Molecular ultrasound imaging is used to image the expression of specific proteins on the surface of blood vessels using the conjugated microbubbles (MBs) that can bind to the targeted proteins, which makes MBs ideal for imaging the protein expressed on blood vessels. However, how to optimally apply MBs in an ultrasound imaging system to detect and quantify the targeted protein expression needs further investigation. To address this issue, objective of this study is to investigate feasibility of developing and applying a new quantitative imaging marker to quantify the expression of protein markers on the surface of cancer cells. To obtain a numeric value proportional to the amount of MBs that bind to the target protein, a standard method for quantification of MBs is applying a destructive pulse, which bursts most of the bubbles in the region of interest. The difference between the signal intensity before and after destruction is used to measure the differential targeted enhancement (dTE). In addition, a dynamic kinetic model is applied to fit the timeintensity curves and a structural similarity model with three metrics is used to detect the differences between images. Study results show that the elevated dTE signals in images acquired from the targeted (MBTar) and isotype (MBIso) are significantly different (p<0.05). Quantitative image features are also successfully computed from the kinetic model and structural similarity model, which provide potential to identify new quantitative image markers that can more accurately differentiate the targeted microbubble status.
Significance: Fluorescence guidance in cancer surgery (FGS) using molecular-targeted contrast agents is accelerating, yet the influence of individual patients’ physiology on the optimal time to perform surgery post-agent-injection is not fully understood.
Aim: Develop a mathematical framework and analytical expressions to estimate patient-specific time-to-maximum contrast after imaging agent administration for single- and paired-agent (coadministration of targeted and control agents) protocols.
Approach: The framework was validated in mouse subcutaneous xenograft studies for three classes of imaging agents: peptide, antibody mimetic, and antibody. Analytical expressions estimating time-to-maximum-tumor-discrimination potential were evaluated over a range of parameters using the validated framework for human cancer parameters.
Results: Correlations were observed between simulations and matched experiments and metrics of tumor discrimination potential (p < 0.05). Based on human cancer physiology, times-to-maximum contrast for peptide and antibody mimetic agents were <200 min, >15 h for antibodies, on average. The analytical estimates of time-to-maximum tumor discrimination performance exhibited errors of <10 % on average, whereas patient-to-patient variance is expected to be greater than 100%.
Conclusion: We demonstrated that analytical estimates of time-to-maximum contrast in FGS carried out patient-to-patient can outperform the population average time-to-maximum contrast used currently in clinical trials. Such estimates can be made with preoperative DCE-MRI (or similar) and knowledge of the targeted agent’s binding affinity.
A paired-agent fluorescent molecular imaging strategy is presented as a method to measure drug target engagement in whole tumor imaging. The protocol involves dynamic imaging of a pair of targeted and control imaging agents prior to and following antibody therapy. Simulations demonstrated that antibody “drug target engagement” can be estimated within a 15%-error over a wide range of tumor physiology (blood flow, vascular permeability, target density) and antibody characteristics (affinity, binding rates). Experimental results demonstrated the first in vivo detection of binding site barrier, highlighting the potential for this methodology to provide novel insights in drug distribution/binding imaging.
We reported the uptake value of a near-infrared imaging agent in an orthotopic glioma mouse models. The imaging agent was IRDy680®. The mice were injected with a trace amount of this imaging agent and imaged on a magnetic resonance imaging device that is coupled with a fluorescence imaging tomography system. By applying a reconstruction method, at each time point, we got a single value for the uptake of this imaging agent in the tumor region. The mean of the uptake values in the mice is reported here.
As the role of immuno-oncological therapeutics expands, the capacity to noninvasively quantify molecular targets and drug-target engagement is increasingly critical to drug development efforts and treatment monitoring. Previously, we showed that MRI-coupled dual-agent fluorescence tomography (FMT) is capable of estimating the concentration of epidermal growth factor receptor (EGFR) in orthotopic glioma models noninvasively. This approach uses the dynamic information of two fluorescent agents (a targeted agent and untargeted isotype) to estimate tumor receptor concentration in vivo. This approach generally relies on the two tracers having similar kinetics in normal tissues, which may not always be the case. Herein, we describe an additional channel added to the MRI-FMT system which measures the uptake of both agents in the normal muscle, data which can be used to compensate for differing kinetic behavior.
Paired-agent kinetic modeling protocols provide one means of estimating cancer cell-surface receptors with in vivo molecular imaging. The protocols employ the coadministration of a control imaging agent with one or more targeted imaging agent to account for the nonspecific uptake and retention of the targeted agent. These methods require the targeted and control agent data be converted to equivalent units of concentration, typically requiring specialized equipment and calibration, and/or complex algorithms that raise the barrier to adoption. This work evaluates a kinetic model capable of correcting for targeted and control agent signal differences. This approach was compared with an existing simplified paired-agent model (SPAM), and modified SPAM that accounts for signal differences by early time point normalization of targeted and control signals (SPAMPN). The scaling factor model (SPAMSF) outperformed both SPAM and SPAMPN in terms of accuracy and precision when the scale differences between targeted and imaging agent signals (α) were not equal to 1, and it matched the performance of SPAM for α = 1. This model could have wide-reaching implications for quantitative cancer receptor imaging using any imaging modalities, or combinations of imaging modalities, capable of concurrent detection of at least two distinct imaging agents (e.g., SPECT, optical, and PET/MR).
Dynamic fluorescence imaging approaches can be used to estimate the concentration of cell surface receptors in vivo. Kinetic models are used to generate the final estimation by taking the targeted imaging agent concentration as a function of time. However, tissue absorption and scattering properties cause the final readout signal to be on a different scale than the real fluorescent agent concentration. In paired-agent imaging approaches, simultaneous injection of a suitable control imaging agent with a targeted one can account for non-specific uptake and retention of the targeted agent. Additionally, the signal from the control agent can be a normalizing factor to correct for tissue optical property differences. In this study, the kinetic model used for paired-agent imaging analysis (i.e., simplified reference tissue model) is modified and tested in simulation and experimental data in a way that accounts for the scaling correction within the kinetic model fit to the data to ultimately extract an estimate of the targeted biomarker concentration.
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