Division of Gastroenterology and Hepatology, University Hospital Zurich, Zurich, Switzerland.
PLoS One. 2011;6(10):e26366. doi: 10.1371/journal.pone.0026366. Epub 2011 Oct 18.
Dynamic contrast enhanced (DCE-) MRI is commonly applied for the monitoring of antiangiogenic therapy in oncology. Established pharmacokinetic (PK) analysis methods of DCE-MRI data do not sufficiently reflect the complex anatomical and physiological constituents of the analyzed tissue. Hence, accepted endpoints such as Ktrans reflect an unknown multitude of local and global physiological effects often rendering an understanding of specific local drug effects impossible. In this work a novel multi-compartment PK model is presented, which for the first time allows the separation of local and systemic physiological effects. DCE-MRI data sets from multiple, simultaneously acquired tissues, i.e. spinal muscle, liver and tumor tissue, of hepatocellular carcinoma (HCC) bearing rats were applied for model development. The full Markov chain Monte Carlo (MCMC) Bayesian analysis method was applied for model parameter estimation and model selection was based on histological and anatomical considerations and numerical criteria. A population PK model (MTL3 model) consisting of 3 measured and 6 latent (unobserved) compartments was selected based on Bayesian chain plots, conditional weighted residuals, objective function values, standard errors of model parameters and the deviance information criterion. Covariate model building, which was based on the histology of tumor tissue, demonstrated that the MTL3 model was able to identify and separate tumor specific, i.e. local, and systemic, i.e. global, effects in the DCE-MRI data. The findings confirm the feasibility to develop physiology driven multi-compartment PK models from DCE-MRI data. The presented MTL3 model allowed the separation of a local, tumor specific therapy effect and thus has the potential for identification and specification of effectors of vascular and tissue physiology in antiangiogenic therapy monitoring.
动态对比增强（DCE-）MRI 常用于监测肿瘤的抗血管生成治疗。已建立的 DCE-MRI 数据药代动力学（PK）分析方法不能充分反映分析组织的复杂解剖和生理成分。因此，公认的终点，如 Ktrans，反映了未知的许多局部和全局生理效应，通常使特定局部药物效应的理解变得不可能。在这项工作中，提出了一种新的多室 PK 模型，该模型首次允许分离局部和全身生理效应。使用来自同时采集的多个组织（即脊柱肌肉、肝脏和肝癌（HCC）荷瘤大鼠的肿瘤组织）的 DCE-MRI 数据集来开发模型。全马尔可夫链蒙特卡罗（MCMC）贝叶斯分析方法用于模型参数估计，模型选择基于组织学和解剖学考虑以及数值标准。基于贝叶斯链图、条件加权残差、目标函数值、模型参数的标准误差和偏差信息标准，选择了由 3 个测量和 6 个潜在（未观察到）室组成的群体 PK 模型（MTL3 模型）。基于肿瘤组织的组织学构建协变量模型表明，MTL3 模型能够识别和分离 DCE-MRI 数据中的肿瘤特异性，即局部和全身，即全局，效应。这些发现证实了从 DCE-MRI 数据开发生理驱动的多室 PK 模型的可行性。所提出的 MTL3 模型允许分离局部、肿瘤特异性治疗效果，因此具有识别和规范抗血管生成治疗监测中血管和组织生理学效应子的潜力。
