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Dynamic contrast-enhanced MRI is becoming a standard tool for imaging-based trials of anti-vascular/angiogenic agents in cancer. So far, however, biomarkers derived from DCE-MRI parameter maps have largely neglected the fact that the maps have spatial structure and instead focussed on distributional summary statistics. Such statistics - e.g., biomarkers based on median values - neglect the spatial arrangement of parameters, which may carry important diagnostic and prognostic information. This article describes two types of heterogeneity biomarker that are sensitive to both parameter values and their spatial arrangement. Methods based on Renyi fractal dimensions and geometrical properties are developed, both of which attempt to describe the complexity of DCE-MRI parameter maps. Experiments using simulated data show that the proposed biomarkers are sensitive to changes that distribution-based summary statistics cannot detect and demonstrate that heterogeneity biomarkers could be applied in the drug trial setting. An experiment using 23 DCE-MRI parameter maps of gliomas - a class of tumour that is graded on the basis of heterogeneity - shows that the proposed heterogeneity biomarkers are able to differentiate between low- and high-grade tumours. Magn Reson Med, 2009. © 2009 Wiley-Liss, Inc.

Imaging in clinical oncology trials provides a wealth of information that contributes to the drug development process, especially in early phase studies. This paper focuses on kinetic modeling in DCE-MRI, inspired by mixed-effects models that are frequently used in the analysis of clinical trials. Instead of summarizing each scanning session as a single kinetic parameter -- such as median $\ktrans$ across all voxels in the tumor ROI -- we propose to analyze all voxel time courses from all scans and across all subjects simultaneously in a single model. The kinetic parameters from the usual non-linear regression model are decomposed into unique components associated with factors from the longitudinal study; e.g., treatment, patient and voxel effects. A Bayesian hierarchical model provides the framework in order to construct a data model, a parameter model, as well as prior distributions. The posterior distribution of the kinetic parameters is estimated using Markov chain Monte Carlo (MCMC) methods. Hypothesis testing at the study level for an overall treatment effect is straightforward and the patient- and voxel-level parameters capture random effects that provide additional information at various levels of resolution to allow a thorough evaluation of the clinical trial. The proposed method is validated with a breast cancer study, where the subjects were imaged before and after two cycles of chemotherapy, demonstrating the clinical potential of this method to longitudinal oncology studies.

Diffusion tensor imaging (DTI) provides a powerful tool for identifying white matter (WM) alterations in clinical populations. The prevalent method for group-level analysis of DTI is statistical comparison of the diffusion tensor fractional anisotropy (FA) metric. The FA metric, however, does not capture the full orientational information contained in the diffusion tensor. For example, the FA test is incapable of detecting group-level differences in diffusion orientation when the level of anisotropy is unaffected. Here, we apply multivariate hypothesis testing procedures to the elements of the diffusion tensor as an alternative to univariate testing using FA. Both parametric and nonparametric tests are proposed with each choice carrying specific assumptions about the diffusion tensor model. Of particular interest is the Cramer test, which works on Euclidean interpoint distances and can be readily adapted to a specific non-Euclidean framework by applying matrix logarithms to the diffusion tensors. Using Monte Carlo simulations, we show that multivariate tests can detect diffusion tensor principal eigenvector differences of 15 degrees with up to 80–90% power under typical design conditions. We also show that some multivariate tests are more sensitive to FA differences, when compared to a univariate test on FA, even if there is no principal eigenvector difference. The Cramer test, using the Euclidean interpoint distances, performed best under both simulation scenarios. When applying the Cramer test of the diffusion tensor in a clinical population with a history of migraine, a 169% increase was observed in the volume of a significant cluster compared to the univariate FA test. Magn Reson Med 57:1065–1074, 2007. © 2007 Wiley-Liss, Inc.

Imaging in clinical oncology trials provides a wealth of information that contributes to the drug development process, especially in early phase studies. This article focuses on kinetic modeling in DCE-MRI, inspired by mixed-effects models that are frequently used in the analysis of clinical trials. Instead of summarizing each scanning session as a single kinetic parameter--such as median k(trans) across all voxels in the tumor ROI-we propose to analyze all voxel time courses from all scans and across all subjects simultaneously in a single model. The kinetic parameters from the usual nonlinear regression model are decomposed into unique components associated with factors from the longitudinal study; e.g., treatment, patient, and voxel effects. A Bayesian hierarchical model provides the framework to construct a data model, a parameter model, as well as prior distributions. The posterior distribution of the kinetic parameters is estimated using Markov chain Monte Carlo (MCMC) methods. Hypothesis testing at the study level for an overall treatment effect is straightforward and the patient- and voxel-level parameters capture random effects that provide additional information at various levels of resolution to allow a thorough evaluation of the clinical trial. The proposed method is validated with a breast cancer study, where the subjects were imaged before and after two cycles of chemotherapy, demonstrating the clinical potential of this method to longitudinal oncology studies.