Reproducibility of perfusion parameters in dynamic contrast-enhanced MRI of lung and liver tumors: effect on estimates of patient sample size in clinical trials and on individual patient responses. AJR Am J Roentgenol. 2010;194:W134-W140. [PMID: 20093564 DOI: 10.2214/ajr.09.3116]

DCE-MRI: a review and applications in veterinary oncology

Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is a functional imaging technique that assesses the physiology of tumour tissue by exploiting abnormal tumour microvasculature. Advances made through DCE-MRI include improvement in the diagnosis of cancer, optimization of treatment choices, assessment of treatment efficacy and non-invasive identification of prognostic information. DCE-MRI enables quantitative assessment of tissue vessel density, integrity, and permeability, and this information can be applied to study of angiogenesis, hypoxia and the evaluation of various biomarkers. Reproducibility of DCE-MRI results is important in determining the significance of observed changes in the parameters. As improvements are made towards the utility of DCE-MRI and interpreting biologic associations, the technique will be applied more frequently in the study of cancer in animals. Given the importance of tumour perfusion with respect to tumour oxygenation and drug delivery, the use of DCE-MRI is a convenient and powerful way to gain basic information about a tumour.

The role of magnetic resonance imaging biomarkers in clinical trials of treatment response in cancer

Current standard-of-care radiological methods for assessing the response of solid tumors to treatment are based on measuring changes in lesion size in a single dimension using high-resolution x-ray computed tomography (CT) or magnetic resonance imaging (MRI). Even if size measurements are adapted to record true volume changes more accurately, the effects of therapeutic drugs on tumor size may not occur for several cycles of treatment. Furthermore, current and future generations of anticancer drugs will be designed to affect highly specific cancer characteristics, and their effects may not be immediately cytotoxic. More sensitive and specific measures are required that can report on tumor status and treatment response early in the course of therapy. Several MRI techniques have matured to the point where they can offer quantitative information on tissue status and greater insight into specific biophysical and physiological characteristics of tumors. Here we review and provide illustrative examples of two MRI methods that have already been incorporated into clinical trials of treatment response in solid tumors: diffusion imaging and dynamic contrast-enhanced MRI. We also discuss the limitations and future research directions required for these techniques to gain greater acceptance and to have their maximum impact.

Opportunities and pitfalls of cancer imaging in clinical trials

Cancer treatment strategies have changed considerably over the past two decades, with increasing emphasis on cancer-specific biological therapies. This situation has led to the incorporation of biomarkers, including those obtained by medical imaging, into trial designs to better understand mechanisms of action and, hopefully, to provide early evidence of treatment efficacy at a molecular or physiological level. Unlike blood tests and tissue samples, an imaging biomarker allows assessment of treatment in the whole tumor, in all tumors in the body, and at multiple time points. This situation has increased the complexity of clinical trials, as each imaging modality has issues related to cost, ease of use, patient compatibility, data analysis, and interpretation. This article reviews strengths and limitations of the current imaging methods available in clinical cancer trials, including MRI, CT, PET, and ultrasonography. The information gained by each test, and the difficulties in acquiring the data and interpreting it are also discussed in order to help researchers plan imaging in clinical trials and interpret data from such studies.

Estimation of intra-operator variability in perfusion parameter measurements using DCE-US

AIM: To investigate intra-operator variability of semi-quantitative perfusion parameters using dynamic contrast-enhanced ultrasonography (DCE-US), following bolus injections of SonoVue^®.

METHODS: The in vitro experiments were conducted using three in-house sets up based on pumping a fluid through a phantom placed in a water tank. In the in vivo experiments, B16F10 melanoma cells were xenografted to five nude mice. Both in vitro and in vivo, images were acquired following bolus injections of the ultrasound contrast agent SonoVue^® (Bracco, Milan, Italy) and using a Toshiba Aplio^® ultrasound scanner connected to a 2.9-5.8 MHz linear transducer (PZT, PLT 604AT probe) (Toshiba, Japan) allowing harmonic imaging (“Vascular Recognition Imaging”) involving linear raw data. A mathematical model based on the dye-dilution theory was developed by the Gustave Roussy Institute, Villejuif, France and used to evaluate seven perfusion parameters from time-intensity curves. Intra-operator variability analyses were based on determining perfusion parameter coefficients of variation (CV).

RESULTS: In vitro, different volumes of SonoVue^® were tested with the three phantoms: intra-operator variability was found to range from 2.33% to 23.72%. In vivo, experiments were performed on tumor tissues and perfusion parameters exhibited values ranging from 1.48% to 29.97%. In addition, the area under the curve (AUC) and the area under the wash-out (AUWO) were two of the parameters of great interest since throughout in vitro and in vivo experiments their variability was lower than 15.79%.

CONCLUSION: AUC and AUWO appear to be the most reliable parameters for assessing tumor perfusion using DCE-US as they exhibited the lowest CV values.

Comparison of dynamic contrast-enhanced MRI and dynamic contrast-enhanced CT biomarkers in bladder cancer

Dynamic contrast-enhanced MRI (DCE-MRI) is frequently used to provide response biomarkers in clinical trials of novel cancer therapeutics but assessment of their physiological accuracy is difficult. DCE-CT provides an independent probe of similar pharmacokinetic processes and may be modeled in the same way as DCE-MRI to provide purportedly equivalent physiological parameters. In this study, DCE-MRI and DCE-CT were directly compared in subjects with primary bladder cancer to assess the degree to which the model parameters report modeled physiology rather than artefacts of the measurement technique and to determine the interchangeability of the techniques in a clinical trial setting. The biomarker K(trans) obtained by fitting an extended version of the Kety model voxelwise to both DCE-MRI and DCE-CT data was in excellent agreement (mean across subjects was 0.085 ± 0.030 min(-1) for DCE-MRI and 0.087 ± 0.033 min(-1) for DCE-CT, intermodality coefficient of variation 9%). The parameter v(p) derived from DCE-CT was significantly greater than that derived from DCE-MRI (0.018 ± 0.006 compared to 0.009 ± 0.008, P = 0.0007) and v(e) was in reasonable agreement only for low values. The study provides evidence that the biomarker K(trans) is a robust parameter indicative of the underlying physiology and relatively independent of the method of measurement.

Imaging in clinical trials

Drug development continues to face challenges to successfully progress the most promising drug candidates through the stages of clinical trials. Given the increasing cost to develop a drug, methods are required to characterise early drug efficacy and safety. Imaging techniques are increasingly used in oncological clinical trials to provide evidence for decision making. With the application of conventional morphological imaging techniques and standardised response criteria based on tumour size measurements, imaging continues to be used to define key study end points. However, functional imaging techniques are likely to play an important role in the evaluation of novel therapeutics, although how these methods are to be optimally applied has yet to be clearly established. The specific challenges of standardising multi-centre imaging in the context of clinical trials are highlighted, including the processes for image acquisition, data analysis and radiological review.