Simultaneous MEMS-based electro-mechanical phenotyping of breast cancer

State-of-the-Art of Breast Cancer Diagnosis in Medical Images via Convolutional Neural Networks (CNNs)

Early detection of breast cancer is crucial for a better prognosis. Various studies have been conducted where tumor lesions are detected and localized on images. This is a narrative review where the studies reviewed are related to five different image modalities: histopathological, mammogram, magnetic resonance imaging (MRI), ultrasound, and computed tomography (CT) images, making it different from other review studies where fewer image modalities are reviewed. The goal is to have the necessary information, such as pre-processing techniques and CNN-based diagnosis techniques for the five modalities, readily available in one place for future studies. Each modality has pros and cons, such as mammograms might give a high false positive rate for radiographically dense breasts, while ultrasounds with low soft tissue contrast result in early-stage false detection, and MRI provides a three-dimensional volumetric image, but it is expensive and cannot be used as a routine test. Various studies were manually reviewed using particular inclusion and exclusion criteria; as a result, 91 recent studies that classify and detect tumor lesions on breast cancer images from 2017 to 2022 related to the five image modalities were included. For histopathological images, the maximum accuracy achieved was around 99 % , and the maximum sensitivity achieved was 97.29 % by using DenseNet, ResNet34, and ResNet50 architecture. For mammogram images, the maximum accuracy achieved was 96.52 % using a customized CNN architecture. For MRI, the maximum accuracy achieved was 98.33 % using customized CNN architecture. For ultrasound, the maximum accuracy achieved was around 99 % by using DarkNet-53, ResNet-50, G-CNN, and VGG. For CT, the maximum sensitivity achieved was 96 % by using Xception architecture. Histopathological and ultrasound images achieved higher accuracy of around 99 % by using ResNet34, ResNet50, DarkNet-53, G-CNN, and VGG compared to other modalities for either of the following reasons: use of pre-trained architectures with pre-processing techniques, use of modified architectures with pre-processing techniques, use of two-stage CNN, and higher number of studies available for Artificial Intelligence (AI)/machine learning (ML) researchers to reference. One of the gaps we found is that only a single image modality is used for CNN-based diagnosis; in the future, a multiple image modality approach can be used to design a CNN architecture with higher accuracy.

RapidET: a MEMS-based platform for label-free and rapid demarcation of tumors from normal breast biopsy tissues

The rapid and label-free diagnosis of malignancies in ex vivo breast biopsy tissues has significant utility in pathology laboratories and operating rooms. We report a MEMS-based platform integrated with microchips that performs phenotyping of breast biopsy tissues using electrothermal sensing. The microchip, fabricated on a silicon substrate, incorporates a platinum microheater, interdigitated electrodes (IDEs), and resistance temperature detectors (RTDs) as on-chip sensing elements. The microchips are integrated onto the platform using a slide-fit contact enabling quick replacement for biological measurements. The bulk resistivity (ρ B ), surface resistivity (ρ S ), and thermal conductivity (k) of deparaffinized and formalin-fixed paired tumor and adjacent normal breast biopsy samples from N = 8 patients were measured. For formalin-fixed samples, the mean ρ B for tumors showed a statistically significant fold change of 4.42 (P = 0.014) when the tissue was heated from 25 °C to 37 °C compared to the adjacent normal tissue, which showed a fold change of 3.47. The mean ρ S measurements also showed a similar trend. The mean k of the formalin-fixed tumor tissues was 0.309 ± 0.02 W m-1 K-1 compared to a significantly higher k of 0.563 ± 0.028 W m-1 K-1 for the adjacent normal tissues. A similar trend was observed in ρ B, ρ S, and k for the deparaffinized tissue samples. An analysis of a combination of ρ B , ρ S , and k using Fisher's combined probability test and linear regression suggests the advantage of using all three parameters simultaneously for distinguishing tumors from adjacent normal tissues with higher statistical significance.

Viscoelastic Properties of Human Autopsy Brain Tissues as Biomarkers for Alzheimer's Diseases

OBJECTIVE

The present study investigates viscoelastic properties of human autopsy brain tissue via nanoindentation to find feasible biomarkers for Alzheimer's disease (AD) in ex vivo condition and to understand the mechanics of the human brain better, especially on the difference before and after progression of AD.

METHODS

Viscoelastic properties of paraformaldehyde-fixed, paraffin-embedded thin (8 [Formula: see text]) sectioned normal and AD affected human autopsy brain tissue samples are investigated via nanoindentation with a combined loading profile of a linear preloading and a sinusoidal loading at various loading frequencies from 0.01 to 10 [Formula: see text]. In 1200 indentation tests for ten human autopsy brain tissue samples from ten different subjects (five AD cases and five normal controls), viscoelastic properties such as Young's modulus, storage modulus, loss modulus, and loss factor of both gray and white matter brain tissues samples from normal and AD affected tissues were measured experimentally.

RESULTS

We found that the normal brain tissues have higher Young's modulus values than the AD affected brain tissues by 23.5 % and 27.9 % on average for gray and white matter, respectively, with statistically significant differences ( ) between the normal and AD affected brain tissues. Additionally, the AD affected brain tissues have much higher loss factor than the normal brain tissues on lower loading frequencies.

SIGNIFICANCE

AD is one of the leading causes of death in America and continues to affect a growing population. The challenges of recognizing the early pathological changes in brain tissue due to AD and diagnosing a patient has led to much research focused on finding biomarkers for the disease. In this regard, understanding the mechanics of brain tissues is increasingly recognized to play an important role in diagnosing brain diseases.

Lab-on-a-Chip Platforms for Biophysical Studies of Cancer with Single-Cell Resolution

Recent cancer research has more strongly emphasized the biophysical aspects of tumor development, progression, and microenvironment. In addition to genetic modifications and mutations in cancer cells, it is now well accepted that the physical properties of cancer cells such as stiffness, electrical impedance, and refractive index vary with tumor progression and can identify a malignant phenotype. Moreover, cancer heterogeneity renders population-based characterization techniques inadequate, as individual cellular features are lost in the average. Hence, platforms for fast and accurate characterization of biophysical properties of cancer cells at the single-cell level are required. Here, we highlight some of the recent advances in the field of cancer biophysics and the development of lab-on-a-chip platforms for single-cell biophysical analyses of cancer cells.

Electromechanical Coupling Factor of Breast Tissue as a Biomarker for Breast Cancer

GOAL

This research aims to validate a new biomarker of breast cancer by introducing electromechanical coupling factor of breast tissue samples as a possible additional indicator of breast cancer. Since collagen fibril exhibits a structural organization that gives rise to a piezoelectric effect, the difference in collagen density between normal and cancerous tissue can be captured by identifying the corresponding electromechanical coupling factor.

METHODS

The design of a portable diagnostic tool and a microelectromechanical systems (MEMS)-based biochip, which is integrated with a piezoresistive sensing layer for measuring the reaction force as well as a microheater for temperature control, is introduced. To verify that electromechanical coupling factor can be used as a biomarker for breast cancer, the piezoelectric model for breast tissue is described with preliminary experimental results on five sets of normal and invasive ductal carcinoma (IDC) samples in the 25-45 temperature range.

CONCLUSION

While the stiffness of breast tissues can be captured as a representative mechanical signature which allows one to discriminate among tissue types especially in the higher strain region, the electromechanical coupling factor shows more distinct differences between the normal and IDC groups over the entire strain region than the mechanical signature. From the two-sample -test, the electromechanical coupling factor under compression shows statistically significant differences ( 0.0039) between the two groups.

SIGNIFICANCE

The increase in collagen density in breast tissue is an objective and reproducible characteristic of breast cancer. Although characterization of mechanical tissue property has been shown to be useful for differentiating cancerous tissue from normal tissue, using a single parameter may not be sufficient for practical usage due to inherent variation among biological samples. The portable breast cancer diagnostic tool reported in this manuscript shows the feasibility of measuring multiple parameters of breast tissue allowing for practical application.