的RMI I相比,灵敏度显著更高(p < 0.0001),分别为77.8(72.5,82.4)、94.9(92.5,96.6)和57.1%(50.6至63.4),但特异性较低(p < 0.002),分别为92.5(90.0,94.4)、84.3(81.3,86.8)和78.2(75.8,80.4)。对于绝经后女性,阈值为27.7(±2)时的ROMA和阈值为10%时的AdNEX与阈值为200时的RMI I相比,灵敏度显著更高(p < 0.001),分别为90.4(87.4,92.7)、97.6(96.2,98.5)和78.7(74.3,82.5),ROMA的特异性相当,而ADneX较低,分别为85.5(81.3,88.9)、81.3(76.9,85.0)(p = 0.155),与RMI 55.2(51.2,59.1)相比(p < 0.001)。结论:在绝经前女性中,ROMA和ADNEX具有显著更高的灵敏度,但特异性显著降低。在绝经后女性中,ROMA显示出显著更高的灵敏度且与RMI I的特异性相当,ADNEX在所有模型中灵敏度最高,但特异性显著降低。与ROMA或ADNEX相比,RMI I的灵敏度较差。在ROMA和ADNEX之间选择作为替代检测方法将取决于成本效益和资源影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4277/9332683/d19825bb6be5/cancers-14-03621-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4277/9332683/4d8ec8f44bbf/cancers-14-03621-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4277/9332683/0960b3f6441f/cancers-14-03621-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4277/9332683/d19825bb6be5/cancers-14-03621-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4277/9332683/4d8ec8f44bbf/cancers-14-03621-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4277/9332683/0960b3f6441f/cancers-14-03621-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4277/9332683/d19825bb6be5/cancers-14-03621-g003.jpg

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