Quantitative diagnosis of breast tumors by characterization of viscoelastic behavior of healthy breast tissue

i-Rheo-optical assay: Measuring the viscoelastic properties of multicellular spheroids

This study introduces a novel mechanobiology assay, named "i-Rheo-optical assay", that integrates rheology with optical microscopy for analysing the viscoelastic properties of multicellular spheroids. These spheroids serve as three-dimensional models resembling tissue structures. The innovative technique enables real-time observation and quantification of morphological responses to applied stress using a cost-effective microscope coverslip for constant compression force application. By bridging a knowledge gap in biophysical research, which has predominantly focused on the elastic properties while only minimally exploring the viscoelastic nature in multicellular systems, the i-Rheo-optical assay emerges as an effective tool. It facilitates the measurement of broadband viscoelastic compressional moduli in spheroids, here derived from cancer (PANC-1) and non-tumoral (NIH/3T3) cell lines during compression tests. This approach plays a crucial role in elucidating the mechanical properties of spheroids and holds potential for identifying biomarkers to discriminate between healthy tissues and their pathological counterparts. Offering comprehensive insights into the biomechanical behaviour of biological systems, i-Rheo-optical assay marks a significant advancement in tissue engineering, cancer research, and therapeutic development.

Biomechanical Modelling of Porcine Kidney

In this study, the viscoelastic properties of porcine kidney in the upper, middle and lower poles were investigated using oscillatory shear tests. The viscoelastic properties were extracted in the form of the storage modulus and loss modulus in the frequency and time domain. Measurements were taken as a function of frequency from 0.1 Hz to 6.5 Hz at a shear strain amplitude of 0.01 and as function of strain amplitude from 0.001 to 0.1 at a frequency of 1 Hz. Measurements were also taken in the time domain in response to a step shear strain. Both the frequency and time domain data were fitted to a conventional Standard Linear Solid (SLS) model and a semi-fractional Kelvin-Voigt (SFKV) model with a comparable number of parameters. The SFKV model fitted the frequency and time domain data with a correlation coefficient of 0.99. Although the SLS model well fitted the time domain data and the storage modulus data in the frequency domain, it was not able to capture the variation in loss modulus with frequency with a correlation coefficient of 0.53. A five parameter Maxwell-Wiechert model was able to capture the frequency dependence in storage modulus and loss modulus better than the SLS model with a correlation of 0.85.

Label-free multimodal electro-thermo-mechanical (ETM) phenotyping as a novel biomarker to differentiate between normal, benign, and cancerous breast biopsy tissues

BACKGROUND

Technologies for quick and label-free diagnosis of malignancies from breast tissues have the potential to be a significant adjunct to routine diagnostics. The biophysical phenotypes of breast tissues, such as its electrical, thermal, and mechanical properties (ETM), have the potential to serve as novel markers to differentiate between normal, benign, and malignant tissue.

RESULTS

We report a system-of-biochips (SoB) integrated into a semi-automated mechatronic system that can characterize breast biopsy tissues using electro-thermo-mechanical sensing. The SoB, fabricated on silicon using microfabrication techniques, can measure the electrical impedance (Z), thermal conductivity (K), mechanical stiffness (k), and viscoelastic stress relaxation (%R) of the samples. The key sensing elements of the biochips include interdigitated electrodes, resistance temperature detectors, microheaters, and a micromachined diaphragm with piezoresistive bridges. Multi-modal ETM measurements performed on formalin-fixed tumour and adjacent normal breast biopsy samples from N = 14 subjects were able to differentiate between invasive ductal carcinoma (malignant), fibroadenoma (benign), and adjacent normal (healthy) tissues with a root mean square error of 0.2419 using a Gaussian process classifier. Carcinoma tissues were observed to have the highest mean impedance (110018.8 ± 20293.8 Ω) and stiffness (0.076 ± 0.009 kNm-1) and the lowest thermal conductivity (0.189 ± 0.019 Wm-1 K-1) amongst the three groups, while the fibroadenoma samples had the highest percentage relaxation in normalized load (47.8 ± 5.12%).

CONCLUSIONS

The work presents a novel strategy to characterize the multi-modal biophysical phenotype of breast biopsy tissues to aid in cancer diagnosis from small-sized tumour samples. The methodology envisions to supplement the existing technology gap in the analysis of breast tissue samples in the pathology laboratories to aid the diagnostic workflow.

Nanoindentation study of the viscoelastic properties of human triple negative breast cancer tissues: Implications for mechanical biomarkers

This paper presents the results of a combined experimental and theoretical study of the structure and viscoelastic properties of human non-tumorigenic mammary breast tissues and triple negative breast cancer (TNBC) tissues of different histological grades. A combination of immunofluorescence and confocal microscopy, and atomic force microscopy is used to study the actin cytoskeletal structures of non-tumorigenic and tumorigenic breast tissues (grade I to grade III). A combination of nanoindentation and statistical techniques is then used to measure viscoelastic properties of non-tumorigenic and human TNBC of different histological grades. A Standard Fluid Model/Anti-Zener Model II is also used to characterize the viscoelastic properties of the non-tumorigenic and tumorigenic TNBC tissues of different grades. The implications of the results are discussed for the potential application of nanoindentation and statistical deconvolution techniques to the development of mechanical biomarkers for TNBC detection/cancer diagnosis. STATEMENT OF SIGNIFICANCE: There is increasing interest in the development of mechanical biomarkers for cancer diagnosis. Here, we show that nanoindentation techniques can be used to characterize the viscoelastic properties of normal breast tissue and TNBC tissues of different histological grades. The Standard Fluid Model (Anti-Zener Model II) is used to classify the viscoelastic properties of breast tissues of different TNBC histological grades. Our results suggest that breast tissue and TNBC tissue viscoelastic properties can be used as mechanical biomarkers for the detection of TNBC at different stages.

Why Are Viscosity and Nonlinearity Bound to Make an Impact in Clinical Elastographic Diagnosis?

The adoption of multiscale approaches by the biomechanical community has caused a major improvement in quality in the mechanical characterization of soft tissues. The recent developments in elastography techniques are enabling in vivo and non-invasive quantification of tissues' mechanical properties. Elastic changes in a tissue are associated with a broad spectrum of pathologies, which stems from the tissue microstructure, histology and biochemistry. This knowledge is combined with research evidence to provide a powerful diagnostic range of highly prevalent pathologies, from birth and labor disorders (prematurity, induction failures, etc.), to solid tumors (e.g., prostate, cervix, breast, melanoma) and liver fibrosis, just to name a few. This review aims to elucidate the potential of viscous and nonlinear elastic parameters as conceivable diagnostic mechanical biomarkers. First, by providing an insight into the classic role of soft tissue microstructure in linear elasticity; secondly, by understanding how viscosity and nonlinearity could enhance the current diagnosis in elastography; and finally, by compounding preliminary investigations of those elastography parameters within different technologies. In conclusion, evidence of the diagnostic capability of elastic parameters beyond linear stiffness is gaining momentum as a result of the technological and imaging developments in the field of biomechanics.

The rise of artificial intelligence in healthcare applications

Big data and machine learning are having an impact on most aspects of modern life, from entertainment, commerce, and healthcare. Netflix knows which films and series people prefer to watch, Amazon knows which items people like to buy when and where, and Google knows which symptoms and conditions people are searching for. All this data can be used for very detailed personal profiling, which may be of great value for behavioral understanding and targeting but also has potential for predicting healthcare trends. There is great optimism that the application of artificial intelligence (AI) can provide substantial improvements in all areas of healthcare from diagnostics to treatment. It is generally believed that AI tools will facilitate and enhance human work and not replace the work of physicians and other healthcare staff as such. AI is ready to support healthcare personnel with a variety of tasks from administrative workflow to clinical documentation and patient outreach as well as specialized support such as in image analysis, medical device automation, and patient monitoring. In this chapter, some of the major applications of AI in healthcare will be discussed covering both the applications that are directly associated with healthcare and those in the healthcare value chain such as drug development and ambient assisted living.

Characterization of breast tissue permeability for detection of vascular breast tumors: An in vitro study

The biological tissue could be considered as a porous matrix filled with the interstitial fluid. The tumoral tissue has a different permeability from the healthy tissue. With regard to this knowledge, the main objective of the present study is to study the tissue permeability as a diagnostic parameter for the detection of breast cancer. To this end, the healthy and the cancerous specimens of the breast tissue are taken from 17 female cases. All cancerous tumors are in the vascular phase of the growth. The samples undergo a uniaxial compression test by a robotic system while the strain rate is set to remain unchanged. Using the stress and the strain rate data, the strain-dependent permeability is determined, which is an exponential function of the strain level. The permeability function is identified by the initial permeability at the zero compressive strain and a material constant. Results show that the initial permeability of the healthy breast tissue is significantly different from the corresponding value for the cancerous tissue. For all cancerous samples, the permeability is less than the healthy tissue samples; as 40-70% reduction in the initial permeability is observed compared to the healthy breast tissue. The evaluation of the permeability between the healthy and the cancerous specimens is accompanied by the biopsy reports and observing structural images of the specimens using environmental scanning electron microscope. Based on the results, the permeability is suggested to have a diagnostic application for the detection of vascular breast tumors.

Correlation between stress drop and applied strain as a biomarker for tumor detection

This is the first study to measure the viscoelastic behavior of tumor tissues using stepwise compression-relaxation testing, and investigate the measured (Δσ-ε) relation between stress drop (Δσ) and applied strain (ε) as a biomarker for tumor detection. Stepwise compression-relaxation testing was implemented via a 2D tactile sensor to measure stress drop at each applied strain of a sample. Pearson correlation analysis was conducted to quantify the measured Δσ-ε relation as slope of stress drop versus applied strain (m=Δσ/ε) and coefficient of determination (R²). The measured results on soft materials revealed no dependency of coefficient of determination on the testing parameters and dependency of slope on them. Three groups of tissues: five mouse breast tumor (BT) tissues ex vivo, two mouse pancreatic tumor (PT) tissues in vivo and six normal tissues, were measured by using different testing parameters. Coefficient of determination was found to show significant difference among the center, edge and outside sites of all the BT tissues, and no difference between the BT outside sites and the normal tissues. Coefficient of determination also revealed significant difference between before and after treatment of the PT tissues, and no difference between the PT tissues after treatment and the normal tissues. Moreover, coefficient of determination of the PT tissues before treatment was found to be significantly different from that of the BT center sites, but slope failed to capture their difference. Dummy tumors made of silicon rubbers were found to behave differently from the native tumors. By removing the need of fitting the time-dependent data with a viscoelastic model, this study offered a time-efficient solution to quantifying the viscosity for tumor detection.