Assessing the accuracy and reproducibility of modality independent elastography in a murine model of breast cancer

Biophysics in tumor growth and progression: from single mechano-sensitive molecules to mechanomedicine

Evidence from physical sciences in oncology increasingly suggests that the interplay between the biophysical tumor microenvironment and genetic regulation has significant impact on tumor progression. Especially, tumor cells and the associated stromal cells not only alter their own cytoskeleton and physical properties but also remodel the microenvironment with anomalous physical properties. Together, these altered mechano-omics of tumor tissues and their constituents fundamentally shift the mechanotransduction paradigms in tumorous and stromal cells and activate oncogenic signaling within the neoplastic niche to facilitate tumor progression. However, current findings on tumor biophysics are limited, scattered, and often contradictory in multiple contexts. Systematic understanding of how biophysical cues influence tumor pathophysiology is still lacking. This review discusses recent different schools of findings in tumor biophysics that have arisen from multi-scale mechanobiology and the cutting-edge technologies. These findings range from the molecular and cellular to the whole tissue level and feature functional crosstalk between mechanotransduction and oncogenic signaling. We highlight the potential of these anomalous physical alterations as new therapeutic targets for cancer mechanomedicine. This framework reconciles opposing opinions in the field, proposes new directions for future cancer research, and conceptualizes novel mechanomedicine landscape to overcome the inherent shortcomings of conventional cancer diagnosis and therapies.

Bayesian Inference of Tissue Heterogeneity for Individualized Prediction of Glioma Growth

Reliably predicting the future spread of brain tumors using imaging data and on a subject-specific basis requires quantifying uncertainties in data, biophysical models of tumor growth, and spatial heterogeneity of tumor and host tissue. This work introduces a Bayesian framework to calibrate the two-/three-dimensional spatial distribution of the parameters within a tumor growth model to quantitative magnetic resonance imaging (MRI) data and demonstrates its implementation in a pre-clinical model of glioma. The framework leverages an atlas-based brain segmentation of grey and white matter to establish subject-specific priors and tunable spatial dependencies of the model parameters in each region. Using this framework, the tumor-specific parameters are calibrated from quantitative MRI measurements early in the course of tumor development in four rats and used to predict the spatial development of the tumor at later times. The results suggest that the tumor model, calibrated by animal-specific imaging data at one time point, can accurately predict tumor shapes with a Dice coefficient 0.89. However, the reliability of the predicted volume and shape of tumors strongly relies on the number of earlier imaging time points used for calibrating the model. This study demonstrates, for the first time, the ability to determine the uncertainty in the inferred tissue heterogeneity and the model-predicted tumor shape.

Preclinical Three-Dimensional Vibrational Shear Wave Elastography for Mapping of Tumour Biomechanical Properties In Vivo

Preclinical investigation of the biomechanical properties of tissues and their treatment-induced changes are essential to support drug-discovery, clinical translation of biomarkers of treatment response, and studies of mechanobiology. Here we describe the first use of preclinical 3D elastography to map the shear wave speed (cs), which is related to tissue stiffness, in vivo and demonstrate the ability of our novel 3D vibrational shear wave elastography (3D-VSWE) system to detect tumour response to a therapeutic challenge. We investigate the use of one or two vibrational sources at vibrational frequencies of 700, 1000 and 1200 Hz. The within-subject coefficients of variation of our system were found to be excellent for 700 and 1000 Hz and 5.4 and 6.2%, respectively. The relative change in cs measured with our 3D-VSWE upon treatment with an anti-vascular therapy ZD6126 in two tumour xenografts reflected changes in tumour necrosis. U-87 MG drug vs vehicle: Δcs = −24.7 ± 2.5 % vs 7.5 ± 7.1%, (p = 0.002) and MDA-MB-231 drug vs vehicle: Δcs = −12.3 ± 2.7 % vs 4.5 ± 4.7%, (p = 0.02). Our system enables rapid (<5 min were required for a scan length of 15 mm and three vibrational frequencies) 3D mapping of quantitative tumour viscoelastic properties in vivo, allowing exploration of regional heterogeneity within tumours and speedy recovery of animals from anaesthesia so that longitudinal studies (e.g., during tumour growth or following treatment) may be conducted frequently.

Reproducibility assessment of a biomechanical model-based elasticity imaging method for identifying changes in left ventricular mechanical stiffness

Purpose

Cardiotoxicity of antineoplastic therapies is increasingly a risk to cancer patients treated with curative intent with years of life to protect. Studies highlight the importance of identifying early cardiac decline in cancer patients undergoing cardiotoxic therapies. Accurate tools to study this are a critical clinical need. Current and emerging methods for assessing cardiotoxicity are too coarse for identifying preclinical cardiac degradation or too cumbersome for clinical implementation.

Approach

In the previous work, we developed a noninvasive biomechanical model-based elasticity imaging methodology (BEIM) to assess mechanical stiffness changes of the left ventricle (LV) based on routine cine cardiac magnetic resonance (CMR) images. We examine this methodology to assess methodological reproducibility. We assessed a cohort of 10 participants that underwent test/retest short-axis CMR imaging at baseline and follow-up sessions as part of a previous publicly available study. We compare test images to retest images acquired within the same session to assess within-session reproducibility. We also compare test and retest images acquired at the baseline imaging session to test and retest images acquired at the follow-up imaging session to assess between-session reproducibility.

Results

We establish the within-session and between-session reproducibility of our method, with global elasticity demonstrating repeatability within a range previously demonstrated in cardiac strain imaging studies. We demonstrate increased repeatability of global elasticity compared to segmental elasticity for both within-session and between-session. Within-subject coefficients of variation for within-session test/retest images globally for all modulus directions and a mechanical fractional mechanical stiffness anisotropy metric ranged from 11% to 28%.

Conclusions

Results suggest that our methodology can reproducibly generate estimates of relative mechanical elasticity of the LV and provides a threshold for distinguishing true changes in myocardial mechanical stiffness from experimental variation. BEIM has applications in identifying preclinical cardiotoxicity in breast cancer patients undergoing antineoplastic therapies.

Developing a biomechanical model-based elasticity imaging method for assessing hormone receptor positive breast cancer treatment-related myocardial stiffness changes

Purpose: Assessing cardiotoxicity as a result of breast cancer therapeutics is increasingly important as breast cancer diagnoses are trending younger and overall survival is increasing. With evidence showing that prevention of cardiotoxicity plays a significant role in increasing overall survival, there is an unmet need for accurate non-invasive methods to assess cardiac injury due to cancer therapies. Current clinical methods are too coarse and emerging research methods have not yet achieved clinical implementation. Approach: As a proof of concept, we examine myocardial elasticity imaging in the setting of premenopausal women diagnosed with hormone receptor positive (HR-positive) breast cancer undergoing severe estrogen depletion, as cardiovascular injury from early estrogen depletion is well-established. We evaluate the ability of our model-based cardiac elasticity imaging analysis method to indicate subclinical cancer therapy-related cardiac decline by examining differences in the change in cardiac elasticity over time in two cohorts of premenopausal women either undergoing severe estrogen depletion for HR-positive breast cancer or triple negative breast cancer patients as comparators. Results: Our method was capable of producing functional mechanical elasticity maps of the left ventricle (LV). Using these elasticity maps, we show significant differences in cardiac mechanical elasticity in the HR-positive breast cancer cohort compared to the comparator cohort. Conclusions: We present our methodology to assess the mechanical stiffness of the LV by interrogating cardiac magnetic resonance images within a computational biomechanical model. Our preliminary study suggests the potential of this method for examining cardiac tissue mechanical stiffness properties as an early indicator of cardiac decline.

Translating preclinical MRI methods to clinical oncology

The complexity of modern in vivo magnetic resonance imaging (MRI) methods in oncology has dramatically changed in the last 10 years. The field has long since moved passed its (unparalleled) ability to form images with exquisite soft-tissue contrast and morphology, allowing for the enhanced identification of primary tumors and metastatic disease. Currently, it is not uncommon to acquire images related to blood flow, cellularity, and macromolecular content in the clinical setting. The acquisition of images related to metabolism, hypoxia, pH, and tissue stiffness are also becoming common. All of these techniques have had some component of their invention, development, refinement, validation, and initial applications in the preclinical setting using in vivo animal models of cancer. In this review, we discuss the genesis of quantitative MRI methods that have been successfully translated from preclinical research and developed into clinical applications. These include methods that interrogate perfusion, diffusion, pH, hypoxia, macromolecular content, and tissue mechanical properties for improving detection, staging, and response monitoring of cancer. For each of these techniques, we summarize the 1) underlying biological mechanism(s); 2) preclinical applications; 3) available repeatability and reproducibility data; 4) clinical applications; and 5) limitations of the technique. We conclude with a discussion of lessons learned from translating MRI methods from the preclinical to clinical setting, and a presentation of four fundamental problems in cancer imaging that, if solved, would result in a profound improvement in the lives of oncology patients. Level of Evidence: 5 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;50:1377-1392.

Toward quantitative quasistatic elastography with a gravity-induced deformation source for image-guided breast surgery

Biomechanical breast models have been employed for applications in image registration and diagnostic analysis, breast augmentation simulation, and for surgical and biopsy guidance. Accurate applications of stress-strain relationships of tissue within the breast can improve the accuracy of biomechanical models that attempt to simulate breast deformations. Reported stiffness values for adipose, glandular, and cancerous tissue types vary greatly. Variations in reported stiffness properties have been attributed to differences in testing methodologies and assumptions, measurement errors, and natural interpatient differences in tissue elasticity. Therefore, the ability to determine patient-specific in vivo breast tissue properties would be advantageous for these procedural applications. While some in vivo elastography methods are not quantitative and others do not measure material properties under deformation conditions that are appropriate to the application of concern, in this study, we developed an elasticity estimation method that is performed using deformations representative of supine therapeutic procedures. More specifically, reconstruction of mechanical properties appropriate for the standard-of-care supine lumpectomy was performed by iteratively fitting two anatomical images before and after deformations taking place in the supine breast configuration. The method proposed is workflow-friendly, quantitative, and uses a noncontact, gravity-induced deformation source.

Breast tissue stiffness estimation for surgical guidance using gravity-induced excitation

Tissue stiffness interrogation is fundamental in breast cancer diagnosis and treatment. Furthermore, biomechanical models for predicting breast deformations have been created for several breast cancer applications. Within these applications, constitutive mechanical properties must be defined and the accuracy of this estimation directly impacts the overall performance of the model. In this study, we present an image-derived computational framework to obtain quantitative, patient specific stiffness properties for application in image-guided breast cancer surgery and interventions. The method uses two MR acquisitions of the breast in different supine gravity-loaded configurations to fit mechanical properties to a biomechanical breast model. A reproducibility assessment of the method was performed in a test-retest study using healthy volunteers and was further characterized in simulation. In five human data sets, the within subject coefficient of variation ranged from 10.7% to 27% and the intraclass correlation coefficient ranged from 0.91-0.944 for assessment of fibroglandular and adipose tissue stiffness. In simulation, fibroglandular content and deformation magnitude were shown to have significant effects on the shape and convexity of the objective function defined by image similarity. These observations provide an important step forward in characterizing the use of nonrigid image registration methodologies in conjunction with biomechanical models to estimate tissue stiffness. In addition, the results suggest that stiffness estimation methods using gravity-induced excitation can reliably and feasibly be implemented in breast cancer surgery/intervention workflows.

MR Imaging Biomarkers in Oncology Clinical Trials

The authors discuss eight areas of quantitative MR imaging that are currently used (RECIST, DCE-MR imaging, DSC-MR imaging, diffusion MR imaging) in clinical trials or emerging (CEST, elastography, hyperpolarized MR imaging, multiparameter MR imaging) as promising techniques in diagnosing cancer and assessing or predicting response of cancer to therapy. Illustrative applications of the techniques in the clinical setting are summarized before describing the current limitations of the methods.

Mechanotransduction in cancer

Tissue stiffness is tightly controlled under normal conditions, but changes with disease. In cancer, tumors often tend to be stiffer than the surrounding uninvolved tissue, yet the cells themselves soften. Within the past decade, and particularly in the last few years, there is increasing evidence that the stiffness of the extracellular matrix modulates cancer and stromal cell mechanics and function, influencing such disease hallmarks as angiogenesis, migration, and metastasis. This review briefly summarizes recent studies that investigate how cancer cells and fibrosis-relevant stromal cells respond to ECM stiffness, the possible sensing appendages and signaling mechanisms involved, and the emergence of novel substrates - including substrates with scar-like fractal heterogeneity - that mimic the in vivo mechanical environment of the cancer cell.