Shimauchi Akiko, Abe Hiroyuki, Schacht David V, Yulei Jian, Pineda Federico D, Jansen Sanaz A, Ganesh Rajiv, Newstead Gillian M
Department of Radiology, University of Chicago, 5841 South Maryland Ave, MC 2026, Chicago, IL, 60637, USA,
Eur Radiol. 2015 Aug;25(8):2470-8. doi: 10.1007/s00330-015-3635-1. Epub 2015 Feb 20.
To quantify kinetic heterogeneity of breast masses that were initially detected with dynamic contrast-enhanced MRI, using whole-lesion kinetic distribution data obtained from computer-aided evaluation (CAE), and to compare that with standard kinetic curve analysis.
Clinical MR images from 2006 to 2011 with breast masses initially detected with MRI were evaluated with CAE. The relative frequencies of six kinetic patterns (medium-persistent, medium-plateau, medium-washout, rapid-persistent, rapid-plateau, rapid-washout) within the entire lesion were used to calculate kinetic entropy (KE), a quantitative measure of enhancement pattern heterogeneity. Initial uptake (IU) and signal enhancement ratio (SER) were obtained from the most-suspicious kinetic curve. Mann-Whitney U test and ROC analysis were conducted for differentiation of malignant and benign masses.
Forty benign and 37 malignant masses comprised the case set. IU and SER were not significantly different between malignant and benign masses, whereas KE was significantly greater for malignant than benign masses (p = 0.748, p = 0.083, and p < 0.0001, respectively). Areas under ROC curve for IU, SER, and KE were 0.479, 0.615, and 0.662, respectively.
Quantification of kinetic heterogeneity of whole-lesion time-curve data with KE has the potential to improve differentiation of malignant from benign breast masses on breast MRI.
• Kinetic heterogeneity can be quantified by computer-aided evaluation of breast MRI • Kinetic entropy was greater in malignant masses than benign masses • Kinetic entropy has the potential to improve differentiation of breast masses.
利用计算机辅助评估(CAE)获得的全病灶动力学分布数据,对最初通过动态对比增强MRI检测到的乳腺肿块的动力学异质性进行量化,并将其与标准动力学曲线分析进行比较。
对2006年至2011年最初通过MRI检测到乳腺肿块的临床MR图像进行CAE评估。使用整个病灶内六种动力学模式(中等持续型、中等平台型、中等廓清型、快速持续型、快速平台型、快速廓清型)的相对频率来计算动力学熵(KE),这是一种增强模式异质性的定量测量指标。从最可疑的动力学曲线中获取初始摄取量(IU)和信号增强率(SER)。采用曼-惠特尼U检验和ROC分析对恶性和良性肿块进行鉴别。
病例组包括40个良性肿块和37个恶性肿块。恶性和良性肿块之间的IU和SER无显著差异,而恶性肿块的KE显著高于良性肿块(分别为p = 0.748、p = 0.083和p < 0.0001)。IU、SER和KE的ROC曲线下面积分别为0.479、0.615和0.662。
用KE对全病灶时间曲线数据的动力学异质性进行量化,有可能提高乳腺MRI对恶性和良性乳腺肿块的鉴别能力。
• 可通过计算机辅助评估乳腺MRI来量化动力学异质性 • 恶性肿块的动力学熵高于良性肿块 • 动力学熵有提高乳腺肿块鉴别的潜力