Technique For Detection Of Breast Cancer Research Articles

New Diagnostic Techniques for Breast Cancer Detection

Breast imaging has made huge advances in the last decade, and along with newer techniques to diagnose primary breast cancer, many novel methods are being used and look promising in detecting distant metastasis, recurrent disease and assessing response to treatment. Full-field digital mammography optimizes the lesion-background contrast and gives better sensitivity, and it is possible to see through the dense tissues by altering computer windows; this may be particularly useful in younger women with dense breasts. The need for repeat imaging is reduced, with the added advantage of reduced radiation dose to patients. Computer-aided detection systems may help the radiologist in interpretation of both conventional and digital mammograms. MRI has a role in screening women at high risk for breast cancer. It also aids in cancer management by assessing response to treatment and can help in deciding appropriate surgery by providing accurate information on the extent of the tumor. Newer diagnostic techniques such as sestamibi scans, optical imaging and molecular diagnostic techniques look promising, but need more investigation into their use. Their roles will appear clearer in coming years, and they may prove to be of help in further investigating lesions that are indeterminate on standard imaging. Other upcoming techniques are contrast-enhanced mammography and tomosynthesis. These may give additional information in indeterminate lesions, and when used in screening they aid in reducing recall rates, as shown in recent studies. PET/computed tomography has a role in detecting local disease recurrence and distant metastasis in breast cancer patients.

Design and evaluation of a novel breast cancer detection system combining both thermoacoustic (TA) and photoacoustic (PA) tomography

We have developed a novel scanner for breast cancer detection, integrating both thermoacoustic and photoacoustic techniques to achieve dual contrast (microwave and light absorption) imaging. This scanner is nonionizing, low cost, and can potentially provide high-resolution, dual modality three-dimensional images of the breast. The scanner uses front instead of side breast compression and dry instead of gel ultrasonic coupling. Here we present the design of the breast scanner along with initial tissue phantom study results as a precursor to an actual patient study.

Highly Accurate Debye Models for Normal and Malignant Breast Tissue Dielectric Properties at Microwave Frequencies

The finite difference time domain (FDTD) method is widely used as a computational tool for development, validation, and optimization of emerging microwave breast cancer detection and treatment techniques. When expressed in terms of Debye parameters, dispersive breast tissue dielectric properties can be efficiently incorporated into FDTD codes. Previously, we experimentally characterized the dielectric properties of a large number of excised normal and malignant breast tissue samples from 0.5 to 20 GHz. We subdivided the large database of normal tissue data into three groups based on the percent adipose tissue present in a particular sample. In addition, we formed a group of all cancer samples that contained at least 30% malignant tissue. We summarized the data using one-pole Cole-Cole models that were rigorously fit to the median dielectric properties of the three normal tissue groups and one malignant tissue group. In this letter, we present computationally simpler one- and two-pole Debye models that retain the high accuracy of the Cole-Cole models. Model parameters are derived for two sets of frequency ranges: the entire measurement frequency range from 0.5 to 20 GHz, and the 3.1-10.6 GHz FCC band allocated for ultrawideband medical applications. The proposed Debye models provide a means for creating computationally efficient FDTD breast models with realistic wideband dielectric properties derived from the largest and most comprehensive experimental study conducted to date on human breast tissue.

Multifrequency Microwave-Induced Thermal Acoustic Imaging for Breast Cancer Detection

Microwave-induced thermal acoustic imaging (TAI) is a promising early breast cancer detection technique, which combines the advantages of microwave stimulation and ultrasound imaging and offers a high imaging contrast, as well as high spatial resolution at the same time. A new multifrequency microwave-induced thermal acoustic imaging scheme for early breast cancer detection is proposed in this paper. Significantly more information about the human breast can be gathered using multiple frequency microwave stimulation. A multifrequency adaptive and robust technique (MART) is presented for image formation. Due to its data-adaptive nature, MART can achieve better resolution and better interference rejection capability than its data-independent counterparts, such as the delay-and-sum method. The effectiveness of this procedure is shown by several numerical examples based on 2-D breast models. The finite-difference time-domain method is used to simulate the electromagnetic field distribution, the absorbed microwave energy density, and the thermal acoustic field in the breast model.

A large-scale study of the ultrawideband microwave dielectric properties of normal, benign and malignant breast tissues obtained from cancer surgeries

The development of microwave breast cancer detection and treatment techniques has been driven by reports of substantial contrast in the dielectric properties of malignant and normal breast tissues. However, definitive knowledge of the dielectric properties of normal and diseased breast tissues at microwave frequencies has been limited by gaps and discrepancies across previously published studies. To address these issues, we conducted a large-scale study to experimentally determine the ultrawideband microwave dielectric properties of a variety of normal, malignant and benign breast tissues, measured from 0.5 to 20 GHz using a precision open-ended coaxial probe. Previously, we reported the dielectric properties of normal breast tissue samples obtained from reduction surgeries. Here, we report the dielectric properties of normal (adipose, glandular and fibroconnective), malignant (invasive and non-invasive ductal and lobular carcinomas) and benign (fibroadenomas and cysts) breast tissue samples obtained from cancer surgeries. We fit a one-pole Cole–Cole model to the complex permittivity data set of each characterized sample. Our analyses show that the contrast in the microwave-frequency dielectric properties between malignant and normal adipose-dominated tissues in the breast is considerable, as large as 10:1, while the contrast in the microwave-frequency dielectric properties between malignant and normal glandular/fibroconnective tissues in the breast is no more than about 10%.For more information on this article, see medicalphysicsweb.org

Feasibility of breast cancer lesion detection using a multi-frequency trans-admittance scanner (TAS) with 10 Hz to 500 kHz bandwidth

We describe a new multi-frequency technique for breast cancer detection. Applying a constant voltage with multiple sinusoidal frequencies between a reference electrode on a distal part of a patient and a scan probe placed on the breast, we measure exit currents from an array of electrodes inside the probe that are kept at the ground potential. The distribution of measured exit currents is called the trans-admittance map and the instrument is called the trans-admittance scanner (TAS). We assume a three-dimensional homogeneous domain including an internal lesion with a complex conductivity different from that of the background. Mathematically analyzing the multi-frequency trans-admittance map obtained on the surface of the domain, we found that both conductivity and permittivity ratios between the background and the lesion are crucial in extracting any useful information about the lesion from the map. Choosing two frequencies in the range of 10 Hz to 500 kHz with one significantly lower than the other and assuming that conductivity values of the background and the lesion do not change much from the low to high frequency, the lesion underneath the probe can be detected only when the conductivity ratio between the background and the lesion is different from the permittivity ratio between the background and the lesion at the chosen high frequency. Results of numerical simulations and saline phantom experiments using a developed TAS system are well matched with the mathematical analysis. The biggest advantage of this multi-frequency technique is that we do not need separately measured reference data in the absence of any lesion. We suggest future studies of a more sophisticated lesion detection algorithm based on the analysis and findings described in this paper.

A large-scale study of the ultrawideband microwave dielectric properties of normal breast tissue obtained from reduction surgeries

The efficacy of emerging microwave breast cancer detection and treatment techniques will depend, in part, on the dielectric properties of normal breast tissue. However, knowledge of these properties at microwave frequencies has been limited due to gaps and discrepancies in previously reported small-scale studies. To address these issues, we experimentally characterized the wideband microwave-frequency dielectric properties of a large number of normal breast tissue samples obtained from breast reduction surgeries at the University of Wisconsin and University of Calgary hospitals. The dielectric spectroscopy measurements were conducted from 0.5 to 20 GHz using a precision open-ended coaxial probe. The tissue composition within the probe's sensing region was quantified in terms of percentages of adipose, fibroconnective and glandular tissues. We fit a one-pole Cole–Cole model to the complex permittivity data set obtained for each sample and determined median Cole–Cole parameters for three groups of normal breast tissues, categorized by adipose tissue content (0–30%, 31–84% and 85–100%). Our analysis of the dielectric properties data for 354 tissue samples reveals that there is a large variation in the dielectric properties of normal breast tissue due to substantial tissue heterogeneity. We observed no statistically significant difference between the within-patient and between-patient variability in the dielectric properties.

Clear-PEM system counting rates: a Monte Carlo study

Positron Emission Mammography (PEM) with 18F-Fluorodeoxyglucose (18F-FDG) is a functional imaging technique for breast cancer detection. The development of dedicated imaging systems with high sensitivity and spatial resolution are crucial for early breast cancer diagnosis and an efficient therapy. Clear-PEM is a dual planar scanner designed for high-resolution breast cancer imaging under development by the Portuguese PET Mammography consortium within the Crystal Clear Collaboration. It brings together a favorable combination of high-density scintillator crystals coupled to compact photodetectors, arranged in a double readout scheme capable of providing depth-of-interaction information.A Monte Carlo study of the Clear-PEM system counting rates is presented in this paper. Hypothetical breast exam scenarios were simulated to estimate the single event rates, true and random coincidence rates. A realistic description of the patient and detector geometry, radiation environment, physics and instrumentation factors was adopted in this work. Special attention was given to the 18F-FDG accumulation in the patient torso organs which, for the Clear-PEM scanner, represent significant activity outside the field-of-view (FOV) contributing to an increase of singles, randoms and scattered coincidences affecting the overall system performance. The potential benefits of patient shielding to minimize the influence of the out-of-field background was explored. The influence of LYSO:Ce crystal intrinsic natural activity due to the presence of the 176Lu isotope on the counting rate performance of the proposed scanner, was also investigated.

Safety assessment of breast cancer detection via ultrawideband microwave radar operating in pulsed‐radiation mode

AbstractWe conduct an finite‐difference time‐domain computational electromagnetic analysis of the safety of ultrawideband microwave breast cancer detection techniques. Pulses are transmitted into anatomically realistic MRI‐derived numerical breast phantoms with different levels of tissue heterogeneity as well as foreign entities such as implants and surgical clips, and the specific absorption is computed. © 2006 Wiley Periodicals, Inc. Microwave Opt Technol Lett 49: 221–225, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.22089

Estimation of the Frequency-Dependent Average Dielectric Properties of Breast Tissue Using a Time-Domain Inverse Scattering Technique

Ultrawideband (UWB) microwave radar imaging techniques for breast cancer detection typically require estimates of the spatially averaged dielectric properties of breast tissue. We propose an algorithm for estimating patient-specific, frequency-dependent average dielectric properties from scattered UWB microwave signals. The algorithm is a variation of an iterative finite-difference time-domain (FDTD) technique for solving the time-domain inverse scattering problem. The assumption is made that the breast consists of two homogeneous regions: skin and underlying tissue. This two-medium geometry results in an inverse scattering problem that is not as ill-posed as those typically encountered in microwave tomography because the number of unknowns being sought is very small. We test the performance of the algorithm on data simulated using anatomically realistic 2-D numerical breast phantoms

Novel Multistatic Adaptive Microwave Imaging Methods for Early Breast Cancer Detection

Multistatic adaptive microwave imaging (MAMI) methods are presented and compared for early breast cancer detection. Due to the significant contrast between the dielectric properties of normal and malignant breast tissues, developing microwave imaging techniques for early breast cancer detection has attracted much interest lately. MAMI is one of the microwave imaging modalities and employs multiple antennas that take turns to transmit ultra-wideband (UWB) pulses while all antennas are used to receive the reflected signals. MAMI can be considered as a special case of the multi-input multi-output (MIMO) radar with the multiple transmitted waveforms being either UWB pulses or zeros. Since the UWB pulses transmitted by different antennas are displaced in time, the multiple transmitted waveforms are orthogonal to each other. The challenge to microwave imaging is to improve resolution and suppress strong interferences caused by the breast skin, nipple, and so forth. The MAMI methods we investigate herein utilize the data-adaptive robust Capon beamformer (RCB) to achieve high resolution and interference suppression. We will demonstrate the effectiveness of our proposed methods for breast cancer detection via numerical examples with data simulated using the finite-difference time-domain method based on a 3D realistic breast model.

Promising techniques for breast cancer detection, diagnosis, and staging using non-ionizing radiation imaging techniques

Traditional imaging for the diagnosis and staging of breast cancer has relied on the tissue morphology of cancers in the background of normal patterns of fibroglandular breast tissue. X-ray mammography and ultrasound have been the primary modalities for the diagnosis and the work-up of breast cancer. New modalities have been validated including magnetic resonance imaging (MRI) and positron emission tomography (PET). New pulse sequences in MRI combined with contrast enhancement kinetic perfusion curves have greatly enhanced detection of mammographically occult cancers. New modalities on the horizon include optical imaging, exploiting again the differential perfusion properties of cancers in a background of normal glandular tissue. Even more specificity can be ach eved with the addition of ductal or intravenous introduction of optical probes specific to tumor associated antigens such as the HER-2/neu receptor in aggressive breast cancers. Quantum dots and other fluorescent dyes coupled to peptides or other probes will greatly enhance our ability to detect cancers earlier and without ionizing radiation.

Genetic analysis of human milk by fluorescence in situ hybridization

Ductal lavage is a technique for early breast cancer detection in high-risk women. During this procedure, exfoliated epithelial cells are flushed out of the milk ducts of nonlactating women and the collected cells are analyzed for cellular changes associated with breast cancer. A recently developed protocol uses interphase fluorescence in situ hybridization (I-FISH) to detect specific chromosomal aneusomies known to be associated with breast cancer. The ability to perform I-FISH on breast milk will prove to be valuable to lactating women who are at high risk for breast cancer or who develop symptoms while breastfeeding. Using established protocols for analyzing peripheral blood lymphocytes, amniocytes, and epithelial cells in urine and breast duct lavage samples as a guide, a protocol for I-FISH of human breast milk has been developed. Cell isolation, fixation, pretreatment, denaturation, hybridization, and washings were optimized to produce slides of high quality, sensitivity, and specificity.

Early neoplastic and metastatic mammary tumours of transgenic mice detected by 5-aminolevulinic acid-stimulated protoporphyrin IX accumulation

A photodynamic technique for human breast cancer detection founded upon the ability of tumour cells to rapidly accumulate the fluorescent product protoporphyrin IX (PpIX) has been applied to transgenic mouse models of mammary tumorigenesis. A major goal of this investigation was to determine whether mouse mammary tumours are reliable models of human disease in terms of PpIX accumulation, for future mechanistic and therapeutic studies. The haeme substrate 5-aminolevulinic acid (5-ALA) (200 mg kg−1) was administered to mouse strains that develop mammary tumours of various histological subtypes upon expression of the transgenic oncogenes HRAS, Polyoma Virus middle T antigen, or Simian Virus 40 large T antigen in the mammary gland. Early neoplastic lesions, primary tumours and metastases showed consistent and rapid PpIX accumulation compared to the normal surrounding tissues, as evidenced by red fluorescence (635 nm) when the tumours were directly illuminated with blue light (380–440 nm). Detection of mouse mammary tumours at the stage of ductal carcinoma in situ by red fluorescence emissions suggests that enhanced PpIX synthesis is a good marker for early tumorigenic processes in the mammary gland. We propose the mouse models provide an ideal experimental system for further investigation of the early diagnostic and therapeutic potential of 5-ALA-stimulated PpIX accumulation in human breast cancer patients.

Scanning Time-domain Optical Mammography: Detection and Characterization of Breast Tumors In Vivo

Optical mammography is one of several new techniques for breast cancer detection and characterization presently under development for clinical use that provide information other than morphologic, in particular on the biochemical and metabolic state of normal and diseased tissue. In breast tissue, scattering of red to near infrared (NIR) light dominates absorption and NIR light may penetrate several centimeters through the breast. Optical mammography avoids the use of ionizing radiation and offers the power of diffuse optical spectroscopy. However, because of strong light scattering, spatial resolution of optical mammography is generally low. The paper reviews the results of a clinical study on scanning time-domain optical mammography comprising 154 patients carrying a total of 102 carcinomas validated by histology. Ninety two of these tumors were detected in optical mammograms retrospectively and for 87 of the detected tumors optical properties and tissue parameters were derived. In addition developments on instrumentation and data analysis are covered and possible improvements of optical mammography are briefly discussed.

Time reversal with the FDTD method for microwave breast cancer detection

The feasibility of microwave breast cancer detection with a time-reversal (TR) algorithm is examined. This algorithm is based on the finite-difference time-domain method, and compensates for the wave decay and, therefore, is suitable for lossy media. In this paper, we consider a two-dimensional breast model based on magnetic resonance imaging data, and examine the focusing abilities of a TR mirror comprised of an array of receivers with a single ultra-wideband pulse excitation. In order to resolve small 3-mm-diameter tumors, a very short duration pulse is necessary, and this requirement may restrict the applicability of the system due to hardware limitations. We propose a way to overcome this obstacle based on the observation that the amplitude and phase information of the tumor response is sufficient to achieve focusing. The robustness of the TR algorithm with respect to breast inhomogeneities is demonstrated, and the good performance of the method suggests it is a promising technique for microwave breast cancer detection.

Using AI to detect breast cancer

Scientists are marshalling AI tools in the fight against the leading cause of cancer among women. Using digital information from traditional breast cancer detection techniques as well as newer detection methods, new applications of data mining and neural networks are helping doctors detect cancers sooner. The AI tools can also reduce the number of biopsies required in the diagnostic process. Neural networks and AI methods of pattern recognition can help indicate the presence of breast cancers.

Microwave imaging via space-time beamforming for early detection of breast cancer

A method of microwave imaging via space-time (MIST) beamforming is proposed for detecting early-stage breast cancer. An array of antennas is located near the surface of the breast and an ultrawideband (UWB) signal is transmitted sequentially from each antenna. The received backscattered signals are passed through a space-time beamformer that is designed to image backscattered signal energy as a function of location. The beamformer spatially focuses the backscattered signals to discriminate against clutter and noise while compensating for frequency-dependent propagation effects. As a consequence of the significant dielectric-properties contrast between normal and malignant tissue, localized regions of large backscatter energy levels in the image correspond to malignant tumors. A data-adaptive algorithm for removing artifacts in the received signals due to backscatter from the skin-breast interface is also presented. The effectiveness of these algorithms is demonstrated using a variety of numerical breast phantoms based on anatomically realistic MRI-derived FDTD models of the breast. Very small (2 mm) malignant tumors embedded within the complex fibroglandular structure of the breast are easily detected above the background clutter. The MIST approach is shown to offer significant improvement in performance over previous UWB microwave breast cancer detection techniques based on simpler focusing schemes.

Numerical investigation of breast tumour detection using multi-static radar

A breast cancer detection technique using multi-static radar is proposed. For the first time, images are produced using this technique, using backscatter data produced from an anatomically realistic 2D MRI-derived FDTD model of the breast. Successful detection of a 2 mm-diameter tumour is demonstrated, although clarity of detection is dependent on mitigating antenna mutual coupling and skin reflections.

EARLY DETECTION AND VISUALIZATION OF BREAST TUMOR WITH THERMOGRAM AND NEURAL NETWORK

Although mammography is still the benchmark technique for breast cancer detection, many advantages of thermography make it a suitable adjunct tool for early detection. This paper describes the development of a computer-aided system for use together with thermography to assist in the detection and visualization/analysis of breast tumors. The system consists of a detection module for predicting the presence of tumors from thermograms, and a visualization module for generating the 3-D volumetric geometry of the suspected tumor inside the breast based on the 2-D thermogram. Detection is achieved through an artificial neural network taking the thermogram image as input, while the visualization is obtained by generating the 3-D model of the breast that produces a matching thermal image as the thermogram under a 3-D finite element analysis. A study with 200 subjects indicate that the detection sensitivity was good but the specificity was poor, but the reverse performance result was true for another back-propagation neural network which used physiological data instead of thermograms as input. This suggests that overall prediction capability can be improved by appropriate combination of the two results.