Breast Ultrasound Screening Research Articles

Prospective multicenter cohort study on breast cancer screening using an automated breast ultrasound with remote reading

Objective: To construct a remote screening network for breast cancer based on automated breast ultrasound (ABUS) and explore the value of ABUS with remote reading for breast cancer screening. Methods: We constructed a remote breast cancer screening network including one remote reading center and 48 image-acquisition centers. We recruited women to participate in breast cancer screening at one of these image-acquisition centers from January 2021 to January 2023. The technicians collected the whole breast images using the ABUS. The images were then sent to the reading center through the PVBUS System and interpreted independently by two radiologists using the Breast Imaging Reporting and Data System (BI-RADS). BI-RADS categories 1 and 2 indicate negative screening results, and women diagnosed with these categories were recommended for annual breast ultrasound screening. BI-RADS categories 3, 4, and 5 indicate positive results. Women with BI-RADS category 3 lesions were recommended for follow-up examinations every 6 months using ABUS or handheld ultrasound, while those with BI-RADS 4 and 5 lesions were suggested to undergo pathological examinations. Results: In our study, we enrolled 10 344 women who completed the ABUS screening and were followed up for more than 12 months. After remote reading, 6 164 women were diagnosed with BI-RADS category 1 and 2 626 woman were within BI-RADS category 2. In contrast, 1 404 women were within BI-RADS category 3, a total of 135 women were within BI-RADS category 4, and 15 women were within BI-RADS category 5. The positive screening rate of ABUS was 15.0% (1 554/10 344). The ABUS with remote reading had a detection rate of 3.7/1 000 (38/10 344) for breast cancer screening, with a sensitivity of 97.4% (38/39) and a specificity of 85.3% (8 789/10 305). Among the 38 breast cancer cases detected, 92.1% (35/38) were invasive carcinomas, and 63.2% (24/38) were stage 0 or Ⅰ breast cancers. Conclusions: Breast cancer screening based on ABUS with remote reading provided an efficient and feasible solution to the problem of unevenly distributed medical resources and medical staff levels in various regions of China, enabling the decentralization of high-quality medical resources and improving the accessibility of high-quality screening services. It has provided an alternative for breast cancer screening in China.

Knowledge, attitude, and practice toward ultrasound screening for breast cancer among women

BackgroundSeveral obstacles can hinder breast cancer screening. This study aimed to investigate the knowledge, attitude, and practice (KAP) toward ultrasound screening for breast cancer in women.MethodsThis cross-sectional study recruited women who visited the breast specialist clinic of Zhongshan City People’s Hospital (a tertiary hospital) between August 2022 and April 2023 through convenience sampling. KAP scores ≥70% were considered adequate.ResultsThis study enrolled 501 participants. The mean knowledge, attitude, and practice levels were 8.56 ± 1.81/12 (possible range 0–12, 71.33%), 29.80 ± 2.71 (possible range 8–40, 74.50%), and 32.04 ± 3.09 (possible range 8–40, 80.10%). Senior high school education (vs. junior high school and below, coefficient = 1.531, 95%CI: 1.013–2.312, p = 0.044), bachelor’s education and above (vs. junior high school and below, coefficient = 5.315, 95%CI: 3.546–7.966, p < 0.001), housewife or unemployed (vs. employed, coefficient = 0.671, 95%CI: 0.466–0.966, p = 0.032), and a history of breast ultrasound (vs. no, coefficient = 1.466, 95%CI: 1.121–1.917, p = 0.005) were independently and positively associated with knowledge. Knowledge (coefficient = 1.303, 95%CI: 1.100–1.544, p = 0.002) and monthly income >10,000 (vs. <5,000, coefficient = 4.364, 95%CI: 1.738–10.956, p = 0.002) were independently and positively associated with attitude. Only attitude (coefficient = 1.212, 95%CI: 1.096–1.340, p < 0.001) was independently and positively associated with the practice. A structural equation modeling (SEM) analysis was used to estimate causality among KAP dimensions, showing that knowledge directly influenced attitude (β = −1.090, p = 0.015), knowledge did not directly influence practice (β = −0.117, p = 0.681) but had an indirect influence (β = 0.826, p = 0.028), and attitude directly influenced practice (β = −0.757, p = 0.016).ConclusionWomen in Zhongshan City had good knowledge, favorable attitudes, and active practice toward breast ultrasound screening for breast cancer. Women’s characteristics associated with a poorer KAP were identified, allowing for more targeted interventions.

Artificial Intelligence for Breast Ultrasound: AJR Expert Panel Narrative Review.

Breast ultrasound is used in a wide variety of clinical scenarios, including both diagnostic and screening applications. Limitations of ultrasound, however, include its low specificity and, for automated breast ultrasound screening, the time necessary to review whole-breast ultrasound images. As of this writing, four AI tools that are approved or cleared by the FDA address these limitations. Current tools, which are intended to provide decision support for lesion classification and/or detection, have been shown to increase specificity among non-specialists and to decrease interpretation times. Potential future applications include triage of patients with palpable masses in low-resource settings, preoperative prediction of axillary lymph node metastasis, and preoperative prediction of neoadjuvant chemotherapy response. Challenges in the development and clinical deployment of AI for ultrasound include: the limited availability of curated training datasets compared to mammography; the high variability in ultrasound image acquisition due to equipment- and operator-related factors (which may limit algorithm generalizability); and the lack of post-implementation evaluation studies. Furthermore, current AI tools for lesion classification were developed based on 2D data, but diagnostic accuracy could potentially be improved if multimodal ultrasound data were used, such as color Doppler, elastography, cine clips, and 3D imaging.

Diagnostic performance of 3D automated breast ultrasound (3D-ABUS) in a clinical screening setting—a retrospective study

ObjectivesTo assess the diagnostic performance of 3D automated breast ultrasound (3D-ABUS) in breast cancer screening in a clinical setting.Materials and methodsAll patients who had 3D-ABUS between January 2014 and January 2022 for screening were included in this retrospective study. The images were reported by 1 of 6 breast radiologists based on the Breast Imaging Reporting and Data Systems (BI-RADS). The 3D-ABUS was reviewed together with the digital breast tomosynthesis (DBT). Recall rate, biopsy rate, positive predictive value (PPV) and cancer detection yield were calculated.ResultsIn total, 3616 studies were performed in 1555 women (breast density C/D 95.5% (n = 3455/3616), breast density A/B 4.0% (n = 144/3616), density unknown (0.5% (n = 17/3616)). A total of 259 lesions were detected on 3D-ABUS (87.6% (n = 227/259) masses and 12.4% (n = 32/259) architectural distortions). The recall rate was 5.2% (n = 188/3616) (CI 4.5–6.0%) with only 36.7% (n = 69/188) cases recalled to another date. Moreover, recall declined over time. There were 3.4% (n = 123/3616) biopsies performed, with 52.8% (n = 65/123) biopsies due to an abnormality detected in 3D-ABUS alone. Ten of 65 lesions were malignant, resulting in a positive predictive value (PPV) of 15.4% (n = 10/65) (CI 7.6–26.5%)). The cancer detection yield of 3D-ABUS is 2.77 per 1000 screening tests (CI 1.30–5.1).ConclusionThe cancer detection yield of 3D-ABUS in a real clinical screening setting is comparable to the results reported in previous prospective studies, with lower recall and biopsy rates. 3D-ABUS also may be an alternative for screening when mammography is not possible or declined.Clinical relevance statement3D automated breast ultrasound screening performance in a clinical setting is comparable to previous prospective studies, with better recall and biopsy rates.Key Points• 3D automated breast ultrasound is a reliable and reproducible tool that provides a three-dimensional representation of the breast and allows image visualisation in axial, coronal and sagittal.• The diagnostic performance of 3D automated breast ultrasound in a real clinical setting is comparable to its performance in previously published prospective studies, with improved recall and biopsy rates.• 3D automated breast ultrasound is a useful adjunct to mammography in dense breasts and may be an alternative for screening when mammography is not possible or declined.

Deep Learning for Differentiation of Breast Masses Detected by Screening Ultrasound Elastography.

Recently, deep learning using convolutional neural networks (CNNs) has yielded consistent results in image-pattern recognition. This study was aimed at investigating the effectiveness of deep learning using CNNs to differentiate benign and malignant breast masses identified by elastography on ultrasound screening. A data set of the elastography images of 245 breast masses (146 benign, 99 malignant) in 239 consecutive patients was retrospectively obtained. The data set was randomly split into training (55%), validation (25%) and test (20%) cohorts. A deep learning model predicting the probability of malignancy was constructed using GoogLeNet architectures (pre-trained by ImageNet) with 50 epochs. The model was then applied to the test data, and the results were compared with those obtained by evaluating the fat-to-lesion ratio (FLR) and by a 5-point visual color assessment (elasticity score). The receiver operating characteristic (ROC) curve was calculated to evaluate the performance of the model. The DeLong test was used to compare the areas under the ROC curve (AUCs). The CNN, FLR and elasticity score had a sensitivity of 0.800, 0.800 and 0.350; specificity of 0.966, 0.586 and 0.931; accuracy of 0.898, 0.673 and 0.694; positive predictive value of 0.941, 0.571 and 0.778; negative predictive value of 0.875, 0.810 and 0.675; and AUC of 0.895, 0.693 and 0.641, respectively. The AUC of the CNN was significantly higher than that of the FLR or elasticity score (p < 0.001). A CNN-based deep learning model for predicting benign or malignant breast masses revealed better diagnostic performance than did FLR or elasticity score-based estimations on ultrasound elastography. The CNN-based model also increased the positive predictive value from 57%-78% to 94%. Therefore, this model may reduce unnecessary biopsy recommendations for masses detected on breast ultrasound screening.

High rate of postoperative upstaging of ductal carcinoma in situ when prioritizing ultrasound evaluation of mammography-detected lesions: a single-center retrospective cohort study

BackgroundThe initial diagnosis of ductal carcinoma in situ (DCIS) can be upstaged to invasive cancer after definitive surgery. This study aimed to identify risk factors for DCIS upstaging using routine breast ultrasonography and mammography (MG) and to propose a prediction model.MethodsIn this single-center retrospective study, patients initially diagnosed with DCIS (January 2016–December 2017) were enrolled (final sample size = 272 lesions). Diagnostic modalities included ultrasound-guided core needle biopsy (US-CNB), MG-guided vacuum-assisted breast biopsy, and wire-localized surgical biopsy. Breast ultrasonography was routinely performed for all patients. US-CNB was prioritized for lesions visible on ultrasound. Lesions initially diagnosed as DCIS on biopsy with a final diagnosis of invasive cancer at definitive surgery were defined as “upstaged.”ResultsThe postoperative upstaging rates were 70.5%, 9.7%, and 4.8% in the US-CNB, MG-guided vacuum-assisted breast biopsy, and wire-localized surgical biopsy groups, respectively. US-CNB, ultrasonographic lesion size, and high-grade DCIS were independent predictive factors for postoperative upstaging, which were used to construct a logistic regression model. Receiver operating characteristic analysis showed good internal validation (area under the curve = 0.88).ConclusionsSupplemental screening breast ultrasonography possibly contributes to lesion stratification. The low upstaging rate for ultrasound-invisible DCIS diagnosed by MG-guided procedures suggests that it is unnecessary to perform sentinel lymph node biopsy for lesions invisible on ultrasound. Case-by-case evaluation of DCIS detected by US-CNB can help surgeons determine if repeating biopsy with vacuum-assisted breast biopsy is necessary or if sentinel lymph node biopsy should accompany breast-preserving surgery.Trial registrationThis single-center retrospective cohort study was conducted with the approval of the institutional review board of our hospital (approval number 201610005RIND). As this was a retrospective review of clinical data, it was not registered prospectively.

AI-assisted Method for Efficiently Generating Breast Ultrasound Screening Reports.

Ultrasound is one of the preferred choices for early screening of dense breast cancer. Clinically, doctors have to manually write the screening report, which is time-consuming and laborious, and it is easy to miss and miswrite. We proposed a new pipeline to automatically generate AI breast ultrasound screening reports based on ultrasound images, aiming to assist doctors in improving the efficiency of clinical screening and reducing repetitive report writing. AI efficiently generated personalized breast ultrasound screening preliminary reports, especially for benign and normal cases, which account for the majority. Doctors then make simple adjustments or corrections based on the preliminary AI report to generate the final report quickly. The approach has been trained and tested using a database of 4809 breast tumor instances. Experimental results indicate that this pipeline improves doctors' work efficiency by up to 90%, greatly reducing repetitive work. Personalized report generation is more widely recognized by doctors in clinical practice than non-intelligent reports based on fixed templates or options to fill in the blanks.

5G-Based Telerobotic Ultrasound System Improves Access to Breast Examination in Rural and Remote Areas: A Prospective and Two-Scenario Study.

Ultrasound (US) plays an important role in the diagnosis and management of breast diseases; however, effective breast US screening is lacking in rural and remote areas. To alleviate this issue, we prospectively evaluated the clinical availability of 5G-based telerobotic US technology for breast examinations in rural and remote areas. Between September 2020 and March 2021, 63 patients underwent conventional and telerobotic US examinations in a rural island (Scenario A), while 20 patients underwent telerobotic US examination in a mobile car located in a remote county (Scenario B) in May 2021. The safety, duration, US image quality, consistency, and acceptability of the 5G-based telerobotic US were assessed. In Scenario A, the average duration of the telerobotic US procedure was longer than that of conventional US (10.3 ± 3.3 min vs. 7.6 ± 3.0 min, p = 0.017), but their average imaging scores were similar (4.86 vs. 4.90, p = 0.159). Two cases of gynecomastia, one of lactation mastitis, and one of postoperative breast effusion were diagnosed and 32 nodules were detected using the two US methods. There was good interobserver agreement between the US features and BI-RADS categories of the identical nodules (ICC = 0.795-1.000). In Scenario B, breast nodules were detected in 65% of the patients using telerobotic US. Its average duration was 10.1 ± 2.3 min, and the average imaging score was 4.85. Overall, 90.4% of the patients were willing to choose telerobotic US in the future, and tele-sonologists were satisfied with 85.5% of the examinations. The 5G-based telerobotic US system is feasible for providing effective breast examinations in rural and remote areas.

Current status of mammography and breast ultrasound combined screening at our facility

現在の対策型乳がん検診の基本はマンモグラフィである。しかし，マンモグラフィは若年者を中心とした高濃度乳房において病変の感度が不十分であると報告されており，さらには高濃度乳房以外でもマンモグラフィだけでは指摘できない乳がんも存在する。「乳がん検診における超音波検査の有効性を検証するための比較試験（J―START）」がLANCET にも掲載されているが，死亡率低減効果が証明されていないため，現時点では対策型としての超音波検診は推奨されていない。 栃木県は，早くから超音波検診を導入していたこともあり，現在はマンモグラフィ超音波併用検診が基本となっている。現在に至るまでの乳がん検診の検査方法の変遷，超音波検査の精度管理，マンモグラフィと超音波検査の関係性，最近導入した分離併用総合判定の成績などを報告する。 マンモグラフィと超音波検査の併用検診は，それぞれの精度管理をしっかり行うことでがん発見率を保ちながら要精検率を絞り込むことは可能である。同時併用総合判定であれば，ほぼ外来診療と同じような感覚で判断できるが，分離併用総合判定の場合は，より慎重に判定することが望まれる。また，総合判定になっても，それぞれの検査方法の精度管理を適切に行うことは必須である。

Feasibility of using AI to auto-catch responsible frames in ultrasound screening for breast cancer diagnosis

The research of AI-assisted breast diagnosis has primarily been based on static images. It is unclear whether it represents the best diagnosis image.To explore the method of capturing complementary responsible frames from breast ultrasound screening by using artificial intelligence. We used feature entropy breast network (FEBrNet) to select responsible frames from breast ultrasound screenings andcompared the diagnostic performance of AI models based on FEBrNet-recommended frames, physician-selected frames, 5-frame interval-selected frames,all frames of video, as well as that of ultrasound and mammography specialists. The AUROC of AI model based on FEBrNet-recommended frames outperformed other frame set based AI models, as well as ultrasound and mammography physicians, indicating that FEBrNet can reach level of medical specialists in frame selection.FEBrNet model can extract video responsible frames for breast nodule diagnosis, whose performance is equivalent to the doctors selected responsible frames.

Ultrasonic breast tumor extraction based on adversarial mechanism and active contour

Background and objectiveBreast cancer is a high incidence of gynecological diseases; breast ultrasound screening can effectively reduce the mortality rate of breast cancer. In breast ultrasound images, the localization and segmentation of tumor lesions are important steps for the extraction of lesions, which helps clinicians evaluate breast lesions quantitatively and makes better clinical diagnosis of the disease. However, the segmentation of breast lesions is difficult due to the blurred and uneven edges of some lesions. In this paper, we propose a segmentation framework combining active contour module and deep learning adversarial mechanism and apply it for the segmentation of breast tumor lesions. MethodWe use a conditional adversarial network as the main framework. The generator is a segmentation network consisting of a Deformed U-Net and an active contour module. Here, the Deformed U-Net performs pixel-level segmentation for breast ultrasound images. The active contour module refines the tumor lesion edges, and the refined result provides loss information for Deformed U-Net. Therefore, the Deformed U-Net can better classify the edge pixels. The discriminator is the Markov discriminator; this discriminator provides loss feedback for the segmentation network. We cross-train the discriminator and segmentation network to implement Adversarial Mechanism for getting a more optimized segmentation network. ResultsThe segmentation performance of the segmentation network for breast ultrasound images is improved by adding a Markov discriminator to provide discriminant loss training. The proposed method for segmenting the tumor lesions in breast ultrasound image obtains dice coefficient: 89.7%, accuracy: 98.1%, precision: 86.3%, mean-intersection-over-union: 82.2%, recall: 94.7%, specificity: 98.5% and F1score: 89.7%. ConclusionComparing with traditional methods, the proposed method gives better performance. The experimental results show that the proposed method can effectively segment the lesions in breast ultrasound images, and then assist doctors to realize the diagnosis of breast lesions.

Multiple Bilateral Circumscribed Masses at Screening Breast Ultrasound: Outcomes of New or Enlarging Masses at Follow-Up.

BACKGROUND. Breast screening ultrasound (US) has limited specificity but is increasingly performed because of widening state and federal legislation regarding breast density. There is a need for evidence-based management guidelines. OBJECTIVE. The purpose of this study was to assess outcomes of new or enlarging oval circumscribed parallel masses in the setting of multiple bilateral circumscribed masses (MBCM) at sequential rounds of US screening. METHODS. In this retrospective study of women found to have MBCM on screening breast US without mammography abnormalities, longitudinal review was performed to identify development of any new or enlarging or changing masses. Outcomes were recorded using biopsy results or minimum of 12 months of follow-up as reference standards. Lesion characteristics, BI-RADS classification, breast density, patient age, demographics, and risk factors were reviewed. Statistical analysis included multivariable logistic regression analysis. RESULTS. There were 284 (2.4%) cases of MBCM in a total of 48,488 bilateral screening US examinations performed in 11,826 asymptomatic women between January 1, 2014, and July 31, 2019, that fit inclusion criteria. Of the 284 women (mean age, 46 years; range, 20-83 years), 150 (52.8%) subsequently developed 465 new, enlarging, and/or changing masses, 107 (23.0%) of which underwent biopsy. Of the 465 masses, 408 (87.7%) were oval circumscribed parallel masses and similar to other MBCM, and 57 (12.3%) were unique findings that were nonoval noncircumscribed masses. None of the new or enlarging oval circumscribed parallel masses were malignant. In total, the malignancy rate was 0% for women with MBCM with follow-up (median, 40.8 months; range, 12-75 months) and 0% for those that underwent biopsy (95% CI, 0-1.2%). Among women with concurrent MBCM and unique findings, four cancers were detected. Three were new irregular masses, and one previously oval mass changed in morphology to have new calcifications and an irregular border. A younger age was related to the likelihood of having enlarging masses (p < .001). CONCLUSION. In the setting of MBCM, new or enlarging oval circumscribed parallel masses are a common and benign event. Concurrent new irregular masses or previously oval masses that develop suspicious morphologic features should be carefully evaluated for malignancy. CLINICAL IMPACT. Breast radiologists who encounter new or enlarging oval circumscribed parallel masses with no suspicious morphologic change in the setting of MBCM can safely defer biopsies.

Do All Intraductal Lesions Identified During Breast Ultrasound Screening Require Further Examination?

Do All Intraductal Lesions Identified During Breast Ultrasound Screening Require Further Examination?

BI-RADS 3 on Screening Breast Ultrasound: What Is It and What Is the Appropriate Management?

US is widely used in breast imaging for diagnostic purposes and is also used increasingly for supplemental screening in women with dense breasts. US frequently depicts masses that are occult on mammography, even after tomosynthesis, and the vast majority of such masses are benign. Many masses seen only on screening US are easily recognized as benign simple cysts. Probably benign, BI-RADS 3, or low suspicion, BI-RADS 4A masses are also common and often prompt short-interval follow-up or biopsy, respectively, yet the vast majority of these are benign. This review details appropriate characterization, classification, and new approaches to the management of probably benign masses seen on screening US that can reduce false positives and, thereby, reduce costs and patient anxiety.

BI-RADS 3 on dense breast screening ultrasound after digital mammography versus digital breast tomosynthesis

ObjectiveCompare the BI-RADS 3 rate and follow-up of dense breast ultrasound (US) screening following digital mammography (DM) versus digital breast tomosynthesis (DBT). MethodsIRB-approved, HIPAA compliant retrospective search was performed of databases at two tertiary breast centers and an office practice for BI-RADS 3 screening US examinations performed 10/1/14–9/30/16. Prior DM versus DBT, downgrade and upgrade rate, and timing and pathology results were recorded. Differences were compared using the two-sample proportions test. Results3183 screening US examinations were performed, 1434/3183 (45.1%) after DM and 1668/3183 (52%) after DBT (2.5% (81/3183) no prior mammogram available). 13.9% (199/1434) had BI-RADS 3 results after DM and 10.6% (177/1668) after DBT (p < 0.01). Median imaging follow-up after DM was 12 months (IQR 6, 24) versus 18 after DBT (IQR 11, 25), p = 0.02. 19.5% (73/375) of patients were lost to follow-up (19.2% (38/198) after DM (68.4% (26/38) no follow-up after initial exam) versus 19.8% (35/177) after DBT (54.3% (19/35) no follow-up after initial exam). 1.3% (5/375) of patients elected biopsy (1.5% (3/198) after DM and 1.1% (2/177) after DBT). 75.2% (282/375) of patients were downgraded (75.3% (149/198) after DM and 75.1% (133/177) after DBT). 2.5% (5/198) were upgraded after DM and 0.6% (1/177) after DBT. Median time to upgrade was 6 months after both DM and DBT. 0.3% (1/375) of patients with BI-RADS 3 results had cancer on follow-up. ConclusionPatients with prior DBT had a lower risk of encountering BI-RADS 3 findings on screening ultrasound. BI-RADS 3 findings on screening ultrasound had an extremely low rate of being cancer.

Added value of deep learning-based computer-aided diagnosis and shear wave elastography to b-mode ultrasound for evaluation of breast masses detected by screening ultrasound.

Low specificity and operator dependency are the main problems of breast ultrasound (US) screening. We investigated the added value of deep learning-based computer-aided diagnosis (S-Detect) and shear wave elastography (SWE) to B-mode US for evaluation of breast masses detected by screening US.Between February 2018 and June 2019, B-mode US, S-Detect, and SWE were prospectively obtained for 156 screening US-detected breast masses in 146 women before undergoing US-guided biopsy. S-Detect was applied for the representative B-mode US image, and quantitative elasticity was measured for SWE. Breast Imaging Reporting and Data System final assessment category was assigned for the datasets of B-mode US alone, B-mode US plus S-Detect, and B-mode US plus SWE by 3 radiologists with varied experience in breast imaging. Area under the receiver operator characteristics curve (AUC), sensitivity, and specificity for the 3 datasets were compared using Delong's method and McNemar test.Of 156 masses, 10 (6%) were malignant and 146 (94%) were benign. Compared to B-mode US alone, the addition of S-Detect increased the specificity from 8%–9% to 31%–71% and the AUC from 0.541–0.545 to 0.658–0.803 in all radiologists (All P < .001). The addition of SWE to B-mode US also increased the specificity from 8%–9% to 41%–75% and the AUC from 0.541–0.545 to 0.709–0.823 in all radiologists (All P < .001). There was no significant loss in sensitivity when either S-Detect or SWE were added to B-mode US.Adding S-Detect or SWE to B-mode US improved the specificity and AUC without loss of sensitivity.

The Added Value of Supplemental Breast Ultrasound Screening for Women With Dense Breasts: A Single Center Canadian Experience.

We evaluated the contribution to cancer detection of supplemental breast ultrasound screening in women with dense breasts based on a single center experience by comparing our results with similar programs elsewhere. We performed a retrospective review of handheld sonographer-performed screening ultrasound exams at our academic breast imaging center, from January 1st to December 31st, 2019. Breast density, breast cancer risk factors, BI-RADS assessment, and lesion pathology were reviewed and tallied, followed by derivation of the biopsy rate, breast cancer detection rate, PPV3 and average tumor size. These values were compared to published results of breast screening programs elsewhere. 695 screening breast ultrasounds for women with dense breasts and negative mammograms were performed in 2019. The biopsy rate was 1.3%, breast cancer detection rate was 7 in 1000, PPV3 was 42%, and the average tumor size was 9.0 ± 1.4 mm. The first-year data of the breast screening ultrasound program at our practice are promising, demonstrating comparable cancer detection rate, higher PPV3, and similar biopsy rate in those with dense breasts compared with similar programs elsewhere. Longitudinal analysis and larger sample size are required for validation. Comparison of incidence and prevalence screening data is also warranted to elucidate the true value of this program.

Feasibility of ABUS as an Alternative to Handheld Ultrasound for Response Control in Neoadjuvant Breast Cancer Treatment

IntroductionThe Invenia Automated Breast Ultrasound Screening (ABUS) is indicated as an adjunct to mammography for breast cancer screening in asymptomatic women with high-density breast tissue. ABUS provides time-efficient evaluation of the 3-dimensional recordings within 3 to 6 minutes. The role and advantages of ABUS in everyday clinical practice, especially in routine examination during neoadjuvant chemotherapy (NACT), is not clear. The aim of this monocentric, noninterventional retrospective study is to evaluate the use of ABUS in patients who are under NACT treatment for response control. MethodsRegular sonographic response check with handheld ultrasound (HHUS) examination and with ABUS were conducted in 83 women who underwent NACT. The response controls were conducted every 3 to 6 weeks during NACT. The handheld sonography was performed with GE Voluson S8. Handheld sonographic measurements and ABUS measurements were compared with the final pathologic tumor size. ResultsThere was no statistical difference between the measurements with HHUS examination or ABUS compared with final pathologic tumor size (P = .47). The average difference from ABUS measured tumor size to final pathologic tumor size was 9.8 mm. The average difference from handheld measured tumor size to final pathologic tumor size was 9/3 mm. Both the specificity of ABUS and HHUS examination in predicting pathologic complete remission was 100%. ConclusionABUS seems to be a suitable method to conduct response control in neoadjuvant breast cancer treatment. ABUS may facilitate preoperative planning and offers remarkable time saving for physicians compared with HHUS examination and thus should be considered for clinical practice

Comparison of Cancers Detected by Screening Breast Ultrasound and Digital Breast Tomosynthesis

Review of outcomes of screening patients imaged with both digital breast tomosynthesis (DBT) and screening ultrasound (US) to compare the cancer detection rates and characteristics of cancers detected by the imaging modalities. This retrospective study reviewed a total of 24,787 screening US exams performed in the time period of January 2013 through December 2017. These exams were in patients with heterogeneously dense or extremely dense breast tissue. In this population, 21,220 (86%) had DBT screening mammography. These cases were further reviewed to identify any pathology-proven malignancy detected with US and/or DBT. The study cohort consisted of 115 breast cancers in patients having screening US and DBT. Of the 115 cancers, 100 were invasive cancers and 15 were ductal carcinoma in situ: 64/115 were seen on DBT, 9 of which were seen only on DBT, and 106 were seen on US, with 51 seen only on US. The cancer detection rate of DBT only was 0.4/1000 (9/21,220) and 3.0/1000 (64/21,220) for those detected on DBT whether with or without additional US, with detection on US only having an incremental cancer detection rate of 2.4/1000 (51/21,220) above DBT detected malignancies. Differences in DBT-detected lesions and US only lesions when comparing median lesion size, lesion type, tumor type (invasive vs noninvasive) and tumor stage were statistically significant (p = 0.0045, p = 0.0113, p = 0.0003, and p = 0.0153, respectively). In review of the outcomes of a screening US program, we found a similar number of breast cancers were detected by DBT and US, and US alone (47.8% vs 44.3%, respectively). Ninety-six percent of the cancers detected by US alone were invasive; 89% of those seen on both modalities were invasive, while most of the breast cancers seen on DBT only were in situ carcinoma. Statistically significant differences between DBT and US, and US alone were found for many lesion characteristics including lesion size, type, tumor size, and tumor stage.

Screening Breast Ultrasound: Update After 10 Years of Breast Density Notification Laws.

OBJECTIVE. Breast density notification laws have grown from the first state legislation in Connecticut in 2009 to a federally mandated update to the Mammography Quality Standards Act in 2019. CONCLUSION. The increasing recognition of limited mammographic sensitivity in women with dense breasts has led to greater utilization of supplemental screening ultrasound. Robust data support improved detection of small node-negative invasive breast cancers with adjunctive ultrasound. Digital breast tomosynthesis and other emerging modalities may also play a role in screening guidelines.