Improving tumour heterogeneity MRI assessment with histograms

Abstract

By definition, tumours are heterogeneous. They are defined by marked differences in cells, microenvironmental factors (oxygenation levels, pH, VEGF, VPF and TGF-α) metabolism, vasculature, structure and function that in turn translate into heterogeneous drug delivery and therapeutic outcome. Ways to estimate quantitatively tumour heterogeneity can improve drug discovery, treatment planning and therapeutic responses. It is therefore of paramount importance to have reliable and reproducible biomarkers of cancerous lesions' heterogeneity. During the past decade, the number of studies using histogram approaches increased drastically with various magnetic resonance imaging (MRI) techniques (DCE-MRI, DWI, SWI etc.) although information on tumour heterogeneity remains poorly exploited. This fact can be attributed to a poor knowledge of the available metrics and of their specific meaning as well as to the lack of literature references to standardised histogram methods with which surrogate markers of heterogeneity can be compared. This review highlights the current knowledge and critical advances needed to investigate and quantify tumour heterogeneity. The key role of imaging techniques and in particular the key role of MRI for an accurate investigation of tumour heterogeneity is reviewed with a particular emphasis on histogram approaches and derived methods.

Figures

Figure 1

Interpretation of the properties of histograms. (A). Definition of mean, median and percentiles. P25=25th percentile; P75=75th percentile. (B). Kurtosis (K) Platykurtosis indicates a flatter peak with negative kurtosis (left, K<0), and leptokurtosis indicates a sharp peak with positive kurtosis (Right, K>0). Skewness: if a histogram has an elongated tail on the left side of the mean, it is negatively skewed. If a histogram has an elongated tail on the right side of the mean, it is positively skewed.