Abstract：
OBJECTIVE: To assess the diagnostic performance of ultrafast magnetic resonance imaging (UF-DCE MRI) in differentiating benign from malignant breast lesions.
METHODS: A comprehensive search was conducted until September 1, 2023, in Medline, Embase, and Cochrane databases. Clinical studies evaluating the diagnostic performance of UF-DCE MRI in breast lesion stratification were screened and included in the meta-analysis. Pooled summary estimates for sensitivity, specificity, diagnostic odds ratio (DOR), and hierarchic summary operating characteristics (SROC) curves were pooled under the random-effects model. Publication bias and heterogeneity between studies were calculated.
RESULTS: A final set of 16 studies analyzing 2090 lesions met the inclusion criteria and were incorporated into the meta-analysis. Using UF-DCE MRI kinetic parameters, the pooled sensitivity, specificity, DOR, and area under the curve (AUC) for differentiating benign from malignant breast lesions were 83% (95% CI 79-88%), 77% (95% CI 72-83%), 18.9 (95% CI 13.7-26.2), and 0.876 (95% CI 0.83-0.887), respectively. We found no significant difference in diagnostic accuracy between the two main UF-DCE MRI kinetic parameters, maximum slope (MS) and time to enhancement (TTE). DOR and SROC exhibited low heterogeneity across the included studies. No evidence of publication bias was identified (p = 0.585).
CONCLUSIONS: UF-DCE MRI as a stand-alone technique has high accuracy in discriminating benign from malignant breast lesions.
CONCLUSIONS: UF-DCE MRI has the potential to obtain kinetic information and stratify breast lesions accurately while decreasing scan times, which may offer significant benefit to patients.
CONCLUSIONS: • Ultrafast breast MRI is a novel technique which captures kinetic information with very high temporal resolution. • The kinetic parameters of ultrafast breast MRI demonstrate a high level of accuracy in distinguishing between benign and malignant breast lesions. • There is no significant difference in accuracy between maximum slope and time to enhancement kinetic parameters.
METHODS: A comprehensive search was conducted until September 1, 2023, in Medline, Embase, and Cochrane databases. Clinical studies evaluating the diagnostic performance of UF-DCE MRI in breast lesion stratification were screened and included in the meta-analysis. Pooled summary estimates for sensitivity, specificity, diagnostic odds ratio (DOR), and hierarchic summary operating characteristics (SROC) curves were pooled under the random-effects model. Publication bias and heterogeneity between studies were calculated.
RESULTS: A final set of 16 studies analyzing 2090 lesions met the inclusion criteria and were incorporated into the meta-analysis. Using UF-DCE MRI kinetic parameters, the pooled sensitivity, specificity, DOR, and area under the curve (AUC) for differentiating benign from malignant breast lesions were 83% (95% CI 79-88%), 77% (95% CI 72-83%), 18.9 (95% CI 13.7-26.2), and 0.876 (95% CI 0.83-0.887), respectively. We found no significant difference in diagnostic accuracy between the two main UF-DCE MRI kinetic parameters, maximum slope (MS) and time to enhancement (TTE). DOR and SROC exhibited low heterogeneity across the included studies. No evidence of publication bias was identified (p = 0.585).
CONCLUSIONS: UF-DCE MRI as a stand-alone technique has high accuracy in discriminating benign from malignant breast lesions.
CONCLUSIONS: UF-DCE MRI has the potential to obtain kinetic information and stratify breast lesions accurately while decreasing scan times, which may offer significant benefit to patients.
CONCLUSIONS: • Ultrafast breast MRI is a novel technique which captures kinetic information with very high temporal resolution. • The kinetic parameters of ultrafast breast MRI demonstrate a high level of accuracy in distinguishing between benign and malignant breast lesions. • There is no significant difference in accuracy between maximum slope and time to enhancement kinetic parameters.
摘要：
目的：评估超快磁共振成像（UF-DCEMRI）在鉴别乳腺良恶性病变中的诊断性能。
方法：进行了全面搜索，直到2023年9月1日，在Medline，Embase,和Cochrane数据库。筛选评估UF-DCEMRI在乳腺病变分层中的诊断性能的临床研究，并将其纳入荟萃分析。敏感性汇总估计，特异性，诊断优势比(DOR)，并在随机效应模型下合并分层汇总操作特征(SROC)曲线。计算研究之间的发表偏倚和异质性。
结果：分析2090个病变的最终16项研究符合纳入标准，并纳入荟萃分析。使用UF-DCEMRI动力学参数，汇集的敏感性，特异性，DOR,用于区分良性和恶性乳腺病变的曲线下面积（AUC）为83％（95％CI79-88％），77%（95%CI72-83%），18.9（95%CI13.7-26.2），和0.876（95%CI0.83-0.887），分别。我们发现两种主要UF-DCEMRI动力学参数之间的诊断准确性没有显着差异，最大斜率(MS)和增强时间(TTE)。在纳入的研究中，DOR和SROC表现出低异质性。没有发现发表偏倚的证据(p=0.585)。
结论：UF-DCEMRI作为一种独立的技术在鉴别乳腺良恶性病变方面具有很高的准确性。
结论：UF-DCEMRI有可能获得动力学信息并准确分层乳腺病变，同时减少扫描时间，这可能会给患者带来显著的好处。
结论：•超快乳腺MRI是一种新颖的技术，以非常高的时间分辨率捕获动力学信息。•超快乳腺MRI的动力学参数表明，在区分良性和恶性乳腺病变方面具有很高的准确性。•最大斜率和增强动力学参数的时间之间的准确度没有显著差异。
方法：进行了全面搜索，直到2023年9月1日，在Medline，Embase,和Cochrane数据库。筛选评估UF-DCEMRI在乳腺病变分层中的诊断性能的临床研究，并将其纳入荟萃分析。敏感性汇总估计，特异性，诊断优势比(DOR)，并在随机效应模型下合并分层汇总操作特征(SROC)曲线。计算研究之间的发表偏倚和异质性。
结果：分析2090个病变的最终16项研究符合纳入标准，并纳入荟萃分析。使用UF-DCEMRI动力学参数，汇集的敏感性，特异性，DOR,用于区分良性和恶性乳腺病变的曲线下面积（AUC）为83％（95％CI79-88％），77%（95%CI72-83%），18.9（95%CI13.7-26.2），和0.876（95%CI0.83-0.887），分别。我们发现两种主要UF-DCEMRI动力学参数之间的诊断准确性没有显着差异，最大斜率(MS)和增强时间(TTE)。在纳入的研究中，DOR和SROC表现出低异质性。没有发现发表偏倚的证据(p=0.585)。
结论：UF-DCEMRI作为一种独立的技术在鉴别乳腺良恶性病变方面具有很高的准确性。
结论：UF-DCEMRI有可能获得动力学信息并准确分层乳腺病变，同时减少扫描时间，这可能会给患者带来显著的好处。
结论：•超快乳腺MRI是一种新颖的技术，以非常高的时间分辨率捕获动力学信息。•超快乳腺MRI的动力学参数表明，在区分良性和恶性乳腺病变方面具有很高的准确性。•最大斜率和增强动力学参数的时间之间的准确度没有显著差异。
