Non-Invasive Imaging of Normalized Solid Stress in Cancers in Vivo

A Novel Poroelastography Method for High-Quality Estimation of Lateral Strain, Solid Stress, and Fluid Pressure In Vivo

Assessment of mechanical and transport properties of tissues using ultrasound elasticity imaging requires accurate estimations of the spatiotemporal distribution of volumetric strain. Due to physical constraints such as pitch limitation and the lack of phase information in the lateral direction, the quality of lateral strain estimation is typically significantly lower than the quality of axial strain estimation. In this paper, a novel lateral strain estimation technique based on the physics of compressible porous media is developed, tested and validated. This technique is referred to as "Poroelastography-based Ultrasound Lateral Strain Estimation" (PULSE). PULSE differs from previously proposed lateral strain estimators as it uses the underlying physics of internal fluid flow within a local region of the tissue as theoretical foundation. PULSE establishes a relation between spatiotemporal changes in the axial strains and corresponding spatiotemporal changes in the lateral strains, effectively allowing assessment of lateral strains with comparable quality of axial strain estimators. We demonstrate that PULSE can also be used to accurately track compression-induced solid stresses and fluid pressure in cancers using ultrasound poroelastography (USPE). In this study, we report the theoretical formulation for PULSE and validation using finite element (FE) and ultrasound simulations. PULSE-generated results exhibit less than 5% percentage relative error (PRE) and greater than 90% structural similarity index (SSIM) compared to ground truth simulations. Experimental results are included to qualitatively assess the performance of PULSE in vivo. The proposed method can be used to overcome the inherent limitations of non-axial strain imaging and improve clinical translatability of USPE.

Mechanical models and measurement methods of solid stress in tumors

In addition to genetic mutations, biomechanical factors also affect the structures and functions of the tumors during tumor growth, including solid stress, interstitial fluid pressure, stiffness, and microarchitecture. Solid stress affects tumors by compressing cancer and stromal cells and deforming blood and lymphatic vessels which reduce supply of oxygen, nutrients and drug delivery, making resistant to treatment. Researchers simulate the stress by creating mechanical models both in vitro and in vivo. Cell models in vitro are divided into two dimensions (2D) and three dimensions (3D). 2D models are simple to operate but exert pressure on apical surface of the cells. 3D models, the multicellular tumor spheres, are more consistent with the actual pathological state in human body. However, the models are more difficult to establish compared with the 2D models. Besides, the procedure of the animal models in vivo is even more complex and tougher to operate. Then, researchers challenged to quantify the solid stress through some measurement methods. We compared the advantages and limitations of these models and methods, which may help to explore new therapeutic targets for normalizing the tumor's physical microenvironment. KEY POINTS: •This is the first review to conclude the mechanical models and measurement methods in tumors. •The merit and demerit of these models and methods are compared. •Insights into further models are discussed.

Bladder Cancer and Artificial Intelligence: Emerging Applications

Bladder cancer is a common and heterogeneous disease that poses a significant burden to the patient and health care system. Major unmet needs include effective early detection strategy, imprecision of risk stratification, and treatment-associated morbidities. The existing clinical paradigm is imprecise, which results in missed tumors, suboptimal therapy, and disease progression. Artificial intelligence holds immense potential to address many unmet needs in bladder cancer, including early detection, risk stratification, treatment planning, quality assessment, and outcome prediction. Despite recent advances, extensive work remains to affirm the efficacy of artificial intelligence as a decision-making tool for bladder cancer management.

Predicting stress and interstitial fluid pressure in tumors based on biphasic theory

The uncontrolled proliferation of cancer cells causes the growth of the tumor mass. Consequently, the normal surrounding tissue exerts a compressive force on the tumor mass to oppose its expansion. These stresses directly promote tumor metastasis and invasion and affect drug delivery. In the past, the mechanical behavior of solid tumors has been extensively studied using linear elastic and nonlinear hyperelastic constitutive models. In this study, we develop a two-dimensional biomechanical model based on the biphasic assumption of the solid matrix and fluid phase of the tissues. Heterogeneous vasculature and nonuniform blood perfusion are also investigated by incorporating in the model a necrotic core and a well-vascularized zone. The findings of our study demonstrate a significant difference between the linear and nonlinear tissue responses to stress, while the interstitial fluid pressure (IFP) distribution is found to be independent of the constitutive model. The proposed biphasic model may be useful for elasticity imaging techniques aiming at predicting stress and IFP in tumors.

Intravital measurements of solid stresses in tumours reveal length-scale and microenvironmentally dependent force transmission

In cancer, solid stresses impede the delivery of therapeutics to tumours and the trafficking and tumour infiltration of immune cells. Understanding such consequences and the origin of solid stresses requires their probing in vivo at the cellular scale. Here we report a method for performing volumetric and longitudinal measurements of solid stresses in vivo, and findings from its applicability to tumours. We used multimodal intravital microscopy of fluorescently labelled polyacrylamide beads injected in breast tumours in mice as well as mathematical modelling to compare solid stresses at the single-cell and tissue scales, in primary and metastatic tumours, in vitro and in mice, and in live mice and post-mortem tissue. We found that solid-stress transmission is scale dependent, with tumour cells experiencing lower stresses than their embedding tissue, and that tumour cells in lung metastases experience substantially higher solid stresses than those in the primary tumours. The dependence of solid stresses on length scale and the microenvironment may inform the development of therapeutics that sensitize cancer cells to such mechanical forces.

Assessment of spinal cord injury using ultrasound elastography in a rabbit model in vivo

The effect of the mechanical micro-environment on spinal cord injury (SCI) and treatment effectiveness remains unclear. Currently, there are limited imaging methods that can directly assess the localized mechanical behavior of spinal cords in vivo. In this study, we apply new ultrasound elastography (USE) techniques to assess SCI in vivo at the site of the injury and at the time of one week post injury, in a rabbit animal model. Eleven rabbits underwent laminectomy procedures. Among them, spinal cords of five rabbits were injured during the procedure. The other six rabbits were used as control. Two neurological statuses were achieved: non-paralysis and paralysis. Ultrasound data were collected one week post-surgery and processed to compute strain ratios. Histologic analysis, mechanical testing, magnetic resonance imaging (MRI), computerized tomography and MRI diffusion tensor imaging (DTI) were performed to validate USE results. Strain ratios computed via USE were found to be significantly different in paralyzed versus non-paralyzed rabbits. The myelomalacia histologic score and spinal cord Young's modulus evaluated in selected animals were in good qualitative agreement with USE assessment. It is feasible to use USE to assess changes in the spinal cord of the presented animal model. In the future, with more experimental data available, USE may provide new quantitative tools for improving SCI diagnosis and prognosis.

Deciphering the Mechanics of Cancer Spheroid Growth in 3D Environments through Microfluidics Driven Mechanical Actuation

Uncontrolled growth of tumor cells is a key contributor to cancer-associated mortalities. Tumor growth is a biomechanical process whereby the cancer cells displace the surrounding matrix that provides mechanical resistance to the growing cells. The process of tumor growth and remodeling is regulated by material properties of both the cancer cells and their surrounding matrix, yet the mechanical interdependency between the two entities is not well understood. Herein, this work develops a microfluidic platform that precisely positions tumor spheroids within a hydrogel and mechanically probes the growing spheroids and surrounding matrix simultaneously. By using hydrostatic pressure to deform the spheroid-laden hydrogel along with confocal imaging and finite element (FE) analysis, this work deduces the material properties of the spheroid and the matrix in situ. For spheroids embedded within soft hydrogels, decreases in the Young's modulus of the matrix are detected at discrete locations accompanied by localized tumor growth. Contrastingly, spheroids within stiff hydrogels do not significantly decrease the Young's modulus of the surrounding matrix, despite exhibiting growth. Spheroids in stiff matrices leverage their high bulk modulus to grow and display a uniform volumetric expansion. Collectively, a quantitative platform is established and new insights into tumor growth within a stiff 3D environment are provided.

Deep GRU-CNN Model for COVID-19 Detection From Chest X-Rays Data

In the current era, data is growing exponentially due to advancements in smart devices. Data scientists apply a variety of learning-based techniques to identify underlying patterns in the medical data to address various health-related issues. In this context, automated disease detection has now become a central concern in medical science. Such approaches can reduce the mortality rate through accurate and timely diagnosis. COVID-19 is a modern virus that has spread all over the world and is affecting millions of people. Many countries are facing a shortage of testing kits, vaccines, and other resources due to significant and rapid growth in cases. In order to accelerate the testing process, scientists around the world have sought to create novel methods for the detection of the virus. In this paper, we propose a hybrid deep learning model based on a convolutional neural network (CNN) and gated recurrent unit (GRU) to detect the viral disease from chest X-rays (CXRs). In the proposed model, a CNN is used to extract features, and a GRU is used as a classifier. The model has been trained on 424 CXR images with 3 classes (COVID-19, Pneumonia, and Normal). The proposed model achieves encouraging results of 0.96, 0.96, and 0.95 in terms of precision, recall, and f1-score, respectively. These findings indicate how deep learning can significantly contribute to the early detection of COVID-19 in patients through the analysis of X-ray scans. Such indications can pave the way to mitigate the impact of the disease. We believe that this model can be an effective tool for medical practitioners for early diagnosis.

Tumor Solid Stress: Assessment with MR Elastography under Compression of Patient-Derived Hepatocellular Carcinomas and Cholangiocarcinomas Xenografted in Mice

Malignant tumors have abnormal biomechanical characteristics, including high viscoelasticity, solid stress, and interstitial fluid pressure. Magnetic resonance (MR) elastography is increasingly used to non-invasively assess tissue viscoelasticity. However, solid stress and interstitial fluid pressure measurements are performed with invasive methods. We studied the feasibility and potential role of MR elastography at basal state and under controlled compression in assessing altered biomechanical features of malignant liver tumors. MR elastography was performed in mice with patient-derived, subcutaneously xenografted hepatocellular carcinomas or cholangiocarcinomas to measure the basal viscoelasticity and the compression stiffening rate, which corresponds to the slope of elasticity versus applied compression. MR elastography measurements were correlated with invasive pressure measurements and digital histological readings. Significant differences in MR elastography parameters, pressure, and histological measurements were observed between tumor models. In multivariate analysis, collagen content and interstitial fluid pressure were determinants of basal viscoelasticity, whereas solid stress, in addition to collagen content, cellularity, and tumor type, was an independent determinant of compression stiffening rate. Compression stiffening rate had high AUC (0.87 ± 0.08) for determining elevated solid stress, whereas basal elasticity had high AUC for tumor collagen content (AUC: 0.86 ± 0.08). Our results suggest that MR elastography compression stiffening rate, in contrast to basal viscoelasticity, is a potential marker of solid stress in malignant liver tumors.

Patient-Specific Characterization of Breast Cancer Hemodynamics Using Image-Guided Computational Fluid Dynamics

The overall goal of this study is to employ quantitative magnetic resonance imaging (MRI) data to constrain a patient-specific, computational fluid dynamics (CFD) model of blood flow and interstitial transport in breast cancer. We develop image processing methodologies to generate tumor-related vasculature-interstitium geometry and realistic material properties, using dynamic contrast enhanced MRI (DCE-MRI) and diffusion weighted MRI (DW-MRI) data. These data are used to constrain CFD simulations for determining the tumor-associated blood supply and interstitial transport characteristics unique to each patient. We then perform a proof-of-principle statistical comparison between these hemodynamic characteristics in 11 malignant and 5 benign lesions from 12 patients. Significant differences between groups (i.e., malignant versus benign) were observed for the median of tumor-associated interstitial flow velocity ( P = 0.028 ), and the ranges of tumor-associated blood pressure (P = 0.016) and vascular extraction rate (P = 0.040). The implication is that malignant lesions tend to have larger magnitude of interstitial flow velocity, and higher heterogeneity in blood pressure and vascular extraction rate. Multivariable logistic models based on combinations of these hemodynamic data achieved excellent differentiation between malignant and benign lesions with an area under the receiver operator characteristic curve of 1.0, sensitivity of 1.0, and specificity of 1.0. This image-based model system is a fundamentally new way to map flow and pressure fields related to breast tumors using only non-invasive, clinically available imaging data and established laws of fluid mechanics. Furthermore, the results provide preliminary evidence for this methodology's utility for the quantitative characterization of breast cancer.

Towards noninvasive estimation of tumour pressure by utilising MR elastography and nonlinear biomechanical models: a simulation and phantom study

The solid and fluid pressures of tumours are often elevated relative to surrounding tissue. This increased pressure is known to correlate with decreased treatment efficacy and potentially with tumour aggressiveness and therefore, accurate noninvasive estimates of tumour pressure would be of great value. We present a proof-of-concept method to infer the total tumour pressure, that is the sum of the fluid and solid parts, by examining stiffness in the peritumoural tissue with MR elastography and utilising nonlinear biomechanical models. The pressure from the tumour deforms the surrounding tissue leading to changes in stiffness. Understanding and accounting for these biases in stiffness has the potential to enable estimation of total tumour pressure. Simulations are used to validate the method with varying pressure levels, tumour shape, tumour size, and noise levels. Results show excellent matching in low noise cases and still correlate well with higher noise. Percent error remains near or below 10% for higher pressures in all noise level cases. Reconstructed pressures were also calculated from experiments with a catheter balloon embedded in a plastisol phantom at multiple inflation levels. Here the reconstructed pressures generally match the increases in pressure measured during the experiments. Percent errors between average reconstructed and measured pressures at four inflation states are 17.9%, 52%, 23.2%, and 0.9%. Future work will apply this method to in vivo data, potentially providing an important biomarker for cancer diagnosis and treatment.