Fan Xiaobing, Medved Milica, Karczmar Gregory S, Yang Cheng, Foxley Sean, Arkani Sanaz, Recant Wendy, Zamora Marta A, Abe Hiroyuki, Newstead Gillian M
Department of Radiology, University of Chicago, Chicago, IL 60637, USA.
Magn Reson Imaging. 2007 Jun;25(5):593-603. doi: 10.1016/j.mri.2006.10.011. Epub 2006 Nov 30.
The purpose of this study was to test whether an empirical mathematical model (EMM) of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can distinguish between benign and malignant breast lesions. A modified clinical protocol was used to improve the sampling of contrast medium uptake and washout. T(1)-weighted DCE magnetic resonance images were acquired at 1.5 T for 22 patients before and after injection of Gd-DTPA. Contrast medium concentration as a function of time was calculated over a small region of interest containing the most rapidly enhancing pixels. Then the curves were fitted with the EMM, which accurately described contrast agent uptake and washout. Results demonstrate that benign lesions had uptake (P<2.0 x 10(-5)) and washout (P<.01) rates of contrast agent significantly slower than those of malignant lesions. In addition, secondary diagnostic parameters, such as time to peak of enhancement, enhancement slope at the peak and curvature at the peak of enhancement, were derived mathematically from the EMM and expressed in terms of primary parameters. These diagnostic parameters also effectively differentiated benign from malignant lesions (P<.03). Conventional analysis of contrast medium dynamics, using a subjective classification of contrast medium kinetics in lesions as "washout," "plateau" or "persistent" (sensitivity=83%, specificity=50% and diagnostic accuracy=72%), was less effective than the EMM (sensitivity=100%, specificity=83% and diagnostic accuracy=94%) for the separation of benign and malignant lesions. In summary, the present research suggests that the EMM is a promising alternative method for evaluating DCE-MRI data with improved diagnostic accuracy.
本研究的目的是测试动态对比增强磁共振成像(DCE-MRI)的经验数学模型(EMM)能否区分乳腺良性和恶性病变。采用改良的临床方案来改善造影剂摄取和洗脱的采样。在1.5T下,对22例患者注射钆喷酸葡胺(Gd-DTPA)前后采集T(1)加权DCE磁共振图像。在包含增强最快像素的小感兴趣区域内计算造影剂浓度随时间的变化。然后用EMM对曲线进行拟合,该模型能准确描述造影剂的摄取和洗脱。结果表明,良性病变的造影剂摄取率(P<2.0×10(-5))和洗脱率(P<.01)明显慢于恶性病变。此外,从EMM中数学推导得出二级诊断参数,如增强峰值时间、峰值增强斜率和增强峰值曲率,并以一级参数表示。这些诊断参数也能有效区分良性和恶性病变(P<.03)。使用病变中造影剂动力学的主观分类为“洗脱”“平台期”或“持续”的传统造影剂动力学分析(敏感性=83%,特异性=50%,诊断准确性=72%),在区分良性和恶性病变方面不如EMM有效(敏感性=100%,特异性=83%,诊断准确性=94%)。总之,本研究表明EMM是一种有前景的替代方法,可用于评估DCE-MRI数据,提高诊断准确性。