Chen Jie, Wu Mingpeng, Liu Rongbo, Li Siyi, Gao Ronghui, Song Bin
Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan Province, P. R. China.
PLoS One. 2015 Feb 6;10(2):e0117661. doi: 10.1371/journal.pone.0117661. eCollection 2015.
To evaluate the diagnostic performance of diffusion-weighted imaging (DWI) in the preoperative prediction of the histological grade of hepatocellular carcinoma (HCC).
A comprehensive literature search was performed in several authoritative databases to identify relevant articles. QUADAS-2 was used to assess the quality of included studies. Data were extracted to calculate the pooled sensitivity, specificity, positive likelihood ratio (PLR) and negative likelihood ratio (NLR). Summary receiver operating characteristic (SROC) curves were derived and areas under the SROC curve (AUC) were computed to indicate the diagnostic accuracy. Heterogeneity test, meta-regression analysis and sensitivity analysis were performed to identify factors and studies contributed to the heterogeneity.
A total of 11 studies with 912 HCCs were included in this meta-analysis. The pooled sensitivity, specificity, PLR and NLR with corresponding 95% confidence intervals (CI) were 0.54(0.47-0.61), 0.90(0.87-0.93), 4.88(2.99-7.97) and 0.46(0.27-0.77) for the prediction of well-differentiated HCC (w-HCC), 0.84(0.78-0.89), 0.48(0.43-0.52), 2.29(1.43-3.69) and 0.30(0.22-0.41) for the prediction of poorly-differentiated HCC (p-HCC). The AUC were 0.9311 and 0.8513 in predicting w-HCC and p-HCC, respectively. Results were further evaluated according to the method of image interpretation. Significant heterogeneity was observed.
DWI had excellent and moderately high diagnostic accuracy for the detection of w-HCC and p-HCC, respectively. Nonetheless, further studies in larger populations and an optimized image acquisition and interpretation are required before DWI-derived parameters can be used as a useful image biomarker for the prediction of the histological grade of HCC.
评估扩散加权成像(DWI)在术前预测肝细胞癌(HCC)组织学分级中的诊断性能。
在多个权威数据库中进行全面的文献检索以识别相关文章。使用QUADAS - 2评估纳入研究的质量。提取数据以计算合并敏感度、特异度、阳性似然比(PLR)和阴性似然比(NLR)。绘制汇总受试者工作特征(SROC)曲线并计算SROC曲线下面积(AUC)以表明诊断准确性。进行异质性检验、meta回归分析和敏感性分析以识别导致异质性的因素和研究。
本meta分析共纳入11项研究,涉及912例HCC。预测高分化HCC(w - HCC)时,合并敏感度、特异度、PLR和NLR及其相应的95%置信区间(CI)分别为0.54(0.47 - 0.61)、0.90(0.87 - 0.93)、4.88(2.99 - 7.97)和0.46(0.27 - 0.77);预测低分化HCC(p - HCC)时,分别为0.84(0.78 - 0.89)、0.48(0.43 - 0.52)、2.29(1.43 - 3.69)和0.30(0.22 - 0.41)。预测w - HCC和p - HCC时的AUC分别为0.9311和0.8513。根据图像解读方法对结果进行了进一步评估。观察到显著的异质性。
DWI分别对w - HCC和p - HCC的检测具有优异和中等偏高的诊断准确性。尽管如此,在DWI衍生参数可作为预测HCC组织学分级的有用图像生物标志物之前,还需要在更大人群中进行进一步研究以及优化图像采集和解读。