Automated Whole Breast Ultrasound

Optimizing the Patient Experience for Women With Disabilities in the Breast Imaging Clinic

While there are varying opinions on what age to begin and at what interval to perform breast cancer screening, screening mammography is recommended for all women irrespective of disability. Unfortunately, women with disabilities are more likely to present with later-stage disease and higher mortality owing to the barriers for more widespread screening in this population. Women with disabilities may experience challenges accessing breast imaging services, and imaging centers may have suboptimal facilities and staff who are inexperienced in caring for this population. Efforts to increase accessibility by employing universal design to increase ease of access and provide training to improve the patient experience will go far to improve outcomes for patients with disabilities. To date, there exists no comprehensive guidance on how to improve breast cancer screening programs for women with disabilities. The purpose of this paper is to review barriers to screening faced by patients with disabilities, describe strategies to overcome these barriers, and provide guidance for radiologists and referring providers in selecting the best exam for the individual patient.

Diagnostic Performance of Artificial Intelligence-Based Computer-Aided Detection Software for Automated Breast Ultrasound

RATIONALE AND OBJECTIVES

This study aimed to evaluate the diagnostic performance of radiologists following the utilization of artificial intelligence (AI)-based computer-aided detection software (CAD) in detecting suspicious lesions in automated breast ultrasounds (ABUS).

MATERIALS AND METHODS

ABUS-detected 262 breast lesions (histopathological verification; January 2020 to December 2022) were included. Two radiologists reviewed the images and assigned a Breast Imaging Reporting and Data System (BI-RADS) category. ABUS images were classified as positive or negative using AI-CAD. The BI-RADS category was readjusted in four ways: the radiologists modified the BI-RADS category using the AI results (AI-aided 1), upgraded or downgraded based on AI results (AI-aided 2), only upgraded for positive results (AI-aided 3), or only downgraded for negative results (AI-aided 4). The AI-aided diagnostic performances were compared to radiologists. The AI-CAD-positive and AI-CAD-negative cancer characteristics were compared.

RESULTS

For 262 lesions (145 malignant and 117 benign) in 231 women (mean age, 52.2 years), the area under the receiver operator characteristic curve (AUC) of radiologists was 0.870 (95% confidence interval [CI], 0.832-0.908). The AUC significantly improved to 0.919 (95% CI, 0.890-0.947; P = 0.001) using AI-aided 1, whereas it improved without significance to 0.884 (95% CI, 0.844-0.923), 0.890 (95% CI, 0.852-0.929), and 0.890 (95% CI, 0.853-0.928) using AI-aided 2, 3, and 4, respectively. AI-CAD-negative cancers were smaller, less frequently exhibited retraction phenomenon, and had lower BI-RADS category. Among nonmass lesions, AI-CAD-negative cancers showed no posterior shadowing.

CONCLUSION

AI-CAD implementation significantly improved the radiologists' diagnostic performance and may serve as a valuable diagnostic tool.

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.

The Automation of Breast Ultrasonography and the Medical Time Dedicated to the Method

In this integrative review, we aimed to describe the records of time devoted by physicians to breast ultrasound in a review of articles in the literature, in order to observe whether the automation of the method enabled a reduction in these values. We selected articles from the Latin American and Caribbean Literature in Health Sciences (LILACS) and MEDLINE databases, through Virtual Health Library (BVS), SciELO (Scientific Electronic Library Online), PubMed, and Scopus. We obtained 561 articles, and, after excluding duplicates and screening procedures, 9 were selected, whose main information related to the guiding question of the research was synthesized and analyzed. It was concluded that the automation of breast ultrasound represents a possible strategy for optimization of the medical time dedicated to the method, but this needs to be better evaluated in comparative studies between both methods (traditional and automated), with methodology directed to the specific investigation of this potentiality.

Evaluation of artificial intelligent breast ultrasound on lesion detection and characterization compared with hand-held ultrasound in asymptomatic women

Introduction

To compare the accuracy of Artificial Intelligent Breast Ultrasound (AIBUS) with hand-held breast ultrasound (HHUS) in asymptomatic women and to offer recommendations for screening in regions with limited medical resources.

Methods

852 participants who underwent both HHUS and AIBUS were enrolled between December 2020 and June 2021. Two radiologists, who were unaware of the HHUS results, reviewed the AIBUS data and scored the image quality on a separate workstation. Breast imaging reporting and data system (BI-RADS) final recall assessment, breast density category, quantified lesion features, and examination time were evaluated for both devices. The statistical analysis included McNemar's test, paired t-test, and Wilcoxon test. The kappa coefficient and consistency rate were calculated in different subgroups.

Results

Subjective satisfaction with AIBUS image quality reached 70%. Moderate agreements were found between AIBUS with good quality images and HHUS for the BI-RADS final recall assessment (κ = 0.47, consistency rate = 73.9%) and breast density category (κ = 0.50, consistency rate = 74.8%). The lesions measured by AIBUS were statistically smaller and deeper than those measured by HHUS (P < 0.001), though they were not significant in clinical diagnosis (all < 3 mm). The total time required for the AIBUS examination and image interpretation was 1.03 (95% CI (0.57, 1.50)) minutes shorter than that of HHUS per case.

Conclusion

Moderate agreement was obtained for the description of the BI-RADS final recall assessment and breast density category. With image quality comparable to that of HHUS, AIBUS was superior for the efficiency of primary screening.

Screening Outcomes of Supplemental Automated Breast US in Asian Women with Dense and Nondense Breasts

Background Automated breast (AB) US effectively depicts mammographically occult breast cancers in Western women. However, few studies have focused on the outcome of supplemental AB US in Asian women who have denser breasts than Western women. Purpose To evaluate the performance of supplemental AB US on mammography-based breast cancer screening in Asian women with dense breasts and those with nondense breasts. Materials and Methods A retrospective database search identified asymptomatic Korean women who underwent digital mammography (DM) and supplemental AB US screening for breast cancer between January 2018 and December 2019. We excluded women without sufficient follow-up, established final diagnosis, or histopathologic results. Performance measures of DM alone and AB US combined with DM (hereafter AB US plus DM) were compared. The primary outcome was cancer detection rate (CDR), and the secondary outcomes were sensitivity and specificity. Subgroup analyses were performed based on mammography density. Results From 2785 screening examinations in 2301 women (mean age, 52 years ± 9 [SD]), 28 cancers were diagnosed (26 screening-detected cancers, two interval cancers). When compared with DM alone, AB US plus DM resulted in a higher CDR of 9.3 per 1000 examinations (95% CI: 7.7, 10.3) versus 6.5 per 1000 examinations (95% CI: 5.2, 7.2; P < .001) and a higher sensitivity of 90.9% (95% CI: 77.3, 100.0) versus 63.6% (95% CI: 40.9, 81.8; P < .001) but a lower specificity of 86.8% (95% CI: 85.2, 88.2) versus 94.6% (95% CI: 93.6, 95.5; P < .001) in women with dense breasts. In women with nondense breasts, AB US plus DM resulted in a higher CDR of 9.5 per 1000 examinations (95% CI: 7.1, 10.6) versus 6.3 per 1000 examinations (95% CI: 3.5, 7.1; P < .001), whereas specificity was lower at 95.2% (95% CI: 93.4, 96.8) versus 97.1% (95% CI: 95.8, 98.4; P < .001). Conclusion In Asian women, the addition of automated breast US to digital mammography showed higher cancer detection rates but lower specificities in both dense and nondense breasts. © RSNA, 2023 Supplemental material is available for this article.

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.

[Breast Imaging Reporting and Data System (BI-RADS): Advantages and Limitations]

Breast Imaging Reporting and Data System (BI-RADS) is a communication and data tracking system that standardizes and controls the quality of reporting by presenting lexicon descriptors, assessment categories, and recommendations for managing breast lesions. Using standardized terminology recommended by BI-RADS, radiologists can concisely and reproducibly communicate breast imaging results to clinicians. They can also provide the estimated malignant probability of the lesions found and guide management for them by determining the final assessment category. The limitations of BI-RADS 5th edition currently in use are that there are some areas for which standardized terminologies still need to be established, and that the diagnostic criteria of MRI assessment categories 3 and 4 are ambiguous compared to those for mammography or ultrasound. The next revision of BI-RADS is expected to include solutions for overcoming current limitations.

Comparison and risk factors analysis of multiple breast cancer screening methods in the evaluation of breast non-mass-like lesions

OBJECTIVE

To compare multiple breast cancer screening methods for evaluating breast non-mass-like lesions (NMLs), and investigate new best screening method for breast non-mass-like lesions and the value of the lexicon of ACR BI-RADS in NML evaluation.

METHODS

This retrospective study examined 253 patients aged 24-68 years who were diagnosed with breast NMLs and described the lexicon of ACR BI-RADS from April 2017 to December 2019. All lesions were evaluated by HHUS, MG, and ABUS to determine BI-RADS category, and underwent pathological examination within six months or at least 2 years of follow-up. The sensitivity, specificity, accuracy, positive predictive values (PPV), and negative predictive values (NPV) of MG, HHUS and ABUS in the prediction of malignancy were compared. Independent risk factors for malignancy were assessed using non-conditional logistic regression.

RESULTS

HHUS, MG and ABUS findings significantly differed between benign and malignant breast NML, including internal echo, hyperechoic spot, peripheral blood flow, internal blood flow, catheter change, peripheral change, coronal features of ABUS, and structural distortion, asymmetry, and calcification in MG. ABUS is superior to MG and HHUS in sensitivity, specificity, PPV, NPV, as well as in evaluating the necessity of biopsy and accuracy in identifying malignancy. MG was superior to HHUS in specificity, PPV, and accuracy in evaluating the need for biopsy.

CONCLUSIONS

ABUS was superior to HHUS and MG in evaluating the need for biopsy in breast NMLs. Compared to each other, HHUS and MG had their own relative advantages. Internal blood flow, calcification, and coronal plane feature was independent risk factors in NMLs Management, and different screening methods had their own advantages in NML management. The lexicon of ACR BI-RADS could be used not only in the evaluation of mass lesions, but also in the evaluation of NML.

What eye tracking can tell us about how radiologists use automated breast ultrasound

Purpose: Automated breast ultrasound (ABUS) presents three-dimensional (3D) representations of the breast in the form of stacks of coronal and transverse plane images. ABUS is especially useful for the assessment of dense breasts. Here, we present the first eye tracking data showing how radiologists search and evaluate ABUS cases. Approach: Twelve readers evaluated single-breast cases in 20-min sessions. Positive findings were present in 56% of the evaluated cases. Eye position and the currently visible coronal and transverse slice were tracked, allowing for reconstruction of 3D "scanpaths." Results: Individual readers had consistent search strategies. Most readers had strategies that involved examination of all available images. Overall accuracy was 0.74 (sensitivity = 0.66 and specificity = 0.84). The 20 false negative errors across all readers can be classified using Kundel's (1978) taxonomy: 17 are "decision" errors (readers found the target but misclassified it as normal or benign). There was one recognition error and two "search" errors. This is an unusually high proportion of decision errors. Readers spent essentially the same proportion of time viewing coronal and transverse images, regardless of whether the case was positive or negative, correct or incorrect. Readers tended to use a "scanner" strategy when viewing coronal images and a "driller" strategy when viewing transverse images. Conclusions: These results suggest that ABUS errors are more likely to be errors of interpretation than of search. Further research could determine if readers' exploration of all images is useful or if, in some negative cases, search of transverse images is redundant following a search of coronal images.

The emerging role of photoacoustic imaging in clinical oncology

Clinical oncology can benefit substantially from imaging technologies that reveal physiological characteristics with multiscale observations. Complementing conventional imaging modalities, photoacoustic imaging (PAI) offers rapid imaging (for example, cross-sectional imaging in real time or whole-breast scanning in 10-15 s), scalably high levels of spatial resolution, safe operation and adaptable configurations. Most importantly, this novel imaging modality provides informative optical contrast that reveals details on anatomical, functional, molecular and histological features. In this Review, we describe the current state of development of PAI and the emerging roles of this technology in cancer screening, diagnosis and therapy. We comment on the performance of cutting-edge photoacoustic platforms, and discuss their clinical applications and utility in various clinical studies. Notably, the clinical translation of PAI is accelerating in the areas of macroscopic and mesoscopic imaging for patients with breast or skin cancers, as well as in microscopic imaging for histopathology. We also highlight the potential of future developments in technological capabilities and their clinical implications, which we anticipate will lead to PAI becoming a desirable and widely used imaging modality in oncological research and practice.

Different Types of Ultrasound Probes Usage for Multi-Angle Conventional 3D Ultrasound Compound Imaging: A Breast Phantom Study

Three-dimensional automated breast ultrasound (ABUS) systems seem to offer excellent results in breast cancer screening tests and its early detection, comparable to handheld ultrasound B-mode scanning, with the benefit of saving physician time and reducing handheld ultrasound issues. Nevertheless, the ABUS systems are not very popular, due to the cost and very narrow application. The multi-angle conventional 3D ultrasound compound imaging method (MACUI) is intended for use with standard B-mode scanners in order to reduce cost but preserve the advantages of ABUS systems. The rotational probe movement is utilized in order to collect images for the three-dimensional reconstruction of the scanned tissue’s anatomy. The authors evaluate the capabilities to increase the scanned volume and quality of reconstructions, which are limited in current MACUI implementations, with a probe tilt and shift. The study shows and discusses the results of the imaging using different probes available for SmartUs Telemed B-Mode scanner at different scanning geometry in order to determine the capabilities of such an ultrasound imaging system. The results discussed in the paper highlight the benefits in quality improvement and scanning area obtained with tilted and shifted probes, as well as the advantages of using a relatively simple convex probe that does not incorporate software beam steering over more advanced devices.

Evaluation of Diagnostic Performance of Automatic Breast Volume Scanner Compared to Handheld Ultrasound on Different Breast Lesions: A Systematic Review

Objective: To compare the diagnostic performance of the automatic breast volume scanner (ABVS) against the handheld ultrasound (HHUS) in the differential diagnosis of benign and malignant breast lesions. Methods: A systematic search and review of studies involving ABVS and HHUS for breast cancer screening were performed. The search involved the data taken from Scopus, PubMed, and science direct databases and was conducted between the year 2011 to 2020. The prospective method was used in determining the inclusion and exclusion criteria while the evidence level was determined using the BI-RADS categories for diagnostic studies. In addition, the parameters of specificity, mean age, sensitivity, tumor number, and diagnostic accuracy of the ABVS and HHUS were summarized. Results: No systematic review or randomized controlled trial were identified in the systematic search while one cross-sectional study, eight retrospective studies, and 10 prospective studies were found. Sufficient follow-up of the subjects with benign and malignant findings were made only in 10 studies, in which only two had used ABVS and HHUS after performing mammographic screening and MRI. Analysis was made of 21 studies, which included 5448 lesions (4074 benign and 1374 malignant) taken from 6009 patients. The range of sensitivity was (0.72–1.0) for ABVS and (0.62–1.0) for HHUS; the specificity range was (0.52–0.98)% for ABVS and (0.49–0.99)% for HHUS. The accuracy range among the 11 studies was (80–99)% and (59–98)% for the HHUS and ABVS, respectively. The identified tumors had a mean size of 2.1 cm, and the detected cancers had a mean percentage of 94% (81–100)% in comparison to the non-cancer in all studies. Conclusions: The evidence available in the literature points to the fact that the diagnostic performance of both ABVS and HHUS are similar with reference to the differentiation of malignant and benign breast lesions.

Reproducibility of Automated Breast Ultrasonography and Handheld Ultrasonography for Breast Lesion Size Measurement

ABSTRACT

The purpose of our study was to evaluate the reproducibility of size measurement of breast lesions using automated breast ultrasonography (ABUS) compared with that with handheld ultrasonography (HHUS). Three breast radiologists performed HHUS and measured the lesions size in 2 different phantoms: lesions with various shape, size, and same stiffness (phantom 1) and lesions with same shape, size, and various stiffness (phantom 2). After 1 month, the same radiologists measured the lesion size of the same breast phantoms in the images obtained using ABUS. We evaluated interobserver variability between 3 radiologists in ABUS and HHUS, and intraobserver variability of radiologists between ABUS and HHUS. Intraclass correlation coefficient (ICC) was used in statistical analysis. The measured size of lesions on HHUS was slightly larger than that on ABUS in both phantom 1 and 2, although not statistically significant (P = 0.314, P = 0.858). There were no significant differences in size measurements between the radiologists' measurements and the reference size in phantom 2 (P = 0.862). The ICCs for the interobserver agreement between the 3 radiologists were 0.98 to 0.99 on ABUS and 0.99 to 1.00 on HHUS, respectively. The ICCs for the intraobserver agreement between ABUS and HHUS were 0.97 to 0.97 in phantom 1 and 0.98 to 0.99 in phantom 2. In conclusion, ABUS showed excellent interobserver and intraobserver agreement with HHUS in measuring size of the lesions, regardless of shape, size, and stiffness. Therefore, ABUS mixed with HHUS can be used reliably in following up breast lesions size.

Automated breast volume scanning combined with shear wave elastography for diagnosis of triple-negative breast cancer and human epidermal growth factor receptor 2-positive breast cancer

OBJECTIVE

To explore the values of automated breast volume scanning (ABVS) combined with shear wave elastography (SWE) in the differential diagnosis of triple-negative breast cancer (TNBC) and human epidermal growth factor receptor 2-positive breast cancers (HER2+BC).

METHODS

In this study, 28 patients with TNBC and 32 patients with HER2+BC were enrolled. The characteristics of ABVS and virtual touch quantification (VTQ) in SWE of all patients were reviewed. The multivariate logistic regression analysis was carried out and the receiver operating characteristic curves of ABVS and ABVS+VTQ were drawn.

RESULTS

In ABVS imaging, the microcalcification, posterior echo, internal echo, shape, and edge had significant difference between TNBC and HER2+BC groups (p<0.05). The regular shape was the independent factor for TNBC (p=0.04, odds ratio [OR]=4.479), and the microcalcification in mass was the independent factor for HER2+BC (p=0.01, OR=2.997). In VTQ imaging, the shear wave velocity (SWV)max, SWVmin, and SWVmean in TNBC group were significantly lower than those in HER2+BC group (p<0.001). The sensitivity, specificity, and accuracy of ABVS+VTQ in diagnosing TNBC were higher than those of ABVS alone.

CONCLUSIONS

ABVS combined with SWE has certain advantages in differentiating TNBC from HER2+BC, which is helpful for the treatment planning and prognosis judgment.

Pros and Cons for Automated Breast Ultrasound (ABUS): A Narrative Review

Automated breast ultrasound (ABUS) is an ultrasound technique that tends to be increasingly used as a supplementary technique in the evaluation of patients with dense glandular breasts. Patients with dense breasts have an increased risk of developing breast cancer compared to patients with fatty breasts. Furthermore, for this group of patients, mammography has a low sensitivity in detecting breast cancers, especially if it is not associated with architectural distortion or calcifications. ABUS is a standardized examination with many advantages in both screening and diagnostic settings: it increases the detection rate of breast cancer, improves the workflow, and reduces the examination time. On the other hand, like any imaging technique, ABUS has disadvantages and even some limitations. Many disadvantages can be diminished by additional attention and training. Disadvantages regarding image acquisition are the inability to assess the axilla, the vascularization, and the elasticity of a lesion, while concerning the interpretation, the disadvantages are the artifacts due to poor positioning, lack of contact, motion or lesion related. This article reviews and discusses the indications, the advantages, and disadvantages of the method and also the sources of error in the ABUS examination.

Improved Inception V3 method and its effect on radiologists' performance of tumor classification with automated breast ultrasound system

BACKGROUND

The automated breast ultrasound system (ABUS) is recognized as a valuable detection tool in addition to mammography. The purpose of this study was to propose a novel computer-aided diagnosis (CAD) system by extracting the textural features from ABUS images and to investigate the efficiency of using this CAD for breast cancer detection.

METHODS

This retrospective study involved 149 breast nodules [maximum diameter: mean size 18.89 mm, standard deviation (SD) 10.238, and range 5-59 mm] in 135. We assigned 3 novice readers (<3 years of experience and 3 experienced readers (≥10 years of experience to review the imaging data and stratify the 149 breast nodules as either malignant or benign. The Improved Inception V3 (II3) method was developed and used as an assistant tool to help the 6 readers to re-interpret the images.

RESULTS

Our method (II3) achieved an accuracy of 88.6% for the final result. The 3 novice readers had an average accuracy of 71.37%±4.067% while the 3 experienced readers was 83.03%±3.371% on the first-reading. With the help of II3 on the second-reading, the average accuracy of the novice readers increased to 84.13%±1.662% and the experienced readers increased to 89.50%±0.346%.The areas under the curve (AUCs) were similar compared with linear algorithms. The mean AUC of the novice readers was improved from 0.7751 (without II3) to 0.8232 (with II3). The mean AUC of the experienced readers was improved from 0.8939 (without II3) to 0.9211 (with II3). The mean AUC for all readers improved in both the second-reading mode (from 0.8345 to 0.8722, P=0.0081<0.05).

CONCLUSIONS

With the help of the II3, the diagnostic accuracy of the two groups were both improved, and II3 was more helpful for novice readers than for experienced readers. Our results showed that II3 is valuable in the differentiation of benign and malignant breast nodules and it also improves the experience and skill of some novice radiologists. The II3 cannot completely replace the influence of experience in the diagnostic process and will retain an auxiliary role in the clinic at present.

A Review of Breast Imaging for Timely Diagnosis of Disease

Breast cancer (BC) is the cancer with the highest incidence in women in the world. In this last period, the COVID-19 pandemic has caused in many cases a drastic reduction of routine breast imaging activity due to the combination of various factors. The survival of BC is directly proportional to the earliness of diagnosis, and especially during this period, it is at least fundamental to remember that a diagnostic delay of even just three months could affect BC outcomes. In this article we will review the state of the art of breast imaging, starting from morphological imaging, i.e., mammography, tomosynthesis, ultrasound and magnetic resonance imaging and contrast-enhanced mammography, and their most recent evolutions; and ending with functional images, i.e., magnetic resonance imaging and contrast enhanced mammography.

Current State of Breast Cancer Diagnosis, Treatment, and Theranostics

Breast cancer is one of the leading causes of cancer-related morbidity and mortality in women worldwide. Early diagnosis and effective treatment of all types of cancers are crucial for a positive prognosis. Patients with small tumor sizes at the time of their diagnosis have a significantly higher survival rate and a significantly reduced probability of the cancer being fatal. Therefore, many novel technologies are being developed for early detection of primary tumors, as well as distant metastases and recurrent disease, for effective breast cancer management. Theranostics has emerged as a new paradigm for the simultaneous diagnosis, imaging, and treatment of cancers. It has the potential to provide timely and improved patient care via personalized therapy. In nanotheranostics, cell-specific targeting moieties, imaging agents, and therapeutic agents can be embedded within a single formulation for effective treatment. In this review, we will highlight the different diagnosis techniques and treatment strategies for breast cancer management and explore recent advances in breast cancer theranostics. Our main focus will be to summarize recent trends and technologies in breast cancer diagnosis and treatment as reported in recent research papers and patents and discuss future perspectives for effective breast cancer therapy.

Comparison of Automated Breast Ultrasound and Hand-Held Breast Ultrasound in the Screening of Dense Breasts

OBJECTIVE

 To compare hand-held breast ultrasound (HHBUS) and automated breast ultrasound (ABUS) as screening tool for cancer.

METHODS

 A cross-sectional study in patients with mammographically dense breasts was conducted, and both HHBUS and ABUS were performed. Hand-held breast ultrasound was acquired by radiologists and ABUS by mammography technicians and analyzed by breast radiologists. We evaluated the Breast Imaging Reporting and Data System (BI-RADS) classification of the exam and of the lesion, as well as the amount of time required to perform and read each exam. The statistical analysis employed was measures of central tendency and dispersion, frequencies, Student t test, and a univariate logistic regression, through the odds ratio and its respective 95% confidence interval, and with p < 0.05 considered of statistical significance.

RESULTS

 A total of 440 patients were evaluated. Regarding lesions, HHBUS detected 15 (7.7%) BI-RADS 2, 175 (89.3%) BI-RADS 3, and 6 (3%) BI-RADS 4, with 3 being confirmed by biopsy as invasive ductal carcinomas (IDCs), and 3 false-positives. Automated breast ultrasound identified 12 (12.9%) BI-RADS 2, 75 (80.7%) BI-RADS 3, and 6 (6.4%) BI-RADS 4, including 3 lesions detected by HHBUS and confirmed as IDCs, in addition to 1 invasive lobular carcinoma and 2 high-risk lesions not detected by HHBUS. The amount of time required for the radiologist to read the ABUS was statistically inferior compared with the time required to read the HHBUS (p < 0.001). The overall concordance was 80.9%. A total of 219 lesions were detected, from those 70 lesions by both methods, 126 only by HHBUS (84.9% not suspicious by ABUS) and 23 only by ABUS.

CONCLUSION

 Compared with HHBUS, ABUS allowed adequate sonographic study in supplemental screening for breast cancer in heterogeneously dense and extremely dense breasts.

The added advantage of automated breast ultrasound to mammographically detected different breast lesions in patients with dense breasts

Background

Breast cancer is the most commonly diagnosed malignancy in women worldwide. Women with dense breast tend to have 15–25% lifetime risk of breast cancer due to decrease of mammographic sensitivity. Automated breast ultrasound (ABUS) is a new promising tool for detection of breast lesions masked by dense glandular tissue at mammography.

Results

The sensitivity of digital mammography in detecting breast lesions was 60.7%, specificity 91.6%, PPV 85%, NPV 75%, and accuracy 78%. The sensitivity of ABUS in detecting breast lesions was 92.86%, specificity 77.78%, PPV 76.47%, NPV 93.33%, and accuracy 84.38%. The sensitivity of handheld ultrasound (HHUS) in detecting breast lesions was 89.29%, specificity 88.89%, PPV 86.21%, NPV 91.43%, and accuracy 89.06%.

Conclusion

The sensitivity of ABUS in detecting breast lesions was much higher than mammography in dense breast while the digital mammography (DM) had higher specificity. So, implementation of both DM and ABUS to get benefit of DM specificity as well as ABUS sensitivity were highly recommended.

Influence of Breast Density on Patient's Compliance during Ultrasound Examination: Conventional Handheld Breast Ultrasound Compared to Automated Breast Ultrasound

Background:

Our aim was to study the influence of breast density on patient's compliance during conventional handheld breast ultrasound (US) or automated breast US (ABUS), which could be used as adjunct screening modalities.

Methods:

Between January 2019 and June 2019, 221 patients (mean age: 53; age range: 24–89 years) underwent both US and ABUS. All participants had independently interpreted US and ABUS regarding patient compliance. The diagnostic experience with US or ABUS was described with a modified testing morbidity index (TMI). The scale ranged from 0 (worst possible experience) to 5 (acceptable experience). Standard statistics was used to compare the data of US and data of ABUS. Breast density was recorded with the Breast Imaging Reporting and Data System (BI-RADS) score.

Results:

The mean TMI score was 4.6 ± 0.5 for US and 4.3 ± 0.8 for ABUS. The overall difference between patients' experience on US and ABUS was statistically significant with P < 0.0001. The difference between patients' experience on US and ABUS in women with BI-RADS C and D for breast density was statistically significant with P < 0.02 in favor of US (4.7 ± 0.4) versus 4.5 ± 0.6 for ABUS. Patients' experience with breast density B was better for US (4.7 ± 0.4) versus 4.3 ± 0.6 for ABUS with P < 0.01. Pain or discomfort occurred during testing, especially in patients >40 years.

Conclusion:

Patient age (>40 years) is a significant predictor of decreased compliance to ABUS. Compliance of ABUS resulted lower that of US independently for breast density.

Harmonic motion imaging of human breast masses: an in vivo clinical feasibility

Non-invasive diagnosis of breast cancer is still challenging due to the low specificity of the imaging modalities that calls for unnecessary biopsies. The diagnostic accuracy can be improved by assessing the breast tissue mechanical properties associated with pathological changes. Harmonic motion imaging (HMI) is an elasticity imaging technique that uses acoustic radiation force to evaluate the localized mechanical properties of the underlying tissue. Herein, we studied the in vivo feasibility of a clinical HMI system to differentiate breast tumors based on their relative HMI displacements, in human subjects. We performed HMI scans in 10 female subjects with breast masses: five benign and five malignant masses. Results revealed that both benign and malignant masses were stiffer than the surrounding tissues. However, malignant tumors underwent lower mean HMI displacement (1.1 ± 0.5 µm) compared to benign tumors (3.6 ± 1.5 µm) and the adjacent non-cancerous tissue (6.4 ± 2.5 µm), which allowed to differentiate between tumor types. Additionally, the excised breast specimens of the same patients (n = 5) were imaged post-surgically, where there was an excellent agreement between the in vivo and ex vivo findings, confirmed with histology. Higher displacement contrast between cancerous and non-cancerous tissue was found ex vivo, potentially due to the lower nonlinearity in the elastic properties of ex vivo tissue. This preliminary study lays the foundation for the potential complementary application of HMI in clinical practice in conjunction with the B-mode to classify suspicious breast masses.

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.

Automated Breast Ultrasound Screening for Dense Breasts

Mammography is the primary screening method for breast cancers. However, the sensitivity of mammographic screening is lower for dense breasts, which are an independent risk factor for breast cancers. Automated breast ultrasound (ABUS) is used as an adjunct to mammography for screening breast cancers in asymptomatic women with dense breasts. It is an effective screening modality with diagnostic accuracy comparable to that of handheld ultrasound (HHUS). Radiologists should be familiar with the unique display mode, imaging features, and artifacts in ABUS, which differ from those in HHUS. The purpose of this study was to provide a comprehensive review of the clinical significance of dense breasts and ABUS screening, describe the unique features of ABUS, and introduce the method of use and interpretation of ABUS.

Automatic breast ultrasound: state of the art and future perspectives

The three-dimensional automated breast ultrasound system (3D ABUS) is a new device which represents a huge innovation in the breast ultrasound field, with several application scenarios of great interest.

ABUS's aim is to solve some of the main defects of traditional ultrasound, such as lack of standardization, high level of skill non-reproducibility, small field of view and high commitment of physician time. ABUS has proven to be an excellent non-ionising alternative to other supplemental screening options for women with dense breast tissue; also, it has appeared to be very promising in daily clinical practice.

The purpose of this paper is to present a summary of current applications of ABUS, focusing on clinical applications and future perspectives as ABUS is particularly promising for studies involving artificial intelligence, radiomics and evaluation of breast molecular subtypes.

Comparison of automated versus hand-held breast US in supplemental screening in asymptomatic women with dense breasts: is there a difference regarding woman preference, lesion detection and lesion characterization?

PURPOSE

To compare automated breast volumetric scanning (ABVS) with hand-held bilateral whole breast ultrasound (HHUS) prospectively in regards to patient workflow, woman preference, efficacy in lesion detection, and characterization.

MATERIALS AND METHODS

Supplemental screening was performed with both ABVS and HHUS to 345 women with dense breasts and negative mammograms. Acquisition and evaluation times were recorded. Lesions were classified according to BIRADS US criteria and compared one to one. Women were recalled for a secondary HHUS examination if ABVS showed any additional lesions. Findings were compared based on biopsy results and/or 36-48 months of follow-up.

RESULTS

Findings could be compared for 340 women. There were two carcinomas which were detected by both methods, with no interval cancers in the follow-up period. Recall rate was 46/340 (13.05%) for ABVS and 4/340 (1.18%) for HHUS. ABVS recalls decreased with experience. HHUS had more true negative (BIRADS 1-2) results, while ABVS had more false positive ones (p < 0.001). Positive predictive value was 4.17% for ABVS and 50% for HHUS. ABVS overdiagnosed shadowings (p < 0.01), distortions (p < 0.034), and irregular nodules (p < 0.001) in comparison to HHUS. At ABVS, 10.6% of women experienced severe pain. 59.7% stated that they would choose HHUS if they had the chance.

CONCLUSION

ABVS is as good as HHUS in lesion detection. However, the recall rate is higher and positive predictive value is lower with ABVS, which could result in more follow-ups, and more anxiety for the women. More than 50% women stated they would prefer HHUS if they were given the chance.

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.

Deeply-Supervised Networks With Threshold Loss for Cancer Detection in Automated Breast Ultrasound

ABUS, or Automated breast ultrasound, is an innovative and promising method of screening for breast examination. Comparing to common B-mode 2D ultrasound, ABUS attains operator-independent image acquisition and also provides 3D views of the whole breast. Nonetheless, reviewing ABUS images is particularly time-intensive and errors by oversight might occur. For this study, we offer an innovative 3D convolutional network, which is used for ABUS for automated cancer detection, in order to accelerate reviewing and meanwhile to obtain high detection sensitivity with low false positives (FPs). Specifically, we offer a densely deep supervision method in order to augment the detection sensitivity greatly by effectively using multi-layer features. Furthermore, we suggest a threshold loss in order to present voxel-level adaptive threshold for discerning cancer vs. non-cancer, which can attain high sensitivity with low false positives. The efficacy of our network is verified from a collected dataset of 219 patients with 614 ABUS volumes, including 745 cancer regions, and 144 healthy women with a total of 900 volumes, without abnormal findings. Extensive experiments demonstrate our method attains a sensitivity of 95% with 0.84 FP per volume. The proposed network provides an effective cancer detection scheme for breast examination using ABUS by sustaining high sensitivity with low false positives. The code is publicly available at https://github.com/nawang0226/abus_code.

False-negative results on computer-aided detection software in preoperative automated breast ultrasonography of breast cancer patients

Purpose

The purpose of this study was to measure the cancer detection rate of computer-aided detection (CAD) software in preoperative automated breast ultrasonography (ABUS) of breast cancer patients and to determine the characteristics associated with false-negative outcomes.

Methods

A total of 129 index lesions (median size, 1.7 cm; interquartile range, 1.2 to 2.4 cm) from 129 consecutive patients (mean age±standard deviation, 53.4±11.8 years) who underwent preoperative ABUS from December 2017 to February 2018 were assessed. An index lesion was defined as a breast cancer confirmed by ultrasonography (US)-guided core needle biopsy. The detection rate of the index lesions, positive predictive value (PPV), and false-positive rate (FPR) of the CAD software were measured. Subgroup analysis was performed to identify clinical and US findings associated with false-negative outcomes.

Results

The detection rate of the CAD software was 0.84 (109 of 129; 95% confidence interval, 0.77 to 0.90). The PPV and FPR were 0.41 (221 of 544; 95% CI, 0.36 to 0.45) and 0.45 (174 of 387; 95% CI, 0.40 to 0.50), respectively. False-negative outcomes were more frequent in asymptomatic patients (P<0.001) and were associated with the following US findings: smaller size (P=0.001), depth in the posterior third (P=0.002), angular or indistinct margin (P<0.001), and absence of architectural distortion (P<0.001).

Conclusion

The CAD software showed a promising detection rate of breast cancer. However, radiologists should judge whether CAD software-marked lesions are true- or false-positive lesions, considering its low PPV and high FPR. Moreover, it would be helpful for radiologists to consider the characteristics associated with false-negative outcomes when reading ABUS with CAD.

Breast ultrasound: automated or hand-held? Exploring patients' experience and preference

Background

Our aim was to compare women’s experience with automated breast ultrasound (ABUS) versus breast hand-held ultrasound (HHUS) and to evaluate their acceptance rate.

Methods

After ethical approval, from October 2017 to March 2018, 79 consecutive patients were enrolled in this prospective study. On the same day, patients underwent HHUS followed by ABUS. Each patient’s experience was assessed using the modified testing morbidities index (TMI) (the lower the score, the better is the experience). Nine items were assessed for both techniques: seven directly related to the examination technique (pain or discomfort immediately before (preparation), during and after testing, fear or anxiety immediately before (preparation) and during testing, physical and mental function after testing) and two indirectly related to the examination technique (embarrassment during testing and overall satisfaction). Finally, we asked patients to choose between the two techniques for a potential next breast examination. Wilcoxon signed ranks test was used.

Results

The median TMI score for the seven items was found to be significantly better for HHUS (8, interquartile range [IQR] 7–11) compared to ABUS (9, IQR 8–12) (p = 0.003). The item ‘pain/discomfort during the test’ (p < 0.001) was significantly higher for ABUS compared to HHUS. Instead, the item ‘fear/anxiety before the test’ was higher for HHUS (p = 0.001). Overall, 40.5% of the patients chose HHUS, 29.1% chose ABUS, and 30.4% were unable to choose.

Conclusions

ABUS and HHUS exams were well tolerated and accepted. However, HHUS was perceived to be less painful than ABUS.

Current and future trends in photoacoustic breast imaging

Non-invasive detection of breast cancer has been regarded as the holy grail of applications for photoacoustic (optoacoustic) imaging right from the early days of re-discovery of the method. Two-and-a-half decades later we report on the state-of-the-art in photoacoustic breast imaging technology and clinical studies. Even within the single application of breast imaging, we find imagers with various measurement geometries, ultrasound detection characteristics, illumination schemes, and image reconstruction strategies. We first analyze the implications on performance of a few of these design choices in a generic imaging system, before going into detailed descriptions of the imagers. Per imaging system we present highlights of patient studies, which barring a couple are mostly in the nature of technology demonstrations and proof-of-principle studies. We close this work with a discussion on several aspects that may turn out to be crucial for the future clinical translation of the method.

Diagnostic value of an automated breast volume scanner compared with a hand-held ultrasound: a meta-analysis

Background

The diagnostic performance of an automated breast volume scanner (ABVS) compared with that of a hand-held ultrasound (HHUS) for breast cancer remains unclear. We performed a meta-analysis to compare the diagnostic performances of the ABVS and HHUS for breast cancer.

Methods

We searched PubMed, EMBASE, Cochrane, and SinoMed databases to identify eligible studies up until November 14, 2018. Studies comparing ABVS and HHUS for differentiating benign and malignant breast tumors were included. A meta-analysis was performed to generate pooled diagnostic accuracy parameters [sensitivity, specificity, diagnostic odds ratio (DOR), area under the curve (AUC), and the Q* index] and detection rates for ABVS and HHUS.

Results

Nine studies involving 1,376 patients and 1,527 lesions were included in the meta-analysis for diagnostic accuracy. The pooled sensitivity was 0.93 [95% confidence interval (CI), 0.91-0.95] for ABVS and 0.90 (95% CI, 0.88-0.92) for HHUS, and the pooled specificity was 0.86 (95% CI, 0.83-0.88) for ABVS and 0.82 (95% CI, 0.79-0.84) for HHUS. The pooled DOR was 88.66 (95% CI, 51.44-152.78) for ABVS and 41.06 for HHUS (95% CI, 26.58-63.42). The AUC of the summary receiver operating characteristic (SROC) was 0.9496 for ABVS and 0.9143 for HHUS, and the Q* index was 0.8899 for ABVS and 0.8469 for HHUS. Meta-regression showed no significant difference between the diagnostic accuracy of ABVS and HHUS (P=0.0771). No publication bias was found. Thirteen published studies involving 1,047 pathologically confirmed malignant lesions were included to generate a pooled detection rate. The pooled detection rate was 1.00 (95% CI, 1.00-1.00) for both ABVS and HHUS, for which a publication bias was found.

Conclusions

ABVS can be used as an appropriate screening tool for breast cancer as well as HHUS in diagnostic accuracy and detection rate. Considering other advantages of ABVS including non-radioactivity, sensitivity to dense breast, three-dimensional reconstruction, time-saving and repeatability, it might be a promising screening tool for young or dense-breast women in the future.

Automatic Breast Volume Scanner versus Handheld Ultrasound in Differentiation of Benign and Malignant Breast Lesions: A Systematic Review and Meta-analysis

The goal of the study described here was to compare the automatic breast volume scanner (ABVS) and handheld ultrasound (HHUS) with respect to diagnostic performance in the differential diagnosis of benign and malignant breast lesions. A literature search of the PubMed, EMBASE and Cochrane Library databases through 30 June 2018 was conducted. Pooled sensitivity, specificity, positive and negative likelihood ratios and diagnostic odds ratios of the ABVS and HHUS were calculated, and summary receiver operating characteristic (SROC) curves were drawn. A total of nine studies, including 1985 lesions (628 malignant and 1357 benign) from 1774 patients, were analyzed. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio and diagnostic odds ratio for ABVS were 90.8% (95% confidence interval: 88.3%-93.0%), 82.2% (80.0%-84.2%), 5.39 (4.26-6.80), 0.10 (0.06-0.15) and 61.68 (32.31-117.76); those for HHUS were 90.6% (88.1%-92.8%), 81.0% (78.8%-83.0%), 5.22 (3.14-8.67), 0.11 (0.08-0.17) and 52.60 (32.06-86.35), respectively. The areas under the SROC curves in the differentiation of benign and malignant breast lesions were 0.93 and 0.94 for ABVS and HHUS, respectively, which were not significantly different (p = 0.853). In conclusion, based on available evidence in the literature, ABVS the diagnostic performance of the ABVS is similar to that of HHUS in the differentiation of benign and malignant breast lesions.

Automated Breast Ultrasound Interpretation Times: A Reader Performance Study

RATIONALE AND OBJECTIVES

This study aimed to determine the average time for breast radiologists of varied experience to interpret automated breast ultrasound (ABUS) examinations.

MATERIALS AND METHODS

A reader performance study was conducted on female patients, with ACR BI-RADS 4 breast density classifications of C or D, who received both an ABUS screening examination and a digital mammogram from 2013 to 2014 at an academic institution. Three faculty breast radiologists with varied levels of ABUS experience (advanced, intermediate, novice) read all ABUS examinations, with interpretation times and final impressions (categorized as "normal" or "abnormal") recorded for each examination.

RESULTS

Ninety-nine patients were included, with all readers demonstrating an average ABUS interpretation time of less than 3 minutes. Compared to the other two readers, the intermediate reader had a significantly longer mean interpretation time at 2.6 minutes (95% confidence interval 2.4-2.8; P < .001). In addition to having the shortest mean interpretation time, the novice reader also demonstrated reduced times in subsequent interpretations, with a significant decrease in interpretation times of 3.1 seconds (95% confidence interval 0.4-5.8) for every 10 ABUS examinations interpreted (P < .05).

CONCLUSIONS

Overall, mean ABUS interpretation time by radiologists of all experience levels was short, at less than 3 minutes per examination, which should not deter radiologists from incorporating ABUS examinations into a busy clinical environment.

Automated Breast Ultrasonography (ABUS) in the Screening and Diagnostic Setting: Indications and Practical Use

Automated breast ultrasonography (ABUS) is a new imaging technology for automatic breast scanning through ultrasound. It was first developed to overcome the limitation of operator dependency and lack of standardization and reproducibility of handheld ultrasound. ABUS provides a three-dimensional representation of breast tissue and allows images reformatting in three planes, and the generated coronal plane has been suggested to improve diagnostic accuracy. This technique has been first used in the screening setting to improve breast cancer detection, especially in mammographically dense breasts. In recent years, numerous studies also evaluated its use in the diagnostic setting: they showed its suitability for breast cancer staging, evaluation of tumor response to neoadjuvant chemotherapy, and second-look ultrasound after magnetic resonance imaging. The purpose of this article is to provide a comprehensive review of the current body of literature about the clinical performance of ABUS, summarize available evidence, and identify gaps in knowledge for future research.

Image quality and artifacts in automated breast ultrasonography

Three-dimensional automated breast ultrasonography (ABUS) has been approved for screening Epub ahead of print studies as an adjunct to mammography. ABUS provides proper orientation and documentation, resulting in better reproducibility. Optimal image quality is essential for a proper diagnosis, and high-quality images should be ensured when ABUS is used in clinical settings. Image quality in ABUS is highly dependent on the acquisition procedure. Artifacts can interfere with the visibility of abnormalities, reduce the overall image quality, and introduce clinical and technical problems. Nipple shadow and reverberation artifacts are some of the artifacts frequently encountered in ABUS. Radiologists should be familiar with proper image acquisition techniques and possible artifacts in order to acquire high-quality images.

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.

Evaluation of an automated breast volume scanner according to the fifth edition of BI-RADS for breast ultrasound compared with hand-held ultrasound

OBJECTIVES

To investigate the automated breast volume scanner (ABVS) in comparison with hand-held ultrasound (HHUS) according to the fifth edition of BI-RADS ultrasound.

MATERIAL AND METHODS

A total of 831 lesions in 786 patients who underwent both HHUS and ABVS were included. Three radiologists independently evaluated the sonographic features of each lesion according to the fifth BI-RADS edition. The kappa coefficient (κ) was calculated for each BI-RADS descriptor and final assessment category. The accuracy of malignancy prediction and diagnostic performance of the BI-RADS descriptors were assessed using multivariate logistic regression and area under the receiver operator characteristic curve (AUC), respectively.

RESULTS

ABVS and HHUS showed moderate to good interobserver agreement (κ = 0.53-0.67 and 0.55-0.70, respectively) except in associated features (κ = 0.31 and 0.36, respectively) for BI-RADS lexicons. Irregular shape, a non-circumscribed margin, and posterior features (combined or shadowing) were independently associated with malignancy in both ABVS and HHUS. Calcification presence on ABVS (odds ratio [OR], 95% confidence interval [CI]: 2.09, 1.11-3.94) and non-parallel orientation on HHUS (OR, 95% CI: 2.04, 1.10-3.78) were independently associated with malignancy. There were no significant differences between ABVS and HHUS in sensitivity (84.2% vs. 84.2%), specificity (80.5% vs. 83.9%), or AUC (0.88 vs. 0.90).

CONCLUSIONS

According to the fifth BI-RADS edition, ABVS is not statistically significantly different from HHUS with regard to interobserver variability and diagnostic performance.

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.

Automated breast volume scanner (ABVS) in assessing breast cancer size: A comparison with conventional ultrasound and magnetic resonance imaging

OBJECTIVES

To compare automated breast volume scanner (ABVS), ultrasound (US) and MRI in measuring breast cancer size, and evaluate the agreement between ABVS and US in assessing lesion location and sonographic features.

METHODS

We retrospectively included 98 women with 100 index cancers who had undergone US and ABVS followed by 1.5T MRI. Images were interpreted by a pool of readers reporting lesion size, location and breast imaging reporting and data system (BI-RADS) features. Bland-Altman analysis (with logarithmic data transformation), intraclass correlation coefficient (ICC) and Cohen's kappa statistic were used for statistical analysis.

RESULTS

MRI showed the best absolute agreement with histology in measuring cancer size (ICC 0.93), with LOA comparable to those of ABVS (0.63-1.99 vs. 0.52-1.73, respectively). Though ABVS and US had highly concordant measurements (ICC 0.95), ABVS showed better agreement with histology (LOA 0.52-1.73 vs. 0.45-1.86, respectively), corresponding to a higher ICC (0.85 vs. 0.75, respectively). Except for posterior features (k=0.39), the agreement between US and ABVS in attributing site and BI-RADS features ranged from substantial to almost perfect (k=0.68-0.85).

CONCLUSIONS

ABVS performs better than US and approaches MRI in predicting breast cancer size. ABVS performs comparably to US in sonographic assessment of lesions.

KEY POINTS

• ABVS approaches MRI in predicting breast cancer size. • ABVS is equivalent to US in localising and characterising breast cancer. • ABVS is more accurate than US in assessing breast cancer size. • ABVS has the potential to replace US in breast cancer staging.

[Diagnostic imaging of breast cancer : An update]

Advances in imaging of the female breast have substantially influenced the diagnosis and probably also the therapy and prognosis of breast cancer in the past few years. This article gives an overview of the most important imaging modalities in the diagnosis of breast cancer. Digital mammography is considered to be the gold standard for the early detection of breast cancer. Digital breast tomosynthesis can increase the diagnostic accuracy of mammography and is used for the assessment of equivocal or suspicious mammography findings. Other modalities, such as ultrasound and contrast-enhanced magnetic resonance imaging (MRI) play an important role in the diagnostics, staging and follow-up of breast cancer. Percutaneous needle biopsy is a rapid and minimally invasive method for the histological verification of breast cancer. New breast imaging modalities, such as contrast-enhanced spectral mammography, diffusion-weighted MRI and MR spectroscopy can possibly further improve breast cancer diagnostics; however, further studies are necessary to prove the advantages of these methods so that they cannot yet be recommended for routine clinical use.

Ultrasound Molecular Imaging With BR55 in Patients With Breast and Ovarian Lesions: First-in-Human Results

Purpose We performed a first-in-human clinical trial on ultrasound molecular imaging (USMI) in patients with breast and ovarian lesions using a clinical-grade contrast agent (kinase insert domain receptor [KDR] -targeted contrast microbubble [MB_KDR]) that is targeted at the KDR, one of the key regulators of neoangiogenesis in cancer. The aim of this study was to assess whether USMI using MB_KDR is safe and allows assessment of KDR expression using immunohistochemistry (IHC) as the gold standard. Methods Twenty-four women (age 48 to 79 years) with focal ovarian lesions and 21 women (age 34 to 66 years) with focal breast lesions were injected intravenously with MB_KDR (0.03 to 0.08 mL/kg of body weight), and USMI of the lesions was performed starting 5 minutes after injection up to 29 minutes. Blood pressure, ECG, oxygen levels, heart rate, CBC, and metabolic panel were obtained before and after MB_KDR administration. Persistent focal MB_KDR binding on USMI was assessed. Patients underwent surgical resection of the target lesions, and tissues were stained for CD31 and KDR by IHC. Results USMI with MB_KDR was well tolerated by all patients without safety concerns. Among the 40 patients included in the analysis, KDR expression on IHC matched well with imaging signal on USMI in 93% of breast and 85% of ovarian malignant lesions. Strong KDR-targeted USMI signal was present in 77% of malignant ovarian lesions, with no targeted signal seen in 78% of benign ovarian lesions. Similarly, strong targeted signal was seen in 93% of malignant breast lesions with no targeted signal present in 67% of benign breast lesions. Conclusion USMI with MB_KDR is clinically feasible and safe, and KDR-targeted USMI signal matches well with KDR expression on IHC. This study lays the foundation for a new field of clinical USMI in cancer.