Breast Tumor Detection Research Articles

Exosome and Breast Cancer

Breast cancer is responsible for the death of millions of women worldwide every year. It is widespread in the world and Iraq that become a genuine problem for public health. Several clinical, diagnostic and pathological techniques have been introduced to get early detection of breast tumors. The uses of current known tumors markers have many limitations. New technique for diagnosis of breast cancer involve detection of extracellular vesicles (EVs) exosomes and its phosphoproteins as a product of cancer cells which represent a non-invasive liquid biopsy that may replace the invasive surgical method.

Electrical Impedance Tomography Technical Contributions for Detection and 3D Geometric Localization of Breast Tumors: A Systematic Review.

Impedance measuring acquisition systems focused on breast tumor detection, as well as image processing techniques for 3D imaging, are reviewed in this paper in order to define potential opportunity areas for future research. The description of reported works using electrical impedance tomography (EIT)-based techniques and methodologies for 3D bioimpedance imaging of breast tissues with tumors is presented. The review is based on searching and analyzing related works reported in the most important research databases and is structured according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) parameters and statements. Nineteen papers reporting breast tumor detection and location using EIT were systematically selected and analyzed in this review. Clinical trials in the experimental stage did not produce results in most of analyzed proposals (about 80%), wherein statistical criteria comparison was not possible, such as specificity, sensitivity and predictive values. A 3D representation of bioimpedance is a potential tool for medical applications in malignant breast tumors detection being capable to estimate an ap-proximate the tumor volume and geometric location, in contrast with a tumor area computing capacity, but not the tumor extension depth, in a 2D representation.

Novel measurement technique to detect breast tumor based on the smallest form factor of UWB patch antenna

AbstractA small ultra-wideband (UWB) patch antenna for microwave breast imaging (MWI) applications is shown off in this paper. However, to improve the antenna performance relating to the bandwidth (BW), the radiating element of the suggested antenna is modified by adding slits in the patch as well as the ground plane. The proposed prototype has a relatively small size of 20 × 19 × 1.6 mm3and it accomplishes a return loss below −10 dB (S11< −10 dB) at an overall BW of 7 GHz (4–11 GHz) with more than 3 dBi realized gain. The antenna is designed and simulated by using a finite integration technique-based simulator. In this way, the characteristics of the fabricated antenna are measured to examine the antenna performance. Indeed, the fidelity factor of face-to-face (FtF) and side-by-side (SbS) scenarios are also noticed for the same frequency range. In the final analysis, a simulation model of the antennas, that operate as a transceiver, and a breast phantom model with tumor sample is proposed for detecting cancerous tumor cells within the breast. Hence, the proposed approach is suitable for UWB based MWI applications in tumor cell detection.

Thermal Simulation and Detection of Breast Tumor Using Passive Acoustic Thermometry

Purpose: For over three decades, various researchers have aimed to construct a model of breast cancer. Most of them have used an infrared thermal model to stimulate breast cancer, but in this study, a novel estimation methodology is presented to detect the breast cancer tumor using the surface measurement obtained by Passive Acoustic Thermometer (PAT). PAT is a safe method for internal temperature estimation that works based on acoustic radiation of materials with a specific temperature. Materials and Methods: This article uses a simulation framework for breast tissue simulation and tumor detection using the PAT methodologies in different scenarios. This framework supports the generation of acoustic radiation, tissue modelling, signal processing, parameter estimation, and temperature reconstruction processes. The proposed framework estimates the temperature in the frequency domain and uses the frequency spectrum of the acquired ultrasound signals captured by a single transducer. Using the proposed framework, PAT has been evaluated in breast cancer detection. Results: According to the results, obtained from the temperature estimation in scenario 3, the sub-band estimation method, which is utilized in practical experiments in this field, shows different errors in each sub-band, making it difficult to select the true estimation. Therefore, a novel formulation is proposed that provides only one estimated temperature for breast tissue with a reasonable error (1.28 degrees) for tumor detection. Conclusion: The results show that it is possible to use this framework to evaluate the PAT in different scenarios for tumor detection. In fact, this method enhances the possibility of examination of different conditions and algorithms. It also reduces the cost of practical experiments.

W-Net: Dense and diagnostic semantic segmentation of subcutaneous and breast tissue in ultrasound images by incorporating ultrasound RF waveform data.

We study the use of raw ultrasound waveforms, often referred to as the "Radio Frequency" (RF) data, for the semantic segmentation of ultrasound scans to carry out dense and diagnostic labeling. We present W-Net, a novel Convolution Neural Network (CNN) framework that employs the raw ultrasound waveforms in addition to the grey ultrasound image to semantically segment and label tissues for anatomical, pathological, or other diagnostic purposes. To the best of our knowledge, this is also the first deep-learning or CNN approach for segmentation that analyzes ultrasound raw RF data along with the grey image. We chose subcutaneous tissue (SubQ) segmentation as our initial clinical goal for dense segmentation since it has diverse intermixed tissues, is challenging to segment, and is an underrepresented research area. SubQ potential applications include plastic surgery, adipose stem-cell harvesting, lymphatic monitoring, and possibly detection/treatment of certain types of tumors. Unlike prior work, we seek to label every pixel in the image, without the use of a background class. A custom dataset consisting of hand-labeled images by an expert clinician and trainees are used for the experimentation, currently labeled into the following categories: skin, fat, fat fascia/stroma, muscle, and muscle fascia. We compared W-Net and attention variant of W-Net (AW-Net) with U-Net and Attention U-Net (AU-Net). Our novel W-Net's RF-Waveform encoding architecture outperformed regular U-Net and AU-Net, achieving the best mIoU accuracy (averaged across all tissue classes). We study the impact of RF data on dense labeling of the SubQ region, which is followed by the analyses of the generalization capability of the networks to patients and analysis on the SubQ tissue classes, determining that fascia tissues, especially muscle fascia in particular, are the most difficult anatomic class to recognize for both humans and AI algorithms. We present diagnostic semantic segmentation, which is semantic segmentation carried out for the purposes of direct diagnostic pixel labeling, and apply it to breast tumor detection task on a publicly available dataset to segment pixels into malignant tumor, benign tumor, and background tissue class. Using the segmented image we diagnose the patient by classifying the breast lesion as either benign or malignant. We demonstrate the diagnostic capability of RF data with the use of W-Net, which achieves the best segmentation scores across all classes.

Breast Tumor Detection in Mammography Using Transfer Learning Techniques]{Breast Cancer: Toward an Accurate Breast Tumor Detection Model in Mammography Using Transfer Learning Techniques

Breast Tumor Detection in Mammography Using Transfer Learning Techniques]{Breast Cancer: Toward an Accurate Breast Tumor Detection Model in Mammography Using Transfer Learning Techniques

A Novel Miniaturized mmWave Antenna Sensor for Breast Tumor Detection and 5G Communication

Breast cancer is one of the deadliest forms of disease that affects women worldwide. X-ray mammography, magnetic resonance imaging, and ultrasound are all helpful methods of medical imaging, but they have their own limitations due to low tissue contrast and side effects. Millimeter wave (mmWave) imaging has been offered as a viable alternative to the conventional screening methods due to their drawbacks. Without exposing the patient to ionizing radiations, malignant lesions can be detected by using the dielectric discontinuities in the tissues. The purpose of this work is to develop a simple, low-cost miniaturized mmWave imaging antenna sensor that can be used to detection of breast tumors or cancers in women by monitoring the changes in the antenna’s S11 parameter. The dimension of the proposed rectangular mmWave antenna is 5 mm × 5 mm × 0.578 mm. The proposed antenna sensor has an operating frequency band range of 32.626 GHz to 33.96 GHz with a peak gain of 6.65 dB and an efficiency of 91.46% in terms of radiation which makes it a suitable system for fifth generation (5G) communication. As well as the antenna sensor can detect very tiny malignant that size ≥1 mm inside the breast fantom.

Multi-agents system for breast tumour detection in mammography by deep learning pre-processing and watershed segmentation

Mammography is the most used process for females to diagnose and screen breast cancer. In this paper, we presented an enhanced automatic watershed segmentation for breast tumour detection and segmentation reinforced with a group of interactive agents. First, we started by a pre-processing based on deep learning (DL), where a convolution neural network (CNN) is applied, to classify the breast density by AlexNet architecture. Second, classic watershed segmentation was applied on these images. Afterward, a multi-agents system (MASs) was introduced. The information within pixels, regions and breast density were explored, to create a region of interest (ROI), and to emerge the MAS segmentation. Experimental results were promising in term of accuracy (ACC), with an overall of 97.18% over three datasets, Mammographic Image Analysis Society (MIAS), INBreast, and a local dataset called Database of Digital Mammograms of Annaba (DDMA). In some cases, our approach was able to detect breast calcification accurately.

Multi-agents system for breast tumour detection in mammography by deep learning pre-processing and watershed segmentation

Multi-agents system for breast tumour detection in mammography by deep learning pre-processing and watershed segmentation

Unlabeled aspirin as an activatable theranostic MRI agent for breast cancer.

Rationale: Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) is emerging as an alternative to gadolinium-based contrast MRI. We have evaluated the possibility of CEST MRI of orthotopic breast tumor xenografts with unlabeled aspirin's conversion to salicylic acid (SA) through various enzymatic activities, most notably inhibition of cyclooxygenase (COX)-1/-2 enzymes.Methods: We measured the COX-1/-2 expression in four breast cancer cell lines by Western Blot analysis and selected the highest and lowest expressing cell lines. We then performed CEST MRI following aspirin treatment to detect SA levels and ELISA to measure levels of downstream prostaglandin E2 (PGE2). We also injected aspirin into the tail vein of mice growing orthotopic tumor xenografts which expressed high and low COX-1/-2 and acquired SA CEST MR images of these tumor xenografts for up to 70 minutes. Tumors were then harvested to perform Western Blot and ELISA experiments to measure COX-1/-2 expression and PGE2 levels, respectively.Results: Western Blots determined that SUM159 cells contained significantly higher COX-1/-2 expression levels than MDA-MB-231 cells, in line with higher levels of downstream PGE2. SA CEST MRI yielded similar contrast at approximately 3% for both cell lines, independent of COX-1/-2 expression level. PGE2 levels decreased by about 50% following aspirin treatment. Results from our mouse study aligned with cultured cells, the overall SA CEST MRI contrast in both MDA-MB-231 and SUM159 tumor xenograft models was 5~8% at one hour post injection. PGE2 levels were ten times higher in SUM159 than MDA-MB-231 and decreased by 50%. The CEST contrast directly depended on the injected dose, with ~6%, ~3% and ~1.5% contrast observed following injection of 100 µL of 300 mM, 200 mM and 150 mM aspirin, respectively.Conclusions: Our data demonstrate the feasibility of using aspirin as a noninvasive activatable CEST MRI contrast agent for breast tumor detection.

Feasibility of Using Feature Entropy Reducation to Auto-Catch Response Frames of Video Ultrasound Data for Deep Learning Model to Detect Breast Tumors

Feasibility of Using Feature Entropy Reducation to Auto-Catch Response Frames of Video Ultrasound Data for Deep Learning Model to Detect Breast Tumors

An Investigation Using Specific Absorption Rate Analysis to Diagnose Early- stage Breast Tumor using UWB Antenna.

This work was focused on the detection of early-stage breast tumors and their location. The most frequently seen disease progression and mortality in women's lives is Breast Cancer. Because of breast cancer/tumors, the risk of mortality for women has risen exponentially. From the 2020 deadline for breast cancer, women who died from carcinoma were 123.8 cases per lac women between 2006-2010. This problem is also overcome by the early identification of the tumor using different detection procedures like X-ray mammography, computerized tomography, ultrasound imaging technique, Positron Emission Tomography (PET), Magnetic Resonance Imaging (MRI), microwave imaging. Researchers carried out multiple studies in these areas. To detect early-stage breast tumors and their location using SAR analysis. The major dielectrical difference between cancerous breast tissues and normal tissues in this technique is the microwave frequency range. The term Specific Absorption Rate (SAR) describes the amount of energy which is absorbed (W/kg) in the breast tissue. This segment illustrates the usefulness to diagnose the tumor position in the breast by means of maximum SAR value coordinates. Changes in breast and tumor size are important for the risk of diagnosis. The power absorbed in connecting with a normal breast and a tumor breast is measured and equivalent for different breast masses. The maximum SAR is also analyzed at distinct tumor locations at various frequency ranges. It is observed that max SAR coordinates are very close to the actual tumor location. So, the maximal value of SAR coordinates indicates the existence of a tumor in the breast phantom. The simulated data above strongly suggests that the Max SAR values were higher in the breast phantom with tumor as compared to the breast without tumor. With different tumor radius (3 mm and 5 mm) analyzed with different resonant frequencies like 3GHz, 4GHz and 5GHz at the actual tumor location of (0, 0, 35). Even though a model representing the real properties of breast tissue is required to assess the validation of any imaging process, so the real-time development of an equivalent breast phantom and its execution is needed.

Modified 16-Quasi Log Periodic Antenna Array for Microwave Imaging of Breast Cancer Detection

In this paper, an effective system for microwave imaging of breast tumor detection using modified 16-planar log periodic antenna (PLPA) array is presented. The modified PLPA operates in the band from 2 to 5 GHz with stable directional patterns in the end-fire direction. Once the results of a single antenna element have been validated, the design is extended to include 16 antenna elements. All 16 transceiver antennas are vertically placed around the phantom in a circular manner where one antenna acts as a transmitter and the rest work as receivers. Delay and Sum (DAS) algorithm is used for post processing the acquired scattered signals from the sensors to reconstruct the image of the breast and to identify the existence of breast tumors. The electromagnetic simulators CST and HFSS are used to design the system, while MATLAB is used to process the data. The developed PLPA array-based microwave imaging system performs admirably, making it one of the most effective systems for detecting tumor cells.

Detection of breast tumor using inset-fed microstrip patch antenna at MICS band for implantable applications

Detection of breast tumor using inset-fed microstrip patch antenna at MICS band for implantable applications

Compact circular polarized CPW antenna for WLAN and biomedical applications

Abstract A compact, circularly polarized, CPW-fed antenna is proposed for wearable applications in ISM Band (5.8 GHz). The antenna is based on DGS, where the ground plane is responsible for impedance matching. The 10 dB impedance of the proposed antenna varies from 5.39 GHz to 5.94 GHz. The circular stub introduced in the ground plane mitigates the surface current and enriches the 3 dB axial ratio from 5.73 GHz to 5.92 GHz. Proposed antenna exhibits the LHCP and RHCP pattern of circular polarization, the antenna can effectively work for biomedical and wearable applications. The antenna is analyzed on the skin phantom model and the SAR value obtained is 1.218 W/kg, which is below the maximum permissible level. The proposed antenna is also used for the detection of breast tumors.

Custom Fluorescence Imaging System Exploiting Hyperspectral Camera to Characterize and Diagnose RNA Breast Cancer

Early diagnosis of breast malignancy is a challenging task to permit this cancer. RNA offers incredible potential as a biomarker for malignant growth because of its noteworthy blood dependability and characteristic articulation in various diseases. We explored the total RNA to select the optimum spectral signature concerning entire blood that could segregate between liver cancer (HCC), early breast cancer growth, and normal persons. A custom Hyperspectral Imaging (HSI) system comprises a hyperspectral camera that works in wavelength (380~1050 nm) with ultraviolet (UV) source light (20 mW, 395 nm). It is associated with the custom software system to measure the total RNA signature of (n=50) subjects (n= 10 HCC patients, n= 15 breast cancer, and n=25 normal persons as a control). The experimental result shows that the scattering absorption of all the investigated samples is high at 395 nm based on (size, shape, medical state). Breast cancer RNA demonstrates fluorescence emission at 431 nm and 493 nm and Phosphorescence at 768 nm compared to the normal and HCC patients. Verifying the experimental results with the RNA cuvette spectral images shows that we could discriminate the HCC from the normal total RNA at 431 nm and the breast cancer from both normal and HCC at 768 nm. This prospective investigation shows that the mutation of the total RNA regarding certain diseases affecting its spectral signature exhibits potential detection of breast tumor and HCC in tissues using extracted RNA from Blood in advance and subsequently surgery in subjects with initial-stage breast cancer. A wavelength of 431 nm was optimum for discrimination between the HCC and the normal total RNA, and a wavelength of 768 nm was ideal for breast cancer discrimination.

A modified source-detector configuration for the discrimination between normal and diseased human breast based on the continuous-wave diffuse optical imaging approach: a simulation study.

Breast tumors are among the most common types of tumors that can affect both genders. It may spread to the whole breast without any symptoms. Therefore, the early detection and accurate diagnosis of breast tumors are significantly important. Current approaches for breast cancer screening such as positron emission tomography (PET) and magnetic resonance imaging (MRI) have some limitations of being time and money-consuming. In addition, mammography screening is not recommended for women under forty. Consequently, optical techniques have been introduced as safe and functional alternatives. Diffuse optical imaging is a non-invasive imaging technique that utilizes near-infrared light to examine biological tissues based on measuring the optical transmission and/or reflection at various locations on the tissue surface. In this paper, we propose a modified arrangement between the laser source and the detectors for distinguishing tumors from normal breast tissue. A three-dimensional model of the normal human breast with three types of tumors is developed using a COMSOL simulation software based on the finite element solution of Helmholtz equation to estimate optical fluence distribution. The breast model consists of four layers: skin, fat, glandular, and muscle, where the tumor is included in the glandular layer. Different wavelengths were used to determine the most proper wavelength for the discrimination between the normal tissue and tumor. The obtained results were verified using the receiver operating characteristic (ROC) method. The resultant fluence images show different features between normal breast and breast with tumor especially using 600-nm incident laser as demonstrated by the obtained ROC curves.

Fabry–Pérot interference principle-based non-contact thermoacoustic imaging system for breast tumor screening

Microwave-induced thermoacoustic imaging (MTAI) is a potential nonionizing candidate for breast tumor detection due to its high contrast of tissue dielectric absorption and high resolution of ultrasonography. However, conventional MTAI systems need to contact the imaging targets through the ultrasound-coupling medium for the efficient transmission of thermoacoustic signals. This is a major drawback for potential applications such as navigation of open surgeries, burns, and ulcer testing. In this paper, we report a Fabry–Pérot (FP) interference principle-based non-contact thermoacoustic imaging system (NCTAIS). The system can infer the ultrasonic intensity by detecting the changes of the acoustically induced refractive index in the FP cavity. The miniaturized FP interferometer with a diameter of 5 mm achieves outstanding imaging sensitivity with a frequency response up to 1 MHz. An adaptive Stolt migration-based imaging reconstruction algorithm is proposed to solve the MTAI image distortion caused by the significant difference in sound velocity between air and a biological tissue. The NCTAIS is capable of achieving an axial resolution of 0.95 mm and a lateral resolution of 1.91 mm. The technical feasibility for breast tumor screening is validated with an in vitro simulated breast tumor model. The results demonstrate that the proposed NCTAIS offers high imaging sensitivity, high contrast, and deep imaging depth and will have more application scenarios than the conventional MTAI system, requiring the ultrasonic coupling medium to contact ultrasonic transducers.

Terahertz Imaging for Breast Cancer Detection.

Terahertz (THz) imaging has the potential to detect breast tumors during breast-conserving surgery accurately. Over the past decade, many research groups have extensively studied THz imaging and spectroscopy techniques for identifying breast tumors. This manuscript presents the recent development of THz imaging techniques for breast cancer detection. The dielectric properties of breast tissues in the THz range, THz imaging and spectroscopy systems, THz radiation sources, and THz breast imaging studies are discussed. In addition, numerous chemometrics methods applied to improve THz image resolution and data collection processing are summarized. Finally, challenges and future research directions of THz breast imaging are presented.

Development and characterization of enzymatic biosensor based on lipase from porcine pancreas for propylparaben detection

PurposeThe purpose of this paper is to study the immobilization of porcine pancreatic lipase (PPL), in an organic matrix by a covalent cross-linking method to sense propylparaben (PP) present in aqueous solution.Design/methodology/approachPPL immobilization was performed by the covalent cross-linking method, using bovine serum albumin (BSA) in the presence of saturated glutaraldehyde vapor (GA). The preparation of the enzymatic membrane involves the incorporation of porcine pancreatic lipase (PPL), bovine serum albumin (BSA) and glycerol into a phosphate buffer solution (PBS). Characterization of this sensor was performed by impedance spectroscopy (EIS) and scanning electron microscope (SEM). The effect of experimental conditions such as PPL activity, potential, scan rate, PP concentration, pH and presence of interfering elements were studied by cyclic voltammetry.FindingsUnder the optimal experimental conditions, a number of significant factors were optimized. The method exhibited good linearity in the range of 10–14to 10–9 mol/L with a good correlation coefficient of 0.957, detection limit (LOD) of 3.66 × 10–15 mol/L and high sensitivity of 1.086 mA mol−1L. The authors also obtained a very good coverage rate of the surface equal to 91.44%, and hydrolytic activity of lipase is evaluated to 26.64 mmol min−1. The stability and the interference were also evaluated. The equivalent circuit used to explain the electrochemical behavior of modified electrode is a Randle circuit.Practical implicationsThe main application of biosensors is the detection of biomolecules that are either indicators of a disease. For example, electrochemical biosensing techniques can be used as clinical tools to detect breast tumors, because these compounds (PP) were found in breast tumors.Originality/valueThe result registered in this paper indicates that the developed sensor is an efficient, fast, simple and inexpensive analytical tool that can be used for the analysis of water containing PP.