Suppr超能文献

扩散峰度成像在乳腺肿瘤特征描述中的诊断效能:一项Meta分析

The Diagnostic Performance of Diffusion Kurtosis Imaging in the Characterization of Breast Tumors: A Meta-Analysis.

作者信息

Li Zhipeng, Li Xinming, Peng Chuan, Dai Wei, Huang Haitao, Li Xie, Xie Chuanmiao, Liang Jianye

机构信息

Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.

Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China.

出版信息

Front Oncol. 2020 Oct 27;10:575272. doi: 10.3389/fonc.2020.575272. eCollection 2020.

Abstract

Diffusion kurtosis imaging (DKI) is a promising imaging technique, but the results regarding the diagnostic performance of DKI in the characterization and classification of breast tumors are inconsistent among published studies. This study aimed to pool all published results to provide more robust evidence of the differential diagnosis between malignant and benign breast tumors using DKI. Studies on the differential diagnosis of breast tumors using DKI-derived parameters were systemically retrieved from PubMed, Embase, and Web of Science without a time limit. Review Manager 5.3 was used to calculate the standardized mean differences (SMDs) and 95% confidence intervals of the mean kurtosis (MK), mean diffusivity (MD), and apparent diffusion coefficient (ADC). Stata 12.0 was used to pool the sensitivity, specificity, and diagnostic odds ratio (DOR) as well as the publication bias and heterogeneity of each parameter. Fagan's nomograms were plotted to predict the post-test probabilities. Thirteen studies including 867 malignant and 460 benign breast lesions were analyzed. Most of the included studies showed a low to unclear risk of bias and low concerns regarding applicability. Breast cancer showed a higher MK (SMD = 1.23, < 0.001) but a lower MD (SMD = -1.29, < 0.001) and ADC (SMD = -1.21, < 0.001) than benign tumors. The MK (SMD = -1.36, = 0.006) rather than the MD (SMD = 0.29, = 0.20) or ADC (SMD = 0.26, = 0.24) can further differentiate invasive ductal carcinoma from ductal carcinoma . The DKI-derived MK (sensitivity = 90%, specificity = 88%, DOR = 66) and MD (sensitivity = 86% and specificity = 88%, DOR = 46) demonstrated superior diagnostic performance and post-test probability (65, 64, and 56% for MK, MD, and ADC) in differentiating malignant from benign breast lesions, with a higher sensitivity and specificity than the DWI-derived ADC (sensitivity = 85% and specificity = 83%, DOR = 29). The DKI-derived MK and MD demonstrate a comparable diagnostic performance in the discrimination of breast tumors based on their microstructures and non-Gaussian characteristics. The MK can further differentiate invasive ductal carcinoma from ductal carcinoma .

摘要

扩散峰度成像(DKI)是一种很有前景的成像技术,但已发表的研究中关于DKI在乳腺肿瘤特征描述和分类方面的诊断性能结果并不一致。本研究旨在汇总所有已发表的结果,以提供更有力的证据,证明使用DKI对乳腺恶性肿瘤和良性肿瘤进行鉴别诊断。我们从PubMed、Embase和Web of Science系统检索了使用DKI衍生参数进行乳腺肿瘤鉴别诊断的研究,无时间限制。使用Review Manager 5.3计算标准化平均差(SMD)以及平均峰度(MK)、平均扩散率(MD)和表观扩散系数(ADC)的95%置信区间。使用Stata 12.0汇总每个参数的敏感性、特异性和诊断比值比(DOR)以及发表偏倚和异质性。绘制Fagan列线图以预测检验后概率。分析了13项研究,包括867例乳腺恶性病变和460例乳腺良性病变。大多数纳入研究显示偏倚风险低至不明确,适用性方面的担忧也较低。乳腺癌的MK高于良性肿瘤(SMD = 1.23,<0.001),但MD(SMD = -1.29,<0.001)和ADC(SMD = -1.21,<0.001)低于良性肿瘤。MK(SMD = -1.36, = 0.006)而非MD(SMD = 0.29, = 0.20)或ADC(SMD = 0.26, = 0.24)能够进一步区分浸润性导管癌和导管原位癌。DKI衍生的MK(敏感性 = 90%,特异性 = 88%,DOR = 66)和MD(敏感性 = 86%,特异性 = 88%,DOR = 46)在鉴别乳腺恶性病变和良性病变方面表现出卓越的诊断性能和检验后概率(MK、MD和ADC分别为65%、64%和56%),其敏感性和特异性高于DWI衍生的ADC(敏感性 = 85%,特异性 = 83%,DOR = 29)。DKI衍生的MK和MD在基于乳腺肿瘤微观结构和非高斯特征的鉴别诊断中表现出相当的诊断性能。MK能够进一步区分浸润性导管癌和导管原位癌。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fe6f/7655131/823c08b22029/fonc-10-575272-g0001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验