Baltzer Pascal A T, Dietzel Matthias, Kaiser Werner A
Institute of Diagnostic and Interventional Radiology, Friedrich Schiller University Jena, Jena, Germany.
J Comput Assist Tomogr. 2011 May-Jun;35(3):361-6. doi: 10.1097/RCT.0b013e31821065c3.
In magnetic resonance imaging (MRI) of the breast, contrast enhancements present as mass or nonmass (NM) lesions. This study aimed to test the usefulness of currently accepted T1-weighted Breast Imaging Reporting and Data System predictors and to determine the incremental value of new T2-weighted predictors for differentiation of benign from malignant NM lesions.
Consecutive patients undergoing surgery after MRI (1.5-T contrast-enhanced T1- and T2-weighted images) were investigated. Lesions were rated by 2 observers in consensus. Breast Imaging Reporting and Data System criteria for NM included spatial distribution, internal enhancement, and dynamic enhancement pattern. Additional criteria on T2-weighted images were signal intensity, presence of intraductal fluid, or cysts at the enhancements location. Independent differentiation criteria (benign vs malignant) were identified by logistic regression followed by receiver operating characteristics analysis.
Of 316 patients, 65 demonstrated NM. The NM lesions were split almost equally into malignant (34) and benign (31) histology. Breast Imaging Reporting and Data System predictors did not differentiate benign from malignant lesions, whereas signal intensity and the presence of cysts on contrast-enhanced T2-weighted images did, with a sensitivity of 91.2% and a specificity of 64.5%.
Differentiation of NM can be improved using additional T2-weighted images.
在乳腺磁共振成像(MRI)中,对比增强表现为肿块或非肿块(NM)病变。本研究旨在测试当前公认的T1加权乳腺影像报告和数据系统预测指标的有效性,并确定新的T2加权预测指标对鉴别良性与恶性NM病变的增量价值。
对MRI(1.5-T对比增强T1加权和T2加权图像)检查后接受手术的连续患者进行研究。由2名观察者共同对病变进行评级。NM的乳腺影像报告和数据系统标准包括空间分布、内部增强和动态增强模式。T2加权图像上的其他标准为信号强度、增强部位是否存在导管内液体或囊肿。通过逻辑回归和受试者操作特征分析确定独立的鉴别标准(良性与恶性)。
316例患者中,65例表现为NM。NM病变在组织学上几乎平均分为恶性(34例)和良性(31例)。乳腺影像报告和数据系统预测指标无法区分良性和恶性病变,而对比增强T2加权图像上的信号强度和囊肿的存在可以区分,敏感性为91.2%,特异性为64.5%。
使用额外的T2加权图像可以改善NM的鉴别。