Electromechanical Coupling Factor of Breast Tissue as a Biomarker for 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.

Fin field-effect-transistor engineered sensor for detection of MDA-MB-231 breast cancer cells: A switching-ratio-based sensitivity analysis

The present study describes the utilization of a gallium-arsenide gate-stack gate-all-around (GaAs-GS-GAA) fin field-effect transistor (FinFET) to accomplish the electrical identification of the breast cancer cell MDA-MB-231 by monitoring the device switching ratio. The proposed sensor uses four nanocavities carved beneath the gate electrodes for enhanced detection sensitivity. MDA-MB-231 (cancerous) and MCF-10A (healthy) breast cells have a distinct dielectric constant, and it changes when exposed to microwave frequencies spanning across 200 MHz and 13.6 GHz, which modifies the electrical characteristics, allowing for early diagnosis. First, a percentage shift in the primary DC characteristics is presented to demonstrate the advantage of GS-GAA FinFET over conventional FinFET. The sensor measures the switching-ratio-based sensitivity, which comes out to be 99.72% for MDA-MB-231 and 47.78% for MCF-10A. The sensor was tested for stability and reproducibility and found to be repeatable and sufficiently stable with settling times of 55.51, 60.80, and 71.58 ps for MDA-MB-231 cells, MCF-10A cells, and air, respectively. It can distinguish between viable and nonviable cells based on electrical response alterations. The possibility of early detection of cancerous breast cells using Bruggeman's model is also discussed. Further, the impact of biomolecule occupancy and frequency variations on the device sensitivity is carried out. This study also explains how to maximize the sensing performance by adjusting the fin height, fin width, work function, channel doping, temperature, and drain voltage. Lastly, this article compared the proposed breast cancer cell detectors to existing literature to evaluate their performance and found considerable improvement. The findings of this research have the potential to establish GaAs-GS-GAA FinFET as a promising contender for MDA-MB-231 breast cancer cell detection.

Deep Learning-Based Real-Time Discriminate Correlation Analysis for Breast Cancer Detection

Breast cancer is the most common cancer in women, and the breast mass recognition model can effectively assist doctors in clinical diagnosis. However, the scarcity of medical image samples makes the recognition model prone to overfitting. A breast mass recognition model integrated with deep pathological information mining is proposed: constructing a sample selection strategy, screening high-quality samples across different mammography image datasets, and dealing with the scarcity of medical image samples from the perspective of data enhancement; mining the pathology contained in limited labeled models from shallow to deep information; and dealing with the shortage of medical image samples from the perspective of feature optimization. The multiview effective region gene optimization (MvERGS) algorithm is designed to refine the original image features, improve the feature discriminate and compress the feature dimension, better match the number of samples, and perform discriminate correlation analysis (DCA) on the advanced new features; in-depth cross-modal correlation between heterogeneous elements, that is, the deep pathological information, can be mined to describe the breast mass lesion area accurately. Based on deep pathological information and traditional classifiers, an efficient breast mass recognition model is trained to complete the classification of mammography images. Experiments show that the key technical indicators of the recognition model, including accuracy and AUC, are better than the mainstream baselines, and the overfitting problem caused by the scarcity of samples is alleviated.

Intelligent and novel multi-type cancer prediction model using optimized ensemble learning

Cancer is known to be highly severe disease and gets incurable even when the treatment has started at the time of diagnosis owing to the occurrence of cancer cells. Diverse machine learning approaches are implemented for predicting the cancer recurrence that needs to be evaluated for showing the appropriate approach for cancer prediction. This paper provides intelligent optimized ensemble learning for predicting multiple types of cancers. At first, the different types of cancer data are collected and performed the data cleansing. Then, the feature extraction is done using statistical features, 'Linear Discriminant Analysis (LDA), and Principal Component Analysis (PCA)'. With these features, a new Adaptive Condition Searched-Harris hawks Whale Optimization (ACS-HWO) is used for selecting the optimal features and transformed into weighted features with meta-heuristic update. The prediction is carried out by Optimized Ensemble-based Multi-disease Detection (OEMD) with Support Vector Machine (SVM), Autoencoder, Adaboost, 'Deep Neural Network (DNN), and Recurrent Neural Network (RNN)' with high ranking strategy. The same ACS-HWO is used for improvising the weighted feature selection and optimized ensemble learning. The comparative analysis over existing models shows that the suggested method can be highly applicable for the healthcare system to ensure the consistent prediction with the multi-type of cancers.

Fuzzy Neural Network Expert System with an Improved Gini Index Random Forest-Based Feature Importance Measure Algorithm for Early Diagnosis of Breast Cancer in Saudi Arabia

Breast cancer is one of the common malignancies among females in Saudi Arabia and has also been ranked as the one most prevalent and the number two killer disease in the country. However, the clinical diagnosis process of any disease such as breast cancer, coronary artery diseases, diabetes, COVID-19, among others, is often associated with uncertainty due to the complexity and fuzziness of the process. In this work, a fuzzy neural network expert system with an improved gini index random forest-based feature importance measure algorithm for early diagnosis of breast cancer in Saudi Arabia was proposed to address the uncertainty and ambiguity associated with the diagnosis of breast cancer and also the heavier burden on the overlay of the network nodes of the fuzzy neural network system that often happens due to insignificant features that are used to predict or diagnose the disease. An Improved Gini Index Random Forest-Based Feature Importance Measure Algorithm was used to select the five fittest features of the diagnostic wisconsin breast cancer database out of the 32 features of the dataset. The logistic regression, support vector machine, k-nearest neighbor, random forest, and gaussian naïve bayes learning algorithms were used to develop two sets of classification models. Hence, the classification models with full features (32) and models with the 5 fittest features. The two sets of classification models were evaluated, and the results of the evaluation were compared. The result of the comparison shows that the models with the selected fittest features outperformed their counterparts with full features in terms of accuracy, sensitivity, and sensitivity. Therefore, a fuzzy neural network based expert system was developed with the five selected fittest features and the system achieved 99.33% accuracy, 99.41% sensitivity, and 99.24% specificity. Moreover, based on the comparison of the system developed in this work against the previous works that used fuzzy neural network or other applied artificial intelligence techniques on the same dataset for diagnosis of breast cancer using the same dataset, the system stands to be the best in terms of accuracy, sensitivity, and specificity, respectively. The z test was also conducted, and the test result shows that there is significant accuracy achieved by the system for early diagnosis of breast cancer.

Analysis of the Mechanism of Breast Metastasis Based on Image Recognition and Ultrasound Diagnosis

Breast cancer is one of the cancers with the highest incidence among women. In the late stage, cancer cells may metastasize to a distance, causing multiple organ diseases, threatening the lives of patients. The detection of lymph node metastasis based on pathological images is a key indicator for the diagnosis and staging of breast cancer, and correct staging decisions are the prerequisite and basis for targeted treatment. At present, the detection of lymph node metastasis mainly relies on manual screening by pathologists, which is time-consuming and labor-intensive, and the diagnosis results are variable and subjective. The automatic staging method based on the panoramic image calculation of the sentinel lymph node of the breast proposed in this paper can provide a set of standardized, high-accuracy, and repeatable objective diagnosis results. However, it is very difficult to automatically detect and locate cancer metastasis areas in highly complex panoramic images of lymph nodes. This paper proposes a novel deep network training strategy based on the sliding window to train an automatic localization model of cancer metastasis area. The training strategy first trains the initial convolutional network in a small amount of data, extracts false-positive and false-negative image blocks, and uses manual screening combined with automatic network screening to reclassify the false-positive blocks to improve the class of negative categories. Using mammography, ultrasound, MRI, and 18F-FDG PET-CT examinations, the detection rate and diagnostic accuracy of primary cancers in the breast of patients with axillary lymph node metastasis as the first diagnosis were obtained. The detection rate and diagnostic accuracy of breast MRI for primary cancers in the breast are much higher than those of X-ray, ultrasound, and 18F-FDG PET-CT (all P values <0.001). Mammography, ultrasound, and PET-CT examinations showed no difference in the detection rate and diagnostic accuracy of primary cancers in the breast of patients with axillary lymph node metastasis as the first diagnosis. Breast MRI should be used as a routine examination for patients with axillary lymph node metastasis as the first diagnosis. The primary breast cancer in the first diagnosed patients with axillary lymph node metastasis is often presented as localized asymmetric compactness or calcification on X-ray; it often appears as small focal mass lesions and ductal lesions without three-dimensional space-occupying effect on ultrasound.

Cellphone enabled point-of-care assessment of breast tumor cytology and molecular HER2 expression from fine-needle aspirates

Management of breast cancer in limited-resource settings is hindered by a lack of low-cost, logistically sustainable approaches toward molecular and cellular diagnostic pathology services that are needed to guide therapy. To address these limitations, we have developed a multimodal cellphone-based platform—the EpiView-D4—that can evaluate both cellular morphology and molecular expression of clinically relevant biomarkers directly from fine-needle aspiration (FNA) of breast tissue specimens within 1 h. The EpiView-D4 is comprised of two components: (1) an immunodiagnostic chip built upon a “non-fouling” polymer brush-coating (the “D4”) which quantifies expression of protein biomarkers directly from crude cell lysates, and (2) a custom cellphone-based optical microscope (“EpiView”) designed for imaging cytology preparations and D4 assay readout. As a proof-of-concept, we used the EpiView-D4 for assessment of human epidermal growth factor receptor-2 (HER2) expression and validated the performance using cancer cell lines, animal models, and human tissue specimens. We found that FNA cytology specimens (prepared in less than 5 min with rapid staining kits) imaged by the EpiView-D4 were adequate for assessment of lesional cellularity and tumor content. We also found our device could reliably distinguish between HER2 expression levels across multiple different cell lines and animal xenografts. In a pilot study with human tissue (n = 19), we were able to accurately categorize HER2-negative and HER2-positve tumors from FNA specimens. Taken together, the EpiView-D4 offers a promising alternative to invasive—and often unavailable—pathology services and may enable the democratization of effective breast cancer management in limited-resource settings.

Optimizing Survival Analysis of XGBoost for Ties to Predict Disease Progression of Breast Cancer

OBJECTIVE

Some excellent prognostic models based on survival analysis methods for breast cancer have been proposed and extensively validated, which provide an essential means for clinical diagnosis and treatment to improve patient survival. To analyze clinical and follow-up data of 12119 breast cancer patients, derived from the Clinical Research Center for Breast (CRCB) in West China Hospital of Sichuan University, we developed a gradient boosting algorithm, called EXSA, by optimizing survival analysis of XGBoost framework for ties to predict the disease progression of breast cancer.

METHODS

EXSA is based on the XGBoost framework in machine learning and the Cox proportional hazards model in survival analysis. By taking Efron approximation of partial likelihood function as a learning objective for ties, EXSA derives gradient formulas of a more precise approximation. It optimizes and enhances the ability of XGBoost for survival data with ties. After retaining 4575 patients (3202 cases for training, 1373 cases for test), we exploit the developed EXSA method to build an excellent prognostic model to estimate disease progress. Risk score of disease progress is evaluated by the model, and the risk grouping and continuous functions between risk scores and disease progress rate at 5- and 10-year are also demonstrated.

RESULTS

Experimental results on test set show that the EXSA method achieves competitive performance with concordance index of 0.83454, 5-year and 10-year AUC of 0.83851 and 0.78155, respectively.

CONCLUSION

The proposed EXSA method can be utilized as an effective method for survival analysis.

SIGNIFICANCE

The proposed method in this paper can provide an important means for follow-up data of breast cancer or other disease research.

A review on computer modeling of bone piezoelectricity and its application to bone adaptation and regeneration

Bone is a hierarchical, multiphasic and anisotropic structure which in addition possess piezoelectric properties. The generation of piezoelectricity in bone is a complex process which has been shown to play a key role both in bone adaptation and regeneration. In order to understand the complex biological, mechanical and electrical interactions that take place during these processes, several computer models have been developed and used to test hypothesis on potential mechanisms behind experimental observations. This paper aims to review the available literature on computer modeling of bone piezoelectricity and its application to bone adaptation and healing. We first provide a brief overview of the fundamentals of piezoelectricity and bone piezoelectric effects. We then review how these properties have been used in computational models of bone adaptation and electromechanical behaviour of bone. In addition, in the last section, we summarize current limitations and potential directions for future work.

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.

Using Brain Activation to Evaluate Arrangements Aiding Hand-Eye Coordination in Surgical Robot Systems

GOAL

To realize intuitive, minimally invasive surgery, surgical robots are often controlled using master-slave systems. However, the surgical robot's structure often differs from that of the human body, so the arrangement between the monitor and master must reflect this physical difference. In this study, we validate the feasibility of an embodiment evaluation method that determines the arrangement between the monitor and master. In our constructed cognitive model, the brain's intraparietal sulcus activates significantly when somatic and visual feedback match. Using this model, we validate a cognitively appropriate arrangement between the monitor and master.

METHODS

In experiments, we measure participants' brain activation using an imaging device as they control the virtual surgical simulator. Two experiments are carried out that vary the monitor and hand positions.

CONCLUSION

There are two common arrangements of the monitor and master at the brain activation's peak: One is placing the monitor behind the master, so the user feels that the system is an extension of his arms into the monitor; the other arranges the monitor in front of the master, so the user feels the correspondence between his own arm and the virtual arm in the monitor.

SIGNIFICANCE

From these results, we conclude that the arrangement between the monitor and master impacts embodiment, enabling the participant to feel apparent posture matches in master-slave surgical robot systems.