Automated Breast Ultrasound Research Articles

Deformable Mapping Method to Relate Lesions in Dedicated Breast CT Images to Those in Automated Breast Ultrasound and Digital Breast Tomosynthesis Images

This work demonstrates the potential for using a deformable mapping method to register lesions between dedicated breast computed tomography (bCT) and both automated breast ultrasound (ABUS) and digital breast tomosynthesis (DBT) images (craniocaudal [CC] and mediolateral oblique [MLO] views). Two multi-modality breast phantoms with external fiducial markers attached were imaged by the three modalities. The DBT MLO view was excluded for the second phantom. The automated deformable mapping algorithm uses biomechanical modeling to determine corresponding lesions based on distances between their centers of mass (dCOM) in the deformed bCT model and the reference model (DBT or ABUS). For bCT to ABUS, the mean dCOM was 5.2 ± 2.6 mm. For bCT to DBT (CC), the mean dCOM was 5.1 ± 2.4 mm. For bCT to DBT (MLO), the mean dCOM was 4.7 ± 2.5 mm. This application could help improve a radiologist's efficiency and accuracy in breast lesion characterization, using multiple imaging modalities.

Breast Cancer Screening and Diagnosis: A Synopsis of the European Breast Guidelines.

The European Commission Initiative for Breast Cancer Screening and Diagnosis guidelines (European Breast Guidelines) are coordinated by the European Commission's Joint Research Centre. The target audience for the guidelines includes women, health professionals, and policymakers. An international guideline panel of 28 multidisciplinary members, including patients, developed questions and corresponding recommendations that were informed by systematic reviews of the evidence conducted between March 2016 and December 2018. GRADE (Grading of Recommendations Assessment, Development and Evaluation) Evidence to Decision frameworks were used to structure the process and minimize the influence of competing interests by enhancing transparency. Questions and recommendations, expressed as strong or conditional, focused on outcomes that matter to women and provided a rating of the certainty of evidence. This synopsis of the European Breast Guidelines provides recommendations regarding organized screening programs for women aged 40 to 75 years who are at average risk. The recommendations address digital mammography screening and the addition of hand-held ultrasonography, automated breast ultrasonography, or magnetic resonance imaging compared with mammography alone. The recommendations also discuss the frequency of screening and inform decision making for women at average risk who are recalled for suspicious lesions or who have high breast density.

Deformable mapping using biomechanical models to relate corresponding lesions in digital breast tomosynthesis and automated breast ultrasound images.

This work investigates the application of a deformable localization/mapping method to register lesions between the digital breast tomosynthesis (DBT) craniocaudal (CC) and mediolateral oblique (MLO) views and automated breast ultrasound (ABUS) images. This method was initially validated using compressible breast phantoms. This methodology was applied to 7 patient data sets containing 9 lesions. The automated deformable mapping algorithm uses finite element modeling and analysis to determine corresponding lesions based on the distance between their centers of mass (dCOM) in the deformed DBT model and the reference ABUS model. This technique shows that location information based on external fiducial markers is helpful in the improvement of registration results. However, use of external markers are not required for deformable registration results described by this methodology. For DBT (CC view) mapped to ABUS, the mean dCOM was 14.9 ± 6.8 mm based on 9 lesions using 6 markers in deformable analysis. For DBT (MLO view) mapped to ABUS, the mean dCOM was 13.7 ± 6.8 mm based on 8 lesions using 6 markers in analysis. Both DBT views registered to ABUS lesions showed statistically significant improvements (p ≤ 0.05) in registration using the deformable technique in comparison to a rigid registration. Application of this methodology could help improve a radiologist's characterization and accuracy in relating corresponding lesions between DBT and ABUS image datasets, especially for cases of high breast densities and multiple masses.

Diagnostic performance of machine learning applied to texture analysis-derived features for breast lesion characterisation at automated breast ultrasound: a pilot study

BackgroundOur aims were to determine if features derived from texture analysis (TA) can distinguish normal, benign, and malignant tissue on automated breast ultrasound (ABUS); to evaluate whether machine learning (ML) applied to TA can categorise ABUS findings; and to compare ML to the analysis of single texture features for lesion classification.MethodsThis ethically approved retrospective pilot study included 54 women with benign (n = 38) and malignant (n = 32) solid breast lesions who underwent ABUS. After manual region of interest placement along the lesions’ margin as well as the surrounding fat and glandular breast tissue, 47 texture features (TFs) were calculated for each category. Statistical analysis (ANOVA) and a support vector machine (SVM) algorithm were applied to the texture feature to evaluate the accuracy in distinguishing (i) lesions versus normal tissue and (ii) benign versus malignant lesions.ResultsSkewness and kurtosis were the only TF significantly different among all the four categories (p < 0.000001). In subsets (i) and (ii), a maximum area under the curve of 0.86 (95% confidence interval [CI] 0.82–0.88) for energy and 0.86 (95% CI 0.82–0.89) for entropy were obtained. Using the SVM algorithm, a maximum area under the curve of 0.98 for both subsets was obtained with a maximum accuracy of 94.4% in subset (i) and 90.7% in subset (ii).ConclusionsTA in combination with ML might represent a useful diagnostic tool in the evaluation of breast imaging findings in ABUS. Applying ML techniques to TFs might be superior compared to the analysis of single TF.

Automated breast ultrasound (ABUS) as a screening tool: initial experience

BackgroundBreast cancer is a major health problem, being the most common cancer in women. Early detection of breast cancer aims to the reduction of mortality and morbidity rates. Conventional screening methods include mammography and ultrasonography; however, both modalities have their limitations. Automated breast ultrasound (ABUS) is a recent technological advancement in the field of breast imaging having the benefit of standardization of the scans and lack of operator dependence as in conventional handheld ultrasound scans. The aim of this work was to report our initial experience of the added value of ABUS as a breast screening tool. The study included 200 patients who had screening mammograms, ultrasound, and ABUS.ResultsA significant difference was found between the number of lesions detected by ABUS and conventional ultrasound. A significant difference was found between lesions detected by ABUS and mammography which was most evident in patients with dense breasts.ConclusionsABUS is a valuable tool in the screening of the breast with improved lesion detection, especially in patients with dense breasts.

Role of ultrasound in detection of breast cancer: Current status

Breast Ultrasonography (US) is now a days considered the first line examination in the Screening of breast for detection of early breast lesions including cancer. Yet only few single center cohort studies analyzing breast US as screening tool could be found in literature. In spite of the fact that mammography is considered as the primary method for screening for its ability to detect microcalcifications, US is good in mass or mass like lesion detection, especially in the dense breast population as proved by the study of ACRIN 6666 (American College Of Radiology Imaging Network). A lobular hypoechoic area, lesion with ductal extension and dilatation, and a hypoechoic nodular lesion with a dilated lactiferous duct leading to the retroareolar region, were the common ultrasound findings in Ductal carcinoma in situ (DCIS) have been found. Recent introduction of Computer programmes have been developed and approved for use in clinical practice, like CAD (computer aided/assisted detection/ diagnosis), ABUS (Automated breast US), elastography and microbubbles in contrast-enhanced ultrasound. The standardized scanning, with addition of computer technology implementation and finding the picture of DCIS may prove an important radiation free modality for detecting early breast cancer. Results: Out of 176 cases in which we analysed US data and compared with histocytological findings sensitivity and specificity were 94.5% and 92.3% respectively. It was concluded that conventional USG examination can very well complement the diagnosis of breast lesions including cancer. Moreover, this method has the lowest cost/efficiency ratio and it is also the most non-invasive and easily accessible imaging method, with an accuracy comparable to MRI.

Novel imaging approaches to screen for breast cancer: Recent advances and future prospects

Aim of the studyOver the past 50 years, the application of mammography – an X-ray of the breast – to screen healthy women has been a successful strategy to reduce breast cancer mortality. The aim of this study was to review the literature on novel imaging approaches that have the potential to replace mammography. MethodsAn online literature search was carried out using PubMed, Google Scholar, ScienceDirect and Google Patents. The search keywords included “breast cancer”, “imaging” and “screening”, with 51 journal articles and five United States patents being selected for review. Seventeen relevant online sources were also identified and referenced. ResultsIn addition to full-field digital mammography (FFDM), a further nine imaging modalities were identified for review. These included: digital breast tomosynthesis (DBT); breast computed tomography (BCT); automated breast ultrasound (ABUS); fusion of FFDM and ABUS; fusion of DBT and ABUS; magnetic resonance imaging (MRI); optical imaging; radio-wave imaging; and tactile sensor imaging. Important parameters were considered: diagnostic success (sensitivity and specificity), especially in dense breasts; time to acquire the images; and capital cost of the equipment. ConclusionsDBT is rapidly replacing FFDM although it still misses invasive cancers in dense tissue. The fusion of ABUS, either with FFDM or DBT, will lead to sensitivity and specificity approaching 100%. The fusion of opto-acoustic imaging with ultrasound holds considerable promise for the future.

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.

Recognizing and Minimizing Artifacts at CT, MRI, US, and Molecular Imaging.

Understanding the physical principles and technical assumptions that underlie image production can help radiologists eliminate or minimize common imaging artifacts and improve diagnostic accuracy.

Performance and Reading Time of Automated Breast US with or without Computer-aided Detection.

BackgroundComputer-aided detection (CAD) systems may be used to help radiologists interpret automated breast (AB) US images. However, the optimal use of CAD with AB US has, to the knowledge of the authors, not been determined.PurposeTo compare the performance and reading time of different readers by using AB US CAD system to detect breast cancer in different reading modes.Materials and MethodsIn this retrospective study, 1485 AB US images (282 with malignant lesions, 695 with benign lesions, and 508 healthy) in 1452 women (mean age, 43.7 years; age range, 19-82 years) including 529 (36.4%) women who were asymptomatic were collected between 2016 and 2017. A CAD system was used to interpret the images. Three novice readers with 1-3 years of US experience and three experienced readers with 5-10 years of US experience were assigned to read AB US images without CAD, at a second reading (after the reader completed a full unaided interpretation), and at concurrent reading (use of CAD at the start of the assessment). Diagnostic performances and reading times were compared by using analysis of variance.ResultsFor all readers, the mean area under the receiver operating characteristic curve improved from 0.88 (95% confidence interval [CI]: 0.85, 0.91) at without-CAD mode to 0.91 (95% CI: 0.89, 0.92; P < .001) at the second-reading mode and 0.90 (95% CI: 0.89, 0.92; P = .002) at the concurrent-reading mode. The mean sensitivity of novice readers in women who were asymptomatic improved from 67% (95% CI: 63%, 74%) at without-CAD mode to 88% (95% CI: 84%, 89%) at both the second-reading mode and the concurrent-reading mode (P = .003). Compared with the without-CAD and second-reading modes, the mean reading time per volume of concurrent reading was 16 seconds (95% CI: 11, 22; P < .001) and 27 seconds (95% CI: 21, 32; P < .001) shorter, respectively.ConclusionComputer-aided detection (CAD) was helpful for novice readers to improve cancer detection at automated breast US in women who were asymptomatic. CAD was more efficient when used concurrently for all readers.© RSNA, 2019Online supplemental material is available for this article.See also the editorial by Slanetz in this issue.

Does Computer-aided Detection Help in Interpretation of Automated Breast US?

Does Computer-aided Detection Help in Interpretation of Automated Breast US?

Three-dimensional Automated Breast US: Facts and Artifacts.

As automated breast (AB) US becomes more integrated into the daily practice of breast imaging, the need to address the artifacts that interfere with AB US image interpretation is becoming more important. Learning methods to detect and subsequently resolve artifacts such as shadowing can enhance the reader's confidence and ability to differentiate artifacts and true abnormalities. Understanding the basic principles of AB US and its image acquisition process are key elements in resolving artifacts. Gaining familiarity with the common patterns of AB US artifacts and placing them into categories of technical, software, physiologic, and breast lesion-related causes can aid in image interpretation. Recognizing specific artifacts, such as dropout and lack of contact, and the ability to distinguish them from true abnormalities, such as surgical scars and suspicious lesions, can help minimize preventable false-positive interpretations. Applying methods to confirm shadowing as artifactual, including the use of a second view, additional planes, and software-related tools such as the rotational tool, can aid the radiologist in resolving artifacts and avoiding preventable recalls, potentially resulting in increased specificity. Presented is a methodical approach to recognizing AB US artifacts and their causes; analyzing shadowing, a challenging entity in the interpretation of AB US imaging studies; differentiating artifact from true abnormality; and reviewing characteristic patterns and basic techniques to resolve artifacts. The goal of this article is to enable the radiologist in applying these methods to help reduce preventable false-positive recommendations and increase efficiency in AB US image interpretation. Online supplemental material is available for this article. ©RSNA, 2019.

Double reading of automated breast ultrasound with digital mammography or digital breast tomosynthesis for breast cancer screening

PurposeTo evaluate the impact of double reading automated breast ultrasound (ABUS) when added to full field digital mammography (FFDM) or digital breast tomosynthesis (DBT) for breast cancer screening. MethodsFrom April 2014 to June 2015, 124 women with dense breasts and intermediate to high breast cancer risk were recruited for screening with FFDM, DBT, and ABUS. Readers used FFDM and DBT in clinical practice and received ABUS training prior to study initiation. FFDM or DBT were first interpreted alone by two independent readers and then with ABUS. All recalled women underwent diagnostic workup with at least one year of follow-up. Recall rates were compared using the sign test; differences in outcomes were evaluated using Fisher's exact test. ResultsOf 121 women with complete follow-up, all had family (35.5%) or personal (20.7%) history of breast cancer, or both (43.8%). Twenty-four women (19.8%) were recalled by at least one modality. Recalls increased from 5.0% to 13.2% (p = 0.002) when ABUS was added to FFDM and from 3.3% to 10.7% (p = 0.004) when ABUS was added to DBT. Findings recalled by both readers were more likely to result in a recommendation for short term follow-up imaging or tissue biopsy compared to findings recalled by only one reader (100% vs. 42.1%, p = 0.041). The cancer detection rate was 8.3 per 1000 screens (1/121); mode of detection: FFDM and DBT. ConclusionsAdding ABUS significantly increased the recall rate of both FFDM and DBT screening. Double reading of ABUS during early phase adoption may reduce false positive recalls.

The application of gel pad for the examination of axilla with automated breast sonography

Background Automated Breast Ultrasonography (ABUS) is mechanically designed to only examine the breast, so there exists a technical limitation on examining the axilla. For this reason, further examination with Hand Held Ultrasonography (HHUS) is currently required to supplement the limitation of ABUS. Objective This study aims to demonstrate technical aspects of making and applying gel pads for the assessment of axilla with ABUS, and to evaluate the usefulness of the gel pad application. Methods For the experiment 1, four gel pads that could be applied to its shape for each individual patients’ axilla were produced by infusing each one of four materials (water, water & 1% super absorbent polymer mixture, ultrasonic gel, ABUS lotion) on every sterilized probe covers, where air bubbles were removed as much as possible. ABUS phantom test was carried out in each gel pads, and the acquired images were qualitatively evaluated to select the best source material. For the experiment 2, 20 patients who visited our institution for breast sonography underwent the axilla examination using HHUS and ABUS with the selected gel pad. The acquired images were qualitatively evaluated for the clarity of anatomical structures and the presence of the artifact to ABUS based on HHUS. Result As a result of experiment 1, gel pads made of each four materials showed total score of 24, 34, 43, and 40, respectively. The gel pad made of ultrasonic gel was found to be the best material among all. Experiment 2 resulted that the image quality of ABUS showed 80% “Equal” and 20% “Insufficient” in the definition of the anatomical structures compared to HHUS, whereas artifacts presented in all cases. Conclusion The application of gel pad during the examination of axilla with ABUS was highly effective. This application can overcome the technical limitation and expand the role of ABUS as a reliable diagnostic tool.

The problems of primary training with newly introduced Automated Breast Ultrasonography（ABUS）

The problems of primary training with newly introduced Automated Breast Ultrasonography（ABUS）

Computer-Aided Detection with Automated Breast Ultrasonography for Suspicious Lesions Detected on Breast MRI

Computer-Aided Detection with Automated Breast Ultrasonography for Suspicious Lesions Detected on Breast MRI

Clinical Applications of Automated Breast Ultrasound: Screening for Breast Cancer

Clinical Applications of Automated Breast Ultrasound: Screening for Breast Cancer

Reliability of automated versus handheld breast ultrasound examinations of suspicious breast masses.

PurposeThe purpose of this study was to assess the reliability of automated breast ultrasound (ABUS) examinations of suspicious breast masses in comparison to handheld breast ultrasound (HHUS) with regard to Breast Imaging Reporting and Data System (BI-RADS) category assessment, and to investigate the factors affecting discrepancies in categorization.MethodsA total of 135 masses that were assessed as BI-RADS categories 4 and 5 on ABUS that underwent ultrasound (US)-guided core needle biopsy from May 2017 to December 2017 were included in this study. The BI-RADS categories were re-assessed using HHUS. Agreement of the BI-RADS categories was evaluated using kappa statistics, and the positive predictive value of each examination was calculated. Logistic regression analysis was performed to identify the mammography and US findings associated with discrepancies in the BI-RADS categorization.ResultsThe overall agreement between ABUS and HHUS in all cases was good (79.3%, kappa=0.61, P<0.001). Logistic regression analysis revealed that accompanying suspicious microcalcifications on mammography (odds ratio [OR], 4.63; 95% confidence interval [CI], 1.83 to 11.71; P=0.001) and an irregular shape on US (OR, 5.59; 95% CI, 1.43 to 21.83; P=0.013) were associated with discrepancies in the BI-RADS categorization.ConclusionThe agreement between ABUS and HHUS examinations in the BI-RADS categorization of suspicious breast masses was good. The presence of suspicious microcalcifications on mammography and an irregular shape on US were factors associated with ABUS yielding a lower level of suspicion than HHUS in terms of the BI-RADS category assessment.

Comparison of mammography, digital breast tomosynthesis, automated breast ultrasound, magnetic resonance imaging in evaluation of residual tumor after neoadjuvant chemotherapy

BackgroundTo compare the accuracy of mammography (MG), digital breast tomosynthesis (DBT), automated breast ultrasound (ABUS) and magnetic resonance imaging (MRI) for the assessment of residual tumor extent in breast cancer after neoadjuvant chemotherapy (NAC). MethodsFifty-one stage II-III breast cancer undergoing NAC were enrolled from March 2015 to December 2016. The longest diameter of residual tumor measured with MG, DBT, ABUS and MRI was compared with the pathologic tumor size. Statistical analysis was performed using intraclass correlation coefficients (ICC) and marginal homogeneity test. Receiver operating characteristics (ROC) analysis was used to evaluate the diagnostic performance for predicting pathologic complete response (pCR). ResultsMRI size correlated well with pathology (ICC = 0.83), significantly better than MG, DBT and ABUS size (ICC = 0.56, ICC = 0.63 and ICC = 0.55, respectively). The discrepancy between MRI and pathology was statistical different from that of MG and ABUS (p = 0.0231 and 0.0039, respectively), but not different from that of DBT (p = 0.5727). For predicting pCR, MRI and DBT had a better performance compared to MG and US (area under the ROC curve: 0.92, 0.84, 0.72, 0.75, respectively; p = 0.3749 for DBT, p = 0.0972 for MG and p = 0.0596 for ABUS, when MRI being reference). ConclusionsMRI and DBT allow more accurate assessment of tumor size compared to pathology compared with MG and ABUS. MRI and DBT outperform MG and ABUS in the prediction of pathologic complete response.

Breast density implications and supplemental screening.

Digital breast tomosynthesis (DBT) has been widely implemented in place of 2D mammography, although it is less effective in women with extremely dense breasts. Breast ultrasound detects additional early-stage, invasive breast cancers when combined with mammography; however, its relevant limitations, including the shortage of trained operators, operator dependence and small field of view, have limited its widespread implementation. Automated breast sonography (ABS) is a promising technique but the time to interpret and false-positive rates need to be improved. Supplemental screening with contrast-enhanced magnetic resonance imaging (MRI) in high-risk women reduces late-stage disease; abbreviated MRI protocols may reduce cost and increase accessibility to women of average risk with dense breasts. Contrast-enhanced digital mammography (CEDM) and molecular breast imaging improve cancer detection but require further validation for screening and direct biopsy guidance should be implemented for any screening modality. This article reviews the status of screening women with dense breasts. KEY POINTS: • The sensitivity of mammography is reduced in women with dense breasts. Supplemental screening with US detects early-stage, invasive breast cancers. • Tomosynthesis reduces recall rate and increases cancer detection rate but is less effective in women with extremely dense breasts. • Screening MRI improves early diagnosis of breast cancer more than ultrasound and is currently recommended for women at high risk. Risk assessment is needed, to include breast density, to ascertain who should start early annual MRI screening.