Furman-Haran Edna, Schechtman Edna, Kelcz Frederick, Kirshenbaum Kevin, Degani Hadassa
Department of Biological Regulation, Weizmann Institute of Science, Rehovot, Israel.
Cancer. 2005 Aug 15;104(4):708-18. doi: 10.1002/cncr.21225.
Tumor perfusion through the microvascular network can be imaged noninvasively by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The objective of the current study was to quantify the microvascular perfusion parameters in various human breast lesions and to determine whether they varied between benign lesions and malignancy and whether they were altered with increased invasiveness.
Perfusion parameters in 22 benign fibrocystic changes, 15 ductal carcinomas in situ (DCIS), 30 infiltrating ductal carcinomas (IDC), and 22 fibroadenomas were measured using high-resolution DCE-MRI. Pixel-by-pixel image analysis yielded parametric images of two perfusion indicators: the influx transcapillary transfer constant (k(trans)) and the efflux transcapillary rate constant (k(ep)). Correlations of lesion type and perfusion parameters were calculated using Spearman correlation. Logistic regression analysis evaluated the best predictors of the kinetic parameters that differentiate between IDC and benign lesions.
The perfusion parameters exhibited a progressive increase from benign fibrocystic changes to DCIS and IDC, with a significant correlation between lesion type and the parameters' values (range of correlation coefficients, 0.56-0.76; P < 0.0001). In addition, k(trans) increased from low-grade DCIS to high-grade DCIS. Fibroadenomas were characterized uniquely by high k(trans) but low k(ep). Stepwise logistic regression selected k(trans) as the best predictor for distinguishing benign fibrocystic changes from IDC, yielding 93% sensitivity and 96% specificity.
The microvascular perfusion parameters in breast lesions were elevated with invasiveness. Quantification of these parameters using high-resolution DCE-MRI was helpful for differentiating between breast lesions and should improve breast carcinoma diagnosis.
通过动态对比增强磁共振成像（DCE-MRI）可以对肿瘤通过微血管网络的灌注进行无创成像。本研究的目的是量化各种人类乳腺病变中的微血管灌注参数，并确定它们在良性病变和恶性病变之间是否存在差异，以及它们是否随着侵袭性增加而改变。
使用高分辨率DCE-MRI测量22例良性纤维囊性变、15例导管原位癌（DCIS）、30例浸润性导管癌（IDC）和22例纤维腺瘤的灌注参数。逐像素图像分析产生了两个灌注指标的参数图像：流入性毛细血管转运常数（k(trans)）和流出性毛细血管速率常数（k(ep)）。使用Spearman相关性计算病变类型与灌注参数的相关性。逻辑回归分析评估了区分IDC和良性病变的动力学参数的最佳预测因子。
灌注参数从良性纤维囊性变到DCIS和IDC呈现逐渐增加，病变类型与参数值之间存在显著相关性（相关系数范围为0.56 - 0.76；P < 0.0001）。此外，k(trans)从低级别DCIS到高级别DCIS增加。纤维腺瘤的独特特征是k(trans)高但k(ep)低。逐步逻辑回归选择k(trans)作为区分良性纤维囊性变与IDC的最佳预测因子，敏感性为93%，特异性为96%。
乳腺病变中的微血管灌注参数随着侵袭性增加而升高。使用高分辨率DCE-MRI对这些参数进行量化有助于区分乳腺病变，并应改善乳腺癌的诊断。
