Quantifying spatial heterogeneity in dynamic contrast‐enhanced MRI parameter maps

Abstract

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 Rényi 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.

Phenotyping of Tumor Biology in Patients by Multimodality Multiparametric Imaging: Relationship of Microcirculation, αvβ3 Expression, and Glucose Metabolism

“…where h sv ffi 0.25 is the hematocrit in small vessels and m 5 rV T is the mass of the soft tissue with density r 5 1.04 g/cm 3 in the examined tissue volume V T . It should be noted that the calculated rBV values may somewhat overestimate the true blood volume, because extravasation of the contrast medium is not explicitly considered by the 1-compartment model used for data analysis.…”

“…Moreover, it can provide biologic information about tissue heterogeneity in living organisms. Although most imaging studies and current quantitative analysis methods discard this spatial heterogeneity information, tissue heterogeneity is increasingly being recognized as an important factor in tumor biology that may have significant diagnostic and predictive utility (3). In the present feasibility study, multimodality multiparametric imaging with 18 F-FDG PET, 18 F-galacto-RGD PET, and DCE MRI was used to investigate noninvasively the spatial relationship of glucose metabolism, a v b 3 expression, and microcirculation in human tumors.…”

Cluster analysis of quantitative parametric maps from DCE-MRI: application in evaluating heterogeneity of tumor response to antiangiogenic treatment

“…Histogram analysis is considered to be more sensitive in detecting changes in tumor heterogeneity after treatment, than conventional summary statistics, looking to changes in histogram shape (kurtosis) and asymmetry (skewness), although it does so at the expense of including spatial information [28,30]. Alternative techniques are those based on texture-analysis, providing quantitative estimates of tumor heterogeneity, also considering their spatial distribution [31]. Similarly, novel methods based on clustering approaches, aiming at grouping pixels sharing similar enhancement properties, have been recently proposed.…”

A comparison of radial keyhole strategies for high spatial and temporal resolution 4D contrast-enhanced MRI in small animal tumor models

“…Indeed, it has been suggested that the complexity of the tumor microenvironment needs to be analyzed with biomarkers that are not only sensitive to kinetic parameters, but also to their spatial distribution. 47 It is in these cases that we see the use of a high spatial resolution method, such as the radial keyhole technique described here, of particular relevance.…”