Pharmacokinetic analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) time-course data allows estimation of quantitative parameters such as K^trans (rate constant for plasma/interstitium contrast agent transfer), v_e (extravascular extracellular volume fraction), and v_p (plasma volume fraction). A plethora of factors in DCE-MRI data acquisition and analysis can affect accuracy and precision of these parameters and, consequently, the utility of quantitative DCE-MRI for assessing therapy response. In this multicenter data analysis challenge, DCE-MRI data acquired at one center from 10 patients with breast cancer before and after the first cycle of neoadjuvant chemotherapy were shared and processed with 12 software tools based on the Tofts model (TM), extended TM, and Shutter-Speed model. Inputs of tumor region of interest definition, pre-contrast T₁, and arterial input function were controlled to focus on the variations in parameter value and response prediction capability caused by differences in models and associated algorithms. Considerable parameter variations were observed with the within-subject coefficient of variation (wCV) values for K^trans and v_p being as high as 0.59 and 0.82, respectively. Parameter agreement improved when only algorithms based on the same model were compared, e.g., the K^trans intraclass correlation coefficient increased to as high as 0.84. Agreement in parameter percentage change was much better than that in absolute parameter value, e.g., the pairwise concordance correlation coefficient improved from 0.047 (for K^trans) to 0.92 (for K^trans percentage change) in comparing two TM algorithms. Nearly all algorithms provided good to excellent (univariate logistic regression c-statistic value ranging from 0.8 to 1.0) early prediction of therapy response using the metrics of mean tumor K^trans and k_ep (= K^trans/v_e, intravasation rate constant) after the first therapy cycle and the corresponding percentage changes. The results suggest that the interalgorithm parameter variations are largely systematic, which are not likely to significantly affect the utility of DCE-MRI for assessment of therapy response.

动态对比增强磁共振成像 (DCE-MRI) 时程数据的药代动力学分析允许估计定量参数，例如K ^trans（血浆/间质造影剂转移的速率常数）、v _e（血管外细胞外体积分数）和v _p（血浆体积分数）。DCE-MRI 数据采集和分析中的众多因素会影响这些参数的准确性和精确度，从而影响定量 DCE-MRI 用于评估治疗反应的效用。在本次多中心数据分析挑战中，在一个中心采集的 10 名乳腺癌患者在第一周期新辅助化疗前后获得的 DCE-MRI 数据通过基于 Tofts 模型（TM）、扩展 TM、和快门速度模型。肿瘤感兴趣区域定义的输入，对比前 T ₁和动脉输入函数被控制以关注由模型和相关算法的差异引起的参数值和响应预测能力的变化。观察到相当大的参数变化，K ^trans和v _p的受试者内变异系数 (wCV) 值分别高达 0.59 和 0.82。当仅比较基于相同模型的算法时，参数一致性得到改善，例如，K ^trans类内相关系数增加到高达 0.84。参数百分比变化的一致性远好于绝对参数值的一致性，例如，成对一致性相关系数从 0.047（对于K^trans ) 到 0.92（对于K ^trans百分比变化）在比较两种 TM 算法时。几乎所有算法都使用平均肿瘤K ^trans和k _ep（= K ^trans / v _e，血管内渗入速率常数）的指标提供良好到优秀（单变量逻辑回归 c 统计值范围从 0.8 到 1.0）的早期治疗反应预测第一个治疗周期和相应的百分比变化。结果表明，算法间参数变化在很大程度上是系统性的，这不太可能显着影响 DCE-MRI 用于评估治疗反应的效用。