Stephanie L. Barnes

Clinically relevant modeling of tumor growth and treatment response.

Noninvasive imaging technologies can help create patient-specific mathematical models to predict tumor growth. Current mathematical models of tumor growth are limited in their clinical application because they require input data that are nearly impossible to obtain with sufficient spatial resolution in patients even at a single time point—for example, extent of vascularization, immune infiltrate, ratio of tumor-to-normal cells, or extracellular matrix status. Here we propose the use of emerging, quantitative tumor imaging methods to initialize a new generation of predictive models. In the near future, these models could be able to forecast clinical outputs, such as overall response to treatment and time to progression, which will provide opportunities for guided intervention and improved patient care.

Quantitative Analysis of Tumor Vascularity in Benign and Malignant Solid Thyroid Nodules

The purpose of our study was to analyze the accuracy of quantitative analysis of tumor vascularity on power Doppler sonograms in differentiating malignant and benign solid thyroid nodules using tumor histologic evaluation as the reference standard.

Practical Dynamic Contrast Enhanced MRI in Small Animal Models of Cancer: Data Acquisition, Data Analysis, and Interpretation

Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) consists of the continuous acquisition of images before, during, and after the injection of a contrast agent. DCE-MRI allows for noninvasive evaluation of tumor parameters related to vascular perfusion and permeability and tissue volume fractions, and is frequently employed in both preclinical and clinical investigations. However, the experimental and analytical subtleties of the technique are not frequently discussed in the literature, nor are its relationships to other commonly used quantitative imaging techniques. This review aims to provide practical information on the development, implementation, and validation of a DCE-MRI study in the context of a preclinical study (though we do frequently refer to clinical studies that are related to these topics).

Multi-parametric MRI characterization of inflammation in murine skeletal muscle.

Myopathies often display a common set of complex pathologies that include muscle weakness, inflammation, compromised membrane integrity, fat deposition, and fibrosis. Multi‐parametric, quantitative, non‐invasive imaging approaches may be able to resolve these individual pathological components. The goal of this study was to use multi‐parametric MRI to investigate inflammation as an isolated pathological feature. Proton relaxation, diffusion tensor imaging (DTI), quantitative magnetization transfer (qMT‐MRI), and dynamic contrast enhanced (DCE‐MRI) parameters were calculated from data acquired in a single imaging session conducted 6–8 hours following the injection of λ‐carrageenan, a local inflammatory agent. T2 increased in the inflamed muscle and transitioned to bi‐exponential behavior. In diffusion measurements, all three eigenvalues and the apparent diffusion coefficient increased, but λ3 had the largest relative change. Analysis of the qMT data revealed that the T1 of the free pool and the observed T1 both increased in the inflamed tissue, while the ratio of exchanging spins in the solid pool to those in the free water pool (the pool size ratio) significantly decreased. DCE‐MRI data also supported observations of an increase in extracellular volume. These findings enriched the understanding of the relation between multiple quantitative MRI parameters and an isolated inflammatory pathology, and may potentially be employed for other single or complex myopathy models. Copyright

Assessing the reproducibility of dynamic contrast enhanced magnetic resonance imaging in a murine model of breast cancer

Quantitative dynamic contrast enhanced magnetic resonance imaging estimates parameters related to tissue vascularity and volume fractions; additionally, semiquantitative parameters such as the initial area under the curve can be utilized to describe tissue behavior. The aim of this study was to establish the reproducibility of quantitative and semiquantitative analysis of dynamic contrast enhanced magnetic resonance imaging in a murine model of breast cancer. For each animal, a T1‐weighted, gradient‐echo sequence was used to acquire two sets of dynamic contrast enhanced magnetic resonance imaging data separated by 5 h. Data were acquired at both a 0.05 mm3 (1282, n = 12) and a 0.2 mm3 (642, n = 12) resolution, and analysis was performed using both the Tofts–Kety (to estimate Ktrans and ve) and extended Tofts–Kety (Ktrans, ve, and vp) models. Reproducibility analysis was performed for both the center slice and the total tumor volume for all parameters. For the total volume analysis, the repeatability index for Ktrans is 0.073 min−1 in the standard model analysis and 0.075 min−1 in the extended model analysis at the 1282 acquisition. For the 642 acquisition, the values are 0.089 and 0.063 min−1 for the standard and extended models, respectively. The repeatability index for initial area under the curve was 0.0039 and 0.0042 mM min for the 1282 and 642 acquisitions, respectively. Magn Reson Med, 2013.

Assessing reproducibility of diffusion-weighted magnetic resonance imaging studies in a murine model of HER2+ breast cancer.

BACKGROUND AND PURPOSE The use of diffusion-weighted magnetic resonance imaging (DW-MRI) as a surrogate biomarker of response in preclinical studies is increasing. However, before a biomarker can be reliably employed to assess treatment response, the reproducibility of the technique must be established. There is a paucity of literature that quantifies the reproducibility of DW-MRI in preclinical studies; thus, the purpose of this study was to investigate DW-MRI reproducibility in a murine model of HER2+ breast cancer. MATERIALS AND METHODS Test-Retest DW-MRI scans separated by approximately six hours were acquired from eleven athymic female mice with HER2+ xenografts using a pulsed gradient spin echo diffusion-weighted sequence with three b values [150, 500, and 800s/mm(2)]. Reproducibility was assessed for the mean apparent diffusion coefficient (ADC) from tumor and muscle tissue regions. RESULTS The threshold to reflect a change in tumor physiology in a cohort of mice is defined by the 95% confidence interval (CI), which was±0.0972×10(-3)mm(2)/s (±11.8%) for mean tumor ADC. The repeatability coefficient defines this threshold for an individual mouse, which was±0.273×10(-3)mm(2)/s. The 95% CI and repeatability coefficient for mean ADC of muscle tissue were±0.0949×10(-3)mm(2)/s (±8.30%) and±0.266×10(-3)mm(2)/s, respectively. CONCLUSIONS Mean ADC of tumors is reproducible and appropriate for detecting treatment-induced changes on both an individual and mouse cohort basis.

Correlation of tumor characteristics derived from DCE-MRI and DW-MRI with histology in murine models of breast cancer

The purpose of this work was to determine the relationship between the apparent diffusion coefficient (ADC, from diffusion‐weighted (DW) MRI), the extravascular, extracellular volume fraction (ve, from dynamic contrast‐enhanced (DCE) MRI), and histological measurement of the extracellular space fraction.

Development of a mechanical testing assay for fibrotic murine liver.

In this article, a novel protocol for mechanical testing, combined with finite element modeling, is presented that allows the determination of the elastic modulus of normal and fibrotic murine livers and is compared to an independent mechanical testing method. The novel protocol employs suspending a portion of murine liver tissue in a cylindrical polyacrylamide gel, imaging with a microCT, conducting mechanical testing, and concluding with a mechanical property determination via a finite element method analysis. More specifically, the finite element model is built from the computerized tomography (CT) images, and boundary conditions are imposed in order to simulate the mechanical testing conditions. The resulting model surface stress is compared to that obtained during mechanical testing, which subsequently allows for direct evaluation of the liver modulus. The second comparison method involves a mechanical indentation test performed on a remaining liver lobe for comparison. In addition, this lobe is used for histological analysis to determine relationships between elasticity measurements and tissue health. This complete system was used to study 14 fibrotic livers displaying advanced fibrosis (injections with irritant), three control livers (injections without irritant), and three normal livers (no injections). The moduli evaluations for nondiseased livers were estimated as 0.62 +/- 0.09 kPa and 0.59 +/- 0.1 kPa for indenter and model-gel-tissue (MGT) assay tests, respectively. Moduli estimates for diseased liver ranged from 0.6-1.64 kPa and 0.96-1.88 kPa for indenter and MGT assay tests, respectively. The MGT modulus, though not equivalent to the modulus determined by indentation, demonstrates a high correlation, thus indicating a relationship between the two testing methods. The results also showed a clear difference between nondiseased and diseased livers. The developed MGT assay system is quite compact and could easily be utilized for controlled evaluation of soft-tissue moduli as shown here. In addition, future work will add the correlative method of elastography such that direct controlled validation of measurement on tissue can be determined.

A novel approach to breast cancer diagnosis via PET imaging of microcalcifications using 18F-NaF

Current radiologic methods for diagnosing breast cancer detect specific morphologic features of solid tumors or any associated calcium deposits. These deposits originate from an early molecular microcalcification process of 2 types: type 1 is calcium oxylate and type II is carbonated calcium hydroxyapatite. Type I microcalcifications are associated mainly with benign tumors, whereas type II microcalcifications are produced internally by malignant cells. No current noninvasive in vivo techniques are available for detecting intratumoral microcalcifications. Such a technique would have a significant impact on breast cancer diagnosis and prognosis in preclinical and clinical settings. 18F-NaF PET has been used solely for bone imaging by targeting the bone hydroxyapatite. In this work, we provide preliminary evidence that 18F-NaF PET imaging can be used to detect breast cancer by targeting the hydroxyapatite lattice within the tumor microenvironment with high specificity and soft-tissue contrast-to-background ratio while delineating tumors from inflammation. Methods: Mice were injected with approximately 106 MDA-MB-231 cells subcutaneously and imaged with 18F-NaF PET/CT in a 120-min dynamic sequence when the tumors reached a size of 200–400 mm3. Regions of interest were drawn around the tumor, muscle, and bone. The concentrations of radiotracer within those regions of interest were compared with one another. For comparison to inflammation, rats with inflamed paws were subjected to 18F-NaF PET imaging. Results: Tumor uptake of 18F− was significantly higher (P < 0.05) than muscle uptake, with the tumor-to-muscle ratio being about 3.5. The presence of type II microcalcification in the MDA-MB-231 cell line was confirmed histologically using alizarin red S and von Kossa staining as well as Raman microspectroscopy. No uptake of 18F− was observed in the inflamed tissue of the rats. Lack of hydroxyapatite in the inflamed tissue was verified histologically. Conclusion: This study provides preliminary evidence suggesting that specific targeting with 18F− of hydroxyapatite within the tumor microenvironment may be able to distinguish between inflammation and cancer.

A Predictive Mathematical Modeling Approach for the Study of Doxorubicin Treatment in Triple Negative Breast Cancer

Doxorubicin forms the basis of chemotherapy regimens for several malignancies, including triple negative breast cancer (TNBC). Here, we present a coupled experimental/modeling approach to establish an in vitro pharmacokinetic/pharmacodynamic model to describe how the concentration and duration of doxorubicin therapy shape subsequent cell population dynamics. This work features a series of longitudinal fluorescence microscopy experiments that characterize (1) doxorubicin uptake dynamics in a panel of TNBC cell lines, and (2) cell population response to doxorubicin over 30 days. We propose a treatment response model, fully parameterized with experimental imaging data, to describe doxorubicin uptake and predict subsequent population dynamics. We found that a three compartment model can describe doxorubicin pharmacokinetics, and pharmacokinetic parameters vary significantly among the cell lines investigated. The proposed model effectively captures population dynamics and translates well to a predictive framework. In a representative cell line (SUM-149PT) treated for 12 hours with doxorubicin, the mean percent errors of the best-fit and predicted models were 14% (±10%) and 16% (±12%), which are notable considering these statistics represent errors over 30 days following treatment. More generally, this work provides both a template for studies quantitatively investigating treatment response and a scalable approach toward predictions of tumor response in vivo.