Automated Breast Ultrasound System Research Articles

Potential Usefulness a Coronal View using an Automated Breast Ultrasound System in Detecting Breast Lesions.

An automated breast ultrasound system (ABUS) combined with screening mammography has increased cancer detection rates; however, supplemental ABUS use has increased recall rates. In this study, we aimed to identify an accurate and efficient method of ABUS interpretation and evaluate the potential usefulness of its coronal view versus the conventional transverse view. This retrospective observer study included comprised 114 ABUS cases (40 normal, 35 benign, 39 malignant). Ten physicians from multiple institutions interpreted the anonymized coronal and transverse views independently. The observers scored their confidence in the lesion detection for each case using a continuous scale and recorded reading times for each coronal and transverse view interpretation. Free-response receiver operating characteristic analysis was employed to compare detection accuracies between views; a paired t-test was used to compare the average reading times. Detection accuracy did not differ significantly between the coronal and transverse views (figure of merit=0.740 and 0.745, respectively; p = 0.72). However, the average reading time for the coronal view was significantly shorter than that for the transverse view (149.7 vs. 200.3 seconds per case, p = 0.003). The coronal view obtained with the ABUS was useful for interpretation and associated with significantly shorter reading times compared with the conventional transverse view while maintaining breast lesion detection accuracy.

Enhancing Ki-67 Prediction in Breast Cancer: Integrating Intratumoral and Peritumoral Radiomics from Automated Breast Ultrasound via Machine Learning

Rationale and ObjectivesTraditional Ki-67 evaluation in breast cancer (BC) via core needle biopsy is limited by repeatability and heterogeneity. The automated breast ultrasound system (ABUS) offers reproducibility but is constrained to morphological and echoic assessments. Radiomics and machine learning (ML) offer solutions, but their integration for improving Ki-67 predictive accuracy in BC remains unexplored. This study aims to enhance ABUS by integrating ML-assisted radiomics for Ki-67 prediction in BC, with a focus on both intratumoral and peritumoral regions. Materials and MethodsA retrospective analysis was conducted on 936 BC patients, split into training (n=655) and testing (n=281) cohorts. Radiomics features were extracted from intra- and peritumoral regions via ABUS. Feature selection involved Z-score normalization, intraclass correlation, Wilcoxon rank sum tests, minimum redundancy maximum relevance, and least absolute shrinkage and selection operator logistic regression. ML classifiers were trained and optimized for enhanced predictive accuracy. The interpretability of the optimized model was further augmented by employing Shapley additive explanations (SHAP). ResultsOf the 2632 radiomics features in each patient, 15 were significantly associated with Ki-67 levels. The support vector machine (SVM) was identified as the optimal classifier, with area under the receiver operating characteristic curve values of 0.868 (training) and 0.822 (testing). SHAP analysis indicated that five peritumoral and two intratumoral features, along with age and lymph node status, were key determinants in the predictive model. ConclusionIntegrating ML with ABUS-based radiomics effectively enhances Ki-67 prediction in BC, demonstrating the SVM model's strong performance with both radiomics and clinical factors.

Transition of traditional method to deep learning based computer-aided system for breast cancer using Automated Breast Ultrasound System (ABUS) images: a review

Transition of traditional method to deep learning based computer-aided system for breast cancer using Automated Breast Ultrasound System (ABUS) images: a review

Optimized Radiomics Nomogram Based on Automated Breast Ultrasound System: A Potential Tool for Preoperative Prediction of Metastatic Lymph Node Burden in Breast Cancer.

Axillary lymph node dissection (ALND) can be safely avoided in women with T1 or T2 primary invasive breast cancer (BC) and one to two metastatic sentinel lymph nodes (SLNs). However, cancellation of ALND based solely on SLN biopsy (SLNB) may lead to adverse outcomes. Therefore, preoperative assessment of LN tumor burden becomes a new focus for ALN status. This study aimed to develop and validate a nomogram incorporating the radiomics score (rad-score) based on automated breast ultrasound system (ABUS) and other clinicopathological features for evaluating the ALN status in patients with early-stage BC preoperatively. Totally 354 and 163 patients constituted the training and validation cohorts. They were divided into ALN low burden (<3 metastatic LNs) and high burden (≥3 metastatic LNs) based on the histopathological diagnosis. The radiomics features of the segmented breast tumor in ABUS images were extracted and selected to generate the rad-score of each patient. These rad-scores, along with the ALN burden predictors identified from the clinicopathologic characteristics, were included in the multivariate analysis to establish a nomogram. It was further evaluated in the training and validation cohorts. High ALN burdens accounted for 11.2% and 10.8% in the training and validation cohorts. The rad-score for each patient was developed based on 7 radiomics features extracted from the ABUS images. The radiomics nomogram was built with the rad-score, tumor size, US-reported LN status, and ABUS retraction phenomenon. It achieved better predictive efficacy than the nomogram without the rad-score and exhibited favorable discrimination, calibration and clinical utility in both cohorts. We developed an ABUS-based radiomics nomogram for the preoperative prediction of ALN burden in BC patients. It would be utilized for the identification of patients with low ALN burden if further validated, which contributed to appropriate axillary treatment and might avoid unnecessary ALND.

Evaluation of a new method of calculating breast tumor volume based on automated breast ultrasound.

ObjectiveTo evaluate the effectiveness and advantages of a new method for calculating breast tumor volume based on an automated breast ultrasound system (ABUS).MethodsA total of 42 patients (18–70 years old) with breast lesions were selected for this study. The Ivenia ABUS 2.0 (General Electric Company, USA) was used, with a probe frequency of 6–15 MHz. Adobe Photoshop CS6 software was used to calculate the pixel ratio of each ABUS image, and to draw an outline of the tumor cross-section. The resulting area (in pixels) was multiplied by the pixel ratio to yield the area of the tumor cross-section. The Wilcoxon signed rank test and Bland-Altman plot were used to compare mean differences and mean values, respectively, between the two methods.ResultsThere was no significant difference between the tumor volumes calculated by pixel method as compared to the traditional method (P>0.05). Repeated measurements of the same tumor volume were more consistent with the pixel method.ConclusionThe new pixel method is feasible for measuring breast tumor volume and has good validity and measurement stability.

Comparison of 3D-Automated Breast Ultrasound With Handheld Breast Ultrasound Regarding Detection and BI-RADS Characterization of Lesions in Dense Breasts: A Study of 592 Cases

We aimed to compare the diagnostic performance of an automated breast ultrasound system (ABUS) with handheld ultrasound (HHUS) in the detection and characterization of lesions regarding BI-RADS classification in women with dense breasts. After ethical approval, from July 2017 to August 2019, 592 consecutive patients were enrolled in this prospective study. On the same day, patients underwent ABUS followed by HHUS. Three breast radiologists participated in this study. The number and type of lesions and BI-RADS categorization of both ABUS and HHUS examinations of each patient were recorded in an excel file. The level of agreement between the two ultrasound systems in terms of lesion number and BI-RADS category were analyzed statistically. ABUS and HHUS detected 1005 and 1491 cystic and 270 and 336 mass lesions in 592 patients respectively. ABUS and HHUS detected 171 and 167 positive/suspicious cases (BIRADS 0/3/4/5). Forty suspicious lesions underwent core needle biopsy whereas 11 malignant lesions were detected by both methods. The remaining lesions were followed with a mean of 31 months. The mean size of solid lesions detected by HHUS and ABUS was 7.67 mm (range 2.1-41 mm) and 7.74 mm (range 2-42 mm) respectively. The agreement for detection of cystic lesions between two methods for each breast was good (kappa: 0.61-0.62 p < 0.001). The agreement of two methods for solid mass lesions for each breast was moderate (k=0.57-0.60 p < 0.001). There was good agreement between the two methods for detecting suspicious lesions (kappa=0.66 p < 0.001). The level of agreement of ABUS and HHUS for dichotomic assignment of BIRADS categories was good. Although ABUS detected fewer lesions compared to HHUS, both methods detected all malignant lesions. ABUS is a reliable method for the detection of malignancy in dense breasts.

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

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. 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. 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). 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 Technical Note on a Novel Technique for the Evaluation of Breast Excision Specimen: Automated Breast Ultrasound System

Specimen ultrasound is a practical, reliable, and efficient method for detecting ultrasound-only breast abnormalities. This technique was developed as a result of an increasing number of breast lesions being observed on ultrasound-only, particularly in younger women. This procedure can be easily performed in either the radiology department or the operative suite and involves a minimal delay during the operation. In this technical note, we want to share our experience with automated 3D breast ultrasound (ABUS) in specimen imaging and its practicability. ABUS can be used as a new technique in specimen ultrasound and gives information about the lesion margin on coronal images. Keywords: Automated breast ultrasound; Automated 3D breast ultrasound; Specimen imaging

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.

Diagnostic performance of automated breast ultrasound and handheld ultrasound in women with dense breasts.

As an adjunct to mammography, ultrasound can improve the detection of breast cancer in women with dense breasts. We aimed to evaluate the diagnostic performance of automated breast ultrasound system (ABUS) and handheld ultrasound (HHUS) in Chinese women with dense breasts, both in combination with mammography and separately. This is a cross-sectional multicenter clinical research study. Nine hundred and thirty-seven women with dense breasts underwent ABUS, HHUS, and mammography at one of five tertiary-care hospitals. The diagnostic performance of ABUS and HHUS was evaluated in combination with mammography, or separately in women with mammography-negative dense breasts. The agreement between ABUS and HHUS in breast cancer detection was also assessed. The sensitivity of the combination of ABUS or HHUS with mammography was 99.1% (219/221), and the specificities were 86.9% (622/716) and 84.9% (608/716), respectively. The area under the curve was 0.93 for ABUS combined with mammography and 0.92 for that of HHUS combined with mammography. Statistically significant agreement between ABUS and HHUS in breast cancer detection was observed (percent agreement = 0.94, κ = 0.85). The incremental cancer detection rate in mammography-negative dense breasts was 42.8 per 1000 ultrasound examinations. Both ABUS and HHUS as adjuncts to mammography can significantly improve the breast cancer detection rate in women with dense breasts, and there is a strong correlation between them. Given the high prevalence of dense breasts and the multiple advantages of ABUS over HHUS, such as less operator dependence and reproducibility, ABUS showed great potential for use in breast cancer early detection, especially in resource-limited areas.

Production and clinical evaluation of breast lesion skin markers for automated three-dimensional ultrasonography of the breast: a pilot study

ObjectivesAutomated ultrasound of the breast has the advantage to have the whole breast scanned by technicians. Consequently, feedback to the radiologist about concurrent focal abnormalities (e.g., palpable lesions) is lost. To enable marking of patient- or physician-reported focal abnormalities, we aimed to develop skin markers that can be used without disturbing the interpretability of the image.MethodsDisk-shaped markers were casted out of silicone. In this IRB-approved prospective study, 16 patients were included with a mean age of 57 (39–85). In all patients, the same volume was imaged twice using an automated breast ultrasound system, once with and once without a marker in place. Nine radiologists from two medical centers filled scoring forms regarding image quality, image interpretation, and confidence in providing a diagnosis based on the images.ResultsMarker adhesion was sufficient for automated scanning. Observer scores showed a significant shift in scores from excellent to good regarding diagnostic yield/image quality (χ2, 15.99, p < 0.01), and image noise (χ2, 21.20, p < 0.01) due to marker presence. In 93% of cases, the median score of observers “agree” with the statement that marker-induced noise did not influence image interpretability. Marker presence did not interfere with confidence in diagnosis (χ2, 6.00, p = 0.20).ConclusionInexpensive, easy producible skin markers can be used for accurate lesion marking in automated ultrasound examinations of the breast while image interpretability is preserved. Any marker-induced noise and decreased image quality did not affect confidence in providing a diagnosis.Key Points• The use of a skin marker enables the reporting radiologist to identify a location which a patient is concerned about.• The developed skin marker can be used for accurate breast lesion marking in ultrasound examinations.

Utility and Diagnostic Performance of Automated Breast Ultrasound System in Evaluating Pure Non-Mass Enhancement on Breast Magnetic Resonance Imaging.

ObjectiveTo compare the utility and diagnostic performance of automated breast ultrasound system (ABUS) with that of hand-held ultrasound (HHUS) in evaluating pure non-mass enhancement (NME) lesions on breast magnetic resonance imaging (MRI).Materials and MethodsOne hundred twenty-six consecutive MRI-visible pure NME lesions of 122 patients with breast cancer were assessed from April 2016 to March 2017. Two radiologists reviewed the preoperative breast MRI, ABUS, and HHUS images along with mammography (MG) findings. The NME correlation rate and diagnostic performance of ABUS were compared with that of HHUS, and the imaging features associated with ABUS visibility were analyzed.ResultsAmong 126 pure NME lesions, 100 (79.4%) were malignant and 26 (20.6%) were benign. The overall correlation rate was 87.3% (110/126) in ABUS and 92.9% (117/126) in HHUS. The sensitivity and specificity were 87% and 50% for ABUS and 92% and 42.3% for HHUS, respectively, with no significant differences (p = 0.180 and 0.727, respectively). Malignant NME was more frequently visualized than benign NME lesions on ABUS (93% vs. 65.4%, p = 0.001). Significant factors associated with the visibility of ABUS were the size of NME lesions on MRI (p < 0.001), their distribution pattern (p < 0.001), and microcalcifications on MG (p = 0.027).ConclusionABUS evaluation of pure NME lesions on MRI in patients with breast cancer is a useful technique with high visibility, especially in malignant lesions. The diagnostic performance of ABUS was comparable with that of conventional HHUS in evaluating NME lesions.

Automated Breast Ultrasound System for Breast Cancer Evaluation: Diagnostic Performance of the Two-View Scan Technique in Women with Small Breasts.

ObjectiveTo comparatively evaluate the scan coverage and diagnostic performance of the two-view scan technique (2-VST) of the automated breast ultrasound system (ABUS) versus the conventional three-view scan technique (3-VST) in women with small breasts.Materials and MethodsBetween March 2016 and May 2017, 136 asymptomatic women with small breasts (bra cup size A) suitable for 2-VST were enrolled. Subsequently, 272 breasts were subjected to bilateral whole-breast ultrasound examinations using ABUS and the hand-held ultrasound system (HHUS). During ABUS image acquisition, one breast was scanned with 2-VST, while the other breast was scanned with 3-VST. In each breast, the breast coverage and visibility of the HHUS detected lesions on ABUS were assessed. The sensitivity and specificity of ABUS were compared between 2-VST and 3-VST.ResultsAmong 136 breasts, eight cases of breast cancer were detected by 2-VST, and 10 cases of breast cancer were detected by 3-VST. The breast coverage was satisfactory in 94.1% and 91.9% of cases under 2-VST and 3-VST, respectively (p = 0.318). All HHUS-detected lesions were visible on the ABUS images regardless of the scan technique. The sensitivities and specificities were similar between 2-VST and 3-VST (100% [8/8] vs. 100% [10/10], and 97.7% [125/128] vs. 95.2% [120/126], respectively), with no significant difference (p > 0.05).Conclusion2-VST of ABUS achieved comparable scan coverage and diagnostic performance to that of conventional 3-VST in women with small breasts.

Automated breast ultrasound system (ABUS): can it replace mammography as a screening tool?

BackgroundMammography is the most accepted, accurate, and effective modality in screening of breast cancer, yet its sensitivity is affected by the density of the breast tissue. Alternative methods for screening are the sonography and MRI but both had their limitations. A new option named ABUS (automated breast ultrasound system) is now proposed to overcome the breast US limitation as it is time-consuming and operator-dependent and to overcome the costly time-consuming MRI. The objectives of the study are to evaluate the accuracy of ABUS in the detection of different breast lesions as a substitution for mammography. This prospective study included 25 women outreached for digital mammography or handheld ultrasound examination at the period between January 2017 and February 2018. Women have no specific age group.ResultsSignificant improvement in the detection of breast lesions by ABUS use with mammogram especially in dense breasts (ACR class C and D)ConclusionABUS is a promising competitor to mammogram in screening of breast lesions

Diagnostic Performance Using Automated Breast Ultrasound System for Breast Cancer in Chinese Women Aged 40 Years or Older: A Comparative Study

The purpose of this study was to investigate the diagnostic performance of the automated breast ultrasound system (ABUS) compared with hand-held ultrasonography (HHUS) and mammography (MG) for breast cancer in women aged 40 y or older. A total of 594 breasts in 385 patients were enrolled in the study. HHUS, ABUS and MG exams were performed for these patients. Follow-up and pathologic findings were used as the reference standard. Based on the reference standard, 519 units were benign or normal and 75 were malignant. The sensitivity, specificity, accuracy and Youden index were 97.33%, 89.79%, 90.74% and 0.87 for HHUS; 90.67%, 92.49%, 92.26% and 0.83 for ABUS; 84.00%, 92.87%, 91.75% and 0.77 for MG, respectively. The specificity of ABUS was significantly superior to that of HHUS (p = 0.024). The area under the receiver operating characteristic curve was 0.936 for HHUS, which was the highest, followed by 0.916 for ABUS and 0.884 for MG. However, the difference was not statistically significant (p > 0.05). In conclusion, the diagnostic performance of ABUS for breast cancer was equivalent to HHUS and MG and potentially can be used as an alternative method for breast cancer diagnosis.

Slice Localization for Three-Dimensional Breast Ultrasound Volume Using Deep Learning

In routine ultrasonography, slice images of the internal organs of the human body are usually generated through an 1D array probe. The position and orientation of the probe is adjusted manually to obtain slice planes with pathological features. This is quite dependent on the experience and technique of a sonographer. This paper aims to locate 2D slice planes in a 3D breast ultrasound volume, which has significant application value in clinical ultrasound examinations. We propose a deep learning approach mapping all possible 2D image slices to their 3D coordinates parameters using a fully connected neural network implemented on MATLAB. We emphasize that this training must be done separately for each patient since the mammary tissue structure varies greatly from one person to another. The trained network can be interpreted as an image-slice location database for each patient. Our study is validated on GE ABUS (Automated Breast Ultrasound System) volume data. Each 2D image slice has four spatial parameters. The method achieves a prediction error of 0.14mm/0.25mm and 0.5 degree/0.3 degree for translation (x/y) and rotation (yaw/roll) parameters respectively, averaged over all practically scannable slices. It takes less than 0.1ms to predict the location of one 64×64 slice image. Thus, slice locations may be displayed with high accuracy in real-time when scanning with a conventional 1D probe, potentially allowing physicians to manipulate the probe to any scan planes of interest.

3D Automated Breast Ultrasound System: Comparison of Interpretation Time of Senior Versus Junior Radiologist.

This study aimed to compare the automated breast ultrasound system (ABUS) reading time of breast radiologist to a radiology resident independent of the clinical outcomes. One hundred women who underwent screening ABUS between July and August 2017 were reviewed retrospectively. Each study was examined sequentially by a breast radiologist who has more than 20 years of experience in breast radiology and third year resident who has 6 months of experience in breast radiology. Data were analyzed with Spearman' correlation, Wilcoxon Signed Ranks Test and Kruskal-Wallis Test and was recorded. The mean age of patients was 42.02±11.423 years (age range16-66). The average time for senior radiologist was 223.36±84.334 seconds (min 118 max 500 seconds). The average time for junior radiologist was 269.48±82.895 seconds (min 150 max 628 seconds). There was a significant difference between the mean time of two radiologists (p=0.00001). There was a significant difference regarding the decrease in the reading time throughout study with the increase of number of cases read by the breast radiologist (p<0.05); but not with the resident radiologist (p=0.687). There was a correlation between BI-RADS category and reading time for both the breast radiologist and the resident (p=0.002, p=0.00043 respectively) indicating that patients who had findings caused longer reading times. ABUS reading time may differ according to the experience of the user, however the times of an experienced and non-experienced user is comparable.

Three-dimensional automated breast ultrasound: Technical aspects and first results

Three-dimensional automated breast ultrasound system (3D ABUS) is an innovation in breast ultrasound that has been developed to uncouple detection from image acquisition and to address the limitations of handheld ultrasound (HHUS). 3D ABUS provides a large field of view using high frequency transducers, producing high-resolution images and covering a large portion of the breast with one sweep. As more data become available on breast density and the impact of supplemental screening, 3D ABUS has gained wider acceptance as an adjunct tool to mammography. Computer-aided detection software significantly reduces interpretation time, improving the workflow for the utilization of 3D ABUS as a supplemental screening tool. In the diagnostic setting, 3D ABUS offers valuable impact in the detectability of breast lesions and the differentiation of malignant from benign lesions, with a high inter-observer agreement. State-of-the art technique, including uniform compression and proper positioning, tends to reduce artifactual posterior shadowing, while combined 3D ABUS-mammography interpretation improves radiologists’ diagnostic performance. Promising results have supported the enhanced efficiency of 3D ABUS in detecting the extent of breast cancer and assessing response to neoadjuvant chemotherapy, whereas its correlation with molecular subtypes of breast cancer is remarkable. Future perspectives include the integration of radiomics and deep learning in the further development of 3D ABUS.

Role of Automated Breast Ultrasound System (ABUS) as Screening Tool in Comparison To Mammogram in Detect of Different Breast Lesions

Background: Mammography is an effective randomized controlled trial-proven method for reducing mortality due to breast cancer. However, the sensitivity of mammography depends on breast density. The current supplemental screening options include breast ultrasonography (US) and magnetic resonance imaging (MRI). Automated breast ultrasound system (ABUS) is an option proposed to overcome the time-consuming and costly nature of handheld, physician-performed whole-breast US (WBUS). Objectives: To evaluate the utility of automated breast ultrasound system (ABUS) in detection of different breast lesions especially in dense breast in comparison to mammogram. Patients and Method: It is prospective study included 25 women outreached for digital mammography or handheld ultrasound examination at the general Egyptian hospitals. Women have no specific age group. Results: The use of ABUS with the mammogram shows significant increased frequency of detection of positive benign lesions in ACR class C and D in comparison to class A and B and insignificance malignant lesions detection. Conclusion: ABUS reflects a promising modality in breast imaging however appears to be on a par with mammogram in terms of diagnostic quality.

Role of Automated Breast Ultrasound System (ABUS) as Screening Tool in Comparison To Mammogram in Detect of Different Breast Lesions

Role of Automated Breast Ultrasound System (ABUS) as Screening Tool in Comparison To Mammogram in Detect of Different Breast Lesions