Classification of breast tissue by electrical impedance spectroscopy

Making Sense of Electrical Stimulation: A Meta-analysis for Wound Healing

Electrical stimulation as a mode of external enhancement factor in wound healing has been explored widely. It has proven to have multidimensional effects in wound healing including antibacterial, galvanotaxis, growth factor secretion, proliferation, transdifferentiation, angiogenesis, etc. Despite such vast exploration, this modality has not yet been established as an accepted method for treatment. This article reviews and analyzes the approaches of using electrical stimulation to modulate wound healing and discusses the incoherence in approaches towards reporting the effect of stimulation on the healing process. The analysis starts by discussing various processes adapted in in vitro, in vivo, and clinical practices. Later it is focused on in vitro approaches directed to various stages of wound healing. Based on the analysis, a protocol is put forward for reporting in vitro works in such a way that the outcomes of the experiment are replicable and scalable in other setups. This work proposes a ground of unification for all the in vitro approaches in a more sensible manner, which can be further explored for translating in vitro approaches to complex tissue stimulation to establish electrical stimulation as a controlled clinical method for modulating wound healing.

Non-redundant implicational base of formal context with constraints using SAT

An implicational base is knowledge extracted from a formal context. The implicational base of a formal context consists of attribute implications which are sound, complete, and non-redundant regarding to the formal context. Non-redundant means that each attribute implication in the implication base cannot be inferred from the others. However, sometimes some attribute implications in the implication base can be inferred from the others together with a prior knowledge. Regarding knowledge discovery, such attribute implications should be not considered as new knowledge and ignored from the implicational base. In other words, such attribute implications are redundant based on prior knowledge. One sort of prior knowledge is a set of constraints that restricts some attributes in data. In formal context, constraints restrict some attributes of objects in the formal context. This article proposes a method to generate non-redundant implication base of a formal context with some constraints which restricting the formal context. In this case, non-redundant implicational base means that the implicational base does not contain all attribute implications which can be inferred from the others together with information of the constraints. This article also proposes a formulation to check the redundant attribute implications and encoding the problem into satisfiability (SAT) problem such that the problem can be solved by SAT Solver, a software which can solve a SAT problem. After implementation, an experiment shows that the proposed method is able to check the redundant attribute implication and generates a non-redundant implicational base of formal context with constraints.

Early breast cancer detection and differentiation tool based on tissue impedance characteristics and machine learning

During Basic screening, it is challenging, if not impossible to detect breast cancer especially in the earliest stage of tumor development. However, measuring the electrical impedance of biological tissue can detect abnormalities even before being palpable. Thus, we used impedance characteristics data of various breast tissue to develop a breast cancer screening tool guided and augmented by a deep learning (DL). A DL algorithm was trained to ideally classify six classes of breast cancer based on electrical impedance characteristics data of the breast tissue. The tool correctly predicted breast cancer in data of patients whose breast tissue impedance was reported to have been measured when other methods detected no anomaly in the tissue. Furthermore, a DL-based approach using Long Short-Term Memory (LSTM) effectively classified breast tissue with an accuracy of 96.67%. Thus, the DL algorithm and method we developed accurately augmented breast tissue classification using electrical impedance and enhanced the ability to detect and differentiate cancerous tissue in very early stages. However, more data and pre-clinical is required to improve the accuracy of this early breast cancer detection and differentiation tool.

On the Applicability of Quantum Machine Learning

In this article, we investigate the applicability of quantum machine learning for classification tasks using two quantum classifiers from the Qiskit Python environment: the variational quantum circuit and the quantum kernel estimator (QKE). We provide a first evaluation on the performance of these classifiers when using a hyperparameter search on six widely known and publicly available benchmark datasets and analyze how their performance varies with the number of samples on two artificially generated test classification datasets. As quantum machine learning is based on unitary transformations, this paper explores data structures and application fields that could be particularly suitable for quantum advantages. Hereby, this paper introduces a novel dataset based on concepts from quantum mechanics using the exponential map of a Lie algebra. This dataset will be made publicly available and contributes a novel contribution to the empirical evaluation of quantum supremacy. We further compared the performance of VQC and QKE on six widely applicable datasets to contextualize our results. Our results demonstrate that the VQC and QKE perform better than basic machine learning algorithms, such as advanced linear regression models (Ridge and Lasso). They do not match the accuracy and runtime performance of sophisticated modern boosting classifiers such as XGBoost, LightGBM, or CatBoost. Therefore, we conclude that while quantum machine learning algorithms have the potential to surpass classical machine learning methods in the future, especially when physical quantum infrastructure becomes widely available, they currently lag behind classical approaches. Our investigations also show that classical machine learning approaches have superior performance classifying datasets based on group structures, compared to quantum approaches that particularly use unitary processes. Furthermore, our findings highlight the significant impact of different quantum simulators, feature maps, and quantum circuits on the performance of the employed quantum estimators. This observation emphasizes the need for researchers to provide detailed explanations of their hyperparameter choices for quantum machine learning algorithms, as this aspect is currently overlooked in many studies within the field. To facilitate further research in this area and ensure the transparency of our study, we have made the complete code available in a linked GitHub repository.

Gelatin-Based Ingestible Impedance Sensor to Evaluate Gastrointestinal Epithelial Barriers

Low-profile and transient ingestible electronic capsules for diagnostics and therapeutics can replace widely used yet invasive procedures such as endoscopies. Several gastrointestinal diseases such as reflux disease, Crohn's disease, irritable bowel syndrome, and eosinophilic esophagitis result in increased intercellular dilation in epithelial barriers. Currently, the primary method of diagnosing and monitoring epithelial barrier integrity is via endoscopic tissue biopsies followed by histological imaging. Here, a gelatin-based ingestible electronic capsule that can monitor epithelial barriers via electrochemical impedance measurements is proposed. Toward this end, material-specific transfer printing methodologies to manufacture soft-gelatin-based electronics, an in vitro synthetic disease model to validate impedance-based sensing, and tests of capsules using ex vivo using porcine esophageal tissue are described. The technologies described herein can advance next generation of oral diagnostic devices that reduce invasiveness and improve convenience for patients.

The complex impedance phase angle of dominant arm: a useful parameter in bone mineral density (BMD) assessment of group 1 post-menopausal women

For widespread osteoporosis (OP) screening, portable and cost-effective screening devices are needed. With 45 menopaused women, when body mass index was below 30 kg.m^- 2, the phase angle (PhA) of complex bioimpedance at 5 kHz, measured from the dominant arm, was correlated with dual-energy X-ray absorptiometry (DEXA) measured central bone mineral densities (BMD), for total hip r = 0.493 and for total lumbar r = 0.411, P< 0.05, and the strength of correlation decreased with increasing measurement frequencies. ANOVA tests showed that, below 15 kHz, PhA was a descriptive marker for bone mineral deficiency. By correlating to hip and lumbar bone mineral densities simultaneously, the dominant arm segment PhA was superior to the characteristic frequency f_c of the body impedance spectroscopy (BIS) and the PhA of whole-body impedance measurements, of the earlier research works. Due to its medium correlation strength with central BMDs, a PhA measuring device could be considered as a complementary tool to the gold standard (DEXA), for OP screening of group 1 post-menopausal women only. For group 2, with 44 subjects with body mass indices over 30 kg.m^- 2, i.e., obese population, the PhA was not correlated with BMD and was no longer a descriptive marker for reduced BMD.

Design of electrical impedance spectroscopy sensing surgical drill using computational modelling and experimental validation

Electrical Impedance Spectroscopy (EIS) sensing surgical instruments could provide valuable and real-time feedback to surgeons about hidden tissue boundaries, therefore reducing the risk of iatrogenic injuries. In this paper, we present an EIS sensing surgical drill as an example instrument and introduce a strategy to optimize the mono-polar electrode geometry using a finite element method (FEM)-based computational model and experimental validation. An empirical contact impedance model and an adaptive mesh refinement protocol were developed to accurately preserve the behaviour of sensing electrodes as they approach high impedance boundaries. Specifically, experiments with drill-bit, cylinder, and conical geometries suggested a 15%-35% increase in resistance as the sensing electrode approached a high impedance boundary. Simulations achieved a maximum mean experiment-to-simulation mismatch of +1.7% for the drill-bit and +/-11% range for other electrode geometries. The simulations preserved the increase in resistance behaviour near the high impedance boundary. This highly accurate simulation framework allows us a mechanism for optimizing sensor geometry without costly experimental evaluation.

Data Anonymization for Pervasive Health Care: Systematic Literature Mapping Study

BACKGROUND

Data science offers an unparalleled opportunity to identify new insights into many aspects of human life with recent advances in health care. Using data science in digital health raises significant challenges regarding data privacy, transparency, and trustworthiness. Recent regulations enforce the need for a clear legal basis for collecting, processing, and sharing data, for example, the European Union's General Data Protection Regulation (2016) and the United Kingdom's Data Protection Act (2018). For health care providers, legal use of the electronic health record (EHR) is permitted only in clinical care cases. Any other use of the data requires thoughtful considerations of the legal context and direct patient consent. Identifiable personal and sensitive information must be sufficiently anonymized. Raw data are commonly anonymized to be used for research purposes, with risk assessment for reidentification and utility. Although health care organizations have internal policies defined for information governance, there is a significant lack of practical tools and intuitive guidance about the use of data for research and modeling. Off-the-shelf data anonymization tools are developed frequently, but privacy-related functionalities are often incomparable with regard to use in different problem domains. In addition, tools to support measuring the risk of the anonymized data with regard to reidentification against the usefulness of the data exist, but there are question marks over their efficacy.

OBJECTIVE

In this systematic literature mapping study, we aim to alleviate the aforementioned issues by reviewing the landscape of data anonymization for digital health care.

METHODS

We used Google Scholar, Web of Science, Elsevier Scopus, and PubMed to retrieve academic studies published in English up to June 2020. Noteworthy gray literature was also used to initialize the search. We focused on review questions covering 5 bottom-up aspects: basic anonymization operations, privacy models, reidentification risk and usability metrics, off-the-shelf anonymization tools, and the lawful basis for EHR data anonymization.

RESULTS

We identified 239 eligible studies, of which 60 were chosen for general background information; 16 were selected for 7 basic anonymization operations; 104 covered 72 conventional and machine learning-based privacy models; four and 19 papers included seven and 15 metrics, respectively, for measuring the reidentification risk and degree of usability; and 36 explored 20 data anonymization software tools. In addition, we also evaluated the practical feasibility of performing anonymization on EHR data with reference to their usability in medical decision-making. Furthermore, we summarized the lawful basis for delivering guidance on practical EHR data anonymization.

CONCLUSIONS

This systematic literature mapping study indicates that anonymization of EHR data is theoretically achievable; yet, it requires more research efforts in practical implementations to balance privacy preservation and usability to ensure more reliable health care applications.

Machine Learning Enhances the Performance of Bioreceptor-Free Biosensors

Since their inception, biosensors have frequently employed simple regression models to calculate analyte composition based on the biosensor's signal magnitude. Traditionally, bioreceptors provide excellent sensitivity and specificity to the biosensor. Increasingly, however, bioreceptor-free biosensors have been developed for a wide range of applications. Without a bioreceptor, maintaining strong specificity and a low limit of detection have become the major challenge. Machine learning (ML) has been introduced to improve the performance of these biosensors, effectively replacing the bioreceptor with modeling to gain specificity. Here, we present how ML has been used to enhance the performance of these bioreceptor-free biosensors. Particularly, we discuss how ML has been used for imaging, Enose and Etongue, and surface-enhanced Raman spectroscopy (SERS) biosensors. Notably, principal component analysis (PCA) combined with support vector machine (SVM) and various artificial neural network (ANN) algorithms have shown outstanding performance in a variety of tasks. We anticipate that ML will continue to improve the performance of bioreceptor-free biosensors, especially with the prospects of sharing trained models and cloud computing for mobile computation. To facilitate this, the biosensing community would benefit from increased contributions to open-access data repositories for biosensor data.

Electric impedance spectroscopy feature extraction for tissue classification with electrode embedded surgical needles through a modified forward stepwise method

There has been a growing interest in developing electric impedance sensing surgical tools for tissue identification during surgery. A key facet of this development is identifying distinct features that can be used to identify tissues from one another. This paper explores several feature extraction techniques and classification methods applied to electric impedance data. Furthermore, a modified forward stepwise method is proposed. The method introduces a scoring metric to help select features to add to the model, that is based off of the coefficient of variation and overlapping index from the feature's probability density functions for each of the classes. The proposed and existing methods were applied to spectral data measured at 23 frequencies, from 132 samples across 6 different tissues including ex-vivo bovine kidney, liver and muscle, poultry liver, as well as freshly excised canine testicle and ovary samples. These methods were able to successfully find impedance spectra features for the investigated biological tissues. The best predictive accuracy was with Boruta feature extraction and a Random Forest classifier but without significantly reducing the number of features in the classifier model. The proposed method was able to reduce the number of features in the model to an average of 5.8 features for all tested classifiers. These methods may have use in finding features to discriminate other tissue types, possibly to aid in targeting lesions in minimally invasive cancer treatment surgeries.

Classification of Metastatic and Non-Metastatic Thoracic Lymph Nodes in Lung Cancer Patients Based on Dielectric Properties Using Adaptive Probabilistic Neural Networks

Objective

Dielectric properties can be used in normal and malignant tissue identification, which requires an effective classifier because of the high throughput nature of the data. With easy training and fast convergence, probabilistic neural networks (PNNs) are widely applied in pattern classification problems. This study aims to propose a classifier to identify metastatic and non-metastatic thoracic lymph nodes in lung cancer patients based on dielectric properties.

Methods

The dielectric properties (permittivity and conductivity) of lymph nodes were measured using an open-ended coaxial probe. The Synthetic Minority Oversampling Technique method was adopted to modify the dataset. Feature parameters were scored to select the appropriate feature vector using a Statistical Dependency algorithm. The dataset was classified using adaptive PNNs with an optimized smooth factor using the simulated annealing PNN (SA-PNN). The results were compared with traditional Probabilistic, Support Vector Machines, k-Nearest Neighbor and the Classify functions in MATLAB.

Results

The conductivity frequencies of 3959, 3958, 3960, 3978, 3510, 3889, 3888, and 3976 MHz were selected as the feature vectors for 219 lymph nodes (178 non-metastatic and 41 metastatic). Compared with the other methods, SA-PNN achieved the highest classification accuracy (92.92%) and the corresponding specificity and sensitivity were 94.72% and 91.11%, respectively.

Conclusions

Compared with the other methods, the SA-PNN proposed in the present study achieved a higher classification accuracy, which provides a new scheme for classification of metastatic and non-metastatic thoracic lymph nodes in lung cancer patients based on dielectric properties.

Electrical Resistive Spiking of Fungi

We study long-term electrical resistance dynamics in mycelium and fruit bodies of oyster fungi P. ostreatus. A nearly homogeneous sheet of mycelium on the surface of a growth substrate exhibits trains of resistance spikes. The average width of spikes is c. 23[Formula: see text]min and the average amplitude is c. 1[Formula: see text]k[Formula: see text]. The distance between neighboring spikes in a train of spikes is c. 30[Formula: see text]min. Typically, there are 4–6 spikes in a train of spikes. Two types of electrical resistance spikes trains are found in fruit bodies: low frequency and high amplitude (28[Formula: see text]min spike width, 1.6[Formula: see text]k[Formula: see text] amplitude, 57[Formula: see text]min distance between spikes) and high frequency and low amplitude (10[Formula: see text]min width, 0.6[Formula: see text]k[Formula: see text] amplitude, 44[Formula: see text]min distance between spikes). The findings could be applied in monitoring of physiological states of fungi and future development of living electronic devices and sensors.

[Discrimination of lung cancer and adjacent normal tissues based on permittivity by optimized probabilistic neural network]

OBJECTIVE

To propose a probabilistic neural network classification method optimized by simulated annealing algorithm (SA-PNN) to discriminate lung cancer and adjacent normal tissues based on permittivity.

METHODS

The permittivity of lung tumors and the adjacent normal tissues was measured by an open-ended coaxial probe, and the statistical dependency (SD) algorithm was used for frequency screening.The permittivity associated with the selected frequency points was taken as the characteristic variable, and SA-PNN was used to discriminate lung cancer and the adjacent normal tissues.

RESULTS

Three frequency points, namely 984 MHz, 2724 MHz and 2723 MHz, were selected by SD algorithm.SA-PNN was used to discriminate 200 samples with the permittivity at the 3 frequency points as the characteristic variable.After 10-fold cross-validation, the final discrimination accuracy was 92.50%, the sensitivity was 90.65%, and the specificity was 94.62%.

CONCLUSIONS

Compared with the traditional probabilistic neural network, BP neural network, RBF neural network and the classification discriminant analysis function (Classify) in MATLAB, the proposed SA-PNN has higher accuracy, sensitivity and specificity for discriminating lung cancer and the adjacent normal tissues based on permittivity.

Mapping oscillating magnetic fields around rechargeable batteries

Power storage devices such as batteries are a crucial part of modern technology. The development and use of batteries has accelerated in the past decades, yet there are only a few techniques that allow gathering vital information from battery cells in a nonivasive fashion. A widely used technique to investigate batteries is electrical impedance spectroscopy (EIS), which provides information on how the impedance of a cell changes as a function of the frequency of applied alternating currents. Building on recent developments of inside-out MRI (ioMRI), a technique is presented here which produces spatially-resolved maps of the oscillating magnetic fields originating from the alternating electrical currents distributed within a cell. The technique works by using an MRI pulse sequence synchronized with a gated alternating current applied to the cell terminals. The approach is benchmarked with a current-carrying wire coil, and demonstrated with commercial and prototype lithium-ion cells. Marked changes in the fields are observed for different cell types.

Monofrequency electrical impedance mammography (EIM) diagnostic system in breast cancer screening

BACKGROUND

Some evidence has shown that malignant breast tumours have lower electrical impedance than surrounding normal tissues. Electrical impedance could be used as an indicator for breast cancer detection. The purpose of our study was to analyse the sensitivity and specificity of electrical impedance mammography (EIM) and its implementation for the differential diagnosis of pathological lesions of the breast, either alone or in combination with mammography/ultrasound, in 1200 women between 25 and 70 years old.

METHODS

This study is a prospective, cross-sectional epidemiological observational study of serial screening. The women were invited to participate and signed a consent letter. Impedance imaging of the mammary gland was evaluated with the computerized mammography equipment of MEIK electroimpedance v.5.6. (0.5 mA, 50 kHz), developed and manufactured by PKF SIM-Technika®. The successful identification of breast cancer along with the sensitivity, specificity, and positive and negative predictive values of EIM were determined as follows: % sensitivity; % specificity; % positive predictive value (PPV); and % negative predictive value (NPV).

RESULTS

EIM had a sensitivity of 85% and a specificity of 96%; the positive predictive value was 12%, and the negative predictive value was 99%. Seven cases were biopsy confirmed cancers. Significant correlations between the electrical conductivity index and body mass index (BMI) (p = 0.04) and patient age were observed (p = 0.01). We also observed that the average conductivity distribution increased according to age group (p = 0.001). We used the chi-squared test to assess the interactions between percent density and BMI (normal < 25 kg/m2 (n = 310), overweight 25-29.9 kg/m2 (n = 418) and obese ≥30 (n = 437)) (p <  0.05). The patients with a diagnosis of mammary carcinoma had a BMI of 35.51 kg/m2.

CONCLUSIONS

Our results demonstrate that the use of monofrequency electrical impedance mammography (EIM) in the detection of breast cancer had a sensitivity and specificity of 85 and 96%, respectively. These findings may support future research in the early detection of breast cancer. EIM is a non-radiation method that may also be used as a screening method for young women with dense breasts and a high risk of developing breast cancer.

The clinical application of electrical impedance technology in the detection of malignant neoplasms: a systematic review

BACKGROUND

Electrical impedance technology has been well established for the last 20 years. Recently research has begun to emerge into its potential uses in the detection and diagnosis of pre-malignant and malignant conditions. The aim of this study was to systematically review the clinical application of electrical impedance technology in the detection of malignant neoplasms.

METHODS

A search of Embase Classic, Embase and Medline databases was conducted from 1980 to 22/02/2018 to identify studies reporting on the use of bioimpedance technology in the detection of pre-malignant and malignant conditions. The ability to distinguish between tissue types was defined as the primary endpoint, and other points of interest were also reported.

RESULTS

731 articles were identified, of which 51 reported sufficient data for analysis. These studies covered 16 different cancer subtypes in a total of 7035 patients. As the studies took various formats, a qualitative analysis of each cancer subtype's data was undertaken. All the studies were able to show differences in electrical impedance and/or related metrics between malignant and normal tissue.

CONCLUSIONS

Electrical impedance technology provides a novel method for the detection of malignant tissue, with large studies of cervical, prostate, skin and breast cancers showing encouraging results. Whilst these studies provide promising insights into the potential of this technology as an adjunct in screening, diagnosis and intra-operative margin assessment, customised development as well as multi-centre clinical trials need to be conducted before it can be reliably employed in the clinical detection of malignant tissue.

Microscale Biosensor Array Based on Flexible Polymeric Platform toward Lab-on-a-Needle: Real-Time Multiparameter Biomedical Assays on Curved Needle Surfaces

In vivo sensing of various physical/chemical parameters is gaining increased attention for early prediction and management of various diseases. However, there are major limitations on the fabrication method of multiparameter needle-based in vivo sensing devices, particularly concerning the uniformity between sensors. To address these challenges, we developed a microscale biosensor array for the measurement of electrical conductivity, pH, glucose, and lactate concentrations on a flexible polymeric polyimide platform with electrodeposited electrochemically active layers. The biosensor array was then transferred to a medical needle toward multiparametric in vivo sensing. The flexibility of the sensor platform allowed an easy integration to the curved surface (φ = 1.2 mm) of the needle. Furthermore, the electrodeposition process was used to localize various active materials for corresponding electrochemical sensors on the microscale electrodes with a high precision (patterning area = 150 μm × 2 mm). The biosensor array-modified needle was aimed to discriminate cancer from normal tissues by providing real-time discrimination of glucose, lactate concentration, pH, and electrical conductivity changes associated with the cancer-specific metabolic processes. The sensor performance was thus evaluated using solution samples, covering the physiological concentrations for cancer discrimination. Finally, the possibility of in vivo electrochemical biosensing during needle insertion was confirmed by utilizing the needle in a hydrogel phantom that mimicked the normal and cancer microenvironments.

Evaluation on Phantoms of the Feasibility of a Smart Bra to Detect Breast Cancer in Young Adults

Breast cancer is the most common cancer observed in women. Although mammography is a recognized method, it remains ionizing and cannot be used routinely or in young adults, leaving up to two years between two diagnoses. Prior to validation on human subjects, this study aims to validate on phantoms the feasibility of quantifying breast density and detecting breast cancer tumors using a smart bra in young women. Six phantoms with various densities and seven phantoms with various volumes of modelized tumor were prepared and measured with a smart bra, including an electrophysiological module. There was a significant difference between the "healthy phantoms" and the "tumor phantoms" with P(Student) = 0.008 (Shapiro-Wilk p = 0.846, samples follow a normal distribution; Fisher variance test, p = 0.287). In addition, this study seems to indicate the possibility of discriminating various types of tumorous and healthy breast tissue using a smart bra, in high density breast. However, a new study on a large sample of human subjects will be required to generate new models, including resistive, capacitive, and other sensor parameters versus reference data collected from imaging.

A Vector Field Design Approach to Animated Transitions

Animated transitions can be effective in explaining and exploring a small number of visualizations where there are drastic changes in the scene over a short interval of time. This is especially true if data elements cannot be visually distinguished by other means. Current research in animated transitions has mainly focused on linear transitions (all elements follow straight line paths) or enhancing coordinated motion through bundling of linear trajectories. In this paper, we introduce animated transition design, a technique to build smooth, non-linear transitions for clustered data with either minimal or no user involvement. The technique is flexible and simple to implement, and has the additional advantage that it explicitly enhances coordinated motion and can avoid crowding, which are both important factors to support object tracking in a scene. We investigate its usability, provide preliminary evidence for the effectiveness of this technique through metric evaluations and user study and discuss limitations and future directions.

Biopsy Needle Integrated with Electrical Impedance Sensing Microelectrode Array towards Real-time Needle Guidance and Tissue Discrimination

A biopsy needle with electrical impedance sensor array based on stainless steel microelectrodes (EIS needle) was developed for real-time four electrode measurement and multi-spot sensing of tissues during the biopsy process. The sensor performance was characterized by using saline solutions with various concentrations, which proved accurate, stable and reliable electrical impedance measurement. The capability of impedance-based tissue sensing was verified by the conductivity measurement of agarose hydrogel based phantom mimicking cancer tissue. Furthermore, multi-spot impedance sensing during needle insertion was demonstrated using porcine meat with muscle and fat layers, which exhibited a clear discrimination between different types of tissues. Also, the electrical impedance difference between normal and fatty livers of mouse model was measured by the EIS needle. We could successfully demonstrate that the EIS needle can provide localized and accurate characterization of biological tissues at the needle tip.

Combining multiple biomarkers linearly to maximize the partial area under the ROC curve

It is now common in clinical practice to make clinical decisions based on combinations of multiple biomarkers. In this paper, we propose new approaches for combining multiple biomarkers linearly to maximize the partial area under the receiver operating characteristic curve (pAUC). The parametric and nonparametric methods that have been developed for this purpose have limitations. When the biomarker values for populations with and without a given disease follow a multivariate normal distribution, it is easy to implement our proposed parametric approach, which adopts an alternative analytic expression of the pAUC. When normality assumptions are violated, a kernel-based approach is presented, which handles multiple biomarkers simultaneously. We evaluated the proposed as well as existing methods through simulations and discovered that when the covariance matrices for the disease and nondisease samples are disproportional, traditional methods (such as the logistic regression) are more likely to fail to maximize the pAUC while the proposed methods are more robust. The proposed approaches are illustrated through application to a prostate cancer data set, and a rank-based leave-one-out cross-validation procedure is proposed to obtain a realistic estimate of the pAUC when there is no independent validation set available.

Mapping breast tissue types by miniature radio-frequency near-field spectroscopy sensor in ex-vivo freshly excised specimens

BACKGROUND

Receiving real-time information on tissue properties while performing biopsy procedures has the potential of improving biopsy accuracy. The study goal was to test the ability of a miniature flexible Radio-Frequency (RF) sensor (Dune Medical Devices), designed to be mounted on the surface of surgical tools, in measuring and mapping the various breast tissue types and abnormalities in terms of electrical properties.

METHODS

Between January and October 2012, 102 patients undergoing lumpectomy, open-biopsy or mastectomy, in 3 medical centers, were enrolled in this study. The device was applied to freshly excised specimens, with registration between device measurements and histology analysis. Based on histology, the dielectric properties of the various tissue types were derived. Additionally, the ability of the device to differentiate between malignant and non-malignant tissue was assessed.

RESULTS

A total of 4322 measurements from 106 specimens from 102 patients were analyzed. The dielectric properties of 10 tissue types in the low RF-frequency range were measured, showing distinct differences between the various types. Based on the dielectric properties, a score variable was derived, which showed a correlation of 90 % between the RF measurements and the tissue types. Differentiation ability between tissue types was characterized using ROC curve analysis, with AUC of 0.96, and sensitivity and specificity of 90 and 91 % respectively, for tissue feature sizes at or above 0.8 mm.

CONCLUSIONS

Using a radio-frequency near-field spectroscopy miniature flexible sensor the dielectric properties of multiple breast tissue types, both normal and abnormal, were evaluated. The results show promise in differentiating between various breast tissue types, and specifically for differentiation between cancer and normal tissues.

Improving cancer therapies by targeting the physical and chemical hallmarks of the tumor microenvironment

Tumors are highly heterogeneous at the patient, tissue, cellular, and molecular levels. This multi-scale heterogeneity poses significant challenges for effective therapies, which ideally must not only distinguish between tumorous and healthy tissue, but also fully address the wide variety of tumorous sub-clones. Commonly used therapies either leverage a biological phenotype of cancer cells (e.g. high rate of proliferation) or indiscriminately kill all the cells present in a targeted volume. Tumor microenvironment (TME) targeting represents a promising therapeutic direction, because a number of TME hallmarks are conserved across different tumor types, despite the underlying genetic heterogeneity. Historically, TME targeting has largely focused on the cells that support tumor growth (e.g. vascular endothelial cells). However, by viewing the intrinsic physical and chemical alterations in the TME as additional therapeutic opportunities rather than barriers, a new class of TME-inspired treatments has great promise to complement or replace existing therapeutic strategies. In this review we summarize the physical and chemical hallmarks of the TME, and discuss how these tumor characteristics either currently are, or may ultimately be targeted to improve cancer therapies.

The Feasibility of a Smart Surgical Probe for Verification of IRE Treatments Using Electrical Impedance Spectroscopy

SIGNIFICANCE

Irreversible electroporation (IRE) is gaining popularity as a focal ablation modality for the treatment of unresectable tumors. One clinical limitation of IRE is the absence of methods for real-time treatment evaluation, namely actively monitoring the dimensions of the induced lesion. This information is critical to ensure a complete treatment and minimize collateral damage to the surrounding healthy tissue.

GOAL

In this study, we are taking advantage of the biophysical properties of living tissues to address this critical demand.

METHODS

Using advanced microfabrication techniques, we have developed an electrical impedance microsensor to collect impedance data along the length of a bipolar IRE probe for treatment verification. For probe characterization and interpretation of the readings, we used potato tuber, which is a suitable platform for IRE experiments without having the complexities of in vivo or ex vivo models. We used the impedance spectra, along with an electrical model of the tissue, to obtain critical parameters such as the conductivity of the tissue before, during, and after completion of treatment. To validate our results, we used a finite element model to simulate the electric field distribution during treatments in each potato.

RESULTS

It is shown that electrical impedance spectroscopy could be used as a technique for treatment verification, and when combined with appropriate FEM modeling can determine the lesion dimensions.

CONCLUSIONS

This technique has the potential to be readily translated for use with other ablation modalities already being used in clinical settings for the treatment of malignancies.

Analysis of biosurfaces by neutron reflectometry: from simple to complex interfaces

Because of its high sensitivity for light elements and the scattering contrast manipulation via isotopic substitutions, neutron reflectometry (NR) is an excellent tool for studying the structure of soft-condensed material. These materials include model biophysical systems as well as in situ living tissue at the solid-liquid interface. The penetrability of neutrons makes NR suitable for probing thin films with thicknesses of 5-5000 Å at various buried, for example, solid-liquid, interfaces [J. Daillant and A. Gibaud, Lect. Notes Phys. 770, 133 (2009); G. Fragneto-Cusani, J. Phys.: Condens. Matter 13, 4973 (2001); J. Penfold, Curr. Opin. Colloid Interface Sci. 7, 139 (2002)]. Over the past two decades, NR has evolved to become a key tool in the characterization of biological and biomimetic thin films. In the current report, the authors would like to highlight some of our recent accomplishments in utilizing NR to study highly complex systems, including in-situ experiments. Such studies will result in a much better understanding of complex biological problems, have significant medical impact by suggesting innovative treatment, and advance the development of highly functionalized biomimetic materials.

A novel clinical decision support system using improved adaptive genetic algorithm for the assessment of fetal well-being

A novel clinical decision support system is proposed in this paper for evaluating the fetal well-being from the cardiotocogram (CTG) dataset through an Improved Adaptive Genetic Algorithm (IAGA) and Extreme Learning Machine (ELM). IAGA employs a new scaling technique (called sigma scaling) to avoid premature convergence and applies adaptive crossover and mutation techniques with masking concepts to enhance population diversity. Also, this search algorithm utilizes three different fitness functions (two single objective fitness functions and multi-objective fitness function) to assess its performance. The classification results unfold that promising classification accuracy of 94% is obtained with an optimal feature subset using IAGA. Also, the classification results are compared with those of other Feature Reduction techniques to substantiate its exhaustive search towards the global optimum. Besides, five other benchmark datasets are used to gauge the strength of the proposed IAGA algorithm.

Resonance-enhanced microfluidic impedance cytometer for detection of single bacteria

This paper reports on a novel impedance-based cytometer, which can detect and characterize sub-micrometer particles and cells passing through a microfluidic channel. The cytometer incorporates a resonator, which is constructed by means of a discrete inductor in series with the measurement electrodes in the microfluidic channel. The use of a resonator increases the sensitivity of the system in comparison to state-of-the-art devices. We demonstrate the functionality and sensitivity of the cytometer by discriminating E. coli and B. subtilis from beads of similar sizes by means of the resonance-enhanced phase shift of the current through the microfluidic channel. The phase shift can be correlated to size and dielectric properties of the measured objects.

Bioelectrical Impedance Methods for Noninvasive Health Monitoring: A Review

Under the alternating electrical excitation, biological tissues produce a complex electrical impedance which depends on tissue composition, structures, health status, and applied signal frequency, and hence the bioelectrical impedance methods can be utilized for noninvasive tissue characterization. As the impedance responses of these tissue parameters vary with frequencies of the applied signal, the impedance analysis conducted over a wide frequency band provides more information about the tissue interiors which help us to better understand the biological tissues anatomy, physiology, and pathology. Over past few decades, a number of impedance based noninvasive tissue characterization techniques such as bioelectrical impedance analysis (BIA), electrical impedance spectroscopy (EIS), electrical impedance plethysmography (IPG), impedance cardiography (ICG), and electrical impedance tomography (EIT) have been proposed and a lot of research works have been conducted on these methods for noninvasive tissue characterization and disease diagnosis. In this paper BIA, EIS, IPG, ICG, and EIT techniques and their applications in different fields have been reviewed and technical perspective of these impedance methods has been presented. The working principles, applications, merits, and demerits of these methods has been discussed in detail along with their other technical issues followed by present status and future trends.

Biomarker selection for medical diagnosis using the partial area under the ROC curve

Background

A biomarker is usually used as a diagnostic or assessment tool in medical research. Finding an ideal biomarker is not easy and combining multiple biomarkers provides a promising alternative. Moreover, some biomarkers based on the optimal linear combination do not have enough discriminatory power. As a result, the aim of this study was to find the significant biomarkers based on the optimal linear combination maximizing the pAUC for assessment of the biomarkers.

Methods

Under the binormality assumption we obtain the optimal linear combination of biomarkers maximizing the partial area under the receiver operating characteristic curve (pAUC). Related statistical tests are developed for assessment of a biomarker set and of an individual biomarker. Stepwise biomarker selections are introduced to identify those biomarkers of statistical significance.

Results

The results of simulation study and three real examples, Duchenne Muscular Dystrophy disease, heart disease, and breast tissue example are used to show that our methods are most suitable biomarker selection for the data sets of a moderate number of biomarkers.

Conclusions

Our proposed biomarker selection approaches can be used to find the significant biomarkers based on hypothesis testing.

Computer-aided diagnosis system: a Bayesian hybrid classification method

A novel method to classify multi-class biomedical objects is presented. The method is based on a hybrid approach which combines pairwise comparison, Bayesian regression and the k-nearest neighbor technique. It can be applied in a fully automatic way or in a relevance feedback framework. In the latter case, the information obtained from both an expert and the automatic classification is iteratively used to improve the results until a certain accuracy level is achieved, then, the learning process is finished and new classifications can be automatically performed. The method has been applied in two biomedical contexts by following the same cross-validation schemes as in the original studies. The first one refers to cancer diagnosis, leading to an accuracy of 77.35% versus 66.37%, originally obtained. The second one considers the diagnosis of pathologies of the vertebral column. The original method achieves accuracies ranging from 76.5% to 96.7%, and from 82.3% to 97.1% in two different cross-validation schemes. Even with no supervision, the proposed method reaches 96.71% and 97.32% in these two cases. By using a supervised framework the achieved accuracy is 97.74%. Furthermore, all abnormal cases were correctly classified.

Intracranial electrical impedance tomography: a method of continuous monitoring in an animal model of head trauma

BACKGROUND

Electrical impedance tomography (EIT) is a method that can render continuous graphical cross-sectional images of the brain's electrical properties. Because these properties can be altered by variations in water content, shifts in sodium concentration, bleeding, and mass deformation, EIT has promise as a sensitive instrument for head injury monitoring to improve early recognition of deterioration and to observe the benefits of therapeutic intervention. This study presents a swine model of head injury used to determine the detection capabilities of an inexpensive bedside EIT monitoring system with a novel intracranial pressure (ICP)/EIT electrode combination sensor on induced intraparenchymal mass effect, intraparenchymal hemorrhage, and cessation of brain blood flow. Conductivity difference images are shown in conjunction with ICP data, confirming the effects.

METHODS

Eight domestic piglets (3-4 weeks of age, mean 10 kg), under general anesthesia, were subjected to 4 injuries: induced intraparenchymal mass effect using an inflated, and later, deflated 0.15-mL Fogarty catheter; hemorrhage by intraparenchymal injection of 1-mL arterial blood; and ischemia/infarction by euthanasia. EIT and ICP data were recorded 10 minutes before inducing the injury until 10 minutes after injury. Continuous EIT and ICP monitoring were facilitated by a ring of circumferentially disposed cranial Ag/AgCl electrodes and 1 intraparenchymal ICP/EIT sensor electrode combination. Data were recorded at 100 Hz. Two-dimensional tomographic conductivity difference (Δσ) images, rendered using data before and after an injury, were displayed in real time on an axial circular mesh. Regions of interest (ROI) within the images were automatically selected as the upper or lower 5% of conductivity data depending on the nature of the injury. Mean Δσ within the ROIs and background were statistically analyzed. ROI Δσ was compared with the background Δσ after an injury event using an unpaired, unequal variance t test. Conductivity change within an ROI after injury was likewise compared with the same ROI before the injury making use of unpaired t tests with unequal variance.

RESULTS

Eight animal subjects were studied, each undergoing 4 injury events including euthanasia. Changes in conductivity due to injury showed expected pathophysiologic effects in an ROI identified within the middle of the left hemisphere; this localization is reasonable given the actual site of injury (left hemisphere) and spatial warping associated with estimating a 3-dimensional conductivity distribution in 2-dimensional space. Results are shown as mean ± 1 SD. When averaged across all 8 animals, balloon inflation caused the mean Δσ within the ROI to shift by -11.4 ± 10.9 mS/m; balloon deflation by +9.4 ± 8.8 mS/m; blood injection by +19.5 ± 11.5 mS/m; death by -12.6 ± 13.2 mS/m. All induced injuries were detectable to statistical significance (P < 0.0001).

CONCLUSION

This study confirms that the bedside EIT system with ICP/EIT combination sensor can detect induced trauma. Such a technique may hold promise for further research in the monitoring and management of traumatically brain-injured individuals.

A feasibility study of magnetic resonance driven electrical impedance tomography using a phantom

Imaging the electrical properties of human tissue may aid in cancer diagnoses or monitoring organ function. Traditionally, the electrical properties are revealed with electrical impedance tomography, where currents are injected into human tissue and voltages are measured on the surface. This paper focuses on a method of measuring the electrical properties using a magnetic resonance (MR) scanner without current injection. In magnetic resonance driven electrical impedance tomography (MRDEIT), the MR phenomenon is used to induce currents in the body and the complex permittivity map is inversely computed from the difference between the modeled electric field and the actual surface electrode measurements. Computer simulations indicate that with noise level under 20%, the contrast is visually discernible in the reconstruction image. A phantom experiment is demonstrated and this supports results from computer simulation studies. The noise level in electrode measurements is evaluated to be approximately 7.8% from repeated experiments, confirming the potential to reconstruct conductivity contrast using MRDEIT. With further improvements in hardware and image reconstruction, MRDEIT may provide an additional contrast mechanism reflecting the electrical properties of human tissue, which may ultimately be used to diagnose a cancer or assist in electroencephalography.

Simulations and phantom evaluations of magnetic resonance electrical impedance tomography (MREIT) for breast cancer detection

MREIT is a new imaging modality that can be used to reconstruct high-resolution conductivity images of the human body. Since conductivity values of cancerous tissues in the breast are significantly higher than those of surrounding normal tissues, breast imaging using MREIT may provide a new noninvasive way of detecting early stage of cancer. In this paper, we present results of experimental and numerical simulation studies of breast MREIT. We built a realistic three-dimensional model of the human breast connected to a simplified model of the chest including the heart and evaluated the ability of MREIT to detect cancerous anomalies in a background material with similar electrical properties to breast tissue. We performed numerical simulations of various scenarios in breast MREIT including assessment of the effects of fat inclusions and effects related to noise levels, such as changing the amplitude of injected currents, effect of added noise and number of averages. Phantom results showed straightforward detection of cancerous anomalies in a background was possible with low currents and few averages. The simulation results showed it should be possible to detect a cancerous anomaly in the breast, while restricting the maximal current density in the heart below published levels for nerve excitation.

Combining extreme learning machines using support vector machines for breast tissue classification

In this paper, we present a new approach for breast tissue classification using the features derived from electrical impedance spectroscopy. This method is composed of a feature extraction method, feature selection phase and a classification step. The feature extraction phase derives the features from the electrical impedance spectra. The extracted features consist of the impedivity at zero frequency (I0), the phase angle at 500 KHz, the high-frequency slope of phase angle, the impedance distance between spectral ends, the area under spectrum, the normalised area, the maximum of the spectrum, the distance between impedivity at I0 and the real part of the maximum frequency point and the length of the spectral curve. The system uses the information theoretic criterion as a strategy for feature selection and the combining extreme learning machines (ELMs) for the classification phase. The results of several ELMs are combined using the support vector machines classifier, and the result of classification is reported as a measure of the performance of the system. The results indicate that the proposed system achieves high accuracy in classification of breast tissues using the electrical impedance spectroscopy.