Automated Whole-Breast Ultrasound: Advancing the Performance of Breast Cancer Screening

Compact reverse time migration: A real-time approach for full waveform ultrasound imaging for breast

We present compact reverse time migration (CRTM), a real-time ultrasound imaging method that can exploit the full waveform information of ultrasonic wave records for imaging breast tissue. Conventional reverse time migration (RTM) computes the gradient of the reflective ultrasound data with respect to the perturbation of the velocity model of the soft tissues and the gradient can indicate the interface between different types of body tissue. In contrast to conventional reflection ultrasound (B-mode), which is based on the high-frequency approximation to the wave equation, the RTM algorithm is based on the complete wave equation, and can thus exploit the full waveform (wide-spectrum) information of the data and provide an image with higher resolution. Unfortunately, the computational burden of RTM is noticeably higher than the ray-based B-mode. This precludes real-time applications, one of the most important features of ultrasound imaging. The proposed CRTM algorithm can significantly reduce the computational costs of RTM, such that it can be applied for real-time imaging. We demonstrate the performance of CRTM through a synthetic experiment of ultrasound breast imaging. CRTM can be potentially adapted to related signal-processing fields, such as seismic imaging, acoustic camera systems, and radar imaging.

Ultrasound for Breast Cancer Screening in Resource-Limited Settings: Current Practice and Future Directions

Breast cancer (BC) is the most prevalent cancer among women globally. Cancer screening can reduce mortality and improve women’s health. In developed countries, mammography (MAM) has been primarily utilized for population-based BC screening for several decades. However, it is usually unavailable in low-resource settings due to the lack of equipment, personnel, and time necessary to conduct and interpret the examinations. Ultrasound (US) with high detection sensitivity for women of younger ages and with dense breasts has become a supplement to MAM for breast examination. Some guidelines suggest using US as the primary screening tool in certain settings where MAM is unavailable and infeasible, but global recommendations have not yet reached a unanimous consensus. With the development of smart devices and artificial intelligence (AI) in medical imaging, clinical applications and preclinical studies have shown the potential of US combined with AI in BC screening. Nevertheless, there are few comprehensive reviews focused on the role of US in screening BC in underserved conditions, especially in technological, economical, and global perspectives. This work presents the benefits, limitations, advances, and future directions of BC screening with technology-assisted and resource-appropriate strategies, which may be helpful to implement screening initiatives in resource-limited countries.

Ultrasound Tomography

Ultrasound tomography (USCT) is a promising imaging modality, mainly aiming at early diagnosis of breast cancer. It provides three-dimensional, reproducible images of higher quality than conventional ultrasound methods and additionally offers quantitative information on tissue properties. This chapter provides an introduction to the background and history of USCT, followed by an overview of image reconstruction algorithms and system design. It concludes with a discussion of current and future applications as well as limitations and their potential solutions.

Best Practice Guidelines for Breast Imaging, Breast Imaging Society, India

Breast imaging is one of the prerequisites for providing high-quality breast health care. Choosing the appropriate investigation is central to diagnosing breast disease or the absence of it in women and men who present to health professionals for treatment. Patients with breast disease present to doctors of different subspecialties as well as general practitioners in our country. It is important therefore to provide uniform guidance to doctors in different health care setups of our country, urban and rural, government and private, for breast diseases to be diagnosed and treated optimally. These guidelines framed by the task group set up by the Breast Imaging Society, India have been formulated focusing primarily on the Indian patients and health care infrastructures. These guidelines aim to provide a framework for the referring doctors and practicing radiologists, to enable them to choose the appropriate investigation for patients with breast symptoms and signs. The guidelines encompass all aspects of breast imaging including mammography, breast ultrasound, breast magnetic resonance imaging, as well as breast interventions. Algorithms for investigation of specific common breast symptoms and signs have been provided in this document. The aim has been to keep this framework simple and practical so that it can guide not only subspecialists in breast care but also help doctors who do not routinely deal with breast diseases, so that breast cancer is not missed. Breast screening is an integral part of breast imaging and guidelines for the same have been incorporated in this document. In the absence of a population-based screening program in India, the guidelines to be followed for those women who wish to be screened by mammography have been provided. Overall, the aim of this document is to provide a holistic approach to standardize breast care imaging services in India.

Supplemental Cancer Screening for Women With Dense Breasts: Guidance for Health Care Professionals

Mammography is the standard for breast cancer screening. The sensitivity of mammography in identifying breast cancer, however, is reduced for women with dense breasts. Thirty-eight states have passed laws requiring that all women be notified of breast tissue density results in their mammogram report. The notification includes a statement that differs by state, encouraging women to discuss supplemental screening options with their health care professionals (HCPs). Several supplemental screening tests are available for women with dense breast tissue, but no established guidelines exist to direct HCPs in their recommendation of preferred supplemental screening test. Tailored screening, which takes into consideration the patient's mammographic breast density and lifetime breast cancer risk, can guide breast cancer screening strategies that are more comprehensive. This review describes the benefits and limitations of the various available supplemental screening tests to guide HCPs and patients in choosing the appropriate breast cancer screening.

Evaluation of the FUSION-X-US-II prototype to combine automated breast ultrasound and tomosynthesis

OBJECTIVE

The FUSION-X-US-II prototype was developed to combine 3D automated breast ultrasound (ABUS) and digital breast tomosynthesis in a single device. We evaluated the performance of ABUS and tomosynthesis in a single examination in a clinical setting.

METHODS

In this prospective feasibility study, digital breast tomosynthesis and ABUS were performed using the FUSION-X-US-II prototype without any change of the breast position in patients referred for clarification of breast lesions with an indication for tomosynthesis. The tomosynthesis and ABUS images of the prototype were interpreted independently from the clinical standard by a breast diagnostics specialist. Any detected lesion was classified using BI-RADS® scores, and results of the standard clinical routine workup (gold standard) were compared to the result of the separate evaluation of the prototype images. Image quality was rated subjectively and coverage of the breast was measured.

RESULTS

One hundred one patients received both ABUS and tomosynthesis using the prototype. The duration of the additional ABUS acquisition was 40 to 60 s. Breast coverage by ABUS was approximately 80.0%. ABUS image quality was rated as diagnostically useful in 86 of 101 cases (85.1%). Thirty-three of 34 malignant breast lesions (97.1%) were identified using the prototype.

CONCLUSION

The FUSION-X-US-II prototype allows a fast ABUS scan in combination with digital breast tomosynthesis in a single device integrated in the clinical workflow. Malignant breast lesions can be localized accurately with direct correlation of ABUS and tomosynthesis images. The FUSION system shows the potential to improve breast cancer screening in the future after further technical improvements.

KEY POINTS

• The FUSION-X-US-II prototype allows the combination of automated breast ultrasound and digital breast tomosynthesis in a single device without decompression of the breast. • Image quality and coverage of ABUS are sufficient to accurately detect malignant breast lesions. • If tomosynthesis and ABUS should become part of breast cancer screening, the combination of both techniques in one device could offer practical and logistic advantages. To evaluate a potential benefit of a combination of ABUS and tomosynthesis in screening-like settings, further studies are needed.

Breast tumor segmentation in 3D automatic breast ultrasound using Mask scoring R-CNN

PURPOSE

Automatic breast ultrasound (ABUS) imaging has become an essential tool in breast cancer diagnosis since it provides complementary information to other imaging modalities. Lesion segmentation on ABUS is a prerequisite step of breast cancer computer-aided diagnosis (CAD). This work aims to develop a deep learning-based method for breast tumor segmentation using three-dimensional (3D) ABUS automatically.

METHODS

For breast tumor segmentation in ABUS, we developed a Mask scoring region-based convolutional neural network (R-CNN) that consists of five subnetworks, that is, a backbone, a regional proposal network, a region convolutional neural network head, a mask head, and a mask score head. A network block building direct correlation between mask quality and region class was integrated into a Mask scoring R-CNN based framework for the segmentation of new ABUS images with ambiguous regions of interest (ROIs). For segmentation accuracy evaluation, we retrospectively investigated 70 patients with breast tumor confirmed with needle biopsy and manually delineated on ABUS, of which 40 were used for fivefold cross-validation and 30 were used for hold-out test. The comparison between the automatic breast tumor segmentations and the manual contours was quantified by I) six metrics including Dice similarity coefficient (DSC), Jaccard index, 95% Hausdorff distance (HD95), mean surface distance (MSD), residual mean square distance (RMSD), and center of mass distance (CMD); II) Pearson correlation analysis and Bland-Altman analysis.

RESULTS

The mean (median) DSC was 85% ± 10.4% (89.4%) and 82.1% ± 14.5% (85.6%) for cross-validation and hold-out test, respectively. The corresponding HD95, MSD, RMSD, and CMD of the two tests was 1.646 ± 1.191 and 1.665 ± 1.129 mm, 0.489 ± 0.406 and 0.475 ± 0.371 mm, 0.755 ± 0.755 and 0.751 ± 0.508 mm, and 0.672 ± 0.612 and 0.665 ± 0.729 mm. The mean volumetric difference (mean and ± 1.96 standard deviation) was 0.47 cc ([-0.77, 1.71)) for the cross-validation and 0.23 cc ([-0.23 0.69]) for hold-out test, respectively.

CONCLUSION

We developed a novel Mask scoring R-CNN approach for the automated segmentation of the breast tumor in ABUS images and demonstrated its accuracy for breast tumor segmentation. Our learning-based method can potentially assist the clinical CAD of breast cancer using 3D ABUS imaging.

Preliminary study of the technical limitations of automated breast ultrasound: from procedure to diagnosis

Objective

To evaluate the main technical limitations of automated breast ultrasound and to determine the proportion of examinations excluded.

Materials and Methods

We evaluated 440 automated breast ultrasound examinations performed, over a 12-month period, by technicians using an established protocol.

Results

In five cases (1.1%), the examination was deemed unacceptable for diagnostic purposes, those examinations therefore being excluded.

Conclusion

Automated breast ultrasound is expected to overcome some of the major limitations of conventional ultrasound in breast cancer screening. In Brazil, this new method can be accepted for inclusion in routine clinical practice only after its advantages have been validated in the national context.

Budget impact analysis of breast cancer screening in Italy: The role of new technologies

Although mammography screening significantly reduces breast cancer mortality, women could present different morphological characteristics that do not allow the correct vision of their breasts and the detection of cancer, resulting in a delay of diagnosis and an increase in the risk of mortality. The present study aims at analyzing potential areas of improvement of the current screening programs and then hypothesizing alternative technologies to use within the diagnostic phase, from an economic point of view. A Budget Impact Analysis approach was implemented, considering the Italian National Healthcare Service perspective, and representing the healthcare expenditure evolution, over three years. In the Budget Impact Analysis model, two distinct phases of the screening programs were considered: (1) the screening/diagnosis phase and (2) the phase related to cancer care and treatments of patients. The results provide clinicians and policy makers with a rational method to forecast economic resources in the screening programs in a general context of limited resources. In particular, results of the Budget Impact Analysis showed that, while the introduction of the ABUS InveniaTM technology into the screening programs would lead to an increase in the screening phase expenditure, it would generate an economic advantage related to the patients treatment and care.

Deformable mapping technique to correlate lesions in digital breast tomosynthesis and automated breast ultrasound images

PURPOSE

To develop a deformable mapping technique to match corresponding lesions between digital breast tomosynthesis (DBT) and automated breast ultrasound (ABUS) images.

METHODS

External fiducial markers were attached to the surface of two CIRS multi-modality compressible breast phantoms (A and B) containing multiple simulated lesions. Both phantoms were imaged with DBT (upright positioning with cranial-caudal compression) and ABUS (supine positioning with anterior-to-chest wall compression). The lesions and markers were manually segmented by three different readers. Reader segmentation similarity and reader reproducibility were assessed using Dice similarity coefficients (DSC) and distances between centers of mass (d_COM ). For deformable mapping between the modalities each reader's segmented dataset was processed with an automated deformable mapping algorithm as follows: First, Morfeus, a finite element (FE) based multi-organ deformable image registration platform, converted segmentations into triangular surface meshes. Second, Altair HyperMesh, a FE pre-processor, created base FE models for the ABUS and DBT data sets. All deformation is performed on the DBT image data; the ABUS image sets remain fixed throughout the process. Deformation was performed on the external skin contour (DBT image set) to match the external skin contour on the ABUS set, and the locations of the external markers were used to morph the skin contours to be within a user-defined distance. Third, the base DBT-FE model was deformed with the FE analysis solver, Optistruct. Deformed DBT lesions were correlated with matching lesions in the base ABUS FE model. Performance (lesion correlation) was assessed with d_COM for all corresponding lesions and lesion overlap. Analysis was performed to determine the minimum number of external fiducial markers needed to create the desired correlation and the improvement of correlation with the use of external markers.

RESULTS

Average DSC for reader similarity ranged from 0.88 to 0.91 (ABUS) and 0.57 to 0.83 (DBT). Corresponding d_COM ranged from 0.20 to 0.36 mm (ABUS) and 0.11 to 1.16 mm (DBT). Lesion correlation is maximized when all corresponding markers are within a maximum distance of 5 mm. For deformable mapping of phantom A, without the use of external markers, only two of six correlated lesions showed overlap with an average lesion d_COM of 6.8 ± 2.8 mm. With use of three external fiducial markers, five of six lesions overlapped and average d_COM improved to 4.9 ± 2.4 mm. For deformable mapping of Phantom B without external markers analysis, four lesions were correlated of seven with overlap between only one of seven lesions, and an average lesion d_COM of 9.7 ± 3.5 mm. With three external markers, all seven possible lesions were correlated with overlap between four of seven lesions. The average d_COM was 8.5 ± 4.0 mm.

CONCLUSION

This work demonstrates the potential for a deformable mapping technique to relate corresponding lesions in DBT and ABUS images by showing improved lesion correspondence and reduced lesion registration errors with the use of external fiducial markers. The technique should improve radiologists' characterization of breast lesions which can reduce patient callbacks, misdiagnoses and unnecessary biopsies.

Performance Goals for an Adjunct Diagnostic Test to Reduce Unnecessary Biopsies After Screening Mammography: Analysis of Costs, Benefits, and Consequences

PURPOSE

Because benign biopsies resulting from false-positive mammographic findings are a known harm of breast cancer screening, physicians and test manufacturers are searching for ways to reduce their frequency. The aim of this study was to estimate potential costs and consequences associated with using an adjunct diagnostic test for triaging women with suspicious mammographic findings before biopsy.

METHODS

A decision model was developed to compare the use of an adjunct test before biopsy to the current standard of care for suspicious mammographic findings. The decision analysis was performed from the perspective of a national health payer, with a 1-year time horizon among women representative of the US screening population aged 40 to 79 years. Three primary outcomes were assessed: (1) incremental costs, (2) number of benign biopsies avoided, and (3) number of missed opportunities for diagnosing cancer per million women screened. Input parameters were obtained from the medical literature and expert opinion. Sensitivity analyses were performed to evaluate the effects of uncertainty in parameter estimates.

RESULTS

The base-case analysis demonstrated that the use of an adjunct diagnostic test with 95% sensitivity, 75% specificity, and a cost of $1,000 would eliminate 8,127 unnecessary breast biopsies per million women screened. However, this would cost the US health care system an additional $6,462,977 and result in 255 missed opportunities for diagnosing cancer per million women screened.

CONCLUSIONS

The addition of an adjunct test for triaging women for breast biopsy after abnormal findings on screening mammography would likely eliminate many unnecessary biopsies but also increase overall health care costs. This exploratory analysis highlights the fact that mammography remains a relatively inexpensive and effective breast cancer screening and diagnostic modality.

Emerging Breast Imaging Technologies on the Horizon

Early detection of breast cancers by mammography in conjunction with adjuvant therapy has contributed to reduction in breast cancer mortality. Mammography remains the "gold-standard" for breast cancer screening but is limited by tissue superposition. Digital breast tomosynthesis and more recently, dedicated breast computed tomography have been developed to alleviate the tissue superposition problem. However, all of these modalities rely upon x-ray attenuation contrast to provide anatomical images, and there are ongoing efforts to develop and clinically translate alternative modalities. These emerging modalities could provide for new contrast mechanisms and may potentially improve lesion detection and diagnosis. In this article, several of these emerging modalities are discussed with a focus on technologies that have advanced to the stage of in vivo clinical evaluation.

Ultrasound Imaging Technologies for Breast Cancer Detection and Management: A Review

Ultrasound imaging is a commonly used modality for breast cancer detection and diagnosis. In this review, we summarize ultrasound imaging technologies and their clinical applications for the management of breast cancer patients. The technologies include ultrasound elastography, contrast-enhanced ultrasound, 3-D ultrasound, automatic breast ultrasound and computer-aided detection of breast ultrasound. We summarize the study results seen in the literature and discuss their future directions. We also provide a review of ultrasound-guided, breast biopsy and the fusion of ultrasound with other imaging modalities, especially magnetic resonance imaging (MRI). For comparison, we also discuss the diagnostic performance of mammography, MRI, positron emission tomography and computed tomography for breast cancer diagnosis at the end of this review. New ultrasound imaging techniques, ultrasound-guided biopsy and the fusion of ultrasound with other modalities provide important tools for the management of breast patients.

Absolute Reconstructions Using Rotational Electrical Impedance Tomography for Breast Cancer Imaging

A rotational Electrical Impedance Tomography (rEIT) methodology is described and shown to produce spatially accurate absolute reconstructions with improved image contrast and an improved ability to distinguish closely spaced inclusions compared to traditional EIT on data recorded from cylindrical and breast-shaped tanks. Rotations of the tank without altering the interior conductivity distribution are used to produce the rEIT data. Quantitatively, rEIT was able to distinguish two inclusions that were 1.5 cm closer together than traditional EIT could achieve for inclusions placed 2 to 3 cm from the center for the cylindrical tank, and rEIT was able to distinguish two tumor-like inclusions where traditional EIT could not reliably do so. Mathematical analysis showed that rEIT improves the number of stable singular vectors by up to 4.2 and 4.7 times than that of traditional EIT for the cylindrical and breast-shaped tanks, respectively, which is an indication of improved resolution. Direct investigations into measurements revealed minimum rotation angles that should yield data uncorrupted by noise. Two inverse approaches (one that inverts then fuses the data (I/DF) and one that fuses the data then inverts (DF/I)) and two mesh modeling approaches were considered. It was found that DF/I produces far better results compared to I/DF and a rotated-mesh approach produces further improvements. The ability to obtain improved absolute reconstructions using rEIT on a practical clinical scenario (breast-shaped tank experiment) is an important step towards using rEIT to improve previous EIT results in medical applications.

Automated Breast Ultrasound: Dual-Sided Compared with Single-Sided Imaging

The design and performance of a mammographically configured, dual-sided, automated breast ultrasound (ABUS) 3-D imaging system are described. Dual-sided imaging (superior and inferior) is compared with single-sided imaging to aid decisions on clinical implementation of the more complex, but potentially higher-quality dual-sided imaging. Marked improvement in image quality and coverage of the breast is obtained in dual-sided ultrasound over single-sided ultrasound. Among hypo-echoic masses imaged, there are increases in the mean contrast-to-noise ratio of 57% and 79%, respectively, for spliced dual-sided versus superior or inferior single-sided imaging. The fractional breast volume coverage, defined as the percentage volume in the transducer field of view that is imaged with clinically acceptable quality, is improved from 59% in both superior and inferior single-sided imaging to 89% in dual-sided imaging. Applying acoustic coupling to the breast requires more effort or sophisticated methods in dual-sided imaging than in single-sided imaging.

Effects of age on the detection and management of breast cancer

Currently, breast cancer affects approximately 12% of women worldwide. While the incidence of breast cancer rises with age, a younger age at diagnosis is linked to increased mortality. We discuss age related factors affecting breast cancer diagnosis, management and treatment, exploring key concepts and identifying critical areas requiring further research. We examine age as a factor in breast cancer diagnosis and treatment relating it to factors such as genetic status, breast cancer subtype, hormone factors and nodal status. We examine the effects of age as seen through the adoption of population wide breast cancer screening programs. Assessing the incidence rates of each breast cancer subtype, in the context of age, we examine the observed correlations. We explore how age affects patient's prognosis, exploring the effects of age on stage and subtype incidence. Finally we discuss the future of breast cancer diagnosis and treatment, examining the potential of emerging tests and technologies (such as microRNA) and how novel research findings are being translated into clinically relevant practices.

Current status of automated breast ultrasonography

Breast ultrasonography (US) is currently considered the first-line examination in the detection and characterization of breast lesions. However, conventional handheld US (HHUS) has several limitations such as operator dependence and the requirement of a considerable amount of radiologist time for whole-breast US. Automated breast US (ABUS), recently approved by the United States Food and Drug Administration for screening purposes, has several advantages over HHUS, such as higher reproducibility, less operator dependence, and less required physician time for image acquisition. In addition, ABUS provides both a coronal view and a relatively large field of view. Recent studies have reported that ABUS is promising in US screening for women with dense breasts and can potentially replace handheld second-look US in a preoperative setting.

Improved digital breast tomosynthesis images using automated ultrasound

PURPOSE

Digital breast tomosynthesis (DBT) offers poor image quality along the depth direction. This paper presents a new method that improves the image quality of DBT considerably through the a priori information from automated ultrasound (AUS) images.

METHODS

DBT and AUS images of a complex breast-mimicking phantom are acquired by a DBT/AUS dual-modality system. The AUS images are taken in the same geometry as the DBT images and the gradient information of the in-slice AUS images is adopted into the new loss functional during the DBT reconstruction process. The additional data allow for new iterative equations through solving the optimization problem utilizing the gradient descent method. Both visual comparison and quantitative analysis are employed to evaluate the improvement on DBT images. Normalized line profiles of lesions are obtained to compare the edges of the DBT and AUS-corrected DBT images. Additionally, image quality metrics such as signal difference to noise ratio (SDNR) and artifact spread function (ASF) are calculated to quantify the effectiveness of the proposed method.

RESULTS

In traditional DBT image reconstructions, serious artifacts can be found along the depth direction (Z direction), resulting in the blurring of lesion edges in the off-focus planes parallel to the detector. However, by applying the proposed method, the quality of the reconstructed DBT images is greatly improved. Visually, the AUS-corrected DBT images have much clearer borders in both in-focus and off-focus planes, fewer Z direction artifacts and reduced overlapping effect compared to the conventional DBT images. Quantitatively, the corrected DBT images have better ASF, indicating a great reduction in Z direction artifacts as well as better Z resolution. The sharper line profiles along the Y direction show enhancement on the edges. Besides, noise is also reduced, evidenced by the obviously improved SDNR values.

CONCLUSIONS

The proposed method provides great improvement on the quality of DBT images. This improvement makes it easier to locate and to distinguish a lesion, which may help improve the accuracy of the diagnosis using DBT imaging.

Computerized detection of breast cancer on automated breast ultrasound imaging of women with dense breasts

PURPOSE

Develop a computer-aided detection method and investigate its feasibility for detection of breast cancer in automated 3D ultrasound images of women with dense breasts.

METHODS

The HIPAA compliant study involved a dataset of volumetric ultrasound image data, "views," acquired with an automated U-Systems Somo●V(®) ABUS system for 185 asymptomatic women with dense breasts (BI-RADS Composition/Density 3 or 4). For each patient, three whole-breast views (3D image volumes) per breast were acquired. A total of 52 patients had breast cancer (61 cancers), diagnosed through any follow-up at most 365 days after the original screening mammogram. Thirty-one of these patients (32 cancers) had a screening-mammogram with a clinically assigned BI-RADS Assessment Category 1 or 2, i.e., were mammographically negative. All software used for analysis was developed in-house and involved 3 steps: (1) detection of initial tumor candidates, (2) characterization of candidates, and (3) elimination of false-positive candidates. Performance was assessed by calculating the cancer detection sensitivity as a function of the number of "marks" (detections) per view.

RESULTS

At a single mark per view, i.e., six marks per patient, the median detection sensitivity by cancer was 50.0% (16/32) ± 6% for patients with a screening mammogram-assigned BI-RADS category 1 or 2--similar to radiologists' performance sensitivity (49.9%) for this dataset from a prior reader study--and 45.9% (28/61) ± 4% for all patients.

CONCLUSIONS

Promising detection sensitivity was obtained for the computer on a 3D ultrasound dataset of women with dense breasts at a rate of false-positive detections that may be acceptable for clinical implementation.

Diagnostic performance of automated breast ultrasound as a replacement for a hand-held second-look ultrasound for breast lesions detected initially on magnetic resonance imaging

To evaluate the diagnostic performance of automated breast ultrasound (ABUS) after breast magnetic resonance imaging (MRI) as a replacement for hand-held second-look ultrasound (HH-SLUS), we evaluated 58 consecutive patients with breast cancer who had additional suspicious lesions on breast MRI. All patients underwent HH-SLUS and ABUS. Three breast radiologists evaluated the detectability, location, characteristics and conspicuity of lesions on ABUS. We also evaluated inter-observer variability and compared the results with HH-SLUS results. Eighty additional suspicious lesions were identified on breast MRI. Fifteen of the 80 lesions (19%) were not detected on HH-SLUS. Eight of the 15 lesions (53%) were detected on ABUS, whereas the remaining 7 were not detected on ABUS. Among the 65 lesions detected on HH-SLUS, only 3 lesions were not detected on ABUS. The intra-class correlation coefficients for lesion location and size all exceeded 0.70, indicating high reliability. Moderate to fair agreement was found for mass shape, orientation, margin and Breast Imaging Reporting and Data System (BI-RADS) final assessment. Therefore, ABUS can reliably detect additional suspicious lesions identified on breast MRI and may help in the decision on biopsy guidance method (US vs. MRI) as a replacement tool for HH-SLUS.

Breast ultrasonography: state of the art

Ultrasonography (US) is an indispensable tool in breast imaging and is complementary to both mammography and magnetic resonance (MR) imaging of the breast. Advances in US technology allow confident characterization of not only benign cysts but also benign and malignant solid masses. Knowledge and understanding of current and emerging US technology, along with the application of meticulous scanning technique, is imperative for image optimization and diagnosis. The ability to synthesize breast US findings with multiple imaging modalities and clinical information is also necessary to ensure the best patient care. US is routinely used to guide breast biopsies and is also emerging as a supplemental screening tool in women with dense breasts and a negative mammogram. This review provides a summary of current state-of-the-art US technology, including elastography, and applications of US in clinical practice as an adjuvant technique to mammography, MR imaging, and the clinical breast examination. The use of breast US for screening, preoperative staging for breast cancer, and breast intervention will also be discussed.

Rapid breast density analysis of partial volumes of automated breast ultrasound images

Rapid volume density analysis (RVDA) for automated breast ultrasound (ABUS) has been proposed as a more efficient method for estimating breast density. In the current experiment, ABUS images were obtained for 67 breasts from 40 patients. For each case, three rectangular volumes of interest (VOIs) were extracted, including the VOIs located at the 6 and 12 o'clock positions relative to the nipple in the anterior to posterior pass and the lateral position relative to the nipple in the lateral pass. The centers of these VOIs were defined to align with the center of nipple, and the depths reached the retromammary fat boundary. The fuzzy c-means classifier was applied to differentiate the fibroglandular and fat tissues to estimate the density. The classification results of the three VOIs were averaged to obtain the breast density. The density correlations between the RVDA and the ABUS methods were 0.98 and 0.96 using Pearson's correlation and linear regression coefficients, respectively. The average computation times for RVDA and ABUS were 4.2 and 17.8 seconds, respectively, using an Intel Core2 2.66 GHz computer with 3.25 GB memory. In conclusion, the RVDA method offers a quantitative and efficient breast density estimation for ABUS.

[Limits of mammography screening: current controversies and perspectives]

In recent years overdiagnosis has been claimed to be a major disadvantage of breast cancer screening. Studies estimating overdiagnosis have shown substantial discrepancies due to differences in the underlying epidemiological methods. Valid estimates based on studies with sufficient follow-up have shown a proportion of overdiagnosis of much less than 10 % of cancer detected by screening. Special attention should be given to intraductal carcinoma in situ as it is a precursor lesion of invasive cancer but the progression potential is poorly understood and it is currently treated similarly to invasive cancer. This highlights the urgent need for better stratification of the disease in research and clinical practice allowing individual risk-adapted appropriate therapy. In this respect new molecular and genetic biomarkers seem to be the most promising approach for predicting disease behavior.Interval carcinoma represents a limitation of breast cancer screening and the impact on screening effectiveness is still unknown. Recent studies have emphasized that interval carcinoma is more aggressive than cancer detected by screening. Hence the occurrence and distribution of interval cancer should be recorded accurately by national cancer registries and evaluated by the screening services. Quality assurance measures may help to reduce the rate of false negative cases to a minimum. Further research focusing on subgroups of screening participants with a higher risk for interval cancer occurrence seems to be a prerequisite for developing future prevention strategies.

Computer-aided tumor detection based on multi-scale blob detection algorithm in automated breast ultrasound images

Automated whole breast ultrasound (ABUS) is an emerging screening tool for detecting breast abnormalities. In this study, a computer-aided detection (CADe) system based on multi-scale blob detection was developed for analyzing ABUS images. The performance of the proposed CADe system was tested using a database composed of 136 breast lesions (58 benign lesions and 78 malignant lesions) and 37 normal cases. After speckle noise reduction, Hessian analysis with multi-scale blob detection was applied for the detection of tumors. This method detected every tumor, but some nontumors were also detected. The tumor like lihoods for the remaining candidates were estimated using a logistic regression model based on blobness, internal echo, and morphology features. The tumor candidates with tumor likelihoods higher than a specific threshold (0.4) were considered tumors. By using the combination of blobness, internal echo, and morphology features with 10-fold cross-validation, the proposed CAD system showed sensitivities of 100%, 90%, and 70% with false positives per pass of 17.4, 8.8, and 2.7, respectively. Our results suggest that CADe systems based on multi-scale blob detection can be used to detect breast tumors in ABUS images.

Screening MR imaging versus screening ultrasound: pros and cons

Data support greater sensitivity of MR imaging compared with mammography and ultrasound in high-risk populations, in particular BRCA 1 and BRCA 2 carriers. Screening ultrasound improves cancer yield versus mammography alone in high-risk patients and in patients with dense breasts and is less expensive. Drawbacks include low positive predictive value, operator dependence, and significant physician time expenditure. Advances, such as refinement of automated whole-breast ultrasound, new outcomes data from ultrasound-detected masses in BI-RADS 3 and 4a categories, and development of new MR imaging sequences that allow rapid screening, potentially without use of contrast, will likely reveal the most appropriate tool over time.

Beyond mammography: new frontiers in breast cancer screening

Breast cancer screening remains a subject of intense and, at times, passionate debate. Mammography has long been the mainstay of breast cancer detection and is the only screening test proven to reduce mortality. Although it remains the gold standard of breast cancer screening, there is increasing awareness of subpopulations of women for whom mammography has reduced sensitivity. Mammography also has undergone increased scrutiny for false positives and excessive biopsies, which increase radiation dose, cost, and patient anxiety. In response to these challenges, new technologies for breast cancer screening have been developed, including low-dose mammography, contrast-enhanced mammography, tomosynthesis, automated whole breast ultrasound, molecular imaging, and magnetic resonance imaging. Here we examine some of the current controversies and promising new technologies that may improve detection of breast cancer both in the general population and in high-risk groups, such as women with dense breasts. We propose that optimal breast cancer screening will ultimately require a personalized approach based on metrics of cancer risk with selective application of specific screening technologies best suited to the individual's age, risk, and breast density.

Conformal ultrasound imaging system for anatomical breast inspection

Ultrasound tomography has considerable potential as a means of breast cancer detection because it reduces the operator-dependency observed in echography. A half-ring transducer array was designed based on breast anatomy, to obtain reflectivity images of the ductolobular structures using tomographic reconstruction procedures. The 3-MHz transducer array comprises 1024 elements set in a 190-degree circular arc with a radius of 100 mm. The front-end electronics incorporate 32 independent parallel transmit/receive channels and a 32-to-1024 multiplexer unit. The transmit and receive circuitries have a variable sampling frequency of up to 80 MHz and 12-bit precision. Arbitrary waveforms are synthesized to improve the signal-to-noise ratio and to increase the spatial resolution when working with low-contrast objects. The setup was calibrated with academic objects and a needle hydrophone to develop the data correction tools and specify the properties of the system. The backscattering field was recorded using a restricted aperture, and tomographic acquisitions were performed with a pair of 0.08-mm-diameter steel wires, a low-contrast 2-D breast phantom, and a breast-shaped phantom containing inclusions. Data were processed with dedicated correction tools and a pulse compression technique. Objects were reconstructed using the elliptical back-projection algorithm.