Breast Imaging Technology Research Articles

Intelligent scoring system based on dynamic optical breast imaging for early detection of breast cancer.

Early detection of breast cancer can significantly improve patient outcomes and five-year survival in clinical screening. Dynamic optical breast imaging (DOBI) technology reflects the blood oxygen metabolism level of tumors based on the theory of tumor neovascularization, which offers a technical possibility for early detection of breast cancer. In this paper, we propose an intelligent scoring system integrating DOBI features assessment and a malignancy score grading reporting system for early detection of breast cancer. Specifically, we build six intelligent feature definition models to depict characteristics of regions of interest (ROIs) from location, space, time and context separately. Similar to the breast imaging-reporting and data system (BI-RADS), we conclude the malignancy score grading reporting system to score and evaluate ROIs as follows: Malignant (≥ 80 score), Likely Malignant (60-80 score), Intermediate (35-60 score), Likely Benign (10-35 score), and Benign (<10 score). This system eliminates the influence of subjective physician judgments on the assessment of the malignant probability of ROIs. Extensive experiments on 352 Chinese patients demonstrate the effectiveness of the proposed system compared to state-of-the-art methods.

Optical Breast Imaging: A Review of Physical Principles, Technologies, and Clinical Applications.

Optical imaging involves the propagation of light through tissue. Current optical breast imaging technologies, including diffuse optical spectroscopy, diffuse optical tomography, and photoacoustic imaging, capitalize on the selective absorption of light in the near-infrared spectrum by deoxygenated and oxygenated hemoglobin. They provide information on the morphological and functional characteristics of different tissues based on their varied interactions with light, including physiologic information on lesion vascular content and anatomic information on tissue vascularity. Fluorescent contrast agents, such as indocyanine green, are used to visualize specific tissues, molecules, or proteins depending on how and where the agent accumulates. In this review, we describe the physical principles, spectrum of technologies, and clinical applications of the most common optical systems currently being used or developed for breast imaging. Most notably, US co-registered photoacoustic imaging and US-guided diffuse optical tomography have demonstrated efficacy in differentiating benign from malignant breast masses, thereby improving the specificity of diagnostic imaging. Diffuse optical tomography and diffuse optical spectroscopy have shown promise in assessing treatment response to preoperative systemic therapy, and photoacoustic imaging and diffuse optical tomography may help predict tumor phenotype. Lastly, fluorescent imaging using indocyanine green dye performs comparably to radioisotope mapping of sentinel lymph nodes and appears to improve the outcomes of autologous tissue flap breast reconstruction.

Novel Technologies in Breast Imaging: A Scoping Review.

Breast cancer is one of the leading causes of death in the United States and can cause considerable suffering for not only the patient but their families as well. The current mainstay of screening is mammography, although this screening modality has its drawbacks. Multiple technologies have been recently explored in hopes of increasing breast cancer detection rates and decreasing false positive rates. Overall, improving breast cancer screening techniques has the potential to decrease cost, patient anxiety, and the use of unnecessary procedures. This review discusses multiple modalities including digital breast tomosynthesis, contrast-enhanced dual-energy digital mammography (CE DE DM), MRI with diffusion-weighted sequences and proton magnetic resonance spectroscopy. This paper was written with the objective of synthesizing information across several databasesto provide clinicians with a more accessible tool to understand the underlying concepts behind these imaging modalities, as well as present reviewed data which highlights the benefits and drawbacks of these breast cancer-detecting techniques.

New Approaches and Recommendations for Risk-Adapted Breast Cancer Screening.

Population-based breast cancer screening using mammography as the gold standard imaging modality has been in clinical practice for over 40 years. However, the limitations of mammography in terms of sensitivity and high false-positive rates, particularly in high-risk women, challenge the indiscriminate nature of population-based screening. Additionally, in light of expanding research on new breast cancer risk factors, there is a growing consensus that breast cancer screening should move toward a risk-adapted approach. Recent advancements in breast imaging technology, including contrast material-enhanced mammography (CEM), ultrasound (US) (automated-breast US, Doppler, elastography US), and especially magnetic resonance imaging (MRI) (abbreviated, ultrafast, and contrast-agent free), may provide new opportunities for risk-adapted personalized screening strategies. Moreover, the integration of artificial intelligence and radiomics techniques has the potential to enhance the performance of risk-adapted screening. This review article summarizes the current evidence and challenges in breast cancer screening and highlights potential future perspectives for various imaging techniques in a risk-adapted breast cancer screening approach. EVIDENCE LEVEL: 1. TECHNICAL EFFICACY: Stage 5.

Gradient-Boosting Algorithm for Microwave Breast Lesion Classification-SAFE Clinical Investigation.

(1) Background: Microwave breast imaging (MBI) is a promising breast-imaging technology that uses harmless electromagnetic waves to radiate the breast and assess its internal structure. It utilizes the difference in dielectric properties of healthy and cancerous tissue, as well as the dielectric difference between different cancerous tissue types to identify anomalies inside the breast and make further clinical predictions. In this study, we evaluate the capability of our upgraded MBI device to provide breast tissue pathology. (2) Methods: Only patients who were due to undergo biopsy were included in the study. A machine learning (ML) approach, namely Gradient Boosting, was used to understand information from the frequency spectrum, collected via SAFE, and provide breast tissue pathology. (3) Results: A total of 54 patients were involved in the study: 29 of them had benign and 25 had malignant findings. SAFE acquired 20 true-positive, 24 true-negative, 4 false-positive and 4 false-negative findings, achieving the sensitivity, specificity and accuracy of 80%, 83% and 81%, respectively. (4) Conclusions: The use of harmless tissue radiation indicates that SAFE can be used to provide the breast pathology of women of any age without safety restrictions. Results indicate that SAFE is capable of providing breast pathology at a high rate, encouraging further clinical investigations.

The Economic Impact of AI on Breast Imaging.

This article explores the development of computer-aided detection (CAD) and artificial or augmented intelligence (AI) for breast radiology examinations and describes the current applications of AI in breast imaging. Although radiologists in other subspecialties may be less familiar with the use of these technologies in their practices, CAD has been used in breast imaging for more than two decades, as mammography CAD programs have been commercially available in the United States since the late 1990s. Likewise, breast radiologists have seen payment for CAD in mammography and breast MRI evolve over time. With the rapid expansion of AI products in radiology in recent years, many new applications for these technologies in breast imaging have emerged. This article outlines the current state of reimbursement for breast radiology AI algorithms within the traditional fee-for-service model used by Medicare and commercial insurers as well as potential future payment pathways. In addition, the inherent challenges of employing the existing payment framework in the United States to AI programs in radiology are detailed for the reader. This article aims to give breast radiologists a better understanding of how AI will be reimbursed as they seek to further incorporate these emerging technologies into their practices to advance patient care and improve workflow efficiency.

Feasibility of Portable Microwave Imaging Device for Breast Cancer Detection.

Purpose: Microwave radar-based breast imaging technology utilizes the principle of radar, in which radio waves reflect at the interface between target and normal tissues, which have different permittivities. This study aims to investigate the feasibility and safety of a portable microwave breast imaging device in clinical practice. Materials and methods: We retrospectively collected the imaging data of ten breast cancers in nine women (median age: 66.0 years; range: 37–78 years) who had undergone microwave imaging examination before surgery. All were Japanese and the tumor sizes were from 4 to 10 cm. Using a five-point scale (1 = very poor; 2 = poor; 3 = fair; 4 = good; and 5 = excellent), a radiologist specialized in breast imaging evaluated the ability of microwave imaging to detect breast cancer and delineate its location and size in comparison with conventional mammography and the pathological findings. Results: Microwave imaging detected 10/10 pathologically proven breast cancers, including non-invasive ductal carcinoma in situ (DCIS) and micro-invasive carcinoma, whereas mammography failed to detect 2/10 breast cancers due to dense breast tissue. In the five-point evaluation, median score of location and size were 4.5 and 4.0, respectively. Conclusion: The results of the evaluation suggest that the microwave imaging device is a safe examination that can be used repeatedly and has the potential to be useful in detecting breast cancer.

A review on image-based approaches for breast cancer detection, segmentation, and classification

The breast cancer as the most life-threatening disease among the woman has emerged in the worldwide. It is supposed that the early testing and treatment for breast cancer detection would be avoided the surgeries and increase the survival rate. A variety of research studies have motivated to improve the diagnostic methods for early diagnosis of breast cancer. This study investigates the automatic and semi-automatic image-based approaches for breast cancer diagnosis. The scope of this research has limited to theimages based diagnosisapplication journal that are published between 2016 and 2020 years. The principles and associated risk factors for diagnosis the breast cancer and existing imaging techniques are presented. The steps ofdiagnosis including preprocessing, segmentation, extracting tumor features, and tumor classification are investigated. The publicly available datasets for breast imaging are briefly introduced as well. The application issues, challenges of breast imaging technologies and future directions are discussed. Based on thedetailed study, most proposed methods use one type of imaging modalities, however, the doctor need to investigate the multiple imaging techniques to accurate diagnosis and effective treatment. Moreover, handling the multiple imaging require the processingof big data using acluster computing framework.

Development of a digital breast phantom for photoacoustic computed tomography.

Photoacoustic (PA) imaging provides morphological and functional information about angiogenesis and thus is potentially suitable for breast cancer diagnosis. However, the development of PA breast imaging has been hindered by inadequate patients and a lack of ground truth images. Here, we report a digital breast phantom with realistic acoustic and optical properties, with which a digital PA-ultrasound imaging pipeline is developed to create a diverse pool of virtual patients with three types of masses: ductal carcinoma in situ, invasive breast cancer, and fibroadenoma. The experimental results demonstrate that our model is realistic, flexible, and can be potentially useful for accelerating the development of PA breast imaging technology.

Radiation Doses and Risks in Breast Screening.

This article describes radiation doses and cancer risks of digital breast imaging technologies used for breast cancer detection. These include digital mammography (DM), digital breast tomosynthesis (DBT), and newer technologies such as contrast-enhanced digital or spectral mammography (CEM), whole-breast computed tomography, breast-specific gamma imaging (BSGI), molecular breast imaging (MBI), and positron emission mammography (PEM). This article describes the basis for radiation risk estimates, compares radiation doses and risks, and provides benefit-to-radiation-risk ratios for different breast imaging modalities that use ionizing radiation. Current x-ray-based screening modalities such as DM and DBT have small to negligible risks of causing radiation-induced cancers in women of normal screening age. Possible new screening modalities such as CEM have similar small cancer risks. Potential screening modalities that involve radionuclide injection such as BSGI, MBI, and PEM have significantly higher cancer risks unless efficient detection systems and reduced administered doses are used. Benefit-to-radiation-risk estimates are highly favorable for screening with DM and other modalities having comparable (or higher) cancer detection rates and comparably low radiation doses.

Abstract P1-01-10: Positron emission mammography in assessment of the clinicopathological characteristics of breast cancers

Abstract Background: Breast cancer is the most prevalent malignant neoplasm in women. Early detection and personalized treatment are very important to decrease mortality. Positron emission mammography(PEM) is a newly developed breast imaging technology using the positron emission tomography(PET) radiopharmaceutical 18F-fluoro-deoxy-D-glucose(18FDG). We investigated the correlation between PEM and known prognostic markers, including estrogen receptor(ER), progesterone receptor(PR), human epidermal growth factor receptor(HER2) status, tumor histology, nuclear grade, and the number of Ki-67-positive tumor cells/10 HPF(Ki-67 index). Methods: Fifty-eight patients were included in this study. PEM imaging was performed on a commercially available PEM unit. Maximum PEM uptake value(PUVmax) and lesion-to-background(LTB) ratio were determined. All resected specimens were evaluated by our institution’s pathology department. PUVmax and LTB ratios were compared with ER and PR status, tumor histology, and nuclear grades. The correlation coefficients between maximum PEM uptake value or LTB and the Ki-67 index were calculated. Results: PUVmax values were significantly different between ductal carcinoma in situ and invasive ductal carcinoma(P = 0.033). PUVmax and LTB showed significant differences between nuclear grade 1 and nuclear grade 3(PUVmax: P = 0.027, LTB: 0.0043). PUVmax, LTB, and Ki-67 index correlated significantly; PUVmax and Ki-67 index: correlation coefficient = 0.558, P = 0.000125; LTB and Ki-67 index: correlation coefficient = 0.572, P = 0.000075. Conclusions: PUVmax and LTB showed remarkable correlation with nuclear grades and the Ki-67 index, suggesting PEM’s utility in assessing the clinicopathological characteristics of breast cancer. Citation Format: Jun Ito, Toshihiko Sato. Positron emission mammography in assessment of the clinicopathological characteristics of breast cancers [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P1-01-10.

Objective assessment of task performance: a comparison of two FFDM detectors using an anthropomorphic breast phantom.

Current digital mammography systems primarily employ one of two types of detectors: indirect conversion, typically using a cesium-iodine scintillator integrated with an amorphous silicon photodiode matrix, or direct conversion, using a photoconductive layer of amorphous selenium (a-Se) combined with thin-film transistor array. The goal of this study was to evaluate a methodology for objectively assessing image quality to compare human observer task performance in detecting microcalcification clusters and extended mass-like lesions achieved with different detector types. The proposed assessment methodology uses a novel anthropomorphic breast phantom fabricated with ink-jet printing. In addition to human observer detection performance, standard linear metrics such as modulation transfer function, noise power spectrum, and detective quantum efficiency (DQE) were also measured to assess image quality. An Analogic Anrad AXS-2430 a-Se detector used in a commercial FFDM/DBT system and a Teledyne Dalsa Xineos-2329 with CMOS pixel readout were evaluated and compared. The DQE of each detector was similar over a range of exposures. Similar task performance in detecting microcalcifications and masses was observed between the two detectors over a range of clinically applicable dose levels, with some perplexing differences in the detection of microcalcifications at the lowest dose measurement. The evaluation approach presented seems promising as a new technique for objective assessment of breast imaging technology.

Let's Get Real about Molecular Breast Imaging and Radiation Risk.

Let's Get Real about Molecular Breast Imaging and Radiation Risk.

A Comprehensive Classification System for Breast Cancer Diagnosis Based on Dynamic Optical Breast Imaging.

The Dynamic Optical Breast Imaging technology is a promising breast cancer diagnosis approach based on tumor angiogenesis or vascular change detection which generally causes an increased blood volume in tumor. By applying sustained pressure to breast tissue under red light, the tissue with abnormal vascularization exhibits different dynamic behaviors of optical properties compared with normal breast tissue. In this paper, we explore a comprehensive classification method to discriminate malignant from benign lesions based on the Dynamic Optical breast Imaging technology. Firstly, we propose an automatic Point of Reference (POR) and Point of Interest (POI) selection algorithm from input images for following comparison procedures. Secondly, an automatic Margin Shape Patterns (MSP) recognition algorithm for Region of Interest (ROI) is explored. Furthermore, we define a new significant temporal and contextual feature named Temporal Curves Similarity Index (TCSI), with the aim of better characterizing and quantifying the difference inside the same breast. Finally, Support Vector Machine (SVM) is utilized for comprehensive classification. Experimental results, sensitivity of 91% and specificity of 71%, on our clinical database verify the efficiency of the proposed method.

Digital Breast Tomosynthesis: What Have We Learned?

Digital breast tomosynthesis (DBT) is increasingly recognized as a superior breast imaging technology compared with 2D digital mammography (DM) alone. Accumulating data confirm increased sensitivity and specificity in the screening setting, resulting in higher cancer detection rates and lower abnormal interpretation (recall) rates. In the diagnostic environment, DBT simplifies the diagnostic work-up and improves diagnostic accuracy. Initial concern about increased radiation exposure resulting from the DBT acquisition added onto a 2D mammogram has been largely alleviated by the development of synthesized 2D mammography (SM). Continued research is underway to reduce artifacts associated with SM, and improve its comparability to DM. Breast cancers detected with DBT are most often small invasive carcinomas with a preponderance for grade 1 histology and luminal A molecular characteristics. Recent data suggest that higher-grade cancers are also more often node negative when detected with DBT. A meta-analysis of early single-institution studies of the effect of DBT on interval cancers has shown a modest decrease when multiple data sets are combined. Because of the greater conspicuity of lesions on DBT imaging, detection of subtle architectural distortion is increased. Such findings include both spiculated invasive carcinomas and benign etiologies such as radial scars. The diagnostic evaluation of architectural distortion seen only with DBT can pose a challenge. When no sonographic correlate can be identified, DBT-guided biopsy and/or localization capability is essential. Initial experience with DBT-guided procedures suggests that DBT biopsy equipment may improve the efficiency of percutaneous breast biopsy with less radiation.

3D-printed breast phantom for multi-purpose and multi-modality imaging.

Breast imaging technology plays an important role in breast cancer planning and treatment. Recently, three-dimensional (3D) printing technology has become a trending issue in phantom constructions for medical applications, with its advantages of being customizable and cost-efficient. However, there is no current practice in the field of multi-purpose breast phantom for quality control (QC) in multi-modalities imaging. The purpose of this study was to fabricate a multi-purpose breast phantom with tissue-equivalent materials via a 3D printing technique for QC in multi-modalities imaging. We used polyvinyl chloride (PVC) based materials and a 3D printing technique to construct a breast phantom. The phantom incorporates structures imaged in the female breast such as microcalcifications, fiber lesions, and tumors with different sizes. Moreover, the phantom was used to assess the sensitivity of lesion detection, depth resolution, and detectability thresholds with different imaging modalities. Phantom tissue equivalent properties were determined using computed tomography (CT) attenuation [Hounsfield unit (HU)] and magnetic resonance imaging (MRI) relaxation times. The 3D-printed breast phantom had an average background value of 36.2 HU, which is close to that of glandular breast tissue (40 HU). T1 and T2 relaxation times had an average relaxation time of 206.81±17.50 and 20.22±5.74 ms, respectively. Mammographic imaging had improved detection of microcalcification compared with ultrasound and MRI with multiple sequences [T1WI, T2WI and short inversion time inversion recovery (STIR)]. Soft-tissue lesion detection and cylindrical tumor contrast were superior with mammography and MRI compared to ultrasound. Hemispherical tumor detection was similar regardless of the imaging modality used. We developed a multi-purpose breast phantom using a 3D printing technique and determined its value for multi-modal breast imaging studies.

Monitoring of Technology Adoption Using Web Content Mining of Location Information and Geographic Information Systems: A Case Study of Digital Breast Tomosynthesis.

To our knowledge, integration of Web content mining of publicly available addresses with a geographic information system (GIS) has not been applied to the timely monitoring of medical technology adoption. Here, we explore the diffusion of a new breast imaging technology, digital breast tomosynthesis (DBT). We used natural language processing and machine learning to extract DBT facility location information using a set of potential sites for the New England region of the United States via a Google search application program interface. We assessed the accuracy of the algorithm using a validated set of publicly available addresses of locations that provide DBT from the DBT technology vendor, Hologic. We quantified precision, recall, and F1 score, aiming for an F1 score of ≥ 95% as the desirable performance. By reverse geocoding on the basis of the results of the Google Maps application program interface, we derived a spatial data set for use in an ArcGIS environment. Within the GIS, a host of spatiotemporal analyses and geovisualization techniques are possible. We developed a semiautomated system that integrated DBT location information into a GIS that was feasible and of reasonable quality. Initial accuracy of the algorithm was poor using only a search term list for information retrieval (precision, 35%; recall, 44%; F1 score, 39%), but performance dramatically improved by leveraging natural language processing and simple machine learning techniques to isolate single, valid instances of DBT location information (precision, 92%; recall, 96%; F1 score, 94%). Reverse geocoding yielded reliable geographic coordinates for easy implementation into a GIS for mapping and planned monitoring. Our novel approach can be applicable to technologies beyond DBT, which may inform equitable access over time and space.

Anthropomorphic breast model repository for research and development of microwave breast imaging technologies

A repository of anthropomorphic numerical breast models is made available for the scientific community to support research and development of microwave imaging technologies for diagnostic and therapeutic applications. These models are constructed from magnetic resonance imaging (MRI) scans acquired at our university hospital. Our 3D breast modelling method is used to translate the MRI scans into 3D models representing the geometry and microwave-frequency properties of tissues in the breast. The reconstructed models demonstrate anatomical realism, reconfigurable complexity, and flexibility to adapt to simulations of various microwave imaging techniques and prototype systems. With these models, realistic and rigorous test scenarios can be defined in simulations to support feasibility analysis, performance verification and design improvements of developing microwave imaging techniques, prior to testing on experimental systems. A repository of breast models is created which includes breasts of varying classification – fatty, scattered, heterogeneous, and dense. In addition, the models include brief documentation to facilitate researchers in selecting a model by matching its features with their requirements.

Development of 3D patient-based super-resolution digital breast phantoms using machine learning

Digital phantoms are important tools for optimization and evaluation of x-ray imaging systems, and should ideally reflect the 3D structure of human anatomy and its potential variability. In addition, they need to include a good level of detail at a high enough spatial resolution to accurately model the continuous nature of the human anatomy. A pipeline to increase the spatial resolution of patient-based digital breast phantoms that can be used for computer simulations of breast imaging is proposed. Given a tomographic breast image of finite resolution, the proposed methods can generate a phantom and increase its resolution at will, not only simply through super-sampling, but also by generating additional random glandular details to account for glandular edges and strands to compensate for those that may have not been detected in the original image due to the limited spatial resolution of the imaging system used. The proposed algorithms use supervised learning to predict the loss in glandularity due to limited resolution, and then to realistically recover this loss by learning the mapping between low and high resolution images. They were trained on high-resolution synchrotron images (detector pixel size 60 μm) reconstructed at seven voxel dimensions (60 μm–480 μm), and applied to patient images acquired with a clinical breast CT system (detector pixel size 194 μm) to generate super-resolution phantoms (voxel sizes 68 μm). Several evaluations were made to assess the appropriateness of the developed methods, both with the synchrotron (relative prediction error 0.010 ± 0.004, recovering accuracy 0.95 ± 0.04), and with the clinical images (average glandularity error at 194 μm: 0.15% ± 0.12%). Finally, a breast radiologist assessed the realism of the developed phantoms by blindly comparing original and phantom images, resulting in not being able to distinguish the real from the phantom images. In conclusion, the proposed method can generate super-resolution phantoms from tomographic breast patient images that can be used for future computer simulations for optimization of new breast imaging technologies.

Comparison of screening CEDM and MRI for women at increased risk for breast cancer: A pilot study

ObjectivesContrast enhanced digital mammography (CEDM) is a new breast imaging technology increasingly used in the diagnostic setting but its utility in the pure screening setting has not been reported. The goal of this pilot study is to prospectively compare screening CEDM to breast MRI in women with an increased risk for breast cancer. MethodsIn this IRB-approved HIPAA-compliant study, 318 women at increased breast cancer risk were consented (December 2012–May 2015) to undergo CEDM in addition to their scheduled MRI. CEDM was performed within 30days of screening MRI. CEDM was interpreted blinded to MRI. The reference standard was defined as a combination of pathology and 2-year imaging follow-up. ResultsData from 307/318 patients were evaluable. Three cancers (two invasive cancers, one ductal carcinoma in situ) were detected at first round screening: MRI detected all three and CEDM detected the two invasive cancers. None of the three cancers was seen on the low energy mammograms which are comparable to conventional mammography. At 2year imaging follow up, there were 5 additional screen detected cancers and no palpable cancers. The positive predictive value 3 (PPV3) for CEDM was 15% (2/13, 95% CI: 2–45%) and 14% for MRI (3/21, 95% CI: 3–36%). The specificity of CEDM and MRI were 94.7% and 94.1% respectively. ConclusionsBoth CEDM and MRI detected additional cancers not seen on conventional mammography, primarily invasive cancers. Our pilot data suggest that CEDM could be valuable as a supplemental imaging exam for women at increased risk for breast cancer who do not meet the criteria for MRI or for whom access to MRI is limited. Validation in larger multi institutional trials is warranted.