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使用磁化传递成像和动态对比增强 MRI 对乳腺良恶性病变的鉴别诊断。

Differentiation of malignant and benign breast lesions using magnetization transfer imaging and dynamic contrast-enhanced MRI.

机构信息

Department of Radiology, NYU School of Medicine, New York, New York 10016, USA.

出版信息

J Magn Reson Imaging. 2013 Jan;37(1):138-45. doi: 10.1002/jmri.23786. Epub 2012 Oct 23.

Abstract

PURPOSE

To evaluate feasibility of using magnetization transfer ratio (MTR) in conjunction with dynamic contrast-enhanced MRI (DCE-MRI) for differentiation of benign and malignant breast lesions at 3 Tesla.

MATERIALS AND METHODS

This prospective study was IRB and HIPAA compliant. DCE-MRI scans followed by MT imaging were performed on 41 patients. Regions of interest (ROIs) were drawn on co-registered MTR and DCE postcontrast images for breast structures, including benign lesions (BL) and malignant lesions (ML). Initial enhancement ratio (IER) and delayed enhancement ratio (DER) were calculated, as were normalized MTR, DER, and IER (NMTR, NDER, NIER) values. Diagnostic accuracy analysis was performed.

RESULTS

Mean MTR in ML was lower than in BL (P < 0.05); mean DER and mean IER in ML were significantly higher than in BL (P < 0.01, P < 0.001). NMTR, NDER, and NIER were significantly lower in ML versus BL (P < 0.007, P < 0.001, P < 0.001). IER had highest diagnostic accuracy (77.6%), sensitivity (86.2%), and area under the ROC curve (.879). MTR specificity was 100%. Logistic regression modeling with NMTR and NIER yielded best results for BL versus ML (sensitivity 93.1%, specificity 80%, AUC 0.884, accuracy 83.7%).

CONCLUSION

Isolated quantitative DCE analysis may increase specificity of breast MR for differentiating BL and ML. DCE-MRI with NMTR may produce a robust means of evaluating breast lesions.

摘要

目的

评估在 3.0T 磁共振下使用磁化传递率(MTR)与动态对比增强磁共振成像(DCE-MRI)联合诊断良恶性乳腺病变的可行性。

材料与方法

本前瞻性研究符合机构审查委员会和健康保险携带和责任法案的规定。对 41 例患者进行了 DCE-MRI 扫描和随后的 MT 成像。在乳腺结构的 MTR 和 DCE 对比后图像上绘制感兴趣区(ROI),包括良性病变(BL)和恶性病变(ML)。计算初始强化比(IER)和延迟强化比(DER),并计算归一化 MTR、DER 和 IER(NMTR、NDER、NIER)值。进行诊断准确性分析。

结果

ML 中的平均 MTR 低于 BL(P < 0.05);ML 中的平均 DER 和平均 IER 明显高于 BL(P < 0.01,P < 0.001)。ML 中的 NMTR、NDER 和 NIER 明显低于 BL(P < 0.007,P < 0.001,P < 0.001)。IER 具有最高的诊断准确性(77.6%)、敏感性(86.2%)和 ROC 曲线下面积(0.879)。MTR 的特异性为 100%。NMTR 和 NIER 的逻辑回归模型对 BL 与 ML 之间的差异具有最佳结果(敏感性 93.1%,特异性 80%,AUC 0.884,准确性 83.7%)。

结论

单独的定量 DCE 分析可能会提高乳腺 MRI 对区分 BL 和 ML 的特异性。NMTR 与 DCE-MRI 联合应用可能为评估乳腺病变提供一种强有力的方法。

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