Electrical impedance scanning as a new breast cancer risk stratification tool for young women

Emerging trends and hot spots on electrical impedance tomography extrapulmonary applications

Objective

Electrical impedance tomography (EIT) develops rapidly in technology and applications. Nowadays EIT is used in multiple clinical and experimental scenarios including pulmonary, brain, and tissue monitoring, etc. The present study explores the research trends and hotspots on EIT extrapulmonary application research by bibliometrics analysis.

Approach

Publications on EIT extrapulmonary applications between 1987 and 2021 were retrieved from the Web of Science Core Collection database. For precise screening, search strategy "electrical impedance tomography" plus "hemodynamic" or "brain" or "nerve" or "cancer" or "venous" or "vessel" or "tumor" or "veterinary" or "tissue" or "cell" or "wearable" or "application" and excluding "lung", "ventilation" "respiratory", "pulmonary", "algorithm", "current", "voltage" or "electrode" were used. CiteSpace and VOSviewer were used to analyze the publication features, collaboration, keywords co-occurrence, and co-cited reference.

Main results

A total of 506 articles were finally identified. The global publication numbers on extrapulmonary applications gradually increased yearly in the past 30 years. The US, UK, and China contributed most three publications concerning EIT extrapulmonary applications. "tissues", "conductivity", "model" were research hotspots, and "cutaneous melanoma", "microstructure", "diagnosis" were recent topics (Portions of this research have previously been presented in poster form).

Significance

Overall, EIT extrapulmonary applications bibliometrics analysis provides a unique insight into research focus, current trends, and future directions.

Electrical Impedance Analysis for Lung Cancer: A Prospective, Multicenter, Blind Validation Study

Hypothesis

Patients with cancer have different impedances or conductances than patients with benign normal tissue; thus, we can apply electrical impedance analysis (EIA) to identify patients with cancer.

Method

To evaluate EIA’s efficacy and safety profile in diagnosing pulmonary lesions, we conducted a prospective, multicenter study among patients with pulmonary lesions recruited from 4 clinical centers (Zhongshan Hospital Ethics Committee, Approval No. 2015-16R and 2017-035(3). They underwent EIA to obtain an Algorithm Composite Score or ‘Prolung Index,’ PI. The classification threshold of 29 was first tested in an analytical validation set of 144 patients and independently validated in a clinical validation set of 418 patients. The subject’s final diagnosis depended on histology and a 2-year follow-up.

Results

In total, 418 patients completed the entire protocol for clinical validation, with 186 true positives, 145 true negatives, 52 false positives, and 35 false negatives. The sensitivity, specificity, and diagnostic yield were 84% (95% CI 79.3%-89.0%), 74% (95% CI 67.4%-79.8%), and 79% (95%CI 75.3%-83.1%), respectively, and did not differ according to age, sex, smoking history, body mass index, or lesion types. The sensitivity of small lesions was comparable to that of large lesions (p = 0.13). Four hundred eighty-four patients who underwent the analysis received a safety evaluation. No adverse events were considered to be related to the test.

Conclusion

Electrical impedance analysis is a safe and efficient tool for risk stratification of pulmonary lesions, especially for patients with a suspicious lung lesion.

The Accuracy of Electrical Impedance Tomography for Breast Cancer Detection: A Systematic Review and Meta-Analysis

Introduction

Incidence of breast cancer (BC) in 2020 is about 2.26 million new cases. It is the first common cancer accounting for 11.7% of all cancer worldwide. Disease complications and the mortality rate of breast cancer are highly dependent on the early diagnosis. Therefore, novel human breast-imaging techniques play an important role in minimizing the breast cancer morbidity and mortality rate. Electrical impedance tomography (EIT) is a noninvasive technique to image the breast using the electrical impedance behavior of the body tissues.

Objectives

The aims of this manuscript are as follows: (1) a comprehensive investigation of the accuracy of EIT for breast cancer diagnosis through searching pieces of evidence in the valid databases and (2) meta-analyses of the results.

Methods

The systematic search was performed in the electronic databases including PubMed, Web of Science, EMBASE, Science Direct, ProQuest, Scopus, and Google Scholar without time and language limitation until January 2021. Search terms were “EIT” and “Breast Cancer” with their synonyms. Relevant studies were included based on PRISMA and study objectives. Quality of studies and risk of bias were performed by QUADAS-2 tools. Then, relevant data were extracted in Excel form. The hierarchical/bivariate meta-analysis was performed with “metandi” package for the ROC plot of sensitivity and specificity. Forest plot of the Accuracy index and double arcsine transformations was applied to stabilize the variance. The heterogeneity of the studies was evaluated by the forest plots, χ2 test (assuming a significance at the a-level of 10%), and the I² statistic for the Accuracy index.

Results

A total of 4027 articles were found. Finally, 12 of which met our criteria. Overall, these articles included studies of 5487 breast cancer patients. EIT had an overall pooled sensitivity and specificity of 75.88% (95% CI, 61.92% to 85.89%) and 82.04% (95% CI, 69.72% to 90.06%), respectively. The pooled diagnostic odds ratio was 14.37 (95% CI, 6.22% to 33.20%), and the pooled effect of accuracy was 0.79 with 95% CI (0.73, 0.83).

Conclusions

This study showed that EIT can be used as a useful method alongside mammography. EIT sensitivity could not be compared with the sensitivity of MRI, but in terms of specificity, it can be considered as a new method that probably can get more attention. Furthermore, large-scale studies will be needed to support the evidence.

Use of Electrical Impedance Spectroscopy for Intraoperative Tissue Differentiation During Thyroid and Parathyroid Surgery

BACKGROUND

Electrical impedance (EI) measures tissue resistance to alternating current across several frequencies and may help identify tissue type. A recent rabbit model demonstrated that electrical impedance spectroscopy (EIS) may facilitate identification of parathyroid glands and potentially improve outcomes following surgery. This study looks at the EI patterns of soft tissues in the human neck to determine whether parathyroid tissue can be accurately identified.

METHODS

This was a phase 1, single-arm interventional study involving 56 patients undergoing thyroid and/or parathyroid surgery. Up to 12 EI readings were taken from in vivo and ex vivo thyroid and parathyroid glands, adipose tissue and muscle of each patient. Each reading consists of a series of measurements over 14 frequencies from each tissue. EI patterns were analysed. Two patients were excluded due to data loss due to device malfunction.

RESULTS

The median age of participants was 53.5 (range 20-85) years. Thirty-five participants had surgery for thyroid pathology, 17 for parathyroid pathology and four for both. Six hundred and six EIS spectra were reviewed for suitability. One hundred and eighty-four spectra were rejected leaving 422 spectra for analysis. The impedance patterns of the soft tissues differed by histological type. The EI ratio of low (152 Hz) to high (312 kHz) frequencies demonstrated a significant difference between the soft tissues (p = 0.006). Using appropriate thresholds, parathyroid tissue can be distinguished from thyroid tissue with a sensitivity of 76% and specificity of 60%.

CONCLUSIONS

This study demonstrates the feasibility of using EIS to aid parathyroid identification and preservation. Further changes to the device and modelling of the EI patterns across the range of frequencies may improve accuracy and facilitate intraoperative use.

TRIAL REGISTRATION

ClinicalTrials.gov (NCT02901873).

Noninvasive pulmonary nodule characterization using transcutaneous bioconductance: Preliminary results of an observational study

We sought to assess the use of an electro pulmonary nodule (EPN) scanner (FreshMedx, Salt Lake City, UT) in the noninvasive characterization of pulmonary nodules using transcutaneous bioconductance.Monocentric prospective study including patients with a pulmonary nodule identified on a chest computed tomography scan. Study protocol approved by the institutional review board and written consent was obtained for every patient. 32 patients (12 females and 20 males), average age 65 years, and average lesion size 33.1 mm (range: 9-123 mm). Data collection by a trained physician, 62 skin surface measurements on the chest, arms, and hands bilaterally. Results were anonymized and mailed to a central data center for analysis and compared to histopathology.Pathology results obtained by percutaneous biopsy (n = 14), surgical biopsy (n = 1), or surgical resection (n = 17) showed 29 malignant lesions (adenocarcinoma n = 21, squamous cell carcinoma n = 5, typical carcinoid n = 1, metastasis n = 2), and 3 benign lesions (necrotic granuloma n = 1, no malignant cells on biopsy n = 2). EPN scanner results had a specificity of 66.67% (95% confidence interval [CI] 0.09-0.99), sensitivity 72.41% (95% CI 0.53-0.87), positive predictive value 95.45% (95% CI 0.81-0.99), and a negative predictive value 20.00% (95% CI 0.08-0.40).This pilot study showed a high positive predictive value of the EPN scanner, allowing aggressive management of lung nodules characterized as malignant. The low negative predictive value warrants further investigation of nodules that are characterized as benign.

Real-time electrical impedance variations in women with and without breast cancer

The chaotic vascular network surrounding malignant tumors leads to pulsatile blood flow patterns that differ from those in benign regions of the breast. This study aimed to determine if high-speed electrical impedance tomography (EIT) is able to detect conductivity changes associated with cyclic blood-volume changes and to gauge the potential of using these signatures to differentiate malignant from benign regions within the breast. EIT imaging of pulsating latex membranes submerged in saline baths provided initial validation of its use for tracking temporally varying conductivities. Nineteen women (10 with cancer, nine without) were imaged with EIT over the course of several heartbeats in synchrony with pulse-oximetry acquisition. Eight parameters ( rs, ϕ(rt,max), rt,max, Plow:full, Phigh:full, Plow:high) relating the conductivity images and pulse-oximeter signatures were extracted and used as a means of comparing malignant and benign regions of the breast. Significant differences between malignant and benign regions of interest were noted in seven of the eight parameters. The maximum correlation between conductivity and pulse-oximeter signals, rt,max , was observed to be the optimal discriminating parameter with a receiver operating characteristic area under the curve of 0.8 and a specificity of 81% at a sensitivity of 77%. Assessing the dynamic conductivity of breast may provide additional clinical utility to that of standard imaging modalities, but further investigation is necessary to better understand the biophysical mechanisms leading to the observed conductivity changes.

Future directions for the early detection of recurrent breast cancer

The main goal of follow-up care after breast cancer treatment is the early detection of disease recurrence. In this review, we emphasize the multidisciplinary approach to this continuity of care from surgery, medical oncology, and radiology. Challenges within each setting are briefly addressed as a means of discussion for the future directions of an effective and efficient surveillance plan of post-treatment breast cancer care.

Breast tumor detection using piezoelectric fingers: first clinical report

BACKGROUND

Mammography is key to detection of breast cancer in high-risk populations. Currently, aside from palpation and risk-assessment questionnaires, there is no prescreening test that can improve the accuracy, safety, and cost effectiveness of screening low-risk populations. The piezoelectric finger (PEF) is a radiation-free, portable, and low-cost breast tumor detector we developed to be used as a prescreening tool.

STUDY DESIGN

Patients presenting with breast abnormalities detected by palpation or imaging were enrolled in this IRB-approved study. The PEF testing was performed with the patient in supine position before undergoing biopsy or surgical excision. The locations of the lesions detected by PEF were compared with those confirmed on imaging or pathology.

RESULTS

A total of 40 patients were enrolled and 46 lesions were confirmed by imaging or pathology. The PEF reported 55 lesions, with 9 false positives and 2 true positives not originally found on imaging or palpation. The overall sensitivity of the PEF test was 87% (40 of 46). In women 40 years old or younger, overall sensitivity was or 100% (19 of 19). In women who had a lesion visible on mammography, PEF had a sensitivity of 83% (24 of 29). Of these, in women aged 40 years or younger, PEF identified all 7 mammographically visible lesions, including 2 malignant lesions. When compared with ultrasound, PEF correctly identified 87% (34 of 39) in this group. Of these, in women aged 40 years or younger, PEF identified 100% (19 of 19) of all ultrasound-visible lesions.

CONCLUSIONS

The PEF identified abnormalities in all 39 patients who presented with breast abnormalities and did not demonstrate any false negatives that would prevent the patients from additional evaluation, which makes it a good prescreening tool. In addition, PEF demonstrated 100% sensitivity in women aged 40 years or younger, a traditionally low-risk population.

Review of electromagnetic techniques for breast cancer detection

Breast cancer is anticipated to be responsible for almost 40,000 deaths in the USA in 2011. The current clinical detection techniques suffer from limitations which motivated researchers to investigate alternative modalities for the early detection of breast cancer. This paper focuses on reviewing the main electromagnetic techniques for breast cancer detection. More specifically, this work reviews the cutting edge research in microwave imaging, electrical impedance tomography, diffuse optical tomography, microwave radiometry, biomagnetic detection, biopotential detection, and magnetic resonance imaging (MRI). The goal of this paper is to provide biomedical researchers with an in-depth review that includes all main electromagnetic techniques in the literature and the latest progress in each of these techniques.

Transcutaneous computed bioconductance measurement in lung cancer: a treatment enabling technology useful for adjunctive risk stratification in the evaluation of suspicious pulmonary lesions

Lung cancer is the number one cause of cancer deaths in North America and is rapidly increasing worldwide. Although there are advances being made in the multidisciplinary management and combined-modality therapies of lung cancers, most cases are still diagnosed in later noncurable stages. Early detection has hinged on clinical risk assessment and on the future possibility of screening by low-dose computed tomography of the chest; however, this will only vastly increase the number of indeterminate pulmonary lesions (IPLs) being detected. Given that the majority of radiographically detected lung lesions are benign, and tissue confirmation by various invasive biopsy tests has increased risks and costs, a noninvasive adjunctive test that can stratify likelihood of an indeterminate lung lesion as malignant or benign will be a useful treatment-enabling technology to speed up diagnosis and treatment of lung cancers at a more curable stage and defer unnecessary invasive procedures that have potential for harm. Measurement of transcutaneous bioconductance using the differential conductivity properties of cancerous versus benign tissue has been previously demonstrated on nonlung lesions. Thus, it has the potential of being a noninvasive, simple-to-perform and repeatable test that may be valuable in assessing lung lesions. In this prospective study of subjects with known thoracic malignancies, computed bioconductance measurements discriminated between malignant lesions (29 primary lung cancers) from benign pathology (12) across a range of IPL sizes (0.8 cm and greater) with a sensitivity of 89.7% (positive predictive value 96.3%) and specificity of 91.7% (negative predictive value 78.5%). The technology seems to be effective across a range of tumor thoracic locations, cell types, and stages. Additional cohorts of subjects will be used to validate testing and for refinement of the current algorithm, which at present has a test performance with a receiver operating characteristic of 90.7%. Noninvasive transcutaneous computed bioconductance measurement can become a standard risk assessment and therapy-enabling tool in the evaluation of IPLs.

A GMM-based breast cancer risk stratification using a resonance-frequency electrical impedance spectroscopy

PURPOSE

The authors developed and tested a multiprobe-based resonance-frequency-based electrical impedance spectroscopy (REIS) system. The purpose of this study was to preliminarily assess the performance of this system in classifying younger women into two groups, those ultimately recommended for biopsy during imaging-based diagnostic workups that followed screening and those rated as negative during mammography.

METHODS

A seven probe-based REIS system was designed, assembled, and is currently being tested in the breast imaging facility. During an examination, contact is made with the nipple and six concentric points on the breast skin. For each measurement channel between the center probe and one of the six external probes, a set of electrical impedance spectroscopy (EIS) signal sweeps is performed and signal outputs ranging from 200 to 800 kHz at 5 kHz interval are recorded. An initial subset of 174 examinations from an ongoing prospective clinical study was selected for this preliminary analysis. An initial set of 35 features, 33 of which represented the corresponding EIS signal differences between the left and right breasts, was established. A Gaussian mixture model (GMM) classifier was developed to differentiate between "positive" (biopsy recommended) cases and "negative" (nonbiopsy) cases. Selecting an optimal feature set was performed using genetic algorithms with an area under a receiver operating characteristic curve (AUC) as the fitness criterion.

RESULTS

The recorded EIS signal sweeps showed that, in general, negative (nonbiopsy) examinations have a higher level of electrical impedance symmetry between the two breasts than positive (biopsy) examinations. Fourteen features were selected by genetic algorithm and used in the optimized GMM classifier. Using a leave-one-case-out test, the GMM classifier yielded a performance level of AUC = 0.78, which compared favorably to other three widely used classifiers including support vector machine, classification tree, and linear discriminant analysis. These results also suggest that the REIS signal based GMM classifier could be used as a prescreening tool to correctly identify a fraction of younger women at higher risk of developing breast cancer (i.e., 47% sensitivity at 90% specificity).

CONCLUSIONS

The study confirms that asymmetry in electrical impedance characteristics between two breasts provides valuable information regarding the presence of a developing breast abnormality; hence, REIS data may be useful in classifying younger women into two groups of "average" and "significantly higher than average" risk of having or developing a breast abnormality that would ultimately result in a later imaging-based recommendation for biopsy.

A preliminary evaluation of multi-probe resonance-frequency electrical impedance based measurements of the breast

RATIONALE AND OBJECTIVES

The aim of this study was to preliminarily assess the performance of a new, resonance-frequency electrical impedance spectroscopy (REIS) system in identifying young women who were recommended to undergo breast biopsy following imaging.

MATERIALS AND METHODS

A seven-probe REIS system was designed and assembled and is currently being prospectively tested. During examination, contact is made with the nipple and six concentric points on the breast skin. Signal sweeps are performed, and outputs ranging from 200 to 800 kHz at 5-kHz intervals are recorded. An initial set of 140 patients, including 56 who eventually had biopsies, 63 who had negative results on screening mammography, and 21 recalled for additional imaging but later determined to have negative results, was used. An initial set of 35 features, 33 representing impedance signal differences between breasts and two representing participant age and average breast density, was assembled and reduced by a genetic algorithm to 14. The performance of an artificial neural network-based classifier was assessed using a case-based leave-one-out method.

RESULTS

The substantially greater asymmetry between signals of mirror-matched regions ascertained from biopsy ("positive") compared to nonbiopsy ("negative") cases resulted in an artificial neural network classifier performance (area under the curve) of 0.830 ± 0.023. At 90% specificity, this classifier, optimized for "recommendation for biopsy" rather than "cancer," detected 30 REIS-positive cases (54%), including six of nine (67%) actual cancer cases and six of nine women (67%) recommended for surgical excision of high-risk lesions.

CONCLUSIONS

Asymmetry in impedance measurements between bilateral breasts may provide valuable discriminatory information regarding the presence of highly suspicious imaging-based findings.

Improving breast cancer risk stratification using resonance-frequency electrical impedance spectroscopy through fusion of multiple classifiers

This study aims to improve breast cancer risk stratification. A seven-probe resonance-frequency-based electrical impedance spectroscopy (REIS) system was designed, assembled, and utilized to establish a data set of examinations from 174 women. Three classifiers, including artificial neural network (ANN), support vector machine (SVM), and Gaussian mixture model (GMM), were independently developed to predict the likelihood of each woman to be recommended for biopsy. The performances of these classifiers were compared, and seven fusion methods for integrating these classifiers were investigated. The results showed that among the three classifiers, the ANN yielded the highest performance with an area under the curve (AUC) of 0.81 for the receiver operating characteristic (ROC), while SVM and GMM achieved AUCs of 0.80 and 0.78, respectively. Improvements of up to 3% were obtained using fusion of the three classifiers, with the largest improvement obtained using either a "minimum score" rule or a "weighted sum" rule. Comparing different combinations of two out of the three classifiers, the weighted sum rule provided the most robust and consistent results, with AUCs of 0.81, 0.83, and 0.82 for the different combinations of ANN and SVM, ANN and GMM, and SVM and GMM, respectively. Furthermore, at 90% specificity, the ANN, the weighted sum- and min rule-based classifiers, all detected 67% of the verified cancer cases as compared with 50, 50, and 60% detection of the high risk cases, respectively. The study demonstrated that REIS examinations provide relevant information for developing breast cancer risk stratification tools and that using fusion of several not-fully-correlated classifiers can improve classification performance.

Computerized detection of breast tissue asymmetry depicted on bilateral mammograms: a preliminary study of breast risk stratification

RATIONALE AND OBJECTIVES

Assessment of the breast tissue pattern asymmetry depicted on bilateral mammograms is routinely used by radiologists when reading and interpreting mammograms. The purpose of this study is to develop an automated scheme to detect breast tissue asymmetry depicted on bilateral mammograms and use the computed asymmetric features to predict the likelihood (or the risk) of women having or developing breast abnormalities or cancer.

MATERIALS AND METHODS

A testing dataset was selected from a large and diverse full-field digital mammography image database, which includes 100 randomly selected negative cases (not recalled during the screening) and 100 positive cases for having or developing breast abnormalities or cancer. Among these positive cases 40 were recalled (biopsy) because of suspicious findings in which 8 were determined as high risk with the lesions surgically removed and the remaining were proven to be benign, and 60 cases were acquired from examinations that were interpreted as negative (without dominant masses or microcalcifications) but the cancers were detected 6-18 months later. A computerized scheme was developed to detect asymmetry of mammographic tissue density represented by the related feature differences computed from bilateral images. Initially, each of 20 features was tested to classify between the positive and the negative cases. To further improve the classification performance, a genetic algorithm (GA) was applied to select a set of optimal features and build an artificial neural network (ANN). The leave-one-case-out validation method was used to evaluate the ANN classification performance.

RESULTS

Using a single feature, the maximum classification performance level measured by the area under the receiver operating characteristic curve (AUC) was 0.681 ± 0.038. Using the GA-optimized ANN, the classification performance level increased to an AUC = 0.754 ± 0.024. At 90% specificity, the ANN classifier yielded 42% sensitivity, in which 42 positive cases were correctly identified. Among them, 30 were the "prior" examinations of the cancer cases and 12 were recalled benign cases, which represent 50% and 30% sensitivity levels in these two subgroups, respectively.

CONCLUSIONS

This study demonstrated that using the computerized detected feature differences related to the bilateral mammographic breast tissue asymmetry, an automated scheme is able to classify a set of testing cases into the two groups of positive or negative of having or developing breast abnormalities or cancer. Hence, further development and optimization of this automated method may eventually help radiologists identify a fraction of women at high risk of developing breast cancer and ultimately detect cancer at an early stage.

Modern breast cancer detection: a technological review

Breast cancer is a serious threat worldwide and is the number two killer of women in the United States. The key to successful management is screening and early detection. What follows is a description of the state of the art in screening and detection for breast cancer as well as a discussion of new and emerging technologies. This paper aims to serve as a starting point for those who are not acquainted with this growing field.

Breast cancer imaging: a perspective for the next decade

Breast imaging is largely indicated for detection, diagnosis, and clinical management of breast cancer and for evaluation of the integrity of breast implants. In this work, a prospective view of techniques for breast cancer detection and diagnosis is provided based on an assessment of current trends. The potential role of emerging techniques that are under various stages of research and development is also addressed. It appears that the primary imaging tool for breast cancer screening in the next decade will be high-resolution, high-contrast, anatomical x-ray imaging with or without depth information. MRI and ultrasonography will have an increasingly important adjunctive role for imaging high-risk patients and women with dense breasts. Pilot studies with dedicated breast CT have demonstrated high-resolution three-dimensional imaging capabilities, but several technological barriers must be overcome before clinical adoption. Radionuclide based imaging techniques and x-ray imaging with intravenously injected contrast offer substantial potential as a diagnostic tools and for evaluation of suspicious lesions. Developing optical and electromagnetic imaging techniques hold significant potential for physiologic information and they are likely to be of most value when integrated with or adjunctively used with techniques that provide anatomic information. Experimental studies with breast specimens suggest that phase-sensitive x-ray imaging techniques can provide edge enhancement and contrast improvement but more research is needed to evaluate their potential role in clinical breast imaging. From the technological perspective, in addition to improvements within each modality, there is likely to be a trend towards multi-modality systems that combine anatomic with physiologic information. We are also likely to transition from a standardized screening, where all women undergo the same imaging exam (mammography), to selection of a screening modality or modalities based an individual-risk or other classification.

Retrospective analyses of pivotal prospective studies with population segmentation: statistically based inferences and clinical relevance

Retrospective analyses of pivotal prospective studies are important for verifying the inferences made as a result of the original studies and for generating new hypotheses. However, careful attention should be given to the comprehensiveness and completeness of a retrospective analysis and how it is ultimately used. A recent retrospective analysis of the Digital Mammographic Imaging Screening Trial (DMIST) underscores several important points related to inference generation and generalization of the results on the basis of summary performance indexes, as well as the importance of incorporating a clinically relevant perspective when generating inferences primarily on the basis of statistical test results. This article highlights three important points related to (1) the use of performance indexes (namely, area under the receiver-operating characteristic curve), (2) applied statistical methods (namely, Bonferroni corrections for multiple comparison), and (3) practical conclusions (namely, consideration of all possible inferences that could be generated from the data), as well as possible implications and limitations of these retrospective analyses. The discussion in this paper is based on one specific retrospective analysis of a prospective study, but the topics addressed are quite basic, general, and potentially applicable to a number of retrospective analyses of data that are experimentally ascertained during pivotal prospective studies, as well as during observer performance studies.

The Use of Tissue Electrical Characteristics for Breast Cancer Detection: A Perspective Review

Breast cancer is the most frequently occurring malignancy in women. It is characterized by a high mortality rate. For the purpose of detecting this life threatening disease, research efforts are being made worldwide to exploit new technologies, to improve the detection accuracy of current devices and to develop new detection devices, comprehensive diagnostic procedures, and protocols. One such technology that is gaining popular attention over the recent years is the usage of electrical characteristics of the breast tissue to differentiate normal and cancerous tissues. Most of the devices using this technology are currently being used as adjunct diagnostic tools to improve the detection accuracy of established techniques like mammography and ultrasound. Also, early detection of breast cancer can help save many thousands of lives every year and can also reduce unnecessary healthcare expenditure caused by advanced stage treatment options. Hence, more research is also being done to adapt these devices into screening tools for early detection of breast cancer.

The main objective of this review is to highlight the features of the currently available commercial devices that use this technology for breast cancer detection. The electrical behavior of normal and cancerous breast tissues is first presented. The various commercial devices that utilize electrical impedance or electropotentials for breast cancer detection are then described. Finally, conclusions and potential areas of research are highlighted.