Monitoring the Effects of Anti-angiogenesis on the Radiation Sensitivity of Pancreatic Cancer Xenografts Using Dynamic Contrast-Enhanced CT

Purpose To image the intra-tumor vascular physiological status of pancreatic tumors xenografts and their response to anti-angiogenic therapy using Dynamic Contrast-Enhanced CT (DCE-CT), and to identify parameters of vascular physiology associated with tumor X-ray sensitivity following anti-angiogenic therapy. Methods and Materials Nude mice bearing human BxPC-3 pancreatic tumor xenografts were treated with 5Gy of radiation therapy (RT), either a low-dose (40mg/kg) or a high-dose (150mg/kg) of DC101, the anti-VEGF receptor-2 anti-angiogenesis antibody, or with combination of low or high dose DC101 and 5Gy RT (DC101-plus-RT). DCE-CT scans were longitudinally acquired over three week period post-DC101 treatment. Parametric maps of tumor perfusion and fractional plasma volume (Fp) were calculated and their averaged values and histogram distributions evaluated and compared to controls, from which a more homogeneous physiological window was observed 1-week post-DC101. Mice receiving a combination of DC101-plus-RT(5Gy) were imaged baseline prior to receiving DC101 and 1-week after DC101 (prior to RT). Changes in perfusion and Fp were compared with alternation in tumor growth delay for RT and DC101-plus-RT(5Gy) treated tumors. Results Pretreatment with low or high doses of DC101 prior to RT significantly delayed tumor growth by an average 7.9 days compared to RT alone (p≤0.01). The increase in tumor growth delay for the DC101-plus-RT treated tumors was strongly associated with changes in tumor perfusion (ΔP>−15%) compared to RT treated tumors alone (p=0.01). In addition, further analysis revealed a trend linking the tumor’s increased growth delay to its tumor volume-to-DC101 dose ratio. Conclusions DCE-CT is capable of monitoring changes in intra-tumor physiological parameter of tumor perfusion in response to anti-angiogenic therapy of a pancreatic human tumor xenograft that was associated with enhanced radiation response

A novel in vivo tumor oxygen profiling assay: Combining functional and molecular imaging with multivariate mathematical modeling

Purpose: The objective of this study is to develop and test a novel high spatio-temporal in vivo assay to quantify tumor oxygenation and hypoxia. The assay implements a biophysical model of oxygen transport to fuse parameters acquired from in vivo functional and molecular imaging modalities. ^ Introduction: Tumor hypoxia plays an important role in carcinogenesis. It triggers pathological angiogenesis to supply more oxygen to the tumor cells and promotes cancer cell metastasis. Preclinical and clinical evidence show that anti-angiogenic treatment is capable of normalizing the tumor vasculature both structurally and functionally. The resulting normalized vasculature provides a more efficient and uniform microcirculation that enhances oxygen and drug delivery to the tumor cells and improves second-line treatments such as traditional radiation or chemotherapy. Early studies using the overall or average tumor hypoxia as a prognostic biomarker of anti-angiogenic therapy efficacy was ambivalent; however, recent studies have discovered that the etiology of hypoxia and its heterogeneity could be used as reliable prognostic biomarkers. The capability to longitudinally map tumor hypoxia with high spatial and temporal resolution has the potential to enhance fundamental cancer research and ultimately cancer patient care. ^ Method: A novel methodology to identify and characterize tumor hypoxia by fusing the physiological hemodynamic parametric maps obtained from functional and molecular imaging modalities and technique using a modified Krogh model of oxygen transport (MPO2) was developed. First, simulations studies were performed to validate this technique. Microscopy data of tumor and brain tissue (control) provided both the vasculature and rheology data. A Green\u27s function algorithm was used to solve the ordinary differential equation and calculate the oxygen profile at a microscopic scale (15 μm) (GPO2), which was used as a reference. From this data, simulated physiological maps (perfusion, fractional plasma volume, fractional interstitial volume) and hemoglobin status (oxygen saturation, hemoglobin concentration) was used as input to MPO2 and used to calculate pO2 levels as a function of scanner spatial resolution and noise. Second, MPO2 was compared to pO2 measurements in xenograft breast tumors using OxyLite oxygen sensor as a Gold Standard, where DCE-CT and PCT-S images were acquired to obtain hemodynamic images. Finally, the vascular physiology measurements obtained from an anti-angiogenic therapeutic study in pancreatic tumors was applied to MPO2 and compared to therapeutic response. ^ Results: The simulation results using Green\u27s function pO2 as standard showed that the MPO2 model performance was dependent on the spatial resolution (voxel size) of the images. Sensitivity and error analysis of this model were also investigated in this study. These oxygen transport simulations results suggest the oxygen saturation and hemoglobin concentration were two key factors in tissue oxygenation, and concomitant with blood perfusion and tumor metabolic rate. Comparisons of the pO2 profile obtained from MPO2 and OxyLite probe in MCF7 tumor model demonstrated a significant correlation and approached a slope of one (after accounting for a few outliers). Simulation studies implementing the physiological data obtained from the anti-angiogenic therapeutic study in pancreatic tumors using the MPO2 model agreed with the experimental findings that blood perfusion is a valuable prognostic biomarker in therapeutic efficacy. This model also predicted the oxygenation improvement difference from two vascular renormalization modes (topological normalization and geometrical normalization). ^ Conclusion: The results from the simulation and in vivo studies demonstrated the feasibility of this novel hypoxia assay. Simulation results of the pancreatic tumors provide an example of the impact the MPO2 model in conjunction with imaging can provide when evaluating the therapeutic significance of various normalization modes in anti-angiogenic therapy, and suggests potential approaches to further improve anti-angiogenic therapy efficacy

Development of a mathematical model to estimate intra-tumor oxygen concentrations through multi-parametric imaging

Background Tumor hypoxia is involved in every stage of solid tumor development: formation, progression, metastasis, and apoptosis. Two types of hypoxia exist in tumors—chronic hypoxia and acute hypoxia. Recent studies indicate that the regional hypoxia kinetics is closely linked to metastasis and therapeutic responses, but regional hypoxia kinetics is hard to measure. We propose a novel approach to determine the local pO2 by fusing the parameters obtained from in vivo functional imaging through the use of a modified multivariate Krogh model. Methods To test our idea and its potential to translate into an in vivo setting through the use of existing imaging techniques, simulation studies were performed comparing the local partial oxygen pressure (pO2) from the proposed multivariate image fusion model to the referenced pO2 derived by Green’s function, which considers the contribution from every vessel segment of an entire three-dimensional tumor vasculature to profile tumor oxygen with high spatial resolution. Results pO2 derived from our fusion approach were close to the referenced pO2 with regression slope near 1.0 and an r2 higher than 0.8 if the voxel size (or the spatial resolution set by functional imaging modality) was less than 200 μm. The simulation also showed that the metabolic rate, blood perfusion, and hemoglobin concentration were dominant factors in tissue oxygenation. The impact of the measurement error of functional imaging to the pO2 precision and accuracy was simulated. A Gaussian error function with FWHM equal to 20 % of blood perfusion or fractional vascular volume measurement contributed to average 7 % statistical error in pO2. Conclusion The simulation results indicate that the fusion of multiple parametric maps through the biophysically derived mathematical models can monitor the intra-tumor spatial variations of hypoxia in tumors with existing imaging methods, and the potential to further investigate different forms of hypoxia, such as chronic and acute hypoxia, in response to cancer therapies

First-order statistical speckle models improve robustness and reproducibility of contrast-enhanced ultrasound perfusion estimates

Contrast-enhanced ultrasound (CEUS) permits the quantification and monitoring of adaptive tumor responses in the face of anti-angiogenic treatment, with the goal of informing targeted therapy. However, conventional CEUS image analysis relies on mean signal intensity as an estimate of tracer concentration in indicator-dilution modeling. This discounts additional information that may be available from the first-order speckle statistics in a CEUS image. Heterogeneous vascular networks, typical of tumor-induced angiogenesis, lead to heterogeneous contrast enhancement of the imaged tumor cross-section. To address this, a linear (B-mode) processing approach was developed to quantify the change in the first-order speckle statistics of B-mode cine loops due to the incursion of microbubbles. The technique, named the EDoF (effective degrees of freedom) method, was developed on tumor bearing mice (MDA-MB-231LN mammary fat pad inoculation) and evaluated using nonlinear (two-pulse amplitude modulated) contrast microbubble-specific images. To improve the potential clinical applicability of the technique, a second-generation compound probability density function for the statistics of two-pulse amplitude modulated contrast-enhanced ultrasound images was developed. The compound technique was tested in an antiangiogenic drug trial (bevacizumab) on tumor bearing mice (MDA-MB-231LN), and evaluated with gold-standard histology and contrast-enhanced X-ray computed tomography. The compound statistical model could more accurately discriminate anti-VEGF treated tumors from untreated tumors than conventional CEUS image. The technique was then applied to a rapid patient-derived xenograft (PDX) model of renal cell carcinoma (RCC) in the chorioallantoic membrane (CAM) of chicken embryos. The ultimate goal of the PDX model is to screen RCC patients for de novo sunitinib resistance. The analysis of the first-order speckle statistics of contrast-enhanced ultrasound cine loops provides more robust and reproducible estimates of tumor blood perfusion than conventional image analysis. Theoretically this form of analysis could quantify perfusion heterogeneity and provide estimates of vascular fractal dimension, but further work is required to determine what physiological features influence these measures. Treatment sensitivity matrices, which combine vascular measures from CEUS and power Doppler, may be suitable for screening of de novo sunitinib resistance in patients diagnosed with renal cell carcinoma. Further studies are required to assess whether this protocol can be predictive of patient outcome

Artificial intelligence in cancer imaging: Clinical challenges and applications

Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care

Non-Invasive Quantitative Imaging Informs Early Assessment of Cancer Therapeutic Response.

Therapeutic response assessment of cancer has long been facilitated by non-invasive imaging methods such as magnetic resonance imaging (MRI) and x-ray computed tomography (CT) in the clinic. Standards of patient care are designed around the most common cases, which may not always be efficacious. However, through evidence-based medicine there has begun a shift toward more individualized care. Standard clinical practice for cancer response assessment utilizes only volumetric change, measured prior and following the completion of therapy, providing no opportunity to adjust the treatment. In addition, novel targeted therapies, which may not result in a substantial decrease in tumor volume, are becoming more prevalent in the treatment of tumors. There is a clear need for non-invasive biomarkers that provide near real-time information on the anatomical and physiological makeup of the tumor post-treatment initiation. Tools for assessing early treatment response may allow physicians to dynamically optimize treatments individually, enhancing patient prognoses and avoiding unnecessary patient morbidity. In the following studies, I have evaluated various non-invasive imaging tools for early detection of treatment response in rodent models of disease. Tissue apparent diffusion coefficients (ADC) are known to correlate well with cellular status in cancer, and have shown promise in the detection of early tumor treatment response. Several different numerical models of higher-order diffusion signal attenuation were evaluated to determine their sensitivity to treatment response compared to the standard diffusion model. Dynamic contrast-enhanced (DCE-) MRI has shown sensitivity to vascular changes in cancer and was evaluated as an imaging biomarker of treatment response using a novel vascular-targeted therapy. Quantitative indices generated from DCE-MRI data were compared to diffusion (ADC) and volumetric MRI readouts for response assessment. The utility of imaging readouts from concurrent MRI, CT, bioluminescence, and fluorescence imaging was also evaluated in a model of bone metastasis. Further, a new voxel-based analytical technique, the parametric response map (PRM), was applied to CT images of metastatic bone disease and osteoporosis to evaluate bone response to treatment and hormone deprivation, respectively. Use of these tools may help improve the clinical effectiveness of cancer patient therapy as well as drug development and testing in preclinical models.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/102381/1/bahoff_1.pd