Using a combination of superb microvascular imaging and other auxiliary ultrasound techniques to increase the accuracy of gray-scale ultrasound for breast masses.

BACKGROUND: Breast ultrasound is highly sensitive, but its specificity is not as high for detecting malignant lesions. Auxiliary modalities like elastography, Color and Power Doppler ultrasound are used as adjuncts to yield both a high sensitivity and specificity. Superb microvascular imaging (SMI) is a newer modality with more accuracy for detecting breast lesions. In this study, our goal was to investigate the role of SMI as an adjunct to ultrasound and find a suitable combination model for the evaluation of breast masses. METHODS: In this cross-sectional study, 132 women with 172 breast masses who underwent ultrasound-guided biopsy were included.. The ultrasound features of the lesion, the strain ratio in strain elastography, the number of vessels for each lesion, their morphology and distribution in Doppler and Power Doppler ultrasound and SMI were recorded for each lesion. A vascular score and a vascular ratio were defined. RESULTS: In the histologic examination, 31 lesions (18%) were malignant and 141 lesions (82%) were benign. The vascular score was more accurate than the vascular ratio in all three modalities. The predictive ability of strain ratio was higher than Doppler and Power Doppler ultrasound and SMI. Adding SMI alone to ultrasound increased the specificity from 46.10% to 61.2% and the accuracy from 55.80% to 70.11%. In the combination of ultrasound with other modalities, the best was the combination of ultrasound, strain elastography, and SMI; which yielded a specificity and sensitivity of 100% and 74.4%, respectively. CONCLUSION: Adding SMI and STE modalities as adjuncts to ultrasound lowers the chance of missing malignant lesions and reduces unnecessary biopsies of breast lesions. A study with a larger sample size using this combination model to evaluate the accuracy with greater precision is recommended.

Fat-signal suppression in breast diffusion-weighted imaging: the Good, the Bad, and the Ugly.

OBJECTIVES: Fat-signal suppression is essential for breast diffusion magnetic resonance imaging (or diffusion-weighted MRI, DWI) as the very low diffusion coefficient of fat tends to decrease absolute diffusion coefficient (ADC) values. Among several methods, the STIR (short-tau inversion recovery) method is a popular approach, but signal suppression/attenuation is not specific to fat contrary to other methods such as SPAIR (spectral adiabatic (or attenuated) inversion recovery). This article focuses on those two techniques to illustrate the importance of appropriate fat suppression in breast DWI, briefly presenting the pros and cons of both approaches. METHODS AND RESULTS: We show here through simulation and data acquired in a dedicated breast DWI phantom made of vials with water and various concentrations of polyvinylpyrrolidone (PVP) how ADC values obtained with STIR DWI may be biased toward tissue components with the longest T1 values: ADC values obtained with STIR fat suppression may be over/underestimated depending on the T1 and ADC profile within tissues. This bias is also illustrated in two clinical examples. CONCLUSION: Fat-specific methods should be preferred over STIR for fat-signal suppression in breast DWI, such as SPAIR which also provides a higher sensitivity than STIR for lesion detection. One should remain aware, however, that efficient fat-signal suppression with SPAIR requires good B0 shimming to avoid ADC underestimation from residual fat contamination. CLINICAL RELEVANCE STATEMENT: The spectral adiabatic (or attenuated) inversion recovery (SPAIR) method should be preferred over short-tau inversion recovery (STIR) for fat suppression in breast DWI. KEY POINTS: Fat-signal suppression is essential for breast DWI; the SPAIR method is recommended. Short-tau inversion recovery (STIR) is not specific to fat; as a result, SNR is decreased and ADC values may be over- or underestimated. The STIR fat-suppression method must not be used after the injection of gadolinium-based contrast agents.

Detection and diagnosis of automated breast ultrasound in patients with BI-RADS category 4 microcalcifications: a retrospective study.

BACKGROUND: Automated Breast Ultrasound (AB US) has shown good application value and prospects in breast disease screening and diagnosis. The aim of the study was to explore the ability of AB US to detect and diagnose mammographically Breast Imaging Reporting and Data System (BI-RADS) category 4 microcalcifications. METHODS: 575 pathologically confirmed mammographically BI-RADS category 4 microcalcifications from January 2017 to June 2021 were included. All patients also completed AB US examinations. Based on the final pathological results, analyzed and summarized the AB US image features, and compared the evaluation results with mammography, to explore the detection and diagnostic ability of AB US for these suspicious microcalcifications. RESULTS: 250 were finally confirmed as malignant and 325 were benign. Mammographic findings including microcalcifications morphology (61/80 with amorphous, coarse heterogeneous and fine pleomorphic, 13/14 with fine-linear or branching), calcification distribution (189/346 with grouped, 40/67 with linear and segmental), associated features (70/96 with asymmetric shadow), higher BI-RADS category with 4B (88/120) and 4 C (73/38) showed higher incidence in malignant lesions, and were the independent factors associated with malignant microcalcifications. 477 (477/575, 83.0%) microcalcifications were detected by AB US, including 223 malignant and 254 benign, with a significantly higher detection rate for malignant lesions (x2 = 12.20, P < 0.001). Logistic regression analysis showed microcalcifications with architectural distortion (odds ratio [OR] = 0.30, P = 0.014), with amorphous, coarse heterogeneous and fine pleomorphic morphology (OR = 3.15, P = 0.037), grouped (OR = 1.90, P = 0.017), liner and segmental distribution (OR = 8.93, P = 0.004) were the independent factors which could affect the detectability of AB US for microcalcifications. In AB US, malignant calcification was more frequent in a mass (104/154) or intraductal (20/32), and with ductal changes (30/41) or architectural distortion (58/68), especially with the both (12/12). BI-RADS category results also showed that AB US had higher sensitivity to malignant calcification than mammography (64.8% vs. 46.8%). CONCLUSIONS: AB US has good detectability for mammographically BI-RADS category 4 microcalcifications, especially for malignant lesions. Malignant calcification is more common in a mass and intraductal in AB US, and tend to associated with architectural distortion or duct changes. Also, AB US has higher sensitivity than mammography to malignant microcalcification, which is expected to become an effective supplementary examination method for breast microcalcifications, especially in dense breasts.

An unusual artifact observed on screening mammography in a patient with an LVAD.

PURPOSE: Screening mammography and digital breast tomosynthesis consist of high-resolution x-ray images to identify findings that are potentially indicative of breast cancer, enabling early detection and reduction of breast cancer mortality. Imaging artifacts can occasionally occur, sometimes due to patient-related medical devices. Because of continuous evolution of new technologies, there is potential for novel artifacts to be encountered. In this technical note, we report an unusual artifact in the screening mammogram of a patient with an Abbott HeartMate 3 left ventricular assist device (LVAD). METHODS: A 72-year-old patient with a HeartMate 3 LVAD presented to our breast imaging facility for a standard screening exam with digital breast tomosynthesis (Selenia Dimensions, Hologic Inc., Bedford, MA) and synthetic 2D images (C-view, Hologic Inc., Bedford, MA). RESULTS: Linear artifacts oriented in the anteroposterior dimension demonstrating a spatial periodicity of ∼1.4 mm were seen on all left breast images, whereas concurrent right breast images did not demonstrate any artifacts. Repeat attempts using two identical digital breast tomosynthesis units demonstrated the same artifacts. No other exam at our imaging center that day demonstrated any such artifacts. Mammogram exams performed on this patient prior to her LVAD placement did not exhibit any similar artifacts. CONCLUSION: Findings support the patient's LVAD as the underlying source of linear artifacts observed on left breast images, particularly given the proximity of the LVAD to the left breast. With the number of patients receiving LVAD placement on the rise, as well as increasing median survival rates status post LVAD implantation, recognition of this LVAD related artifact on mammography may be important.

Review of Microwave Near-Field Sensing and Imaging Devices in Medical Applications.

Microwaves can safely and non-destructively illuminate and penetrate dielectric materials, making them an attractive solution for various medical tasks, including detection, diagnosis, classification, and monitoring. Their inherent electromagnetic properties, portability, cost-effectiveness, and the growth in computing capabilities have encouraged the development of numerous microwave sensing and imaging systems in the medical field, with the potential to complement or even replace current gold-standard methods. This review aims to provide a comprehensive update on the latest advances in medical applications of microwaves, particularly focusing on the near-field ones working within the 1-15 GHz frequency range. It specifically examines significant strides in the development of clinical devices for brain stroke diagnosis and classification, breast cancer screening, and continuous blood glucose monitoring. The technical implementation and algorithmic aspects of prototypes and devices are discussed in detail, including the transceiver systems, radiating elements (such as antennas and sensors), and the imaging algorithms. Additionally, it provides an overview of other promising cutting-edge microwave medical applications, such as knee injuries and colon polyps detection, torso scanning and image-based monitoring of thermal therapy intervention. Finally, the review discusses the challenges of achieving clinical engagement with microwave-based technologies and explores future perspectives.

Practice of Mammography in a Private Nigerian Cancer Foundation: Our Experience.

BACKGROUND: Despite the proven effectiveness of mammography in screening and early breast cancer detection, there is still a huge disparity in both access to breast care and the quality of services provided in Nigeria. Non-governmental organizations (NGOs) have attempted to bridge this gap through awareness campaigns and subsidized breast imaging services. OBJECTIVES: To document the mammographic findings of adult females in a private NGO and assess the benefits of mammography practice in our locality. MATERIAL AND METHODS: This was a retrospective evaluation of mammographic examinations carried out over a two-year period (January 2020- December 2021) in a private cancer foundation in Abuja, Nor t h Ce nt r al Nigeria. Demographic details, clinical and mammographic features were analyzed with a statistical level of significance set at p≤0.05. RESULT: The age range of 565 women evaluated in this study was 31-84 years with the majority (55.7%) of them in the 40-49 year range. More than half (52.7%) of the women had had at least one previous mammogram. Screening was the predominant indication for mammograms in 361 women (63.9%) while 204(36.1%) were symptomatic. Breast pain (59.6%) and breast lump (26.3%) were the most common clinical indications. The predominant breast density pattern was the American College of Radiologists Breast Imaging and Reporting Data System (ACR BIRADS) type B (Scattered fibroglandular densities) in 241 women (42.7%). Mammogram was normal in 206 women (34.7%) while 52 (8.8%) had intraparenchymal findings. The final assessment showed that most of the mammograms were BIRADS category 1(69.6%) and 2(13.8%) signifying normal and benign findings. Body mass index, parity, age at first pregnancy, menopausal status, and breast density had significant relationships with the final BIRADS category. CONCLUSION: Mammography is an invaluable part of breast care in our locality. Evaluation of mammographic services in our private NGO showed a predominance of screening mammography while a majority of the women with symptomatic breast diseases had normal and benign findings.

CONTEXTE: Malgré l'efficacité avérée de la mammographie dans le dépistage et la détection précoce du cancer du sein, il existe encore une énorme disparité tant dans l'accès aux soins du sein que dans la qualité des services fournis au Nigeria. Les organisations non gouvernementales (ONG) ont tenté de combler cette lacune grâce à des campagnes de sensibilisation et à des services d'imagerie mammaire subventionnés. OBJECTIFS: Documenter les résultats mammographiques des femmes adultes dans une ONG privée et évaluer les avantages de la pratique de la mammographie dans notre localité. MATÉRIEL ET MÉTHODES: Il s'agissait d'une évaluation rétrospective des examens mammographiques réalisés sur une période de deux ans (janvier 2020 - décembre 2021) dans une fondation de lutte contre le cancer privée à Abuja, au Nigeria. Les détails démographiques, les caractéristiques cliniques et mammographiques ont été analysés avec un niveau de signification statistique fixé à p ≤ 0,05. RÉSULTAT: La tranche d'âge des 565 femmes évaluées dans cette étude était de 31 à 84 ans, la majorité (55,7 %) d'entre elles se situant dans la tranche d'âge de 40 à 49 ans. Plus de la moitié (52,7 %) des femmes avaient déjà subi au moins une mammographie précédente. Le dépistage était l'indication prédominante pour les mammographies chez 361 femmes (63,9 %), tandis que 204 (36,1 %) étaient symptomatiques. Les douleurs mammaires (59,6 %) et les masses mammaires (26,3 %) étaient les indications cliniques les plus courantes. Le motif de densité mammaire prédominant était de type B du système de notation et de rapport d'imagerie mammaire du Collège Américain des Radiologues (ACR BIRADS) chez 241 femmes (42,7 %). La mammographie était normale chez 206 femmes ( 34, 7 %) , t andi s que 52 ( 8, 8 %) présent ai ent des anomal i es intraparenchymateuses. L'évaluation finale a montré que la plupart des mammographies étaient classées BIRADS catégorie 1 (69,6 %) et 2 (13,8 %), ce qui signifie des résultats normaux et bénins. L'indice de masse corporelle, la parité, l'âge à la première grossesse, le statut ménopausique et la densité mammaire avaient des relations significatives avec la catégorie BIRADS finale. CONCLUSION: La mammographie est un élément inestimable des soins du sein dans notre localité. L'évaluation des services mammographiques dans notre ONG privée a montré une prédominance de la mammographie de dépistage, tandis que la majorité des femmes atteintes de maladies mammaires symptomatiques présentaient des résultats normaux et bénins. MOTS-CLÉS: Mammographie, Femmes, Nigeria, Soins du sein, Imagerie mammaire, Organisation non gouvernementale.

Surviving the COVID-19 pandemic: navigating the recovery of breast imaging services in a safety-net hospital.

PURPOSE: The purpose of this study was to determine the impact of COVID-19 on county safety-net breast imaging services and describe the steps taken to actively manage and mitigate delays. METHODS: This was an IRB exempt retrospective review of our county safety-net breast imaging practice analyzed for 4 distinct time periods: (1) "Shut-down period": March 17, 2020 to May 17, 2020; (2) "Phased re-opening": May 18, 2020 to June 30, 2020; (3) "Ramp-up": July 1, 2020 to September 30, 2020; and (4) "Current state": October 1, 2020 to September 30, 2021. These time periods were compared to identical time periods 1 year prior. For "Current state," given that the 1-year prior comparison encompassed the first 3 periods of the pandemic, the identical time period 2 years prior was also compared. RESULTS: Our safety-net practice sustained significant volume losses during the first 3 time periods with a 99% reduction in screening mammography in the shut-down period. Cancers diagnosed decreased by 17% in 2020 (n = 229) compared to 2019 (n = 276). By implementing multiple initiatives that targeted improved access to care, including building community-hospital partnerships and engagement through outreach events and a community education roadshow, we were able to recover and significantly exceed our pandemic screening volumes by 48.1% (27,279 vs 18,419) from October 1, 2020 to September 30, 2021 compared to the identical time period 1 year prior, and exceed our pre-pandemic screening volume by 17.4% (27,279 vs 23,234) compared to the identical time period 2 years prior. CONCLUSION: Through specific community outreach programs and optimized navigation, our safety-net breast imaging practice was able to mitigate the impact of COVID-19 on our patient population by increasing patient engagement and breast imaging services.

Automated Breast Density Assessment in MRI Using Deep Learning and Radiomics: Strategies for Reducing Inter-Observer Variability.

BACKGROUND: Accurate breast density evaluation allows for more precise risk estimation but suffers from high inter-observer variability. PURPOSE: To evaluate the feasibility of reducing inter-observer variability of breast density assessment through artificial intelligence (AI) assisted interpretation. STUDY TYPE: Retrospective. POPULATION: Six hundred and twenty-one patients without breast prosthesis or reconstructions were randomly divided into training (N = 377), validation (N = 98), and independent test (N = 146) datasets. FIELD STRENGTH/SEQUENCE: 1.5 T and 3.0 T; T1-weighted spectral attenuated inversion recovery. ASSESSMENT: Five radiologists independently assessed each scan in the independent test set to establish the inter-observer variability baseline and to reach a reference standard. Deep learning and three radiomics models were developed for three classification tasks: (i) four Breast Imaging-Reporting and Data System (BI-RADS) breast composition categories (A-D), (ii) dense (categories C, D) vs. non-dense (categories A, B), and (iii) extremely dense (category D) vs. moderately dense (categories A-C). The models were tested against the reference standard on the independent test set. AI-assisted interpretation was performed by majority voting between the models and each radiologist's assessment. STATISTICAL TESTS: Inter-observer variability was assessed using linear-weighted kappa (κ) statistics. Kappa statistics, accuracy, and area under the receiver operating characteristic curve (AUC) were used to assess models against reference standard. RESULTS: In the independent test set, five readers showed an overall substantial agreement on tasks (i) and (ii), but moderate agreement for task (iii). The best-performing model showed substantial agreement with reference standard for tasks (i) and (ii), but moderate agreement for task (iii). With the assistance of the AI models, almost perfect inter-observer variability was obtained for tasks (i) (mean κ = 0.86), (ii) (mean κ = 0.94), and (iii) (mean κ = 0.94). DATA CONCLUSION: Deep learning and radiomics models have the potential to help reduce inter-observer variability of breast density assessment. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 1.

Deep Learning k-Space-to-Image Reconstruction Facilitates High Spatial Resolution and Scan Time Reduction in Diffusion-Weighted Imaging Breast MRI.

BACKGROUND: For time-consuming diffusion-weighted imaging (DWI) of the breast, deep learning-based imaging acceleration appears particularly promising. PURPOSE: To investigate a combined k-space-to-image reconstruction approach for scan time reduction and improved spatial resolution in breast DWI. STUDY TYPE: Retrospective. POPULATION: 133 women (age 49.7 ± 12.1 years) underwent multiparametric breast MRI. FIELD STRENGTH/SEQUENCE: 3.0T/T2 turbo spin echo, T1 3D gradient echo, DWI (800 and 1600 sec/mm2 ). ASSESSMENT: DWI data were retrospectively processed using deep learning-based k-space-to-image reconstruction (DL-DWI) and an additional super-resolution algorithm (SRDL-DWI). In addition to signal-to-noise ratio and apparent diffusion coefficient (ADC) comparisons among standard, DL- and SRDL-DWI, a range of quantitative similarity (e.g., structural similarity index [SSIM]) and error metrics (e.g., normalized root mean square error [NRMSE], symmetric mean absolute percent error [SMAPE], log accuracy error [LOGAC]) was calculated to analyze structural variations. Subjective image evaluation was performed independently by three radiologists on a seven-point rating scale. STATISTICAL TESTS: Friedman's rank-based analysis of variance with Bonferroni-corrected pairwise post-hoc tests. P < 0.05 was considered significant. RESULTS: Both DL- and SRDL-DWI allowed for a 39% reduction in simulated scan time over standard DWI (5 vs. 3 minutes). The highest image quality ratings were assigned to SRDL-DWI with good interreader agreement (ICC 0.834; 95% confidence interval 0.818-0.848). Irrespective of b-value, both standard and DL-DWI produced superior SNR compared to SRDL-DWI. ADC values were slightly higher in SRDL-DWI (+0.5%) and DL-DWI (+3.4%) than in standard DWI. Structural similarity was excellent between DL-/SRDL-DWI and standard DWI for either b value (SSIM ≥ 0.86). Calculation of error metrics (NRMSE ≤ 0.05, SMAPE ≤ 0.02, and LOGAC ≤ 0.04) supported the assumption of low voxel-wise error. DATA CONCLUSION: Deep learning-based k-space-to-image reconstruction reduces simulated scan time of breast DWI by 39% without influencing structural similarity. Additionally, super-resolution interpolation allows for substantial improvement of subjective image quality. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 1.

New Approaches and Recommendations for Risk-Adapted Breast Cancer Screening.

Population-based breast cancer screening using mammography as the gold standard imaging modality has been in clinical practice for over 40 years. However, the limitations of mammography in terms of sensitivity and high false-positive rates, particularly in high-risk women, challenge the indiscriminate nature of population-based screening. Additionally, in light of expanding research on new breast cancer risk factors, there is a growing consensus that breast cancer screening should move toward a risk-adapted approach. Recent advancements in breast imaging technology, including contrast material-enhanced mammography (CEM), ultrasound (US) (automated-breast US, Doppler, elastography US), and especially magnetic resonance imaging (MRI) (abbreviated, ultrafast, and contrast-agent free), may provide new opportunities for risk-adapted personalized screening strategies. Moreover, the integration of artificial intelligence and radiomics techniques has the potential to enhance the performance of risk-adapted screening. This review article summarizes the current evidence and challenges in breast cancer screening and highlights potential future perspectives for various imaging techniques in a risk-adapted breast cancer screening approach. EVIDENCE LEVEL: 1. TECHNICAL EFFICACY: Stage 5.

Prophylactic mastectomy and occult malignancy: Surgical and imaging considerations.

BACKGROUND: Sentinel node biopsy (SLNB) is not routinely recommended for patients undergoing prophylactic mastectomy (PM), yet omission remains a subject of debate among surgeons. A modern patient cohort was examined to determine occult malignancy (OM) incidence within PM specimens to reinforce current recommendations. METHODS: All PM performed over a 5-year period were retrospectively identified, including women with unilateral breast cancer who underwent synchronous or delayed contralateral PM or women with elevated cancer risk who underwent bilateral PM. RESULTS: The study population included 772 patients (598 CPM, 174 BPM) with a total of 39 OM identified: 17 invasive cancers (14 CPM, 3 BPM) and 22 DCIS (19 CPM, 3 BPM). Of the 86 patients for whom SLNB was selectively performed, 1 micrometastasis was identified. In the CPM cohort, risk of OM increased with age, presence of LCIS of either breast, or presence of a non-BRCA high-penetrance gene mutation, while preoperative magnetic resonance imaging was associated with lower likelihood of OM. CONCLUSIONS: Given the low incidence of invasive OM in this updated series, routine SLNB is of low value for patients undergoing PM. For patients with indeterminate radiographic findings, discordant preoperative biopsies, LCIS, or non-BRCA high-penetrance gene mutations, selective SLNB implementation could be considered.

Bleeding Events After Image-Guided Breast Biopsies: Comparison of Patients Temporarily Discontinuing Versus Maintaining Antithrombotic Therapy During Biopsy.

BACKGROUND. Antithrombic (AT) therapy is commonly temporarily discontinued before breast core needle biopsy (CNB), introducing risks of thrombotic events and diagnostic delay. OBJECTIVE. The purpose of this article was to compare the frequency of postbiopsy bleeding events among patients without AT use, patients temporarily discontinuing AT therapy, and patients maintaining AT therapy during breast CNB. METHODS. This retrospective study included 5302 patients (median age, 52 years) who underwent image-guided breast or axillary CNB between January 1, 2014, and December 31, 2019. From January 1, 2014, to December 31, 2016, patients temporarily discontinued all AT therapy for 5 days before CNB; from January 1, 2017, to December 31, 2019, patients maintained AT therapy during CNB. Immediate postbiopsy mammograms were reviewed for imaging-apparent hematoma. Patients were called 24-48 hours after biopsy and asked regarding palpable hematoma and breast bruise. The EMR was reviewed for clinically significant postbiopsy hematoma (i.e., hematoma requiring drainage, primary care or emergency department visit for persistent symptoms, or hospital admission). Bleeding events were compared among groups, including Firth bias-reduced multivariable logistic regression analysis. RESULTS. During CNB, 4665 patients were not receiving AT therapy, 423 temporarily discontinued AT therapy, and 214 maintained AT therapy. Imaging-apparent hematoma occurred in 3% of patients without AT use, 6% of patients discontinuing AT therapy, and 7% of patients maintaining AT therapy (p = .60 [discontinuing vs maintaining]). Palpable hematoma occurred in 2% of patients without AT use, 4% of patients maintaining AT therapy, and 4% of patients discontinuing AT therapy (p = .92 [discontinuing vs maintaining]). Breast bruise occurred in 2% of patients without AT use, 1% of patients discontinuing AT therapy, and 6% of patients maintaining AT therapy (p < .001 [discontinuing vs maintaining]). In multivariable analysis adjusting for age, biopsy imaging modality, needle gauge, number of biopsy samples, and pathologic result, discontinued AT therapy (using maintained AT therapy as reference) was not a significant independent predictor of imaging-apparent hematoma (p = .23) or palpable hematoma (p = .91) but independently predicted decreased risk of bruise (OR = 0.11, p < .001). No patient developed clinically significant postbiopsy hematoma. CONCLUSION. Frequencies of imaging-apparent and palpable hematoma were not significantly different between patients temporarily discontinuing versus maintaining AT therapy. CLINICAL IMPACT. The findings support the safety of continuing AT therapy during CNB. Patients who maintain AT therapy should be counseled regarding risk of bruise.

Contrast-Enhanced In-Phase Dixon Sequence: Impact on Biopsy Clip Detection on Breast MRI.

BACKGROUND. Identification of breast biopsy clips using conventional MRI sequences may be challenging. A contrast-enhanced in-phase Dixon sequence may have greater conspicuity for areas of susceptibility compared with standard clinical sequences. OBJECTIVE. The purpose of this article is to compare detection of breast biopsy clips on MRI between the contrast-enhanced in-phase Dixon sequence and three routine clinical sequences. METHODS. This retrospective study included 164 patients (mean age, 50.3 years) with a total of 281 breast biopsy clips who underwent contrast-enhanced breast MRI between January 2, 2019, and April 16, 2020. Three radiologists, blinded to the clip location and sequence used, independently annotated biopsy clip locations on three clinical sequences (T1-weighted non-fat-suppressed [NFS], STIR, and first phase from dynamic contrast-enhanced T1-weighted fat-suppressed [FS]) and on a contrast-enhanced in-phase Dixon sequence and then recorded confidence scores (1-4 scale). A study coordinator used all available imaging and reports to localize clips on MRI, which served as the reference standard. A physicist measured clip CNR. Sequences were compared using the McNemar test and two-tailed Wilcoxon signed rank tests. RESULTS. Among the three readers, pooled sensitivity and PPV were 78.2% and 96.2% for T1-weighted NFS, 26.6% and 92.7% for STIR, 61.7% and 95.9% for contrast-enhanced T1-weighted FS, and 85.1% and 95.1% for contrast-enhanced in-phase Dixon sequence. Pooled sensitivity was higher for contrast-enhanced in-phase Dixon sequence than for the other sequences (all p < .05); pooled PPV was not significantly different between contrast-enhanced in-phase Dixon and the other sequences (all p > .05). Mean confidence scores (pooled across readers for true-positive assessments) and mean CNR were 3.0 ± 0.9 (SD) and 1.21 ± 0.61 for T1-weighted NFS, 1.7 ± 0.9 and 0.57 ± 0.69 for STIR, 2.5 ± 1.0 and 0.54 ± 0.61 for contrast-enhanced T1-weighted FS, and 3.5 ± 0.8 and 4.05 ± 2.6 for the contrast-enhanced in-phase Dixon sequence. Pooled mean confidence scores and CNR were higher for contrast-enhanced in-phase Dixon than for the other sequences (all p < .001). CONCLUSION. Compared with clinical sequences, the contrast-enhanced in-phase Dixon sequence had higher sensitivity for detecting breast biopsy clips on MRI and higher reader confidence and CNR, without change in PPV. CLINICAL IMPACT. The contrast-enhanced in-phase Dixon sequence may help address a current challenge in clinical breast MRI interpretation.

Axillary Lymphadenopathy After a COVID-19 Vaccine Booster Dose: Time to Resolution on Ultrasound Follow-Up and Associated Factors.

BACKGROUND. Because administration of booster doses of COVID-19 vaccines is ongoing, radiologists are continuing to encounter COVID-19 vaccine-related axillary lymphadenopathy on imaging. OBJECTIVE. The purposes of this study were to assess time to resolution of COVID-19 vaccine-related axillary lymphadenopathy identified on breast ultrasound after administration of a booster dose and to assess factors potentially associated with time to resolution. METHODS. This retrospective single-institution study included 54 patients (mean age, 57 years) with unilateral axillary lymphadenopathy ipsilateral to the site of injection of a booster dose of messenger RNA COVID-19 vaccine visualized on ultrasound (whether an initial breast imaging examination or follow-up to prior screening or diagnostic breast imaging) performed between September 1, 2021, and December 31, 2022, and who underwent follow-up ultrasound examinations until resolution of lymphadenopathy. Patient information was extracted from the EMR. Univariable and multivariable linear regression analyses were used to identify predictors of time to resolution. Time to resolution was compared with that in a previously described sample of 64 patients from the study institution that was used to evaluate time to resolution of axillary lymphadenopathy after the initial vaccination series. RESULTS. Six of the 54 patients had a history of breast cancer, and two had symptoms related to axillary lymphadenopathy (axillary pain in both patients). Among the 54 initial ultrasound examinations showing lymphadenopathy, 33 were screening examinations and 21 were diagnostic examinations. Lymphadenopathy had resolved a mean of 102 ± 56 (SD) days after administration of the booster dose and 84 ± 49 days after the initial ultrasound showing lymphadenopathy. Age, vaccine booster type (Moderna vs Pfizer-BioNTech), and history of breast cancer were not significantly associated with time to resolution in univariable or multivariable analyses (all p > .05). Time to resolution after administration of a booster dose was significantly shorter than time to resolution after administration of the first dose in the initial series (mean, 129 ± 37 days) (p = .01). CONCLUSION. Axillary lymphadenopathy after administration of a COVID-19 vaccine booster dose has a mean time to resolution of 102 days, shorter than the time to resolution after the initial vaccination series. CLINICAL IMPACT. The time to resolution after administration of a booster dose supports the current recommendation for a follow-up interval of at least 12 weeks when vaccine-related lymphadenopathy is suspected.

PPV of the Molecular Breast Imaging Lexicon.

BACKGROUND. Molecular breast imaging (MBI) is used for various breast imaging indications. An MBI lexicon has been developed, although the likelihood of malignancy of the lexicon descriptors has not been assessed to our knowledge. OBJECTIVE. The purpose of this article was to evaluate the PPV for malignancy of the MBI lexicon imaging descriptors. METHODS. This retrospective study included MBI examinations performed from August 1, 2005, through August 31, 2017, that were positive (BI-RADS analogous categories 0, 3, 4, 5, or 6) according to the clinical report and had an available reference standard. Examinations were performed using dual-detector cadmium zinc telluride MBI systems after injection of 99mTc sestamibi. Category 3 lesions had pathologic correlation, at least 2 years of imaging follow-up, or final resolution on follow-up imaging as category 1 or 2; category 4 and 5 lesions had pathologic correlation. MBI examinations were reviewed by one of two radiologists to assess lesions on the basis of the published MBI lexicon for type (mass vs nonmass uptake), distribution (if nonmass uptake), uptake intensity, and number of MBI views on which the lesion was seen. PPV for malignancy was summarized. RESULTS. The analysis included 643 lesions (479 benign, 164 malignant; 83 mass, 560 nonmass uptake) in 509 patients (median age, 56 years). PPV was 73.5% (61/83) for masses and 18.4% (103/560) for nonmass uptake. Among the nonmass uptake lesions, PPV was 36.2% (17/47) for segmental, 20.1% (77/384) for focal, 30.8% (4/13) for diffuse, and 4.3% (5/116) for regional or multiple regional distribution. PPV was 5.3% (5/94) for one view, 15.2% (32/210) for two views, 14.6% (13/89) for three views, and 45.4% (113/249) for four views showing the lesion. PPV was 14.0% (43/307) for mild, 22.4% (51/228) for moderate, and 64.8% (70/108) for marked uptake intensity. CONCLUSION. The MBI lexicon lesion descriptors are associated with likelihood of malignancy. PPV was higher for masses, lesions seen on multiple MBI views, and lesions with marked uptake intensity. Among nonmass uptake lesions, PPV was highest for those with segmental distribution. CLINICAL IMPACT. Insight into the likelihood of malignancy associated with the MBI lexicon descriptors can inform radiologists' interpretations and guide potential future incorporation of the MBI lexicon into the ACR BI-RADS Atlas.

Value of inversion imaging to diagnosis in differentiating malignant from benign breast masses.

BACKGROUND: We aimed to evaluate the added value of inversion imaging in differentiating between benign and malignant breast masses when combined with the Breast Imaging Reporting and Data System (BI-RADS). METHODS: A total of 364 patients with 367 breast masses (151 benign and 216 malignant) who underwent conventional ultrasound and inversion imaging prior to breast surgery were included. A 5-point inversion score (IS) scale was proposed based on the masses' internal echogenicity and distribution characteristics in the inversion images. The combination of IS and BI-RADS was compared with BI-RADS alone to evaluate the value of inversion imaging for breast mass diagnosis. The diagnostic performance of the BI-RADS and its combination with IS for breast masses were analyzed using area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). RESULTS: The IS for malignant breast masses (3.96 ± 0.77) was significantly higher than benign masses (2.58 ± 0.98) (P < 0.001). The sensitivity, specificity, accuracy, PPV, and NPV of BI-RADS were 86.1%, 81.5%, 84.2%, 86.9%, and 80.4%, respectively, and an AUC was 0.909. By compared with BI-RADS, 72 breast masses were downgraded from suspected malignancy to benign, and 6 masses were upgraded from benign to suspected malignancy. Thus, the specificity was increased from 81.5 to 84.8%, it allows 72 benign masses avoid biopsy. CONCLUSION: The combination of inversion imaging with BI-RADS can effectively improve the diagnostic efficacy of breast masses, and inversion imaging could help benign masses avoid biopsy.

High-temporal resolution DCE-MRI improves assessment of intra- and peri-breast lesions categorized as BI-RADS 4.

BACKGROUND: BI-RADS 4 breast lesions are suspicious for malignancy with a range from 2 to 95%, indicating that numerous benign lesions are unnecessarily biopsied. Thus, we aimed to investigate whether high-temporal-resolution dynamic contrast-enhanced MRI (H_DCE-MRI) would be superior to conventional low-temporal-resolution DCE-MRI (L_DCE-MRI) in the diagnosis of BI-RADS 4 breast lesions. METHODS: This single-center study was approved by the IRB. From April 2015 to June 2017, patients with breast lesions were prospectively included and randomly assigned to undergo either H_DCE-MRI, including 27 phases, or L_DCE-MRI, including 7 phases. Patients with BI-RADS 4 lesions were diagnosed by the senior radiologist in this study. Using a two-compartment extended Tofts model and a three-dimensional volume of interest, several pharmacokinetic parameters reflecting hemodynamics, including Ktrans, Kep, Ve, and Vp, were obtained from the intralesional, perilesional and background parenchymal enhancement areas, which were labeled the Lesion, Peri and BPE areas, respectively. Models were developed based on hemodynamic parameters, and the performance of these models in discriminating between benign and malignant lesions was evaluated by receiver operating characteristic (ROC) curve analysis. RESULTS: A total of 140 patients were included in the study and underwent H_DCE-MRI (n = 62) or L_DCE-MRI (n = 78) scans; 56 of these 140 patients had BI-RADS 4 lesions. Some pharmacokinetic parameters from H_DCE-MRI (Lesion_Ktrans, Kep, and Vp; Peri_Ktrans, Kep, and Vp) and from L_DCE-MRI (Lesion_Kep, Peri_Vp, BPE_Ktrans and BPE_Vp) were significantly different between benign and malignant breast lesions (P < 0.01). ROC analysis showed that Lesion_Ktrans (AUC = 0.866), Lesion_Kep (AUC = 0.929), Lesion_Vp (AUC = 0.872), Peri_Ktrans (AUC = 0.733), Peri_Kep (AUC = 0.810), and Peri_Vp (AUC = 0.857) in the H_DCE-MRI group had good discrimination performance. Parameters from the BPE area showed no differentiating ability in the H_DCE-MRI group. Lesion_Kep (AUC = 0.767), Peri_Vp (AUC = 0.726), and BPE_Ktrans and BPE_Vp (AUC = 0.687 and 0.707) could differentiate between benign and malignant breast lesions in the L_DCE-MRI group. The models were compared with the senior radiologist's assessment for the identification of BI-RADS 4 breast lesions. The AUC, sensitivity and specificity of Lesion_Kep (0.963, 100.0%, and 88.9%, respectively) in the H_DCE-MRI group were significantly higher than those of the same parameter in the L_DCE-MRI group (0.663, 69.6% and 75.0%, respectively) for the assessment of BI-RADS 4 breast lesions. The DeLong test was conducted, and there was a significant difference only between Lesion_Kep in the H_DCE-MRI group and the senior radiologist (P = 0.04). CONCLUSIONS: Pharmacokinetic parameters (Ktrans, Kep and Vp) from the intralesional and perilesional regions on high-temporal-resolution DCE-MRI, especially the intralesional Kep parameter, can improve the assessment of benign and malignant BI-RADS 4 breast lesions to avoid unnecessary biopsy.

Automated breast ultrasound (ABUS) for intraoperative margin control on surgical specimens in breast conserving surgery.

PURPOSE: As breast-conserving surgery (BCS) has become the standard for treatment of early breast cancer, the need for new technologies to improve intraoperative margin assessment has become clear. Close or positive margins during BCS lead to additional surgeries, treatment delay, additional stress for patients and increasing healthcare cost. Automated three-dimensional breast ultrasound (ABUS) systems are meant to overcome the shortcomings of hand-held ultrasound (HHUS). In this study, we investigate the feasibility of ABUS to conduct ultrasound on surgical specimens in breast conserving therapy. METHODS: In this monocentric, non-interventional study, specimens of 40 women were examined via ABUS. A construction with isotonic saline solution, gel pads and ABUS membranes was invented by our team to produce images of breast cancer specimens using ABUS. Evaluation of the ABUS images was carried out by two independent physicians trained on ABUS evaluation. RESULTS: ABUS was conducted on 40 specimens. 90% of the generated images were of high quality. Measured tumor sizes with ABUS were bigger than measured tumor size with HHUS (mean tumor size 22.9 vs. 18.1 mm, CI 2.38-7.35, p < 0.05). The mean difference between the ABUS tumor size and the pathological tumor size was 1.8 mm (CI - 0.84-4.53, p = 0.17). The mean difference between the HHUS tumor size and the pathological tumor size was 3.2 mm (CI - 5.35 to - 1.03, p = 0.005). CONCLUSION: ABUS seems to be a suitable method to conduct specimen ultrasound. Further studies are required to evaluate the accuracy of ABUS for intraoperative margin assessment and possible implementation in clinical work routine.

Mitigating Under-Sampling Artifacts in 3D Photoacoustic Imaging Using Res-UNet Based on Digital Breast Phantom.

In recent years, photoacoustic (PA) imaging has rapidly grown as a non-invasive screening technique for breast cancer detection using three-dimensional (3D) hemispherical arrays due to their large field of view. However, the development of breast imaging systems is hindered by a lack of patients and ground truth samples, as well as under-sampling problems caused by high costs. Most research related to solving these problems in the PA field were based on 2D transducer arrays or simple regular shape phantoms for 3D transducer arrays or images from other modalities. Therefore, we demonstrate an effective method for removing under-sampling artifacts based on deep neural network (DNN) to reconstruct high-quality PA images using numerical digital breast simulations. We constructed 3D digital breast phantoms based on human anatomical structures and physical properties, which were then subjected to 3D Monte-Carlo and K-wave acoustic simulations to mimic acoustic propagation for hemispherical transducer arrays. Finally, we applied a 3D delay-and-sum reconstruction algorithm and a Res-UNet network to achieve higher resolution on sparsely-sampled data. Our results indicate that when using a 757 nm laser with uniform intensity distribution illuminated on a numerical digital breast, the imaging depth can reach 3 cm with 0.25 mm spatial resolution. In addition, the proposed DNN can significantly enhance image quality by up to 78.4%, as measured by MS-SSIM, and reduce background artifacts by up to 19.0%, as measured by PSNR, even at an under-sampling ratio of 10%. The post-processing time for these improvements is only 0.6 s. This paper suggests a new 3D real time DNN method addressing the sparse sampling problem based on numerical digital breast simulations, this approach can also be applied to clinical data and accelerate the development of 3D photoacoustic hemispherical transducer arrays for early breast cancer diagnosis.

Review and Analysis of Tumour Detection and Image Quality Analysis in Experimental Breast Microwave Sensing.

This review evaluates the methods used for image quality analysis and tumour detection in experimental breast microwave sensing (BMS), a developing technology being investigated for breast cancer detection. This article examines the methods used for image quality analysis and the estimated diagnostic performance of BMS for image-based and machine-learning tumour detection approaches. The majority of image analysis performed in BMS has been qualitative and existing quantitative image quality metrics aim to describe image contrast-other aspects of image quality have not been addressed. Image-based diagnostic sensitivities between 63 and 100% have been achieved in eleven trials, but only four articles have estimated the specificity of BMS. The estimates range from 20 to 65%, and do not demonstrate the clinical utility of the modality. Despite over two decades of research in BMS, significant challenges remain that limit the development of this modality as a clinical tool. The BMS community should utilize consistent image quality metric definitions and include image resolution, noise, and artifacts in their analyses. Future work should include more robust metrics, estimates of the diagnostic specificity of the modality, and machine-learning applications should be used with more diverse datasets and with robust methodologies to further enhance BMS as a viable clinical technique.