Rapid T 1 -weighted 3D spoiled gradient-echo (GRE) data sets were acquired in the abdomen of 23 cancer patients during a total of 113 separate visits to allow dynamic contrast-enhanced MRI (DCE-MRI) analysis of tumor microvasculature. The arterial input function (AIF) was measured in each patient at each visit using an automated AIF extraction method following a standardized bolus administration of gadodiamide. The AIFs for each patient were combined to obtain a mean AIF that is representative for any individual. T 1 -weighted dynamic contrast-enhanced (DCE)-MRI is an established method for assessing microvascular changes associated with disease in tissues. It is most commonly used in cancer imaging (1-15), but has also been applied in a range of inflammatory conditions (16,17,41) and in cerebral (18) and cardiac (19) ischemia. Quantitative DCE-MRI has the potential to provide physiological information related to the functional status of tissue microvasculature. This information is available via the application of a tracer kinetic model-usually a compartmental model that describes the rate of transfer of contrast agent between the blood pool and the extracellular extravascular space (EES) (20).All models of contrast agent kinetics require the concentration of contrast agent in the blood pool (the arterial input function (AIF)) to be determined. Simple models assume a simplified functional form for the AIF, and additionally assume that the same functional form is valid for all individuals (16,21). However, it has been shown that using a simplified standard AIF leads to large systematic errors in model output parameters such as the volume transfer constant K trans and blood volume v b (22,42). Additionally, it is generally assumed that interpatient variability in factors such as heart rate and kidney function will lead to differences in the true form of AIF between individuals. An AIF that is accurately measured in each patient studied is therefore the accepted aim for kinetic modeling using contrast agents, even if it one that is met in only a minority of studies (6,13,23).In many settings it is not possible to perform an AIF measurement reliably, due either to data acquisition constraints or the lack of a suitable artery within the imaging field of view (FOV) from which to obtain an AIF. In such cases it would be desirable to utilize an assumed form of AIF that provides sufficient information to allow an accurate estimation of model parameters. Here we present a functional form of AIF that meets this requirement. We obtained this AIF from a population of 67 individually measured AIFs from the abdomens of 23 patients. We also show that the variability associated with the population of AIFs is low. Finally, we show that the use of the new functional form of the population AIF improves the reproducibility of tracer kinetic model parameters, and conclude that it is valid to use an assumed form of AIF if it is not possible to acquire AIFs from individual patients. MATERIALS AND METHODS PatientsTwenty-three patients (...

Imaging biomarkers (IBs) are integral to the routine management of patients with cancer. IBs used daily in oncology include clinical TNM stage, objective response and left ventricular ejection fraction. Other CT, MRI, PET and ultrasonography biomarkers are used extensively in cancer research and drug development. New IBs need to be established either as useful tools for testing research hypotheses in clinical trials and research studies, or as clinical decision-making tools for use in healthcare, by crossing ‘translational gaps’ through validation and qualification. Important differences exist between IBs and biospecimen-derived biomarkers and, therefore, the development of IBs requires a tailored ‘roadmap’. Recognizing this need, Cancer Research UK (CRUK) and the European Organisation for Research and Treatment of Cancer (EORTC) assembled experts to review, debate and summarize the challenges of IB validation and qualification. This consensus group has produced 14 key recommendations for accelerating the clinical translation of IBs, which highlight the role of parallel (rather than sequential) tracks of technical (assay) validation, biological/clinical validation and assessment of cost-effectiveness; the need for IB standardization and accreditation systems; the need to continually revisit IB precision; an alternative framework for biological/clinical validation of IBs; and the essential requirements for multicentre studies to qualify IBs for clinical use.

Tumors exhibit genomic and phenotypic heterogeneity, which has prognostic significance and may influence response to therapy. Imaging can quantify the spatial variation in architecture and function of individual tumors through quantifying basic biophysical parameters such as CT density or MRI signal relaxation rate; through measurements of blood flow, hypoxia, metabolism, cell death, and other phenotypic features; and through mapping the spatial distribution of biochemical pathways and cell signaling networks using PET, MRI, and other emerging molecular imaging techniques. These methods can establish whether one tumor is more or less heterogeneous than another and can identify subregions with differing biology. In this article, we review the image analysis methods currently used to quantify spatial heterogeneity within tumors. We discuss how analysis of intratumor heterogeneity can provide benefit over more simple biomarkers such as tumor size and average function. We consider how imaging methods can be integrated with genomic and pathology data, instead of being developed in isolation. Finally, we identify the challenges that must be overcome before measurements of intratumoral heterogeneity can be used routinely to guide patient care. Clin Cancer Res; 21(2); 249-57. Ó2014 AACR.