GlaxoSmithKline Clinical Imaging Centre, Hammersmith Hospital, Imperial College, London W120HS, UK.
MAGMA. 2011 Apr;24(2):85-96. doi: 10.1007/s10334-010-0238-3. Epub 2011 Jan 4.
Pharmacokinetic parameters from dynamic contrast-enhanced MRI (DCE-MRI) were used to assess the perfusion effects due to treatment response using a tyrosine kinase inhibitor. A Bayesian hierarchical model (BHM) is proposed, as an alternative to voxel-wise estimation procedures, to test for a treatment effect while explicitly modeling known sources of variability.
Nine subjects from a randomized, blinded, placebo-controlled, multicenter, phase II study of lapatinib were examined before and after treatment. Kinetic parameters were estimated, with an extended compartmental model and subject-specific arterial input function, on a voxel-by-voxel basis.
The group treated with lapatinib had a decrease in median K(trans) of 0.17 min⁻¹, when averaged across all voxels in the tumor ROIs, compared with no change in the placebo group based on nonlinear regression. A hypothesis test of equality between pre- and posttreatment K (trans) could not be rejected against a one-sided alternative (P = 0.09). Equality between median K(trans) in placebo and lapatinib groups posttreatment could also not be rejected using the BHM (P = 0.32). Across all scans acquired in the study, estimates of K(trans) at one site were greater on average than those at the other site by including a site effect in the BHM. The inter-voxel variability is of similar order (within 15%) when compared to the inter-patient variability.
Though the study contained a small number of subjects and no significant difference was found, the Bayesian hierarchical model provided estimates of variability from known sources in the study and confidence intervals for all estimated parameters. We believe the BHM provides a straightforward and thorough interrogation of the imaging data at the level of voxels, patients or sites in this multicenter clinical study.
利用动态对比增强磁共振成像（DCE-MRI）的药代动力学参数来评估因治疗反应而产生的灌注效应，方法是使用一种酪氨酸激酶抑制剂。提出了一种贝叶斯层次模型（BHM），作为一种替代体素估计程序的方法，用于测试治疗效果，同时明确建模已知的变异性来源。
对来自拉帕替尼随机、双盲、安慰剂对照、多中心、二期研究的 9 名受试者进行了治疗前后的检查。采用扩展的房室模型和个体动脉输入函数，对每个体素进行了动力学参数的估计。
与安慰剂组相比，在肿瘤 ROI 的所有体素上，接受拉帕替尼治疗的组的平均 K（trans）中位数下降了 0.17 min⁻¹，基于非线性回归，这一变化具有统计学意义。基于单侧替代假设，对治疗前后 K（trans）的假设检验不能拒绝相等（P = 0.09）。使用 BHM，也不能拒绝安慰剂和拉帕替尼治疗后 K（trans）中位数相等的假设（P = 0.32）。在整个研究中采集的所有扫描中，在 BHM 中包括部位效应，一个部位的 K（trans）估计值的平均值大于另一个部位。当与个体间变异性进行比较时，体素间变异性的差异相似（在 15%以内）。
尽管该研究包含的受试者数量较少，且未发现显著差异，但贝叶斯层次模型提供了研究中已知来源的变异性估计值以及所有估计参数的置信区间。我们认为，BHM 为多中心临床研究中的体素、患者或部位水平的成像数据提供了一种直接而全面的分析。
