Emerging Techniques in Breast MRI

As indicated throughout this chapter, there is a constant effort to move to more sensitive, specific, and quantitative methods for characterizing breast tissue via magnetic resonance imaging (MRI). In the present chapter, we focus on six emerging techniques that seek to quantitatively interrogate the physiological and biochemical properties of the breast. At the physiological scale, we present an overview of ultrafast dynamic contrast-enhanced MRI and magnetic resonance elastography which provide remarkable insights into the vascular and mechanical properties of tissue, respectively. Moving to the biochemical scale, magnetization transfer, chemical exchange saturation transfer, and spectroscopy (both “conventional” and hyperpolarized) methods all provide unique, noninvasive, insights into tumor metabolism. Given the breadth and depth of information that can be obtained in a single MRI session, methods of data synthesis and interpretation must also be developed. Thus, we conclude the chapter with an introduction to two very different, though complementary, methods of data analysis: (1) radiomics and habitat imaging, and (2) mechanism-based mathematical modeling

Computer simulation of glioma growth and morphology

Despite major advances in the study of glioma, the quantitative links between intra-tumor molecular/cellular properties, clinically observable properties such as morphology, and critical tumor behaviors such as growth and invasiveness remain unclear, hampering more effective coupling of tumor physical characteristics with implications for prognosis and therapy. Although molecular biology, histopathology, and radiological imaging are employed in this endeavor, studies are severely challenged by the multitude of different physical scales involved in tumor growth, i.e., from molecular nanoscale to cell microscale and finally to tissue centimeter scale. Consequently, it is often difficult to determine the underlying dynamics across dimensions. New techniques are needed to tackle these issues. Here, we address this multi-scalar problem by employing a novel predictive three-dimensional mathematical and computational model based on first-principle equations (conservation laws of physics) that describe mathematically the diffusion of cell substrates and other processes determining tumor mass growth and invasion. The model uses conserved variables to represent known determinants of glioma behavior, e.g., cell density and oxygen concentration, as well as biological functional relationships and parameters linking phenomena at different scales whose specific forms and values are hypothesized and calculated based on in vitro and in vivo experiments and from histopathology of tissue specimens from human gliomas. This model enables correlation of glioma morphology to tumor growth by quantifying interdependence of tumor mass on the microenvironment (e.g., hypoxia, tissue disruption) and on the cellular phenotypes (e.g., mitosis and apoptosis rates, cell adhesion strength). Once functional relationships between variables and associated parameter values have been informed, e.g., from histopathology or intra-operative analysis, this model can be used for disease diagnosis/prognosis, hypothesis testing, and to guide surgery and therapy. In particular, this tool identifies and quantifies the effects of vascularization and other cell-scale glioma morphological characteristics as predictors of tumor-scale growth and invasion

Patient-specific, mechanistic models of tumor growth incorporating artificial intelligence and big data

Despite the remarkable advances in cancer diagnosis, treatment, and management that have occurred over the past decade, malignant tumors remain a major public health problem. Further progress in combating cancer may be enabled by personalizing the delivery of therapies according to the predicted response for each individual patient. The design of personalized therapies requires patient-specific information integrated into an appropriate mathematical model of tumor response. A fundamental barrier to realizing this paradigm is the current lack of a rigorous, yet practical, mathematical theory of tumor initiation, development, invasion, and response to therapy. In this review, we begin by providing an overview of different approaches to modeling tumor growth and treatment, including mechanistic as well as data-driven models based on ``big data" and artificial intelligence. Next, we present illustrative examples of mathematical models manifesting their utility and discussing the limitations of stand-alone mechanistic and data-driven models. We further discuss the potential of mechanistic models for not only predicting, but also optimizing response to therapy on a patient-specific basis. We then discuss current efforts and future possibilities to integrate mechanistic and data-driven models. We conclude by proposing five fundamental challenges that must be addressed to fully realize personalized care for cancer patients driven by computational models

Tumor Microvasculature: Endothelial Leakiness and Endothelial Pore Size Distribution in a Breast Cancer Model

Tumor endothelial leakiness is quantified in a rat mammary adenocarcinoma model using dynamic contrast enhancement MRI and contrast agents of widely varying sizes. The contrast agents were constructed to be of globular configuration and have their uptake rate into tumor interstitium be driven by the same diffusion process and limited only by the availability of endothelial pores of passable size. It was observed that the endothelial pore distribution has a steep power law dependence on size, r−β, with an exponent of −4.1. The model of large pore dominance in tumor leakiness as reported in some earlier investigation with fluorescent probes and optical chamber methods is rejected for this tumor model and a number of other tumor types including chemically induced tumors. This steep power law dependence on size is also consistent with observations on human breast cancer

Quantitative Perfusion-Sensitive Mri Phantoms

Perfusion-sensitive MR methods are increasingly utilized in preclinical and clinical MR research studies with the promise of providing quantitative estimates of parameters that describe in vivo microvasculature. One of these techniques, dynamic contrast enhanced: DCE) MRI, has found particularly common use in oncology for the detection, staging, and monitoring of highly vascularized tumors. DCE-MRI has been qualitatively validated by various studies that show a high correlation between modeled parameters from DCE and histologically measured microvascular density: MVD). However, in the absence of a matching gold-standard technique, DCE-MRI has not yet been quantitatively validated: i.e., the accuracy of the estimated parameters is unknown). Partly because of this inability to determine the accuracy of the measured parameters, there remains debate in the literature about which DCE signal model(s) best reflect(s) experimental data. In order to address these scientific challenges, realistic DCE tissue phantoms have been constructed. These phantoms implement semi-permeable hollow fibers, found commonly in commercial hemodialysis cartridges, to simulate leaky vasculature. Their design and construction are cataloged in this thesis. In addition, the phantoms have been experimentally characterized. In conjunction with these experiments, an interesting example of diffusion driven longitudinal relaxation was observed and is described herein. Lastly, the permeability of the fiber wall with respect to Gd-based contrast agents has been measured independently and compared with values derived from a mock-DCE experiment performed on the phantoms. In general, the results of these experiments support current DCE-MRI methods

MRI in Cancer: Improving Methodology for Measuring Vascular Properties and Assessing Radiation Treatment Effects in Brain

Tumors cannot survive, progress and metastasize without recruiting new blood vessels. Vascular properties, including perfusion and permeability, provide valuable information for characterizing cancers and assessing therapeutic outcomes. Dynamic contrast-enhanced (DCE) MRI is a non-invasive imaging technique that affords quantitative parameters describing the underlying vascular structure of tissue. To date, the clinical application of DCE-MRI has been hampered by the lack of standardized and validated quantitative modeling approaches for data analysis. From a therapeutic perspective, radiation therapy is a central component of the standard treatment for patients with cancer. Besides killing cancer cells, radiation also induces parenchymal and stromal changes in normal tissue, limiting radiation dose and complicating treatment response evaluation. Further, emerging evidence suggest that the radiation-modulated tumor microenvironment may also contribute to the enhanced tumor regrowth and resistance to therapy. Given these clinical problems, the objectives of this dissertation were to: i) improve the DCE MRI-based measurements of vascular properties; and ii) assess the radiation treatment effects on normal tissue (parenchyma) and the interaction between radiation-modulated parenchyma and tumor growth. For the first goal, Bayesian probability theory-based model selection was employed to evaluate four commonly employed DCE-MRI tracer kinetic models against both in silico DCE-MRI data and high-quality clinical data collected from patients with advanced-staged cervical cancer. Further, a constrained local arterial input function (cL-AIF) modeling approach was developed to improve the pharmacokinetic analysis of DCE-MRI data. For the second goal, a novel mouse model of radiation-mediated effects on normal brain was developed. The efficacy of anti-vascular endothelial growth factor (VEGF) antibody treatment of delayed, radiation-induced necrosis (RN) was evaluated. Also, the effects of radiation-modulated brain parenchyma on glioblastoma cell growth were studied. It was found that 1) complex DCE-MRI signal models are more sensitive to noise than simpler models with respect to parameter estimation accuracy and precision. Caution is thus advised when considering application of complex DCE-MRI kinetic models. It follows that data-driven model selection is an important prerequisite to DCE-MRI data analysis; 2) the proposed cL-AIF method, which estimates an unique local-AIF amplitude and arrival time for each voxel within the tissue of interest, provides better measurements vascular properties than the conventional approach employing a single, remotely measured AIF; 3) anti-VEGF antibody decreased MR-derived RN lesion volumes, while large areas of focal calcification formed and the expression of VEGF remained high post-treatment. More effective therapeutic strategies for RN are still needed; 4) the radiation-modulated brain parenchyma promotes aggressive, infiltrative glioma growth. The histologic features of such tumors are consistent with those commonly observed in recurrent high-grade tumors in patients. These findings afford new insights into the highly aggressive tumor regrowth patterns observed following radiotherapy

Targeted Anti-Angiogenic Therapy in Metastatic Renal Cell Carcinoma and Methodological Improvements in Assessment of Therapeutic Response with Imaging Biomarkers

Background: Drugs targeting angiogenic pathway remain the mainstay of treatment for metastatic renal cell carcinoma (mRCC). Tyrosine Kinase Inhibitors (TKI) as Sunitinib, Pazopanib as single agents and humanised monoclonal antibody bevacizumab (Bev) in combination with Interferon- α2a (IFN) have established as the first-line therapy for mRCC. Despite improvements in treatment, there are multiple questions which remain unanswered. In the combination of Bev and IFN, the respective role of each drug and whether any additional anti-angiogenic activity is gained by adding IFN to Bev remains unknown. As the clinical benefit obtained with these cytostatic agents does not always correlate with the conventional response assessment techniques as RECIST, it is necessary to reconsider the methods by which we assess benefit from these therapies. In this thesis, I report three studies aiming to answer these questions. Methods: With the clinical trial reported here, I explore whether Bev induced changes in vascular parameters measured by Dynamic Contrast Enhanced MRI (DCE-MRI) is significantly enhanced by the addition of IFN. In a phase II, randomised, open labelled, multicentre trial, treatment naïve mRCC patients were randomised to receive Bev on its own or in combination with a low dose (3MU) or standard dose (9MU) IFN. DCE-MRI was used to assess the changes in vascularity with the primary endpoint being, changes in transfer coefficient (Ktrans) after six weeks of treatment. I also report two retrospective imaging-based studies, using contrast-enhanced CT scans, performed to improve the methodology of response assessment for these antiangiogenic therapeutics. Here I explore the use of a) combining changes in size and arterial phase contrast enhancement measured using CT scan and b) changes in CT texture as methods of therapeutic response assessment in mRCC patients treated with TKI. Results: With the phase 2 clinical trial, we faced significant difficulty in recruitment as a result of restrictions in access to treatment in NHS, other competing studies and restrictions proposed by the DCE-MRI inclusion criteria. With slow recruitment, an unplanned analysis was performed after 21 patients were recruited. Analysis of primary endpoint showed no trend in the difference between arms with no correlation found between change in Ktrans and addition of IFN to bevacizumab. Effect size analysis performed due to the small numbers recruited failed to show any significance in the observed difference in Ktrans. Change in Ktrans and Kep may identify a group of patients likely to have PFS > 6 months, but this observation needs to evaluation in a larger sample size. Measuring size and change in arterial phase enhancement retrospectively using CT, a new criterion "modified" Choi, which prerequisite a combination of a decrease in arterial phase density by 15% and a decrease in size by 10% for response was proposed. Response assessment was measured with RECIST, Choi and modified Choi individually in 20 evaluable patients retrospectively and clinical benefit compared with Kaplan-Meier statistics and Log-Rank test. Response assessment as defined by the modified Choi criteria successfully identified patients who received clinical benefit from the treatment. Time to progression (TTP) was 448 days for the partial response and 89 days for stable disease as per the new criteria which were statistically significant with a p-value of 0.002. The second retrospective analysis explored the textural changes in enhanced CT scan. Performed in collaboration with researchers from Brighton University who developed the software algorithm used to assess changes in entropy and uniformity, 87 metastases from 39 patients with mRCC were analysed at baseline and after two cycles of TKI treatment. Textural parameters and response assessment criteria were correlated with TTP. After two cycles of TKI, the decrease in tumour entropy was 3%-45%, and increase in uniformity was 5%-21%. At a threshold change of -2% with uniformity, on a coarse scale of 2.5, the textural change was able to separate responders from non-responders. With Kaplan-Meier analysis comparing all four criteria, the percentage change in uniformity was statistically more significant than for RECIST, Choi, and Modified Choi criteria. Cox regression analysis showed that texture uniformity was an independent predictor of time to progression. Discussion: With the studies reported here, I was able to demonstrate the importance of improving the methodology in assessment of therapeutic response to targeted anti-angiogenic therapy in metastatic renal cell carcinoma. Even though the clinical trial, terminated early due to slow recruitment, did not reach its primary endpoint, changes in other vascular parameters as Kep combined with changes Ktrans showed tendency towards identifying a group of patients who derived clinical benefit of >6months with these therapies. This is particularly exciting as given the vascular stabilisation effect proposed for bevacizumab, the effusion parameter Kep may be a better tool in assessing response rather than Ktrans and warrants further assessment in a larger cohort. Modified choi criterion and textural analysis are two important methodological improvements in response assessment of cytostatic anti-angiogenic therapy. In the analyses reported here, both techniques have shown superiority over RECIST in response assessment and differentiating mRCC patients who is likely to gain clinical benefit by TKI therapy. Validation of these criteria on a larger patient cohort is important. As these criterions are assessed on standard enhanced CT scans, incorporating these criteria, especially modified choi criterion, as part of standard CT assessment could be performed and will provide a real world validation. Retrospective assessment using larger cohort of patients from previous phase 3 trials or inclusion of these parameters prospectively in phase 3 trials would also help us in evaluating these modalities further

Oncology and mechanics: landmark studies and promising clinical applications

Clinical management of cancer has continuously evolved for several decades. Biochemical, molecular and genomics approaches have brought and still bring numerous insights into cancerous diseases. It is now accepted that some phenomena, allowed by favorable biological conditions, emerge via mechanical signaling at the cellular scale and via mechanical forces at the macroscale. Mechanical phenomena in cancer have been studied in-depth over the last decades, and their clinical applications are starting to be understood. If numerous models and experimental setups have been proposed, only a few have led to clinical applications. The objective of this contribution is to propose to review a large scope of mechanical findings which have consequences on the clinical management of cancer. This review is mainly addressed to doctoral candidates in mechanics and applied mathematics who are faced with the challenge of the mechanics-based modeling of cancer with the aim of clinical applications. We show that the collaboration of the biological and mechanical approaches has led to promising advances in terms of modeling, experimental design and therapeutic targets. Additionally, a specific focus is brought on imaging-informed mechanics-based models, which we believe can further the development of new therapeutic targets and the advent of personalized medicine. We study in detail several successful workflows on patient-specific targeted therapies based on mechanistic modeling