University of Utah Department of Radiology, Salt Lake City, Utah 84132, USA.
J Magn Reson Imaging. 2010 Jun;31(6):1371-8. doi: 10.1002/jmri.22179.
To prospectively investigate whether a rapid dynamic MRI protocol, in conjunction with pharmacokinetic modeling, could provide diagnostically useful information for discriminating biopsy-proven benign lesions from malignancies.
Patients referred to breast biopsy based on suspicious screening findings were eligible. After anatomic imaging, patients were scanned using a dynamic protocol with complete bilateral breast coverage. Maps of pharmacokinetic parameters representing transfer constant (K(trans)), efflux rate constant (k(ep)), blood plasma volume fraction (v(p)), and extracellular extravascular volume fraction (v(e)) were averaged over lesions and used, with biopsy results, to generate receiver operating characteristic curves for linear classifiers using one, two, or three parameters.
Biopsy and imaging results were obtained from 93 lesions in 74 of 78 study patients. Classification based on K(trans) and k(ep) gave the greatest accuracy, with an area under the receiver operating characteristic curve of 0.915, sensitivity of 91%, and specificity of 85%, compared with values of 88% and 68%, respectively, obtained in a recent study of clinical breast MRI in a similar patient population.
Pharmacokinetic classification of breast lesions is practical on modern MRI hardware and provides significant accuracy for identification of malignancies. Sensitivity of a two-parameter linear classifier is comparable to that reported in a recent multicenter study of clinical breast MRI, while specificity is significantly higher.
前瞻性研究快速动态 MRI 方案与药代动力学模型相结合是否能为鉴别经活检证实的良性病变与恶性病变提供有诊断价值的信息。
符合可疑筛查结果而行乳腺活检的患者符合入选条件。在进行解剖成像后，对患者进行双侧乳腺完整覆盖的动态方案扫描。将药代动力学参数图（代表转移常数（K(trans)）、流出率常数（k(ep)）、血浆体积分数（v(p)）和细胞外细胞外体积分数（v(e)））表示的转移常数（K(trans)）和流出率常数（k(ep)）进行平均，用于生成基于线性分类器的受试者工作特征曲线，使用一个、两个或三个参数。
78 例研究患者中的 74 例获得了活检和影像学结果。基于 K(trans)和 k(ep)的分类具有最高的准确性，受试者工作特征曲线下面积为 0.915，灵敏度为 91%，特异性为 85%，而在最近一项对类似患者人群的临床乳腺 MRI 的研究中，灵敏度分别为 88%和 68%。
乳腺病变的药代动力学分类在现代 MRI 硬件上是可行的，并且为识别恶性肿瘤提供了显著的准确性。双参数线性分类器的灵敏度与最近一项临床乳腺 MRI 的多中心研究相似，而特异性则显著更高。
