In-vitro Strain and Modulus Measurements in Porcine Cervical Lymph Nodes

Macro-indentation testing of soft biological materials and assessment of hyper-elastic material models from inverse finite element analysis

Mechanical characterization of hydrogels and ultra-soft tissues is a challenging task both from an experimental and material parameter estimation perspective because they are much softer than many biological materials, ceramics, or polymers. The elastic modulus of such materials is within the 1 - 100 kPa range, behaving as a hyperelastic solid with strain hardening capability at large strains. In the current study, indentation experiments have been performed on agarose hydrogels, bovine liver, and bovine lymph node specimens. This work reports on the reliable determination of the elastic modulus by indentation experiments carried out at the macro-scale (mm) using a spherical indenter. However, parameter identification of the hyperelastic material properties usually requires an inverse finite element analysis due to the lack of an analytical contact model of the indentation test. Hence a comprehensive study on the spherical indentation of hyperelastic soft materials is carried out through robust computational analysis. Neo-Hookean and first-order Ogden hyperelastic material models were found to be most suitable. A case study on known anisotropic hyperelastic material showed the inability of the inverse finite element method to uniquely identify the whole material parameter set.

Endocrine Tumor Classification via Machine-Learning-Based Elastography: A Systematic Scoping Review

Elastography complements traditional medical imaging modalities by mapping tissue stiffness to identify tumors in the endocrine system, and machine learning models can further improve diagnostic accuracy and reliability. Our objective in this review was to summarize the applications and performance of machine-learning-based elastography on the classification of endocrine tumors. Two authors independently searched electronic databases, including PubMed, Scopus, Web of Science, IEEEXpress, CINAHL, and EMBASE. Eleven (n = 11) articles were eligible for the review, of which eight (n = 8) focused on thyroid tumors and three (n = 3) considered pancreatic tumors. In all thyroid studies, the researchers used shear-wave ultrasound elastography, whereas the pancreas researchers applied strain elastography with endoscopy. Traditional machine learning approaches or the deep feature extractors were used to extract the predetermined features, followed by classifiers. The applied deep learning approaches included the convolutional neural network (CNN) and multilayer perceptron (MLP). Some researchers considered the mixed or sequential training of B-mode and elastographic ultrasound data or fusing data from different image segmentation techniques in machine learning models. All reviewed methods achieved an accuracy of ≥80%, but only three were ≥90% accurate. The most accurate thyroid classification (94.70%) was achieved by applying sequential training CNN; the most accurate pancreas classification (98.26%) was achieved using a CNN-long short-term memory (LSTM) model integrating elastography with B-mode and Doppler images.

Determining the elastography strain ratio cut off value for differentiating benign from malignant breast lesions: systematic review and meta-analysis

Background

Elastography is an addition to grey-scale ultrasonic examination that has gained substantial traction within the last decade. Strain ratio (SR) has been incorporated as a semiquantitative measure within strain elastography, thus a potential imaging biomarker. The World Federation for Ultrasound in Medicine and Biology (WFUMB) published guidelines in 2015 for breast elastography. These guidelines acknowledge the marked variance in SR cut-off values used in differentiating benign from malignant lesions. The objective of this review was to include more recent evidence and seek to determine the optimal strain ratio cut off value for differentiating between benign and malignant breast lesions.

Methods

Comprehensive search of MEDLINE and Web of Science electronic databases with additional searches via Google Scholar and handsearching set from January 2000 to May 2020 was carried out. For retrieved studies, screening for eligibility, data extraction and analysis was done as per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Diagnostic Test Accuracy (PRISMA-DTA) Statement guidelines of 2018. Quality and risk of bias assessment of the studies were performed using the revised Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool.

Results

A total of 424 articles, 412 from electronic database and 12 additional searches were retrieved and 65 studies were included in the narrative synthesis and subgroup analysis. The overall threshold effect indicated significant heterogeneity among the studies with Spearman correlation coefficient of Logit (TPR) vs Logit (FPR) at − 0.301, p-value = 0.015. A subgroup under machine model consisting seven studies with 783 patients and 844 lesions showed a favourable threshold, Spearman’s correlation coefficient,0.786 (p = 0.036).

Conclusion

From our review, currently the optimal breast SR cut-off point or value remains unresolved despite the WFUMB guidelines of 2015. Machine model as a possible contributor to cut-off value determination was suggested from this review which can be subjected to more industry and multi-center research determination.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40644-022-00447-5.

Displacement Patterns in Magnetomotive Ultrasound Explored by Finite Element Analysis

Magnetomotive ultrasound is an emerging technique that enables detection of magnetic nanoparticles. This has implications for ultrasound molecular imaging, and potentially addresses clinical needs regarding determination of metastatic infiltration of the lymphatic system. Contrast is achieved by a time-varying magnetic field that sets nanoparticle-laden regions in motion. This motion is governed by vector-valued mechanical and magnetic forces. Understanding how these forces contribute to observed displacement patterns is important for the interpretation of magnetomotive ultrasound images. Previous studies have captured motion adjacent to nanoparticle-laden regions that was attributed to diamagnetism. While diamagnetism could give rise to a force, it cannot fully account for the observed displacements in magnetomotive ultrasound. To isolate explanatory variables of the observed displacements, a finite element model is set up. Using this model, we explore potential causes of the unexplained motion by comparing numerical models with earlier experimental findings. The simulations reveal motion outside particle-laden regions that could be attributed to mechanical coupling and the principle of mass conservation. These factors produced a motion that counterbalanced the time-varying magnetic excitation, and whose extent and distribution was affected by boundary conditions as well as compressibility and stiffness of the surroundings. Our findings emphasize the importance of accounting for the vector-valued magnetic force in magnetomotive ultrasound imaging. In an axisymmetric geometry, that force can be represented by a simple scalar expression, an oversimplification that rapidly becomes inaccurate with distance from the symmetry axis. Additionally, it results in an underestimation of the vertical force component by up to 30%. We therefore recommend using the full vector-valued force to capture the magnetic interaction. This study enhances our understanding of how forces govern magnetic nanoparticle displacement in tissue, contributing to accurate analysis and interpretation of magnetomotive ultrasound imaging.

Breast Tumour Classification Using Ultrasound Elastography with Machine Learning: A Systematic Scoping Review

Simple Summary

Breast cancer is one of the most common cancers among women globally. Early and accurate screening of breast tumours can improve survival. Ultrasound elastography is a non-invasive and non-ionizing imaging approach to characterize lesions for breast cancer screening, while machine learning techniques could improve the accuracy and reliability of computer-aided diagnosis. This review focuses on the state-of-the-art development and application of the machine learning model in breast tumour classification.

Abstract

Ultrasound elastography can quantify stiffness distribution of tissue lesions and complements conventional B-mode ultrasound for breast cancer screening. Recently, the development of computer-aided diagnosis has improved the reliability of the system, whilst the inception of machine learning, such as deep learning, has further extended its power by facilitating automated segmentation and tumour classification. The objective of this review was to summarize application of the machine learning model to ultrasound elastography systems for breast tumour classification. Review databases included PubMed, Web of Science, CINAHL, and EMBASE. Thirteen (n = 13) articles were eligible for review. Shear-wave elastography was investigated in six articles, whereas seven studies focused on strain elastography (5 freehand and 2 Acoustic Radiation Force). Traditional computer vision workflow was common in strain elastography with separated image segmentation, feature extraction, and classifier functions using different algorithm-based methods, neural networks or support vector machines (SVM). Shear-wave elastography often adopts the deep learning model, convolutional neural network (CNN), that integrates functional tasks. All of the reviewed articles achieved sensitivity ≥80%, while only half of them attained acceptable specificity ≥95%. Deep learning models did not necessarily perform better than traditional computer vision workflow. Nevertheless, there were inconsistencies and insufficiencies in reporting and calculation, such as the testing dataset, cross-validation, and methods to avoid overfitting. Most of the studies did not report loss or hyperparameters. Future studies may consider using the deep network with an attention layer to locate the targeted object automatically and online training to facilitate efficient re-training for sequential data.

Transcutaneous Ultrasound: Elastographic Lymph Node Evaluation. Current Clinical Applications and Literature Review

Distinguishing malignant versus benign lymphadenopathies is a major diagnostic dilemma in clinical medicine. Metastatic deposits in normal-sized lymph nodes (LNs) can be smaller than a millimeter, thus presenting a diagnostic challenge. In most clinical settings, however, enlarged LNs detected on imaging need to be classified as malignant or benign. Ultrasound seems to be a very reliable method for LN characterization because of the high resolution, especially in the subcutaneous areas. However, B-mode and Doppler-ultrasound criteria for characterization of a lymphadenopathy as benign or malignant are lacking specificity. Newer methods such as elastography seem to be valuable for identifying metastatic deposits within LNs and may help discriminate malignant and benign LNs. This review summarizes the different elastographic methods available and provides an overview of the relevant publications. According to the literature, elastography can be used for identifying metastatic deposits, to guide fine needle aspiration and to non-invasively choose the most suspicious LN of a group of enlarged LNs for targeted biopsy.

Leukocytes as carriers for targeted cancer drug delivery

INTRODUCTION

Metastasis contributes to over 90% of cancer-related deaths. Numerous nanoparticle platforms have been developed to target and treat cancer, yet efficient delivery of these systems to the appropriate site remains challenging. Leukocytes, which share similarities to tumor cells in terms of their transport and migration through the body, are well suited to serve as carriers of drug delivery systems to target cancer sites.

AREAS COVERED

This review focuses on the use and functionalization of leukocytes for therapeutic targeting of metastatic cancer. Tumor cell and leukocyte extravasation, margination in the bloodstream, and migration into soft tissue are discussed, along with the potential to exploit these functional similarities to effectively deliver drugs. Current nanoparticle-based drug formulations for the treatment of cancer are reviewed, along with methods to functionalize delivery vehicles to leukocytes, either on the surface and/or within the cell. Recent progress in this area, both in vitro and in vivo, is also discussed, with a particular emphasis on targeting cancer cells in the bloodstream as a means to interrupt the metastatic process.

EXPERT OPINION

Leukocytes interact with cancer cells both in the bloodstream and at the site of solid tumors. These interactions can be utilized to effectively deliver drugs to targeted areas, which can reduce both the amount of drug required and various nonspecific cytotoxic effects within the body. If drug delivery vehicle functionalization does not interfere with leukocyte function, this approach may be utilized to neutralize tumor cells in the bloodstream to prevent the formation of new metastases, and also to deliver drugs to metastatic sites within tissues.

The roles of cellular nanomechanics in cancer

The biomechanical properties of cells and tissues may be instrumental in increasing our understanding of cellular behavior and cellular manifestations of diseases such as cancer. Nanomechanical properties can offer clinical translation of therapies beyond what are currently employed. Nanomechanical properties, often measured by nanoindentation methods using atomic force microscopy, may identify morphological variations, cellular binding forces, and surface adhesion behaviors that efficiently differentiate normal cells and cancer cells. The aim of this review is to examine current research involving the general use of atomic force microscopy/nanoindentation in measuring cellular nanomechanics; various factors and instrumental conditions that influence the nanomechanical properties of cells; and implementation of nanoindentation methods to distinguish cancer cells from normal cells or tissues. Applying these fundamental nanomechanical properties to current discoveries in clinical treatment may result in greater efficiency in diagnosis, treatment, and prevention of cancer, which ultimately can change the lives of patients.

Sweeping lymph node micrometastases off their feet: an engineered model to evaluate natural killer cell mediated therapeutic intervention of circulating tumor cells that disseminate to the lymph nodes

Approximately 90% of cancer related deaths are due to metastasis. Cells from the primary tumor can metastasize through either the vascular or lymphatic circulation. Cancer cells in circulation are called circulating tumor cells (CTCs) and it has been shown that bone marrow is a niche for homing of blood borne CTCs from several epithelial tumors. Cancer cells found in bone marrow are termed disseminated tumor cells (DTCs). Likewise, CTCs in the lymphatic circulation are more often seeded in the sentinel lymph nodes (SLN) that drain the tumor. Micrometastases (<2 mm) occur after the arrest and implantation of DTCs in lymph nodes over time. This paper presents a cell culture platform termed microbubbles formed in polydimethylsiloxane (PDMS) from a microfabricated silicon wafer for mimicking lymph node micrometastases. We cultured lymph node seeking cancer cells in microbubbles to evaluate the efficacy of natural killer (NK) mediated therapy for targeting lymph node micrometastasis. The microbubble platform consists of an array of microcavities that provides a unique microenvironment for mimicking the deep cortical unit of the lymph nodes. We show that cancer cells cultured in microbubbles with therapeutic NK cells undergo apoptosis after 24 h in culture. Since lymph node metastases are prevalent across several types of cancer, this platform may be useful for developing improved cancer therapies for targeting lymph node micrometastases.

New ultrasound techniques for lymph node evaluation

Conventional ultrasound (US) is the recommended imaging method for lymph node (LN) diseases with the advantages of high resolution, real time evaluation and relative low costs. Current indications of transcutaneous ultrasound and endoscopic ultrasound include the detection and characterization of lymph nodes and the guidance for LN biopsy. Recent advances in US technology, such as contrast enhanced ultrasound (CEUS), contrast enhanced endoscopic ultrasound (CE-EUS), and real time elastography show potential to improve the accuracy of US for the differential diagnosis of benign and malignant lymph nodes. In addition, CEUS and CE-EUS have been also used for the guidance of fine needle aspiration and assessment of treatment response. Complementary to size criteria, CEUS could also be used to evaluate response of tumor angiogenesis to anti-angiogenic therapies. In this paper we review current literature regarding evaluation of lymphadenopathy by new and innovative US techniques.

Three-dimensional elastography for cervical lymph node volume measurements: a study to investigate feasibility, accuracy and reliability

This study investigated the feasibility of using three-dimensional (3-D) elastography in measuring cervical lymph node volume and compared the accuracy and reliability of 3-D elastography and 3-D grayscale ultrasound in measurement of ill-defined cervical nodes. Eighteen porcine lymph nodes from the neck were embedded in tissue-mimicking phantoms and scanned with the two ultrasound techniques. Ultrasound measurements were compared with the volume determined by water-displacement method to evaluate measurement accuracy. Inter-observer reproducibility and intra-observer repeatability of measurements were evaluated. Four patients with enlarged neck nodes were included to evaluate intra-observer repeatability of ultrasound measurements. Results demonstrated that lymph nodes that appeared ill-defined on grayscale ultrasound showed well-defined boundaries on elastography. 3-D elastography has higher measurement accuracy (84.2%), reproducibility (intraclass correlation coefficient, ICC = 0.909) and repeatability (ICC = 0.964-0.988) than does 3-D grayscale ultrasound (62.2%, 0.777 and 0.863-0.906 respectively). As a conclusion, 3-D elastography is accurate and reliable in volume measurement of ill-defined lymph nodes and has potential for accurate assessment of lymph node volume.