Modern breast cancer detection: a technological review

A novel textile-based UWB patch antenna for breast cancer imaging

Breast cancer is the second leading cause of death for women worldwide, and detecting cancer at an early stage increases the survival rate by 97%. In this study, a novel textile-based ultrawideband (UWB) microstrip patch antenna was designed and modeled to work in the 2-11.6 GHz frequency range and a simulation was used to test its performance in early breast cancer detection. The antenna was designed with an overall size of 31*31 mm2 using a denim substrate and 100% metal polyamide-based fabric with copper, silver, and nickel to provide comfort for the wearer. The designed antenna was tested in four numerical breast models. The models ranged from simple tumor-free to complex models with small tumors. The size, structure, and position of the tumor were modified to test the suggested ability of the antenna to detect cancers with different shapes, sizes, and positions. The specific absorption rate (SAR), return loss (S11), and voltage standing wave ratio (VSWR) were calculated for each model to measure the antenna performance. The simulation results showed that SAR values were between 1.6 and 2 W/g (10 g SAR) and were within the allowed range for medical applications. Additionally, the VSWR remained in an acceptable range from 1.15 to 2. Depending on the size and location of the tumor, the antenna return losses of the four models ranged from - 36 to - 18.5 dB. The effect of bending was tested to determine the flexibility. The antenna proved to be highly effective and capable of detecting small tumors with diameters of up to 2 mm.

Canine Cancer Diagnostics by X-ray Diffraction of Claws

We report the results of X-ray diffraction (XRD) measurements of the dogs' claws and show the feasibility of using this approach for early, non-invasive cancer detection. The obtained two-dimensional XRD patterns can be described by Fourier coefficients, which were calculated for the radial and circular (angular) directions. We analyzed these coefficients using the supervised learning algorithm, which implies optimization of the random forest classifier by using samples from the training group and following the calculation of mean cancer probability per patient for the blind dataset. The proposed algorithm achieved a balanced accuracy of 85% and ROC-AUC of 0.91 for a blind group of 68 dogs. The transition from samples to patients additionally improved the ROC-AUC by 10%. The best specificity and sensitivity values for 68 patients were 97.4% and 72.4%, respectively. We also found that the structural parameter (biomarker) most important for the diagnostics is the intermolecular distance.

Novel electrochemical biosensor for breast cancer detection, based on a nanocomposite of carbon nanofiber, metal-organic framework, and magnetic graphene oxide

In this study, we present the newly developed a novel microRNA biosensor based on magnetic rod carbon paste electrodes for breast cancer detection by using a relatively new MOF structure as a substrate. The major goal of manufacturing biosensors, suitable for clinical diagnostics, is to measure very low amount of microRNA 155 in complex environments. Therefore, we used a combination of different materials, including carbon nanofibers, CuBTC-AIA (CuMOF), and Fe@rGO, to improve the electrode surface-to-volume ratio and facilitate the electron transfer process. In this method, 1-pyrenebutyric acid N-hydroxysuccinimide ester was used to bind the microRNAs to the electrode surface. The hybridization process on the modified electrode surface was investigated using cyclic voltammetry, electrochemical impedance spectroscopy, and differential pulse voltammetry across the potential range, in which the accumulated hematoxylin was electroactive. Under optimal conditions, a very low detection limit of 0.08 fM and an adequate dynamic range of 0.2 fM-500 pM were achieved. The fabricated sensor was reported to be reproducible and selective when tested using different types of mismatched target sequences. And finally, the real human serum samples were used to confirm the capability of the nanobiosensor to detect microRNA 155 without any significant interference from other molecules and components.

CSPG4 as a target for the specific killing of triple-negative breast cancer cells by a recombinant SNAP-tag-based antibody-auristatin F drug conjugate

PURPOSE

Triple-negative breast cancer (TNBC) is phenotypic of breast tumors lacking expression of the estrogen receptor (ER), the progesterone receptor (PgR), and the human epidermal growth factor receptor 2 (HER2). The paucity of well-defined molecular targets in TNBC, coupled with the increasing burden of breast cancer-related mortality, emphasizes the need to develop targeted diagnostics and therapeutics. While antibody-drug conjugates (ADCs) have emerged as revolutionary tools in the selective delivery of drugs to malignant cells, their widespread clinical use has been hampered by traditional strategies which often give rise to heterogeneous mixtures of ADC products.

METHODS

Utilizing SNAP-tag technology as a cutting-edge site-specific conjugation method, a chondroitin sulfate proteoglycan 4 (CSPG4)-targeting ADC was engineered, encompassing a single-chain antibody fragment (scFv) conjugated to auristatin F (AURIF) via a click chemistry strategy.

RESULTS

After showcasing the self-labeling potential of the SNAP-tag component, surface binding and internalization of the fluorescently labeled product were demonstrated on CSPG4-positive TNBC cell lines through confocal microscopy and flow cytometry. The cell-killing ability of the novel AURIF-based recombinant ADC was illustrated by the induction of a 50% reduction in cell viability at nanomolar to micromolar concentrations on target cell lines.

CONCLUSION

This research underscores the applicability of SNAP-tag in the unambiguous generation of homogeneous and pharmaceutically relevant immunoconjugates that could potentially be instrumental in the management of a daunting disease like TNBC.

Detection and Classification of Histopathological Breast Images Using a Fusion of CNN Frameworks

Breast cancer is responsible for the deaths of thousands of women each year. The diagnosis of breast cancer (BC) frequently makes the use of several imaging techniques. On the other hand, incorrect identification might occasionally result in unnecessary therapy and diagnosis. Therefore, the accurate identification of breast cancer can save a significant number of patients from undergoing unnecessary surgery and biopsy procedures. As a result of recent developments in the field, the performance of deep learning systems used for medical image processing has showed significant benefits. Deep learning (DL) models have found widespread use for the aim of extracting important features from histopathologic BC images. This has helped to improve the classification performance and has assisted in the automation of the process. In recent times, both convolutional neural networks (CNNs) and hybrid models of deep learning-based approaches have demonstrated impressive performance. In this research, three different types of CNN models are proposed: a straightforward CNN model (1-CNN), a fusion CNN model (2-CNN), and a three CNN model (3-CNN). The findings of the experiment demonstrate that the techniques based on the 3-CNN algorithm performed the best in terms of accuracy (90.10%), recall (89.90%), precision (89.80%), and f1-Score (89.90%). In conclusion, the CNN-based approaches that have been developed are contrasted with more modern machine learning and deep learning models. The application of CNN-based methods has resulted in a significant increase in the accuracy of the BC classification.

Hybrid Techniques for Diagnosing Endoscopy Images for Early Detection of Gastrointestinal Disease Based on Fusion Features

Gastrointestinal (GI) diseases, particularly tumours, are considered one of the most widespread and dangerous diseases and thus need timely health care for early detection to reduce deaths. Endoscopy technology is an effective technique for diagnosing GI diseases, thus producing a video containing thousands of frames. However, it is difficult to analyse all the images by a gastroenterologist, and it takes a long time to keep track of all the frames. Thus, artificial intelligence systems provide solutions to this challenge by analysing thousands of images with high speed and effective accuracy. Hence, systems with different methodologies are developed in this work. The first methodology for diagnosing endoscopy images of GI diseases is by using VGG-16 + SVM and DenseNet-121 + SVM. The second methodology for diagnosing endoscopy images of gastrointestinal diseases by artificial neural network (ANN) is based on fused features between VGG-16 and DenseNet-121 before and after high-dimensionality reduction by the principal component analysis (PCA). The third methodology is by ANN and is based on the fused features between VGG-16 and handcrafted features and features fused between DenseNet-121 and the handcrafted features. Herein, handcrafted features combine the features of gray level cooccurrence matrix (GLCM), discrete wavelet transform (DWT), fuzzy colour histogram (FCH), and local binary pattern (LBP) methods. All systems achieved promising results for diagnosing endoscopy images of the gastroenterology data set. The ANN network reached an accuracy, sensitivity, precision, specificity, and an AUC of 98.9%, 98.70%, 98.94%, 99.69%, and 99.51%, respectively, based on fused features of the VGG-16 and the handcrafted.

Nontoxic Fluorescent Nanoprobes for Multiplexed Detection and 3D Imaging of Tumor Markers in Breast Cancer

Multiplexed fluorescent immunohistochemical analysis of breast cancer (BC) markers and high-resolution 3D immunofluorescence imaging of the tumor and its microenvironment not only facilitate making the disease prognosis and selecting effective anticancer therapy (including photodynamic therapy), but also provides information on signaling and metabolic mechanisms of carcinogenesis and helps in the search for new therapeutic targets and drugs. The characteristics of imaging nanoprobe efficiency, such as sensitivity, target affinity, depth of tissue penetration, and photostability, are determined by the properties of their components, fluorophores and capture molecules, and by the method of their conjugation. Regarding individual nanoprobe components, fluorescent nanocrystals (NCs) are widely used for optical imaging in vitro and in vivo, and single-domain antibodies (sdAbs) are well established as highly specific capture molecules in diagnostic and therapeutic applications. Moreover, the technologies of obtaining functionally active sdAb–NC conjugates with the highest possible avidity, with all sdAb molecules bound to the NC in a strictly oriented manner, provide 3D-imaging nanoprobes with strong comparative advantages. This review is aimed at highlighting the importance of an integrated approach to BC diagnosis, including the detection of biomarkers of the tumor and its microenvironment, as well as the need for their quantitative profiling and imaging of their mutual location, using advanced approaches to 3D detection in thick tissue sections. The existing approaches to 3D imaging of tumors and their microenvironment using fluorescent NCs are described, and the main comparative advantages and disadvantages of nontoxic fluorescent sdAb–NC conjugates as nanoprobes for multiplexed detection and 3D imaging of BC markers are discussed.

Nuclear Deformability of Breast Cells Analyzed from Patients with Malignant and Benign Breast Diseases

Breast cancer is a heterogeneous and dynamic disease, in which cancer cells are highly responsive to alterations in the microenvironment. Today, conventional methods of detecting cancer give a rather static image of the condition of the disease, so dynamic properties such as invasiveness and metastasis are difficult to capture. In this study, conventional molecular-level evaluations of the patients with breast adenocarcinoma were combined with in vitro methods on micropatterned poly(methyl methacrylate) (PMMA) biomaterial surfaces that deform cells. A correlation between deformability of the nuclei and cancer stemness, invasiveness, and metastasis was sought. Clinical patient samples were from regions of the breast with different proximities to the tumor. Responses at the single-cell level toward the micropatterned surfaces were studied using CD44/24, epithelial cell adhesion marker (EpCAM), MUC1, and PCK. Results showed that molecular markers and shape descriptors can discriminate the cells from different proximities to the tumor center and from different patients. The cells with the most metastatic and invasive properties showed both the highest deformability and the highest level of metastatic markers. In conclusion, by using a combination of molecular markers together with nuclear deformation, it is possible to improve detection and separation of subpopulations in heterogenous breast cancer specimens at the single-cell level.

High-Frequency Excitation and Surface Temperature Analysis of Breast Tissue for Detection of Anomaly

Techniques used for breast cancer detection usually incorporate Infrared Thermography (IRT) to locate abnormal hotspots or asymmetry in a thermal texture map. This can be unreliable due to various individual differences from one person to another. In this paper, a detection method that is independent of the aforementioned limitations is proposed. This technique is a combination of thermal imaging and high-frequency excitation. This technique is based on the fact that the differences in electromagnetic and thermal properties of abnormal (malignant) tissue and the surrounding normal tissue will result in a noticeable difference in temperature increase after exposure to high-frequency excitation. A three-dimensional (3-D) finite-element method (FEM) has been used to simulate the thermal behavior of breast tissue exposed to antenna excitations. Finally, the effectiveness of this technique was tested in a series of experiments using a life-sized breast phantom.

Advanced Enhancement Techniques for Breast Cancer Classification in Mammographic Images

Background:

Breast cancer is one of the most significant health problems in the world. Early diagnosis of breast cancer is very important for treatment. Image enhancement techniques have been used to improve the captured images for quick and accurate diagnosis. These techniques include median filtering, edge enhancement, dilation, erosion, and contrast-limited adaptive histogram equalization. Although these techniques have been used in many studies, their results have not reached optimum values based on image properties and the methods used for feature extraction and classification.

Methods:

In this study, enhancement techniques were implemented to guarantee the best image enhancement. They were applied to 319 images collected from the Mammographic Image Analysis Society (MIAS) database. The Gabor filter and local binary pattern were used as feature extraction methods together with support vector machine (SVM), linear discriminant analysis (LDA), and nearest neighbor (KNN) classifiers.

Results:

The experimental work indicates that by merging the features of the Gabor filter and local binary pattern, the results were 97.8%, 100%, and 94.6% for normal/abnormal and 85.1%, 88.7%, and 81.9% for benign/malignant using the SVM, LDA, and KNN classifiers, respectively.

Conclusion:

The best results were obtained by combining the features of the two tested strategies and using LDA as a classifier.

A Novel Hybrid Deep Learning Model for Metastatic Cancer Detection

Cancer has been found as a heterogeneous disease with various subtypes and aims to destroy the body's normal cells abruptly. As a result, it is essential to detect and prognosis the distinct type of cancer since they may help cancer survivors with treatment in the early stage. It must also divide cancer patients into high- and low-risk groups. While realizing efficient detection of cancer is frequently a time-taking and exhausting task with the high possibility of pathologist errors and previous studies employed data mining and machine learning (ML) techniques to identify cancer, these strategies rely on handcrafted feature extraction techniques that result in incorrect classification. On the contrary, deep learning (DL) is robust in feature extraction and has recently been widely used for classification and detection purposes. This research implemented a novel hybrid AlexNet-gated recurrent unit (AlexNet-GRU) model for the lymph node (LN) breast cancer detection and classification. We have used a well-known Kaggle (PCam) data set to classify LN cancer samples. This study is tested and compared among three models: convolutional neural network GRU (CNN-GRU), CNN long short-term memory (CNN-LSTM), and the proposed AlexNet-GRU. The experimental results indicated that the performance metrics accuracy, precision, sensitivity, and specificity (99.50%, 98.10%, 98.90%, and 97.50) of the proposed model can reduce the pathologist errors that occur during the diagnosis process of incorrect classification and significantly better performance than CNN-GRU and CNN-LSTM models. The proposed model is compared with other recent ML/DL algorithms to analyze the model's efficiency, which reveals that the proposed AlexNet-GRU model is computationally efficient. Also, the proposed model presents its superiority over state-of-the-art methods for LN breast cancer detection and classification.

Microwave Imaging for Early Breast Cancer Detection: Current State, Challenges, and Future Directions

Breast cancer is the most commonly diagnosed cancer type and is the leading cause of cancer-related death among females worldwide. Breast screening and early detection are currently the most successful approaches for the management and treatment of this disease. Several imaging modalities are currently utilized for detecting breast cancer, of which microwave imaging (MWI) is gaining quite a lot of attention as a promising diagnostic tool for early breast cancer detection. MWI is a noninvasive, relatively inexpensive, fast, convenient, and safe screening tool. The purpose of this paper is to provide an up-to-date survey of the principles, developments, and current research status of MWI for breast cancer detection. This paper is structured into two sections; the first is an overview of current MWI techniques used for detecting breast cancer, followed by an explanation of the working principle behind MWI and its various types, namely, microwave tomography and radar-based imaging. In the second section, a review of the initial experiments along with more recent studies on the use of MWI for breast cancer detection is presented. Furthermore, the paper summarizes the challenges facing MWI as a breast cancer detection tool and provides future research directions. On the whole, MWI has proven its potential as a screening tool for breast cancer detection, both as a standalone or complementary technique. However, there are a few challenges that need to be addressed to unlock the full potential of this imaging modality and translate it to clinical settings.

Automatic breast tissue segmentation in MRIs with morphology snake and deep denoiser training via extended Stein's unbiased risk estimator

Accurate segmentation of the breast tissue is a significant challenge in the analysis of breast MR images, especially analysis of breast images with low contrast. Most of the existing methods for breast segmentation are semi-automatic and limited in their ability to achieve accurate results. This is because of difficulties in removing landmarks from noisy magnetic resonance images (MRI). Especially, when tumour is imaged for scanning, how to isolate the tumour region from chest will directly affect the accuracy for tumour to be detected. Due to low intensity levels and the close connection between breast and chest portion in MRIs, this study proposes an innovative, fully automatic and fast segmentation approach which combines histogram with inverse Gaussian gradient for morphology snakes, along with extended Stein's unbiased risk estimator (eSURE) applied for unsupervised learning of deep neural network Gaussian denoisers, aimed at accurate identification of landmarks such as chest and breast.

Positron Emission Mammography (PEM) in the diagnosis of breast cancer: A systematic review and economic evaluation

Background: Positron Emission Mammography (PEM) is an imaging technique which is increasing focuses on imaging the chest instead of imaging the whole body. The aim of this study was to conduct a systematic review of the clinical efficacy and coste-ffectiveness of PEM technology, as compared with PET, as a diagnostic method used for breast cancer patients.

Methods: The present study was a Health Technology Assessment (HTA), which was conducted via a systematic review of clinical efficacy and cost-effectiveness of the methods based on domestic evidence. To evaluate the efficacy of the PEM diagnostic method, as compared with PET, we used efficacy indices, including Sensitivity, Specificity, Accuracy, PPV, and NPV. The required data were collected through a meta-analysis of studies published in electronic databases from 1990 to 2016. In addition, direct costs in both methods were estimated and finally, a cost-effectiveness analysis was performed using the results of the study. Also, a one-way sensitivity analysis was performed to examine the effects of parameters’ uncertainty in the model. In this study, we used STATA software to integrate the results of studies with similar parameters.

Results: A total of 722 cases (N) were obtained from the five final studies. The results of the meta-analysis performed on the collected data showed that the two methods were identical in terms of the Specificity and PPV parameters. However, as to Sensitivity, NPV, and Accuracy parameters, the PEM method was superior to the PET for diagnosis of primary breast cancer. The total cost of using PEM and PET was $1737385.7 and $1940903.5, respectively, and the cost of a one-time scan (cost per unit) using PEM and PET devices was $86.82 and $157.63, respectively. As compared with the PET method, the use of the PEM diagnostic method for diagnosis of breast cancer was cost-effective in terms of all the five studied parameters (it was definitely cost-effective for four parameters and was also considered as cost-effective for another index, since ICER was below the threshold).

Conclusion: The results showed that the use of PEM technology for the diagnosis of primary breast cancer is more cost-effective than PET technology; thus, due to the wide range of PET technology in different fields, it is recommended that this method should be used in other areas of priority.

An introduction to the Cyrcadia Breast Monitor: A wearable breast health monitoring device

BACKGROUND

The most common breast cancer detection modalities are generally limited by radiation exposure, discomfort, high costs, inter-observer variabilities in image interpretation, and low sensitivity in detecting cancer in dense breast tissue. Therefore, there is a clear need for an affordable and effective adjunct modality that can address these limitations. The Cyrcadia Breast Monitor (CBM) is a non-invasive, non-compressive, and non-radiogenic wearable device developed as an adjunct to current modalities to assist in the detection of breast tissue abnormalities in any type of breast tissue.

METHODS

The CBM records thermodynamic metabolic data from the breast skin surface over a period of time using two wearable biometric patches consisting of eight sensors each and a data recording device. The acquired multi-dimensional temperature time series data are analyzed to determine the presence of breast tissue abnormalities. The objective of this paper is to present the scientific background of CBM and also to describe the history around the design and development of the technology.

RESULTS

The results of using the CBM device in the initial clinical studies are also presented. Twenty four-hour long breast skin temperature circadian rhythm data was collected from 93 benign and 108 malignant female study subjects in the initial clinical studies. The predictive model developed using these datasets could differentiate benign and malignant lesions with 78% accuracy, 83.6% sensitivity and 71.5% specificity. A pilot study of 173 female study subjects is underway, in order to validate this predictive model in an independent test population.

CONCLUSIONS

The results from the initial studies indicate that the CBM may be valuable for breast health monitoring under physician supervision for confirmation of any abnormal changes, potentially prior to other methods, such as, biopsies. Studies are being conducted and planned to validate the technology and also to evaluate its ability as an adjunct breast health monitoring device for identifying abnormalities in difficult-to-diagnose dense breast tissue.

Using Convolutional Neural Network with Cheat Sheet and Data Augmentation to Detect Breast Cancer in Mammograms

The American Cancer Society expected to diagnose 276,480 new cases of invasive breast cancer in the USA and 48,530 new cases of noninvasive breast cancer among women in 2020. Early detection of breast cancer, followed by appropriate treatment, can reduce the risk of death from this disease. DL through CNN can assist imaging specialists in classifying the mammograms accurately. Accurate classification of mammograms using CNN needs a well-trained CNN by a large number of labeled mammograms. Unfortunately, a large number of labeled mammograms are not always available. In this study, a novel procedure to aid imaging specialists in detecting normal and abnormal mammograms has been proposed. The procedure supplied the designed CNN with a cheat sheet for some classical attributes extracted from the ROI and an extra number of labeled mammograms through data augmentation. The cheat sheet aided the CNN through encoding easy-to-recognize artificial patterns in the mammogram before passing it to the CNN, and the data augmentation supported the CNN with more labeled data points. Fifteen runs of 4 different modified datasets taken from the MIAS dataset were conducted and analyzed. The results showed that the cheat sheet, along with data augmentation, enhanced CNN's accuracy by at least 12.2% and enhanced the precision of the CNN by at least 2.2. The mean accuracy, sensitivity, and specificity obtained using the proposed procedure were 92.1, 91.4, and 96.8, respectively, while the average area under the ROC curve was 94.9.

Applications of Computational Methods in Biomedical Breast Cancer Imaging Diagnostics: A Review

With the exponential increase in new cases coupled with an increased mortality rate, cancer has ranked as the second most prevalent cause of death in the world. Early detection is paramount for suitable diagnosis and effective treatment of different kinds of cancers, but this is limited to the accuracy and sensitivity of available diagnostic imaging methods. Breast cancer is the most widely diagnosed cancer among women across the globe with a high percentage of total cancer deaths requiring an intensive, accurate, and sensitive imaging approach. Indeed, it is treatable when detected at an early stage. Hence, the use of state of the art computational approaches has been proposed as a potential alternative approach for the design and development of novel diagnostic imaging methods for breast cancer. Thus, this review provides a concise overview of past and present conventional diagnostics approaches in breast cancer detection. Further, we gave an account of several computational models (machine learning, deep learning, and robotics), which have been developed and can serve as alternative techniques for breast cancer diagnostics imaging. This review will be helpful to academia, medical practitioners, and others for further study in this area to improve the biomedical breast cancer imaging diagnosis.

Breast Tumor Malignancy Classification using Smartphone Compression-induced Sensing System and Deformation Index Ratio

Smartphone-based compression-induced sensing system uses the light diffusion pattern to characterize the early-stage breast tumor noninvasively. The system is built on a smartphone and cloud platform to capture, transfer, and interface with the user. The compressed tissue's deforming pattern creates distinctive tactile images due to the size and hardness of the tumor. From the compression-induced images, we estimate the size of the tumor using projection analysis and the tumor's malignancy using the tissue deformation index ratio. Deformation index ratio is based on the changes of a healthy region over the tumorous region. By using the projection analysis, the human patient tumor size estimation resulted in 52.3% of the average error. For a small number (seven) of the feasibility test, the tumor's malignancy was classified based on the deformation index ratio with 67.0% of sensitivity and 100% specificity.

A Brief Review on Breast Carcinoma and Deliberation on Current Non Invasive Imaging Techniques for Detection

BACKGROUND

Breast carcinoma is a life threatening disease that accounts for 25.1% of all carcinoma among women worldwide. Early detection of the disease enhances the chance for survival.

DISCUSSION

This paper presents comprehensive report on breast carcinoma disease and its modalities available for detection and diagnosis, as it delves into the screening and detection modalities with special focus placed on the non-invasive techniques and its recent advancement work done, as well as a proposal on a novel method for the application of early breast carcinoma detection.

CONCLUSION

This paper aims to serve as a foundation guidance for the reader to attain bird's eye understanding on breast carcinoma disease and its current non-invasive modalities.

Recent advances in aptamer-based sensors for breast cancer diagnosis: special cases for nanomaterial-based VEGF, HER2, and MUC1 aptasensors

Cancer is one of the most common and important diseases with a high mortality rate. Breast cancer is among the three most common types of cancer in women, and the mortality rate has reached 0.024% in some countries. For early-stage preclinical diagnosis of breast cancer, sensitive and reliable tools are needed. Today, there are many types of biomarkers that have been identified for cancer diagnosis. A wide variety of detection strategies have also been developed for the detection of these biomarkers from serum or other body fluids at physiological concentrations. Aptamers are single-stranded DNA or RNA oligonucleotides and promising in the production of more sensitive and reliable biosensor platforms in combination with a wide range of nanomaterials. Conformational changes triggered by the target analyte have been successfully applied in fluorometric, colorimetric, plasmonic, and electrochemical-based detection strategies. This review article presents aptasensor approaches used in the detection of vascular endothelial growth factor (VEGF), human epidermal growth factor receptor 2 (HER2), and mucin-1 glycoprotein (MUC1) biomarkers, which are frequently studied in the diagnosis of breast cancer. The focus of this review article is on developments of the last decade for detecting these biomarkers using various sensitivity enhancement techniques and nanomaterials.

MicroRNA-155 complementation on a chemically functionalized dual electrode surface for determining breast cancer

This study correlated and quantified the expression of microRNA-155 with breast cancer to determine breast cancer progression. The target microRNA-155 sequence was identified by complementation on a capture-probe sequence-immobilized interdigitated dual electrode surface. The sensitivity was found to be 1 fM, and the limit of detection fell between 1 and 10 fM. The specific sequence selectivity with single mismatches, triple mismatches, and noncomplementary bases failed to complement the capture-probe sequence. The obtained results demonstrate the selective determination of the microRNA-155 sequence and can help to diagnose breast cancer.

Is there an advantage to using computer aided detection for the early detection of pulmonary nodules within chest X-Ray imaging?

OBJECTIVE

Using published literature, this research examines whether Computer-aided Detection (CAD) identifies more Pulmonary Nodules (PN) within Chest X-ray (CXR) systems, compared to radiologist diagnosis without CAD.

KEY FINDINGS

Although the primary papers were pointing to CAD being a beneficial system in the diagnosis of PN detection, a regression analysis of the data available within these papers showed no correlation between the higher sensitivity of CAD against the detrimental high False Positives (FP) of CAD. Findings of the studies were deemed inconclusive.

CONCLUSION

Further research is recommended to review the potential of CAD on CXR PN detection.

IMPLICATIONS FOR PRACTICE

CAD acting as a second reader could potentially reduce interpreter error rate.

Thermogram Breast Cancer Detection: A Comparative Study of Two Machine Learning Techniques

Breast cancer is considered one of the major threats for women’s health all over the world. The World Health Organization (WHO) has reported that 1 in every 12 women could be subject to a breast abnormality during her lifetime. To increase survival rates, it is found that it is very effective to early detect breast cancer. Mammography-based breast cancer screening is the leading technology to achieve this aim. However, it still can not deal with patients with dense breast nor with tumor size less than 2 mm. Thermography-based breast cancer approach can address these problems. In this paper, a thermogram-based breast cancer detection approach is proposed. This approach consists of four phases: (1) Image Pre-processing using homomorphic filtering, top-hat transform and adaptive histogram equalization, (2) ROI Segmentation using binary masking and K-mean clustering, (3) feature extraction using signature boundary, and (4) classification in which two classifiers, Extreme Learning Machine (ELM) and Multilayer Perceptron (MLP), were used and compared. The proposed approach is evaluated using the public dataset, DMR-IR. Various experiment scenarios (e.g., integration between geometrical feature extraction, and textural features extraction) were designed and evaluated using different measurements (i.e., accuracy, sensitivity, and specificity). The results showed that ELM-based results were better than MLP-based ones with more than 19%.

Palpreast—A New Wearable Device for Breast Self-Examination

Breast cancer is the most commonly diagnosed cancer in women worldwide. Although targeted screening programs using mammography have facilitated earlier detection and improved treatment has resulted in a significant reduction in mortality, some negative aspects related to cost, the availability of trained staff, the duration of the procedure, and its non-generalizability to all women must be taken into consideration. Breast palpation is a simple non-invasive procedure that can be performed by lay individuals for detecting possible malignant nodules in the breast. It is a simple test, based on the haptic perception of different stiffness between healthy and abnormal tissues. According to a survey we carried out, despite being safe and simple, breast self-examination is not carried by women because they are not confident of their ability to detect a lump. In this study, a non-invasive wearable device designed to mimic the process of breast self-examination using pressure sensing textiles and thus increase the confidence and self-awareness of women is proposed. Combined with other screening methods, the device can increase the odds of early detection for better prognosis. Here, we present the physical implementation of the device and a finite element analysis of the mechanics underlying its working principle. Characterization of the device using models of large and medium breast phantoms with rigid inclusions demonstrates that it can detect nodules in much the same way as does the human hand during breast self-examination.

Thermal imaging as a tool for evaluating tumor treatment efficacy

Breast cancer is the most frequently diagnosed cancer among women in the Western world. Thermography is a nonionizing, noninvasive, portable, and low-cost method that can be used in an outpatient clinic. It was tried as a tool to detect breast cancer tumors, however, it had too many false readings. Thermography has been extensively studied as a breast cancer detection tool but was not used as a treatment monitoring tool. The purpose of this study was to investigate the possibility of using thermal imaging as a feedback system to optimize radiation therapy. Patients were imaged with a thermal camera prior and throughout the radiotherapy sessions. At the end of the session, the images were analyzed for temporal vasculature changes through vessels segmentation image processing tools. Tumors that were not responsive to treatment were observed before the radiation therapy sessions were concluded. Assessing the efficacy of radiotherapy during treatment makes it possible to change the treatment regimen, dose, and radiation field during treatment as well as to individualize treatment schedules to optimize treatment effectiveness.

Cancer Detection Based on Electrical Impedance Spectroscopy: A Clinical Study

An electrical Impedance based tool is designed and developed to aid physicians performing clinical exams focusing on cancer detection. Current research envisions improvement in sensor-based measurement technology to differentiate malignant and benign lesions in human subjects. The tool differentiates malignant anomalies from nonmalignant anomalies using Electrical Impedance Spectroscopy (EIS). This method exploits cancerous tissue behavior by using EIS technique to aid early detection of cancerous tissue. The correlation between tissue electrical properties and tissue pathologies is identified by offering an analysis technique based on the Cole model. Additional classification and decision-making algorithm is further developed for cancer detection. This research suggests that the sensitivity of tumor detection will increase when supplementary information from EIS and built-in intelligence are provided to the physician.

Quantitative assessment of soft tissue deformation using digital speckle pattern interferometry: studies on phantom breast models

Assessment of mechanical properties of soft matter is a challenging task in a purely noninvasive and noncontact environment. As tissue mechanical properties play a vital role in determining tissue health status, such noninvasive methods offer great potential in framing large-scale medical screening strategies. The digital speckle pattern interferometry (DSPI)-based image capture and analysis system described here is capable of extracting the deformation information from a single acquired fringe pattern. Such a method of analysis would be required in the case of the highly dynamic nature of speckle patterns derived from soft tissues while applying mechanical compression. Soft phantoms mimicking breast tissue optical and mechanical properties were fabricated and tested in the DSPI out of plane configuration set up. Hilbert transform (HT)-based image analysis algorithm was developed to extract the phase and corresponding deformation of the sample from a single acquired fringe pattern. The experimental fringe contours were found to correlate with numerically simulated deformation patterns of the sample using Abaqus finite element analysis software. The extracted deformation from the experimental fringe pattern using the HT-based algorithm is compared with the deformation value obtained using numerical simulation under similar conditions of loading and the results are found to correlate with an average %error of 10. The proposed method is applied on breast phantoms fabricated with included subsurface anomaly mimicking cancerous tissue and the results are analyzed.

Fourier rebinning and consistency equations for time-of-flight PET planograms

Due to the unique geometry, dual-panel PET scanners have many advantages in dedicated breast imaging and on-board imaging applications since the compact scanners can be combined with other imaging and treatment modalities. The major challenges of dual-panel PET imaging are the limited-angle problem and data truncation, which can cause artifacts due to incomplete data sampling. The time-of-flight (TOF) information can be a promising solution to reduce these artifacts. The TOF planogram is the native data format for dual-panel TOF PET scanners, and the non-TOF planogram is the 3D extension of linogram. The TOF planograms is five-dimensional while the objects are three-dimensional, and there are two degrees of redundancy. In this paper, we derive consistency equations and Fourier-based rebinning algorithms to provide a complete understanding of the rich structure of the fully 3D TOF planograms. We first derive two consistency equations and John's equation for 3D TOF planograms. By taking the Fourier transforms, we obtain two Fourier consistency equations and the Fourier-John equation, which are the duals of the consistency equations and John's equation, respectively. We then solve the Fourier consistency equations and Fourier-John equation using the method of characteristics. The two degrees of entangled redundancy of the 3D TOF data can be explicitly elicited and exploited by the solutions along the characteristic curves. As the special cases of the general solutions, we obtain Fourier rebinning and consistency equations (FORCEs), and thus we obtain a complete scheme to convert among different types of PET planograms: 3D TOF, 3D non-TOF, 2D TOF and 2D non-TOF planograms. The FORCEs can be used as Fourier-based rebinning algorithms for TOF-PET data reduction, inverse rebinnings for designing fast projectors, or consistency conditions for estimating missing data. As a byproduct, we show the two consistency equations are necessary and sufficient for 3D TOF planograms. Finally, we give numerical examples of implementation of a fast 2D TOF planogram projector and Fourier-based rebinning for a 2D TOF planograms using the FORCEs to show the efficacy of the Fourier-based solutions.

Optical imaging for breast cancer prescreening

Breast cancer prescreening is carried out prior to the gold standard screening using X-ray mammography and/or ultrasound. Prescreening is typically carried out using clinical breast examination (CBE) or self-breast examinations (SBEs). Since CBE and SBE have high false-positive rates, there is a need for a low-cost, noninvasive, non-radiative, and portable imaging modality that can be used as a prescreening tool to complement CBE/SBE. This review focuses on the various hand-held optical imaging devices that have been developed and applied toward early-stage breast cancer detection or as a prescreening tool via phantom, in vivo, and breast cancer imaging studies. Apart from the various optical devices developed by different research groups, a wide-field fiber-free near-infrared optical scanner has been developed for transillumination-based breast imaging in our Optical Imaging Laboratory. Preliminary in vivo studies on normal breast tissues, with absorption-contrasted targets placed in the intramammary fold, detected targets as deep as 8.8 cm. Future work involves in vivo imaging studies on breast cancer subjects and comparison with the gold standard X-ray mammography approach.

Computer vision-based breast self-examination stroke position and palpation pressure level classification using artificial neural networks and wavelet transforms

This paper focuses on breast self-examination (BSE) stroke position and palpation level classification for the development of a computer vision-based BSE training and guidance system. In this study, image frames are extracted from a BSE video and processed considering the color information, shape, and texture by wavelet transform and first order color moment. The new approach using artificial neural network and wavelet transform can identify BSE stroke positions and palpation levels, i.e. light, medium, and deep, at 97.8 % and 87.5 % accuracy respectively.

Digital tomosynthesis: a new future for breast imaging?

The aim of this article is to review the major limitations in current mammography and to describe how these may be addressed by digital breast tomosynthesis (DBT). DBT is a novel imaging technology in which an x-ray fan beam sweeps in an arc across the breast, producing tomographic images and enabling the production of volumetric, three-dimensional (3D) data. It can reduce tissue overlap encountered in conventional two-dimensional (2D) mammography, and thus has the potential to improve detection of breast cancer, reduce the suspicious presentations of normal tissues, and facilitate accurate differentiation of lesion types. This paper reviews the latest studies of this new technology. Issues including diagnostic efficacy, reading time, radiation dose, and level of compression; cost and new innovations are considered.