Meng Xiang-Fu, Zhu Shi-Cai, Sun Shao-Juan, Guo Ji-Cai, Wang Xue
Department of Radiology, Linyi Traditional Chinese Medicine Hospital, Linyi, Shandong 276003, P.R. China.
Department of Respiratory Medicine, Linyi Traditional Chinese Medicine Hospital, Linyi, Shandong 276003, P.R. China.
Oncol Lett. 2016 Jun;11(6):3795-3802. doi: 10.3892/ol.2016.4445. Epub 2016 Apr 18.
In order to assess the diagnostic accuracy of diffusion weighted imaging (DWI) in differentiating between benign and malignant ovarian neoplasms, a systemic meta-analysis was conducted. Relevant studies were retrieved from scientific literature databases, including the PubMed, Wiley, EBSCO, Ovid, Web of Science, Wanfang, China National Knowledge Infrastructure and VIP databases. Following a multi-step screening and study selection process, the relevant data was extracted for use in the present study. Statistical analyses were performed using Meta-disc software version 1.4 and STATA statistical software version 12.0. A total of 285 articles were retrieved from the database searches. Following a careful screening process, 10 case-control studies were selected for the present meta-analysis. The 10 studies investigated the efficacy of DWI in diagnosing ovarian neoplasms, and included a combined total of 1,159 subjects, of which 559 patients had malignant lesions and 600 had benign lesions. The results showed that the pooled sensitivity, pooled specificity, pooled positive likelihood ratio, pooled negative likelihood ratio, pooled diagnostic odds ratio (DOR) and area under the curve of the summary receiver operating characteristics curve of DWI for differentiating between benign and malignant ovarian neoplasms were 0.93, 0.89, 7.58, 0.10, 85.33 and 0.95, respectively. A subgroup analysis based on ethnicity revealed no significant difference between Asians and Caucasians. Another subgroup analysis by magnetic resonance imaging (MRI) type showed that the DORs for GE Healthcare Life Sciences and Siemens AG machines were 100.76 [95% confidence interval (CI), 65.28-155.53] and 30.85 (95% CI, 10.40-91.53), respectively; this indicates that the diagnostic efficiency of the GE Healthcare Life Sciences MRI is superior compared with the Siemens AG MRI. The DWI demonstrated an excellent diagnostic performance in discriminating between benign and malignant ovarian neoplasms, and predicted the surgical outcome in ovarian neoplasms.
为了评估扩散加权成像（DWI）在鉴别卵巢良恶性肿瘤中的诊断准确性，进行了一项系统的荟萃分析。从科学文献数据库中检索相关研究，包括PubMed、Wiley、EBSCO、Ovid、Web of Science、万方、中国知网和维普数据库。经过多步骤筛选和研究选择过程，提取相关数据用于本研究。使用Meta-disc软件1.4版和STATA统计软件12.0版进行统计分析。通过数据库检索共获取285篇文章。经过仔细筛选过程，选择了10项病例对照研究进行本荟萃分析。这10项研究调查了DWI在诊断卵巢肿瘤中的疗效，总共纳入1159名受试者，其中559例患者有恶性病变，600例有良性病变。结果显示，DWI鉴别卵巢良恶性肿瘤的汇总敏感度、汇总特异度、汇总阳性似然比、汇总阴性似然比、汇总诊断比值比（DOR）以及汇总受试者工作特征曲线下面积分别为0.93、0.89、7.58、0.10、85.33和0.95。基于种族的亚组分析显示亚洲人和高加索人之间无显著差异。另一项按磁共振成像（MRI）类型进行的亚组分析表明，通用电气医疗生命科学公司和西门子公司设备的DOR分别为100.76[95%置信区间（CI），65.28 - 155.53]和30.85（95%CI，10.40 - 91.53）；这表明通用电气医疗生命科学公司MRI的诊断效率优于西门子公司MRI。DWI在鉴别卵巢良恶性肿瘤方面表现出优异的诊断性能，并可预测卵巢肿瘤的手术结果。
