Huang Wei, Chen Yiyi, Fedorov Andriy, Li Xia, Jajamovich Guido H, Malyarenko Dariya I, Aryal Madhava P, LaViolette Peter S, Oborski Matthew J, O'Sullivan Finbarr, Abramson Richard G, Jafari-Khouzani Kourosh, Afzal Aneela, Tudorica Alina, Moloney Brendan, Gupta Sandeep N, Besa Cecilia, Kalpathy-Cramer Jayashree, Mountz James M, Laymon Charles M, Muzi Mark, Schmainda Kathleen, Cao Yue, Chenevert Thomas L, Taouli Bachir, Yankeelov Thomas E, Fennessy Fiona, Li Xin
Oregon Health and Science University, Portland, OR.
Brigham and Women's Hospital and Harvard Medical School, Boston, MA.
Tomography. 2016 Mar;2(1):56-66. doi: 10.18383/j.tom.2015.00184.
Dynamic contrast-enhanced MRI (DCE-MRI) has been widely used in tumor detection and therapy response evaluation. Pharmacokinetic analysis of DCE-MRI time-course data allows estimation of quantitative imaging biomarkers such as K(rate constant for plasma/interstitium contrast reagent (CR) transfer) and v (extravascular and extracellular volume fraction). However, the use of quantitative DCE-MRI in clinical prostate imaging islimited, with uncertainty in arterial input function (AIF, , the time rate of change of the concentration of CR in the blood plasma) determination being one of the primary reasons. In this multicenter data analysis challenge to assess the effects of variations in AIF quantification on estimation of DCE-MRI parameters, prostate DCE-MRI data acquired at one center from 11 prostate cancer patients were shared among nine centers. Each center used its site-specific method to determine the individual AIF from each data set and submitted the results to the managing center. Along with a literature population averaged AIF, these AIFs and their reference-tissue-adjusted variants were used by the managing center to perform pharmacokinetic analysis of the DCE-MRI data sets using the Tofts model (TM). All other variables including tumor region of interest (ROI) definition and pre-contrast T were kept the same to evaluate parameter variations caused by AIF variations only. Considerable pharmacokinetic parameter variations were observed with the within-subject coefficient of variation (wCV) of K obtained with unadjusted AIFs as high as 0.74. AIF-caused variations were larger in K than v and both were reduced when reference-tissue-adjusted AIFs were used. The parameter variations were largely systematic, resulting in nearly unchanged parametric map patterns. The CR intravasation rate constant, k (= K/v), was less sensitive to AIF variation than K (wCV for unadjusted AIFs: 0.45 for k 0.74 for K), suggesting that it might be a more robust imaging biomarker of prostate microvasculature than K.
动态对比增强磁共振成像（DCE-MRI）已广泛应用于肿瘤检测和治疗反应评估。对DCE-MRI时间历程数据进行药代动力学分析，可以估计诸如K（血浆/间质对比剂（CR）转移的速率常数）和v（血管外和细胞外体积分数）等定量成像生物标志物。然而，定量DCE-MRI在临床前列腺成像中的应用有限，动脉输入函数（AIF，即血浆中CR浓度的时间变化率）测定的不确定性是主要原因之一。在这项多中心数据分析挑战中，为了评估AIF量化变化对DCE-MRI参数估计的影响，来自一个中心的11例前列腺癌患者的前列腺DCE-MRI数据在9个中心之间共享。每个中心使用其特定地点的方法从每个数据集中确定个体AIF，并将结果提交给管理中心。管理中心将这些AIF及其经参考组织调整的变体与文献中的人群平均AIF一起，使用Tofts模型（TM）对DCE-MRI数据集进行药代动力学分析。所有其他变量，包括肿瘤感兴趣区域（ROI）的定义和对比前T，均保持不变，以仅评估由AIF变化引起的参数变化。观察到相当大的药代动力学参数变化，未调整AIF时获得的K的受试者内变异系数（wCV）高达0.74。AIF引起的K的变化大于v，使用经参考组织调整的AIF时两者均减小。参数变化在很大程度上是系统性的，导致参数图模式几乎不变。CR内渗速率常数k（=K/v）对AIF变化的敏感性低于K（未调整AIF时k的wCV为0.45，K为0.74），这表明它可能是比K更稳健的前列腺微血管成像生物标志物。
