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To develop a simple ultrasound (US) based scoring system to reduce benign breast biopsies. Women with BI-RADS 4 or 5 breast lesions underwent shear-wave elastography (SWE) imaging before biopsy. Standard US and color Doppler US (CDUS) parameters were recorded, and the size ratio (Sz R =longest/shortest diameter) was calculated. Measured/calculated SWE parameters were minimum (SWV Min) and maximum (SWV Max) shear velocity, velocity heterogeneity (SWV H =SWV Max -SWV Min), velocity ratio (SWV R =SWV Min /SWV Max), and normalized SWV R (SWV Rn =(SWV Max -SWV Min)/SWV Min). Linear regression analysis was performed by converting continuous parameters into categorical corresponding equivalents using decision tree analyses. Linear regression models were fitted using stepwise regression analysis and optimal coefficients for the predictors in the models were determined. A scoring model was devised from the results and validated using a different data set from another center consisting of 187 cases with BI-RADS 3, 4, and 5 lesions. A total of 418 lesions (238 benign, 180 malignant) were analyzed. US and CDUS parameters exhibited poor (AUC=0.592-0.696), SWE parameters exhibited poor-good (AUC=0.607-0.816) diagnostic performance in benign/malignant discrimination. Linear regression models of US+CDUS and US+SWE parameters revealed an AUC of 0.819 and 0.882, respectively. The developed scoring system could have avoided biopsy in 37.8% of benign lesions while missing 1.1% of malignant lesions. The scoring system was validated with a 100% NPV rate with a specificity of 74.6%. The linear regression model using US+SWE parameters performed better than any single parameter alone. The developed scoring method could lead to a significant decrease in benign biopsies. [ABSTRACT FROM AUTHOR]

Purpose: To assess the accuracy of preoperative sonographic staging in patients with primary invasive breast cancer., Methods: We retrospectively analyzed a prospectively kept service database of patients with newly diagnosed, unifocal, cT1-3, invasive breast cancer. All patients were diagnosed at a single center institution between January 2013 and December 2021. Clinical T stage was assessed preoperatively by ultrasound and correlated with the definite postoperative pathologic T stage. Demographics, clinical and pathological characteristics were collected. Factors influencing accuracy, over- and underdiagnosis of sonographic staging were analyzed with multivariable regression analysis., Results: A total of 2478 patients were included in the analysis. Median patients' age was 65 years. 1577 patients (63.6%) had clinical T1 stage, 864 (34.9%) T2 and 37 (1.5%) T3 stage. The overall accuracy of sonography and histology was 76.5% (n = 1896), overestimation was observed in 9.1% (n = 225) of all cases, while underestimation occurred in 14.4% (n = 357) of all cases. Accuracy increased when clinical tumor stage cT was higher (OR 1.23; 95% CI 1.10-1.38, p ≤ 0.001). The highest accuracy was seen for patients with T2 stage (82.8%). The accuracy was lower in Luminal B tumors compared to Luminal A tumors (OR 0.71; 95% CI 0.59-0.87, p ≤ 0.001). We could not find any association between sonographic accuracy in HER2 positive patients, and demographic characteristics, or tumor-related factors., Conclusion: Our unicentric study showed a high accuracy of sonography in predicting T stage, especially for tumors with clinical T2 stage. Tumor stage and biological tumor factors do affect the accuracy of sonographic staging., (© 2024. The Author(s).)

Introduction: In academic breast surgery, ultrasound use tends to be limited to radiology departments, thus formal surgical resident training in breast ultrasound is sparse. Building on residents' ultrasound skills in our general surgery training program, we developed a novel curriculum to teach ultrasound-guided breast procedures (UGBPs), including core needle biopsy (CNB) and wire localization (WL). We hypothesized that learning UGBPs on cadavers would be preferred to learning with a breast phantom model using chicken breasts., Methods: Residents received a 1-h lecture on breast CNB and WL followed by a 1-h hands-on laboratory session. Olives stuffed with red pimentos were used to replicate breast masses and implanted in chicken breasts and the breasts of lightly embalmed and unembalmed female cadavers. All residents practiced UGBPs with a course instructor on both models. Residents completed anonymous prelaboratory and postlaboratory surveys utilizing five-point Likert scales., Results: A total of 35 trainees participated in the didactics; all completed the prelaboratory survey and 28 completed the postlaboratory survey. Participant clinical year ranged from 1 to 6. Residents' confidence in describing and performing CNBs and WLs increased significantly on postlaboratory surveys, controlling for clinical year (P < 0.001). Eighty-point seven percent preferred learning UGBPs on cadavers over phantoms most commonly citing that the cadaver was more realistic., Conclusions: Following a novel 2-h UGBP training curriculum using phantom and cadaveric models, resident confidence in describing and performing UGBPs significantly improved. Most favored the cadaveric model and reported that the course prepared them for real-life procedures., (Copyright © 2024. Published by Elsevier Inc.)

Purpose: The aim of this study was to develop and test a post-processing technique for detection and classification of lesions according to the BI-RADS atlas in automated breast ultrasound (ABUS) based on deep convolutional neural networks (dCNNs). Methods and materials: In this retrospective study, 645 ABUS datasets from 113 patients were included; 55 patients had lesions classified as high malignancy probability. Lesions were categorized in BI-RADS 2 (no suspicion of malignancy), BI-RADS 3 (probability of malignancy < 3%), and BI-RADS 4/5 (probability of malignancy > 3%). A deep convolutional neural network was trained after data augmentation with images of lesions and normal breast tissue, and a sliding-window approach for lesion detection was implemented. The algorithm was applied to a test dataset containing 128 images and performance was compared with readings of 2 experienced radiologists. Results: Results of calculations performed on single images showed accuracy of 79.7% and AUC of 0.91 [95% CI: 0.85–0.96] in categorization according to BI-RADS. Moderate agreement between dCNN and ground truth has been achieved (κ: 0.57 [95% CI: 0.50–0.64]) what is comparable with human readers. Analysis of whole dataset improved categorization accuracy to 90.9% and AUC of 0.91 [95% CI: 0.77–1.00], while achieving almost perfect agreement with ground truth (κ: 0.82 [95% CI: 0.69–0.95]), performing on par with human readers. Furthermore, the object localization technique allowed the detection of lesion position slice-wise. Conclusions: Our results show that a dCNN can be trained to detect and distinguish lesions in ABUS according to the BI-RADS classification with similar accuracy as experienced radiologists. Key Points: • A deep convolutional neural network (dCNN) was trained for classification of ABUS lesions according to the BI-RADS atlas. • A sliding-window approach allows accurate automatic detection and classification of lesions in ABUS examinations. [ABSTRACT FROM AUTHOR]

Objectives: Breast cancer screening consists of batch interpretation of two-view (cranio-caudal CC- and medio-lateral oblique MLO) digital mammography (DM) per breast. The DM-MLO view was substituted by an MLO-digital breast tomosynthesis (DBT) and its synthetic (2D-synthetic mammography (SM)-MLO) view. The performance of this hybrid protocol was evaluated in a one-stop-shop screening visit, providing immediate reading and additional work up. Methods: Retrospective, observational review, comparing the cancer detection rate (CDR), breast US rates, and biopsy rates in 13,048 women screened with DM from June 2015 to November 2016 and 8639 women screened with SM-DBT/DM from January 2017 to July 2018. Chi-square tests or Fisher's exact tests were used to compare proportions between the two screening imaging methods. Results: SM + DBT/DM significantly increased the overall CDR (10.8‰) versus DM (7.5‰) (p = 0.0120) with more invasive lobular carcinoma (14% versus 4%) (p = 0.0357) detected and overall more invasive cancers among women with breast density type B (p = 0.0411) and those aged between 60 and 70 (p = 0.0306). This was achieved at the expense of additional sonographic examinations performed (33.5% in DBT group versus 26.7% in DM group) (p < 0.0001), more BI-RADS category III assigned (1.8% in SM-DBT/DM group versus 1.5% in DM group) (p = 0.0443) and more biopsy rates (3.0 % in SM-DBT/DM group versus 1.7% in DM group) (p < 0.0001). Conclusions: Hybrid mammographic protocol replacing 2D-MLO by DBT-MLO and SM-MLO views in a one-stop-shop screening visit improved CDR, at the expense of more sonographic examinations, biopsies, and BI-RADS III lesions. Breast US alone detected 9.2% of all breast cancers in this cohort. Key Points: • Hybrid protocol including MLO (DBT + SM) with 2D DM CC may improve CDR compared to standard 4 views 2D DM in a screening program providing immediate interpretation. • Adding screening breast US, when perceived necessary, in the same visit of a screening mammography, increases cancer detection rate of 9.2%. • Based on our results, hybrid protocol including DBT + SM in MLO plane and DM in CC plane could be safely implemented as a transition towards DBT and SM alone, without any compromise in the cancer detection ability. Our results may vary according to the properties of machines from different vendors. [ABSTRACT FROM AUTHOR]

ABSTRACT Objective To evaluate the impact of COVID-19 pandemic on breast cancer diagnosis in a breast imaging center. Methods This was a retrospective cohort study that included women submitted to breast exams and procedures in a private hospital in São Paulo, SP, Brazil, as from the period of most strict social isolation measures, in 2020 (separated in first period of social isolation, March 24 to June 21, 2020, and second period, June 22 to December 31, 2020), as compared to the same period in 2019. The number of exams, cancer detection rates, pathologic findings and risk factors were analyzed. Results A total of 32,144 patients were included in the study. Breast imaging exams and procedures decreased by 78.9% in the first period, and 2.7% in the second period, in 2020. By the end of 2020, the number of breast cancer lesions detected was just six cases less than in 2019, although the number of patients submitted to mammograms was 35% lower. Conclusion There was a drop in number of breast exams and cancer diagnoses in the first 90 days of the pandemic. The decrease in diagnosis of cancer was partially compensated in the second period, but the number of patients submitted to mammograms by the end of 2020 was lower, still considering a large number of patients with delayed exams.

Purpose: The Ugandan Ministry of Health adopted BI-RADS as standard of care in 2016. The authors performed a medical audit of breast ultrasound practices at four tertiary-level hospitals to assess interpretive performance. The authors also determined the effect of a low-cost navigation program linking breast imaging and pathology on the percentage of patients completing diagnostic care., Methods: The authors retrieved 966 consecutive diagnostic breast ultrasound reports, with complete data, for studies performed on women aged >18 years presenting with symptoms of breast cancer between 2018 and 2020 from participating hospitals. Ultrasound results were linked to tumor registries and patient follow-up. A medical audit was performed according to the ACR's BI-RADS Atlas, fifth edition, and results were compared with those of a prior audit performed in 2013. At Mulago Hospital, an intervention was piloted on the basis of patient navigation, cost sharing, and same-day imaging, tissue sampling, and pathology., Results: In total, 888 breast ultrasound examinations (91.9%) were eligible for inclusion. Compared with 2013, the postintervention cancer detection rate increased from 38 to 148.7 cancers per 1,000 examinations, positive predictive value 2 from 29.6% to 48.9%, and positive predictive value 3 from 62.7% to 79.9%. Specificity decreased from 90.5% to 87.7% and sensitivity from 92.3% to 81.1%. The mean time from tissue sampling to receipt of a diagnosis decreased from 60 to 7 days. The intervention increased the percentage of patients completing diagnostic care from 0% to 100%., Conclusions: Efforts to establish a culture of continuous quality improvement in breast ultrasound require robust data collection that links imaging results to pathology and patient follow-up. Interpretive performance met BI-RADS benchmarks for palpable masses, except sensitivity. This resource-appropriate strategy linking imaging, tissue sampling, and pathology interpretation decreased time to diagnosis and rates of loss to follow-up and improved the precision of the audit., (Copyright © 2024 American College of Radiology. Published by Elsevier Inc. All rights reserved.)

Objectives: To investigate whether magnetic resonance imaging (MRI) best detects early malignancy in high-risk women., Methods: A retrospective, cross-sectional study, carried out at King Abdulaziz University Hospital, Jeddah, Saudi Arabia, included 419 female breast cancer patients aged 16-84 years (mean age of 49). Data were collected from the radiological department's database to compare the MRI, ultrasound (US), and mammography results, with or without tissue biopsy., Results: In diagnosing benign versus malignant lesions, MRI showed significant agreement with tissue biopsy, with high sensitivity (70%) and specificity (87%); its positive predictive value (PPV) was 92% and negative predictive value (NPV) was 56%. While US has a PPV of 84% and NPV of 63%; with a sensitivity (79%) and specificity (71%). In patients without tissue biopsy, there was little difference between mammography and US compared with MRI results., Conclusion: Magnetic resonance imaging is more effective than US and mammography for early detection of BC. It showed high sensitivity in detecting breast lesions and high specificity in characterizing their nature when correlated with pathological results. Ultrasound screening followed by MRI is suggested for undetected or suspected lesions. This will increase the breast lesion detection rate, reduce unneeded tissue biopsies, and enhance the disease's survival rate., (Copyright: © Saudi Medical Journal.)

Background: Tuberculous mastitis (TBM), is an uncommon form of extra-pulmonary tuberculosis. Clinical and radiological overlap of tuberculous mastitis with malignancy and other granulomatous conditions, along with its paucibacillary nature, make it a diagnostic challenge. In our study, we aim to assess the radiological response of microbiologically negative granulomatous mastitis cases to anti-tuberculous treatment (ATT) in an endemic country., Methods: Eighty-seven cases demonstrating granulomatous lesions on breast biopsy were identified. Of these, 49 patients who were treated with ATT and had at least two serial ultrasound follow-ups were included in our study. Mammogram and ultrasound were used for initial imaging. Subsequently, ultrasound was used for serial follow-up. Mantoux skin test, acid fast staining and histological examination of tissue sample were the other investigations used., Results: Radiologically, on ultrasound, well-circumscribed hypoechoic masses were noted in 18 patients, followed by ill-defined collections with tubular extensions in 15 cases, abscesses in 8, and a focal heterogeneity in 8 patients. Following ATT, 17 patients showed radiological resolution in 4 weeks, 18 of them at 3 months, and nine of them in 6 months., Conclusion: Excellent and prompt radiological response to ATT, indicates the need for a high degree of suspicion for tuberculous mastitis (TBM), in endemic countries, even though microbiological tests may turn out negative., Competing Interests: Conflict of interest The authors have none to declare., (Copyright © 2023 Tuberculosis Association of India. Published by Elsevier B.V. All rights reserved.)

Aims/Background Breast leukaemia (BL) is a rare breast malignancy that is treated differently from other malignant conditions. However, it is easily confused with other conditions; therefore, how to accurately diagnose is crucial. We retrospectively analysed the imaging findings of 13 patients to provide a diagnostic reference. Methods From January 2015 to April 2023, 13 patients with BL confirmed by biopsy who underwent imaging in Peking University People's hospital were retrospectively analysed. The imaging findings obtained via ultrasound (US), mammography (MMG), magnetic resonance imaging (MRI), and positron emission tomography/computed tomography (PET/CT) were analysed, and the detection rates of these methods for diagnosing BL were compared. Results Twenty-nine lesions were detected in the 13 patients. These patients presented with palpable masses or breast swelling several months after treatment for leukaemia, mainly involving the bilateral breasts. Ultrasonography was performed for 13 patients, and all lesions were detected. Most of the identified masses were hypoechoic and had indistinct boundaries, irregular shapes, no enhancement of the posterior echo, and no abundant blood flow. MMG was performed for five patients, revealing breast masses, architectural distortion, and no abnormalities. MRI was performed for four patients, and all lesions were detected; most of the lesions were hypointense on T1-weighted imaging and hyperintense on T2-weighted imaging and diffusion-weighted imaging, with a decreased apparent diffusion coefficient and inhomogeneous enhancement. The enhancement curves were mostly inflow patterns. PET/CT was performed for four patients; two patients had hypermetabolism, and the other two had no obvious radioactive uptake. Conclusion Compared to MMG and PET/CT, US and MRI have higher detection rates. Furthermore, compared to MRI, US is inexpensive, convenient and efficient; therefore, it should be the first choice for diagnosing BL.

Importance: Breast cancer is a leading cause of cancer mortality for US women. Trials have established that screening mammography can reduce mortality risk, but optimal screening ages, intervals, and modalities for population screening guidelines remain unclear., Objective: To review studies comparing different breast cancer screening strategies for the US Preventive Services Task Force., Data Sources: MEDLINE, Cochrane Library through August 22, 2022; literature surveillance through March 2024., Study Selection: English-language publications; randomized clinical trials and nonrandomized studies comparing screening strategies; expanded criteria for screening harms., Data Extraction and Synthesis: Two reviewers independently assessed study eligibility and quality; data extracted from fair- and good-quality studies., Main Outcomes and Measures: Mortality, morbidity, progression to advanced cancer, interval cancers, screening harms., Results: Seven randomized clinical trials and 13 nonrandomized studies were included; 2 nonrandomized studies reported mortality outcomes. A nonrandomized trial emulation study estimated no mortality difference for screening beyond age 74 years (adjusted hazard ratio, 1.00 [95% CI, 0.83 to 1.19]). Advanced cancer detection did not differ following annual or biennial screening intervals in a nonrandomized study. Three trials compared digital breast tomosynthesis (DBT) mammography screening with digital mammography alone. With DBT, more invasive cancers were detected at the first screening round than with digital mammography, but there were no statistically significant differences in interval cancers (pooled relative risk, 0.87 [95% CI, 0.64-1.17]; 3 studies [n = 130 196]; I2 = 0%). Risk of advanced cancer (stage II or higher) at the subsequent screening round was not statistically significant for DBT vs digital mammography in the individual trials. Limited evidence from trials and nonrandomized studies suggested lower recall rates with DBT. An RCT randomizing individuals with dense breasts to invitations for supplemental screening with magnetic resonance imaging reported reduced interval cancer risk (relative risk, 0.47 [95% CI, 0.29-0.77]) and additional false-positive recalls and biopsy results with the intervention; no longer-term advanced breast cancer incidence or morbidity and mortality outcomes were available. One RCT and 1 nonrandomized study of supplemental ultrasound screening reported additional false-positives and no differences in interval cancers., Conclusions and Relevance: Evidence comparing the effectiveness of different breast cancer screening strategies is inconclusive because key studies have not yet been completed and few studies have reported the stage shift or mortality outcomes necessary to assess relative benefits.

Importance: Among all US women, breast cancer is the second most common cancer and the second most common cause of cancer death. In 2023, an estimated 43 170 women died of breast cancer. Non-Hispanic White women have the highest incidence of breast cancer and non-Hispanic Black women have the highest mortality rate., Objective: The USPSTF commissioned a systematic review to evaluate the comparative effectiveness of different mammography-based breast cancer screening strategies by age to start and stop screening, screening interval, modality, use of supplemental imaging, or personalization of screening for breast cancer on the incidence of and progression to advanced breast cancer, breast cancer morbidity, and breast cancer-specific or all-cause mortality, and collaborative modeling studies to complement the evidence from the review., Population: Cisgender women and all other persons assigned female at birth aged 40 years or older at average risk of breast cancer., Evidence Assessment: The USPSTF concludes with moderate certainty that biennial screening mammography in women aged 40 to 74 years has a moderate net benefit. The USPSTF concludes that the evidence is insufficient to determine the balance of benefits and harms of screening mammography in women 75 years or older and the balance of benefits and harms of supplemental screening for breast cancer with breast ultrasound or magnetic resonance imaging (MRI), regardless of breast density., Recommendation: The USPSTF recommends biennial screening mammography for women aged 40 to 74 years. (B recommendation) The USPSTF concludes that the current evidence is insufficient to assess the balance of benefits and harms of screening mammography in women 75 years or older. (I statement) The USPSTF concludes that the current evidence is insufficient to assess the balance of benefits and harms of supplemental screening for breast cancer using breast ultrasonography or MRI in women identified to have dense breasts on an otherwise negative screening mammogram. (I statement).

Background: Radial scars/radial sclerosing lesions (RS) are benign breast lesions identified on core needle biopsy (CNB) which can upgrade to malignancy at excision. There is limited data on RS detection and upgrade rates with more sensitive imaging such as magnetic resonance imaging (MRI) and none during their detection for breast cancer workup and its implication on patient treatment decisions., Methods: A retrospective institutional study of RS diagnosed on CNB between January 2008 and December 2017 was conducted. Clinicopathologic and radiologic features of RS, patient treatment decisions, upgrade rates and long-term follow-up were examined., Results: We identified 133 patients with RS on CNB, of whom 106 opted for surgery for an upgrade rate to malignancy of 1.9%, 2 patients. Radial scar was diagnosed on mammogram in 60%, MRI in 25% and ultrasound in 15% of patients. In this cohort, 32 patients had their RS detected during breast cancer workup (coexistent group) and they were more likely to have their radial scar detected by MRI (60% vs. 14%, P < .001) and undergo more extensive surgery (94% vs. 75%, P = .02). Among the 27 patients electing observation of their RS, only one (3.7%) developed breast cancer., Conclusions: Our results show an extremely low upgrade rate to malignancy of RS, regardless if there is coexisting breast cancer elsewhere. Despite this, RS still prompted more extensive surgical excisions. The findings do not support excision of RS even among breast cancer patients when identified at a separate site from their cancer., Competing Interests: Disclosure The authors have stated that they have no conflicts of interest., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Clinical and pathologic characteristics of the invasive ductal carcinoma (IDC) presenting as a thick-walled breast cyst are little known. Three female patients were included in this report. A palpable, nontender breast lump was found in all cases. While mammography showed a hyperdense mass, ultrasonography demonstrated a thick-walled cystic mass. Magnetic resonance imaging clearly showed the cystic breast lesions with ring-like or irregular rim enhancement. A grade III IDC was confirmed in all cases. All IDCs but one were estrogen receptor negative, progesterone receptor negative, and human epidermal growth factor receptor 2 negative, with merely weak progesterone receptor positivity (5%) in one case. All cases underwent surgical management first and postoperative chemotherapy. Breast malignancy presenting as a thick-walled cystic mass could be a highly aggressive IDC, even triple-negative breast cancer. It is imperative for breast cancer-related practitioners to identify the potentially malignant cystic lesions timely and adopt appropriate management., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Objective: The FUSION-X-US-II prototype was developed to combine 3D automated breast ultrasound (ABUS) and digital breast tomosynthesis in a single device. We evaluated the performance of ABUS and tomosynthesis in a single examination in a clinical setting. Methods: In this prospective feasibility study, digital breast tomosynthesis and ABUS were performed using the FUSION-X-US-II prototype without any change of the breast position in patients referred for clarification of breast lesions with an indication for tomosynthesis. The tomosynthesis and ABUS images of the prototype were interpreted independently from the clinical standard by a breast diagnostics specialist. Any detected lesion was classified using BI-RADS® scores, and results of the standard clinical routine workup (gold standard) were compared to the result of the separate evaluation of the prototype images. Image quality was rated subjectively and coverage of the breast was measured. Results: One hundred one patients received both ABUS and tomosynthesis using the prototype. The duration of the additional ABUS acquisition was 40 to 60 s. Breast coverage by ABUS was approximately 80.0%. ABUS image quality was rated as diagnostically useful in 86 of 101 cases (85.1%). Thirty-three of 34 malignant breast lesions (97.1%) were identified using the prototype. Conclusion: The FUSION-X-US-II prototype allows a fast ABUS scan in combination with digital breast tomosynthesis in a single device integrated in the clinical workflow. Malignant breast lesions can be localized accurately with direct correlation of ABUS and tomosynthesis images. The FUSION system shows the potential to improve breast cancer screening in the future after further technical improvements. Key Points: • The FUSION-X-US-II prototype allows the combination of automated breast ultrasound and digital breast tomosynthesis in a single device without decompression of the breast. • Image quality and coverage of ABUS are sufficient to accurately detect malignant breast lesions. • If tomosynthesis and ABUS should become part of breast cancer screening, the combination of both techniques in one device could offer practical and logistic advantages. To evaluate a potential benefit of a combination of ABUS and tomosynthesis in screening-like settings, further studies are needed. [ABSTRACT FROM AUTHOR]

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

Introduction. Breast cancer is a major health and economic problem in Serbia. On the one hand, most patients see a the doctor at an advanced stage of the disease, and on the other hand, screening response rate is low. Early detection increases the chances of survival. Digital mammography is the gold standard in breast cancer imaging, together with supplemental ultrasound and tomosynthesis. Mammography combined with tomosynthesis has a greater detection rate regardless of breast density, although the difference is more pronounced in high-density lesions, and it has fewer false negative results than mammography alone. Ultrasound, apart from being a complementary method, is ideal for ultra sound guided breast biopsy. Magnetic resonance mammography is an imaging method with the highest sensitivity in the detection of invasive breast cancer and has an important role in the evaluation of the post-therapeutic response and in preoperative preparation. Magnetic resonance spectroscopy measures the choline levels and helps to differentiate malignant from benign lesions. It is also a promising method for early assessment of the post-therapeutic response. Positron emission tomography is likely to gain more significant roles in breast cancer imaging in the near future, especially in measuring the treatment response. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the impact of COVID-19 pandemic on breast cancer diagnosis in a breast imaging center. Methods: This was a retrospective cohort study that included women submitted to breast exams and procedures in a private hospital in São Paulo, SP, Brazil, as from the period of most strict social isolation measures, in 2020 (separated in first period of social isolation, March 24 to June 21, 2020, and second period, June 22 to December 31, 2020), as compared to the same period in 2019. The number of exams, cancer detection rates, pathologic findings and risk factors were analyzed. Results: A total of 32,144 patients were included in the study. Breast imaging exams and procedures decreased by 78.9% in the first period, and 2.7% in the second period, in 2020. By the end of 2020, the number of breast cancer lesions detected was just six cases less than in 2019, although the number of patients submitted to mammograms was 35% lower. Conclusion: There was a drop in number of breast exams and cancer diagnoses in the first 90 days of the pandemic. The decrease in diagnosis of cancer was partially compensated in the second period, but the number of patients submitted to mammograms by the end of 2020 was lower, still considering a large number of patients with delayed exams. [ABSTRACT FROM AUTHOR]

Breast pain is extremely common, occurring in 70% to 80% of women. Most cases of breast pain are from physiologic or benign causes, and patients should be reassured and offered treatment strategies to alleviate symptoms, often without diagnostic imaging. A complete clinical history and physical examination is key for distinguishing intrinsic breast pain from extramammary pain. Breast pain without other suspicious symptoms and with a negative history and physical examination result is rarely associated with malignancy, although it is a common reason for women to undergo diagnostic imaging. When breast imaging is indicated, guidelines according to the American College of Radiology Appropriateness Criteria should be followed as to whether mammography, US, or both are recommended. This review article summarizes the initial clinical evaluation of breast pain and evidence-based guidelines for imaging. Additionally, the article reviews cyclical and noncyclical breast pain and provides an image-rich discussion of the imaging presentation and management of benign and malignant breast pain etiologies., (© Society of Breast Imaging 2024. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Syringomatous tumor of the nipple is a benign, locally infiltrative tumor. There are reports in the literature of tumor recurrence in cases of incomplete excision. Clinical and mammographic findings in syringomatous tumors are like those of breast carcinoma and the pathologist has a fundamental role in final tumor diagnosis. Therefore, the aim of this study was to report a case of syringoma located in the areolar region. A 33-year-old woman reported that she had noticed a nodule in her left areolar region 4 years previously (February 2019). A breast ultrasound was performed, detecting intraparenchymatous breast cysts. Surgical resection of the nodule was indicated although it was not performed. Two years later, in August 2021, the patient underwent a mastopexy with prosthesis inclusion. Histopathology study of the surgical specimen revealed a syringomatous tumor with positive margins. Thirteen (13) months after diagnosis (September 3, 2021 - October 16, 2022), the patient is doing well and receives clinical follow-up., Competing Interests: The authors declare no competing interests., (Copyright: Ana Vitória de Jesus Félix et al.)

Objectives: The authors aimed to assess the performance of a deep learning (DL) model, based on a combination of ultrasound (US) and mammography (MG) images, for predicting malignancy in breast lesions categorized as Breast Imaging Reporting and Data System (BI-RADS) US 4A in diagnostic patients with dense breasts., Methods: A total of 992 patients were randomly allocated into the training cohort and the test cohort at a proportion of 4:1. Another, 218 patients were enrolled to form a prospective validation cohort. The DL model was developed by incorporating both US and MG images. The predictive performance of the combined DL model for malignancy was evaluated by sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). The combined DL model was then compared to a clinical nomogram model and to the DL model trained using US image only and to that trained MG image only., Results: The combined DL model showed satisfactory diagnostic performance for predicting malignancy in breast lesions, with an AUC of 0.940 (95% CI: 0.874-1.000) in the test cohort, and an AUC of 0.906 (95% CI: 0.817-0.995) in the validation cohort, which was significantly higher than the clinical nomogram model, and the DL model for US or MG alone ( P <0.05)., Conclusions: The study developed an objective DL model combining both US and MG imaging features, which was proven to be more accurate for predicting malignancy in the BI-RADS US 4A breast lesions of patients with dense breasts. This model may then be used to more accurately guide clinicians' choices about whether performing biopsies in breast cancer diagnosis., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.)

Improving the quality of breast ultrasound images is of great significance for clinical diagnosis which can greatly boost the diagnostic accuracy of ultrasonography. However, due to the influence of ultrasound imaging principles and acquisition equipment, the collected ultrasound images naturally contain a large amount of speckle noise, which leads to a decrease in image quality and affects clinical diagnosis. To overcome this problem, we propose an improved denoising algorithm combining multi-filter DFrFT (Discrete Fractional Fourier Transform) and the adaptive fast BM3D (Block Matching and 3D collaborative filtering) method. Firstly, we provide the multi-filtering DFrFT method for preprocessing the original breast ultrasound image so as to remove some speckle noise early in fractional transformation domain. Based on the fractional frequency spectrum characteristics of breast ultrasound images, three types of filters are designed correspondingly in low, medium, and high frequency domains. And by integrating filtered images, the enhanced images are obtained which not only remove some speckle noise in background but also preserve the details of breast lesions. Secondly, for further enhancing the image quality on the basis of multi-filter DFrFT, we propose the adaptive fast BM3D method by introducing the DBSCAN-based super pixel segmentation to block matching process, which utilizes super pixel segmentation labels to provide a reference on how similar it is between target block and retrieval blocks. It reduces the number of blocks to be retrieved and make the matched blocks with more similar features. At last, the local noise parameter estimation is also adopted in the hard threshold filtering process of traditional BM3D algorithm to achieve local adaptive filtering and further improving the denoising effect. The synthetic data and real breast ultrasound data examples show that this combined method can improve the speckle suppression level and keep the fidelity of structure effectively without increasing time cost., Competing Interests: Declaration of competing interest The authors declare that they have no potential conflicts of interest with respect to the research, author- ship, and/or publication of this article., (Copyright © 2024. Published by Elsevier B.V.)

This study aims to establish an effective benign and malignant classification model for breast tumor ultrasound images by using conventional radiomics and transfer learning features. We collaborated with a local hospital and collected a base dataset (Dataset A) consisting of 1050 cases of single lesion 2D ultrasound images from patients, with a total of 593 benign and 357 malignant tumor cases. The experimental approach comprises three main parts: conventional radiomics, transfer learning, and feature fusion. Furthermore, we assessed the model's generalizability by utilizing multicenter data obtained from Datasets B and C. The results from conventional radiomics indicated that the SVM classifier achieved the highest balanced accuracy of 0.791, while XGBoost obtained the highest AUC of 0.854. For transfer learning, we extracted deep features from ResNet50, Inception-v3, DenseNet121, MNASNet, and MobileNet. Among these models, MNASNet, with 640-dimensional deep features, yielded the optimal performance, with a balanced accuracy of 0.866, AUC of 0.937, sensitivity of 0.819, and specificity of 0.913. In the feature fusion phase, we trained SVM, ExtraTrees, XGBoost, and LightGBM with early fusion features and evaluated them with weighted voting. This approach achieved the highest balanced accuracy of 0.964 and AUC of 0.981. Combining conventional radiomics and transfer learning features demonstrated clear advantages over using individual features for breast tumor ultrasound image classification. This automated diagnostic model can ease patient burden and provide additional diagnostic support to radiologists. The performance of this model encourages future prospective research in this domain., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 IPEM. Published by Elsevier Ltd. All rights reserved.)

To establish a predictive model incorporating conventional ultrasound, strain elastography and clinicopathological features for Ki-67 expression in small breast cancer (SBC) which defined as maximum diameter less than2 cm. In this retrospective study, 165 SBC patients from our hospital were allocated to a high Ki-67 group ( n  = 104) and a low Ki-67 group ( n  = 61). Multivariate regression analysis was performed to identify independent indicators for developing predictive models. The area under the receiver operating characteristic (AUC) curve was also determined to establish the diagnostic performance of different predictive models. The corresponding sensitivities and specificities of different models at the cutoff value were compared. Conventional ultrasound parameters (spiculated margin, absence of posterior shadowing and Adler grade 2-3), strain elastic scores and clinicopathological information (HER2 positive) were significantly correlated with high expression of Ki-67 in SBC (all p  < .05). Model 2, which incorporated conventional ultrasound features and strain elastic scores, yielded good diagnostic performance (AUC = 0.774) with better sensitivity than model 1, which only incorporated ultrasound characteristics (78.85%vs. 55.77%, p  = .000), with specificities of 77.05% and 62.30% ( p  = .035), respectively. Model 3, which incorporated conventional ultrasound, strain elastography and clinicopathological features, yielded better performance (AUC = 0.853) than model 1 (AUC = 0.694) and model 2 (AUC = 0.774), and the specificity was higher than model 1 (86.89% vs. 77.05%, p  = .001). The predictive model combining conventional ultrasound, strain elastic scores and clinicopathological features could improve the predictive performance of Ki-67 expression in SBC., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Objective: Various efforts have been made to improve the accuracy of breast cancer screening. This study aimed to report differences in the contribution of ultrasonography to cancer screening assessments of dense and non-dense breasts., Methods: The participants in this study were 29,640 Japanese women in their 40 s who underwent breast cancer screening at the Iwate Cancer Society between 2018 and 2021. This included women who chose mammography alone or mammography with adjunctive ultrasonography (overall assessment). They were classified into two groups according to the breast density in mammography: dense breasts and non-dense breasts. Recall rate, breast cancer detection rate, and positive predictive value of the two screening-type groups were evaluated for each breast density group., Results: Of the 29,640 women analyzed, 18,861 (63.6%) underwent mammography alone and 10,779 (36.3%) were by overall assessments. The number of women recalled was higher in the overall assessment group than in the mammography-alone group (2.9% vs. 1.9%, p < 0.01). The proportion of women in whom breast cancer was detected was higher in the overall assessment group than in the mammography-alone group (0.31% [n = 33] vs. 0.15% [n = 28], p < 0.01). For non-dense breasts, there were no significant differences in either the recall rate or the breast cancer detection rate between those who underwent mammography alone and those who underwent overall assessment. Conversely, for dense breasts, the recall rate after mammography alone was lower than that after overall assessment (1.8% vs. 3.8%, p < 0.01), and the breast cancer detection rate was higher after overall assessment than after mammography alone (0.40% vs. 0.18%, p < 0.01)., Conclusion: We found the benefits of adjunctive ultrasonography with mammography to differ depending on breast density. This could be used to tailor the selection of screening modalities to individuals., (© 2023. The Author(s), under exclusive licence to The Japanese Breast Cancer Society.)

Background: A malignancy might be found at surgery in cases of atypical ductal hyperplasia (ADH) diagnosed via US-guided core needle biopsy (CNB). The objective of this study was to investigate the diagnostic performance of contrast-enhanced ultrasound (CEUS) in predicting ADH diagnosed by US-guided CNB that was upgraded to malignancy after surgery., Methods: In this retrospective study, 110 CNB-diagnosed ADH lesions in 109 consecutive women who underwent US, CEUS, and surgery between June 2018 and June 2023 were included. CEUS was incorporated into US BI-RADS and yielded a CEUS-adjusted BI-RADS. The diagnostic performance of US BI-RADS and CEUS-adjusted BI-RADS for ADH were analyzed and compared., Results: The mean age of the 109 women was 49.7 years ± 11.6 (SD). The upgrade rate of ADH at CNB was 48.2% (53 of 110). The sensitivity, specificity, positive predictive value, and negative predictive value of CEUS for identification of malignant upgrading were 96.2%, 66.7%,72.9%, and 95.0%, respectively, based on BI-RADS category 4B threshold. The two false-negative cases were low-grade ductal carcinoma in situ. Compared with the US, CEUS-adjusted BI-RADS had better specificity for lesions smaller than 2 cm (76.7% vs. 96.7%, P = 0.031). After CEUS, 16 (10 malignant and 6 nonmalignant) of the 45 original US BI-RADS category 4A lesions were up-classified to BI-RADS 4B, and 3 (1 malignant and 2 nonmalignant) of the 41 original US BI-RADS category 4B lesions were down-classified to BI-RADS 4A., Conclusions: CEUS is helpful in predicting malignant upgrading of ADH, especially for lesions smaller than 2 cm and those classified as BI-RADS 4A and 4B on ultrasound., (© 2024. The Author(s).)

Background: The incidence of breast cancer among Chinese women has gradually increased in recent years. This study aims to analyze the situation of breast cancer screening programs in China and compare the cancer detection rates (CDRs), early-stage cancer detection rates (ECDRs), and the proportions of early-stage cancer among different programs., Methods: We conducted a systematic review and meta-analysis of studies in multiple literature databases. Studies that were published between January 1, 2010 and June 30, 2023 were retrieved. A random effects model was employed to pool the single group rate, and subgroup analyses were carried out based on screening model, time, process, age, population, and follow-up method., Results: A total of 35 studies, including 47 databases, satisfied the inclusion criteria. Compared with opportunistic screening, the CDR (1.32‰, 95% CI: 1.10‰-1.56‰) and the ECDR (0.82‰, 95% CI: 0.66‰-0.99‰) were lower for population screening, but the proportion of early-stage breast cancer (80.17%, 95% CI: 71.40%-87.83%) was higher. In subgroup analysis, the CDR of population screening was higher in the urban group (2.28‰, 95% CI: 1.70‰-2.94‰), in the breast ultrasonography (BUS) in parallel with mammography (MAM) group (3.29‰, 95% CI: 2.48‰-4.21‰), and in the second screening follow-up group (2.47‰, 95% CI: 1.64‰-3.47‰), and the proportion of early-stage breast cancer was 85.70% (95% CI: 68.73%-97.29%), 88.18% (95% CI: 84.53%-91.46%), and 90.05% (95% CI: 84.07%-94.95%), respectively., Conclusion: There were significant differences between opportunistic and population screening programs. The results of these population screening studies were influenced by the screening process, age, population, and follow-up method. In the future, China should carry out more high-quality and systematic population-based screening programs to improve screening coverage and service., (© 2024. The Author(s).)

Ultrasound is an adjunct tool to mammography that can quickly and safely aid physicians in diagnosing breast abnormalities. Clinical ultrasound often assumes a constant sound speed to form diagnostic B-mode images. However, the components of breast tissue, such as glandular tissue, fat, and lesions, differ in sound speed. Given a constant sound speed assumption, these differences can degrade the quality of reconstructed images via phase aberration. Sound speed images can be a powerful tool for improving image quality and identifying diseases if properly estimated. To this end, we propose a supervised deep-learning approach for sound speed estimation from analytic ultrasound signals. We develop a large-scale simulated ultrasound dataset that generates representative breast tissue samples by modeling breast gland, skin, and lesions with varying echogenicity and sound speed. We adopt a fully convolutional neural network architecture trained on a simulated dataset to produce an estimated sound speed map. The simulated tissue is interrogated with a plane wave transmit sequence, and the complex-value reconstructed images are used as input for the convolutional network. The network is trained on the sound speed distribution map of the simulated data, and the trained model can estimate sound speed given reconstructed pulse-echo signals. We further incorporate thermal noise augmentation during training to enhance model robustness to artifacts found in real ultrasound data. To highlight the ability of our model to provide accurate sound speed estimations, we evaluate it on simulated, phantom, and in-vivo breast ultrasound data., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Jeremy Dahl reports financial support was provided by National Institute of Health., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Background: Accurate medical image segmentation is crucial for disease diagnosis and surgical planning. Transformer networks offer a promising alternative for medical image segmentation as they can learn global features through self-attention mechanisms. To further enhance performance, many researchers have incorporated more Transformer layers into their models. However, this approach often results in the model parameters increasing significantly, causing a potential rise in complexity. Moreover, the datasets of medical image segmentation usually have fewer samples, which leads to the risk of overfitting of the model., Purpose: This paper aims to design a medical image segmentation model that has fewer parameters and can effectively alleviate overfitting., Methods: We design a MultiIB-Transformer structure consisting of a single Transformer layer and multiple information bottleneck (IB) blocks. The Transformer layer is used to capture long-distance spatial relationships to extract global feature information. The IB block is used to compress noise and improve model robustness. The advantage of this structure is that it only needs one Transformer layer to achieve the state-of-the-art (SOTA) performance, significantly reducing the number of model parameters. In addition, we designed a new skip connection structure. It only needs two 1× 1 convolutions, the high-resolution feature map can effectively have both semantic and spatial information, thereby alleviating the semantic gap., Results: The proposed model is on the Breast UltraSound Images (BUSI) dataset, and the IoU and F1 evaluation indicators are 67.75 and 87.78. On the Synapse multi-organ segmentation dataset, the Param, Hausdorff Distance (HD) and Dice Similarity Cofficient (DSC) evaluation indicators are 22.30, 20.04 and 81.83., Conclusions: Our proposed model (MultiIB-TransUNet) achieved superior results with fewer parameters compared to other models., (© 2023 American Association of Physicists in Medicine.)

A new detailed dataset of breast ultrasound scans (BrEaST) containing images of benign and malignant lesions as well as normal tissue examples, is presented. The dataset consists of 256 breast scans collected from 256 patients. Each scan was manually annotated and labeled by a radiologist experienced in breast ultrasound examination. In particular, each tumor was identified in the image using a freehand annotation and labeled according to BIRADS features and lexicon. The histopathological classification of the tumor was also provided for patients who underwent a biopsy. The BrEaST dataset is the first breast ultrasound dataset containing patient-level labels, image-level annotations, and tumor-level labels with all cases confirmed by follow-up care or core needle biopsy result. To enable research into breast disease detection, tumor segmentation and classification, the BrEaST dataset is made publicly available with the CC-BY 4.0 license., (© 2024. The Author(s).)

Objective. Automated segmentation of targets in ultrasound (US) images during US-guided liver surgery holds the potential to assist physicians in fast locating critical areas such as blood vessels and lesions. However, this remains a challenging task primarily due to the image quality issues associated with US, including blurred edges and low contrast. In addition, studies specifically targeting liver segmentation are relatively scarce possibly since studying deep abdominal organs under US is difficult. In this paper, we proposed a network named BAG-Net to address these challenges and achieve accurate segmentation of liver targets with varying morphologies, including lesions and blood vessels. Approach. The BAG-Net was designed with a boundary detection module together with a position module to locate the target, and multiple attention-guided modules combined with the depth supervision strategy to enhance detailed segmentation of the target area. Main Results. Our method was compared to other approaches and demonstrated superior performance on two liver US datasets. Specifically, the method achieved 93.9% precision, 91.2% recall, 92.4% Dice coefficient, and 86.2% IoU to segment the liver tumor. Additionally, we evaluated the capability of our network to segment tumors on the breast US dataset (BUSI), where it also achieved excellent results. Significance. Our proposed method was validated to effectively segment liver targets with diverse morphologies, providing suspicious areas for clinicians to identify lesions or other characteristics. In the clinic, the method is anticipated to improve surgical efficiency during US-guided surgery., (© 2024 Institute of Physics and Engineering in Medicine.)