Imaging biomarkers (IBs) are used extensively in drug development and cancer research, but important differences exist between IBs and biospecimen-derived biomarkers. A tailored 'roadmap' is required for the development of new IBs to be used either in clinical research or for decision-making in healthcare. In this Consensus statement, a group of experts assembled by CRUK and the EORTC present 14 key recommendations for accelerating the clinical translation of IBs. 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.

Detailed knowledge of the anatomy and connectivity pattern of cortico-basal ganglia circuits is essential to an understanding of abnormal cortical function and pathophysiology associated with a wide range of neurological and neuropsychiatric diseases. We aim to study the spatial extent and topography of human basal ganglia connectivityin vivo. Additionally, we explore at an anatomical level the hypothesis of coexistent segregated and integrative cortico-basal ganglia loops. We use probabilistic tractography on magnetic resonance diffusion weighted imaging data to segment basal ganglia and thalamus in 30 healthy subjects based on their cortical and subcortical projections. We introduce a novel method to define voxel-based connectivity profiles that allow representation of projections from a source to more than one target region. Using this method, we localize specific relay nuclei within predefined functional circuits. We find strong correlation between tractography-based basal ganglia parcellation and anatomical data from previously reported invasive tracing studies in nonhuman primates. Additionally, we showin vivothe anatomical basis of segregated loops and the extent of their overlap in prefrontal, premotor, and motor networks. Our findings in healthy humans support the notion that probabilistic diffusion tractography can be used to parcellate subcortical gray matter structures on the basis of their connectivity patterns. The coexistence of clearly segregated and also overlapping connections from cortical sites to basal ganglia subregions is a neuroanatomical correlate of both parallel and integrative networks within them. We believe that this method can be used to examine pathophysiological concepts in a number of basal ganglia-related disorders.

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. ©2014 AACR.

Rapid T₁‐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. The functional form of this general AIF may be useful for studies in which AIF measurements are not possible. Improvements in the reproducibility of DCE‐MRI model parameters (K^trans, v_e, and v_p) were observed when this new, high‐temporal‐resolution population AIF was used, indicating the potential for increased sensitivity to therapy‐induced change. Magn Reson Med, 2006.

Purpose: To establish a general methodology for quantifying streamline‐based diffusion fiber tracking methods in terms of probability of connection between points and/or regions.Materials and Methods: The commonly used streamline approach is adapted to exploit the uncertainty in the orientation of the principal direction of diffusion defined for each image voxel. Running the streamline process repeatedly using Monte Carlo methods to exploit this inherent uncertainty generates maps of connection probability. Uncertainty is defined by interpreting the shape of the diffusion orientation profile provided by the diffusion tensor in terms of the underlying microstructure.Results: Two candidates for describing the uncertainty in the diffusion tensor are proposed and maps of probability of connection to chosen start points or regions are generated in a number of major tracts.Conclusion: The methods presented provide a generic framework for utilizing streamline methods to generate probabilistic maps of connectivity. J. Magn. Reson. Imaging 2003;18:242–254.