Zhang Jing, Li Longchao, Zhang Li, Zhe Xia, Tang Min, Lei Xiaoyan, Zhang Xiaoling
Department of Magnetic Resonance Imaging (MRI), Shaanxi Provincial People's Hospital, Xi'an, Shaanxi, China.
Front Oncol. 2024 Mar 12;14:1332783. doi: 10.3389/fonc.2024.1332783. eCollection 2024.
The objective of this study was to conduct a meta-analysis comparing the diagnostic efficacy of models based on diffusion-weighted imaging (DWI)-MRI, dynamic contrast enhancement (DCE)-MRI, and combination models (DCE and DWI) in distinguishing benign from malignant non-mass enhancement (NME) breast lesions.
PubMed, Embase, and Cochrane Library were searched, from inception to January 30, 2023, for studies that used DCE or DWI-MRI for the prediction of NME breast cancer patients. A bivariate random-effects model was used to calculate the meta-analytic sensitivity, specificity, and area under the curve (AUC) of the DCE, DWI, and combination models. Subgroup analysis and meta-regression analysis were performed to find the source of heterogeneity.
Of the 838 articles screened, 18 were eligible for analysis (13 on DCE, five on DWI, and four studies reporting the diagnostic accuracy of both DCE and DWI). The funnel plot showed no publication bias ( > 0.5). The pooled sensitivity and specificity and the AUC of the DCE, DWI, and combination models were 0.58, 0.72, and 0.70, respectively; 0.84, 0.69, and 0.84, respectively; and 0.88, 0.79, 0.90, respectively. The meta-analysis found no evidence of a threshold effect and significant heterogeneity among trials in terms of DCE sensitivity and specificity, as well as DWI specificity alone (I > 75%). The meta-regression revealed that different diagnostic criteria contributed to the DCE study's heterogeneity ( < 0.05). Different reference criteria significantly influenced the heterogeneity of the DWI model ( < 0.05). Subgroup analysis revealed that clustered ring enhancement (CRE) had the highest pooled specificity (0.92) among other DCE features. The apparent diffusion coefficient (ADC) with a mean threshold <1.3 × 10 mm/s had a slightly higher sensitivity of 0.86 compared to 0.82 with an ADC of ≥1.3 × 10 mm/s.
The combination model (DCE and DWI) outperformed DCE or DWI alone in identifying benign and malignant NME lesions. The DCE-CRE feature was the most specific test for ruling in NME cancers.
本研究的目的是进行一项荟萃分析,比较基于扩散加权成像(DWI)-MRI、动态对比增强(DCE)-MRI以及联合模型(DCE和DWI)区分乳腺非肿块强化(NME)病变良恶性的诊断效能。
检索PubMed、Embase和Cochrane图书馆,检索时间从建库至2023年1月30日,查找使用DCE或DWI-MRI预测NME乳腺癌患者的研究。采用双变量随机效应模型计算DCE、DWI和联合模型的荟萃分析敏感性、特异性和曲线下面积(AUC)。进行亚组分析和荟萃回归分析以找出异质性来源。
在筛选的838篇文章中,18篇符合分析条件(13篇关于DCE,5篇关于DWI,4篇研究报告了DCE和DWI的诊断准确性)。漏斗图显示无发表偏倚(>0.5)。DCE、DWI和联合模型的合并敏感性、特异性及AUC分别为0.58、0.72和0.70;0.84、0.69和0.84;以及0.88、0.79、0.90。荟萃分析未发现阈值效应的证据,且在DCE敏感性和特异性以及单独的DWI特异性方面试验间存在显著异质性(I>75%)。荟萃回归显示不同的诊断标准导致了DCE研究的异质性(<0.05)。不同的参考标准显著影响了DWI模型的异质性(<0.05)。亚组分析显示,在其他DCE特征中,簇状环形强化(CRE)具有最高的合并特异性(0.92)。平均阈值<1.3×10⁻³mm²/s的表观扩散系数(ADC)的敏感性略高,为0.86,而ADC≥1.3×10⁻³mm²/s时敏感性为0.82。
联合模型(DCE和DWI)在鉴别NME病变的良恶性方面优于单独的DCE或DWI。DCE-CRE特征是诊断NME癌症最具特异性的检查。