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Purpose: This study quantifies the impact on budget and cost per health benefit of implementing digital breast tomosynthesis (DBT) in place of digital mammography (DM) for breast cancer screening among asymptomatic women in Italy. Methods: A budget impact analysis and a cost consequence analysis were conducted using parameters from the MAITA project and literature. The study considered four scenarios for DBT implementation, i.e., DBT for all women, DBT for women aged 45–49 years, DBT based on breast density (BI-RADS C + D or D only), and compared these to the current DM screening. Healthcare provider's perspective was adopted, including screening, diagnosis, and cancer treatment costs. Results: Introducing DBT for all women would increase overall screening costs by 20%. Targeting DBT to women aged 45–49 years or with dense breasts would result in smaller cost increases (3.2% for age-based and 1.4–10.7% for density-based scenarios). The cost per avoided interval cancer was significantly higher when DBT was applied to all women compared to targeted approaches. The cost per gained early-detected cancer slightly increases in targeted approaches, while the assumptions on the clinical significance and overdiagnosis of cancers detected by DBT and not by DM have a strong impact. Conclusions: Implementing DBT as a primary breast cancer test in screening programs in Italy would lead to a substantial increase in costs. Tailoring DBT use to women aged 45–49 or with dense breasts could enhance the feasibility and sustainability of the intervention. Further research is needed to clarify the impact of DBT on overdiagnosis and the long-term outcomes. [ABSTRACT FROM AUTHOR]

Introduction: Screening and diagnostic mammography are associated with some risk of radiation-induced breast cancer. This study was conducted to establish the National Diagnostic Reference Levels (NDRLs) for digital diagnostic and screening mammography in Uganda to achieve breast radiation dose optimization. Methods: A cross-sectional study was conducted among female participants recruited by consecutive sampling from three selected hospitals with digital mammography in Uganda. The study variables extracted from the mammography machines were exposure factors, compressed breast thickness (CBT), and Average Glandular Dose (AGD) of two standard mammogram views. The stratified National DRL was derived by calculating the 75th percentile of the AGD across all the samples at various CBT ranges for both screening and diagnostic mammography in craniocaudal (CC) and mediolateral oblique (MLO) views. Results: We included 300 participants with mean ages of 50.28±9.32 and 47.45±13.45 years for the screening and diagnostic mammography, respectively. There were statistically significant positive correlations between AGD and exposure factors (mAs, kVp) (all p-values<0.0001). For screening mammography, mAs demonstrated a strong positive correlation (r = 0.8369 in CC, 0.8133 in MLO), whereas kVp showed a positive correlation with relatively lower coefficients (r = 0.3700 in CC, 0.3080 in MLO). For diagnostic mammography, mAs exhibited an even stronger positive correlation (r = 0.8987 in CC, 0.8762 in MLO), and kVp maintained a positive correlation with somewhat lower coefficients (r = 0.4954 in CC, 0.3597 in MLO). In screening mammography, for CBT within the range of (7–39) mm, the NDRLs were (1.5mGy, 1.66mGy) in CC) and MLO views. For CBT in the range of (40–59) mm, the NDRLs were (1.78mGy, 1.87mGy), and for CBT in the range of (60–99) mm, the NDRLs were (2.18mGy, 2.22mGy). For diagnostic mammography, the NDRLs were established as (1.7mGy, 1.91mGy), (2.00mGy, 2.09mGy), and (2.63mGy, 2.81mGy) for CBT ranges of (7–39) mm, (40–59) mm, and (60–99) mm, respectively. Conclusion: The NDRLs for digital screening and diagnostic mammography in Uganda have been proposed for the first time. The NDRL values in mammography should be specific to CBT ranges and mammographic views for both diagnostic and screening mammography. [ABSTRACT FROM AUTHOR]

Aim Methods Results Conclusion Manual detection and scoring of Ki67 hotspots is difficult and prone to variability, limiting its clinical utility. Automated hotspot detection and scoring by digital image analysis (DIA) could improve the assessment of the Ki67 hotspot proliferation index (PI). This study compared the clinical performance of Ki67 hotspot detection and scoring DIA algorithms based on virtual dual staining (VDS) and deep learning (DL) with manual Ki67 hotspot PI assessment.Tissue sections of 135 consecutive invasive breast carcinomas were immunohistochemically stained for Ki67. Two DIA algorithms, based on VDS and DL, automatically determined the Ki67 hotspot PI. For manual assessment; two independent observers detected hotspots and calculated scores using a validated scoring protocol.Automated hotspot detection and assessment by VDS and DL could be performed in 73% and 100% of the cases, respectively. Automated hotspot detection by VDS and DL led to higher Ki67 hotspot PIs (mean 39.6% and 38.3%, respectively) compared to manual consensus Ki67 PIs (mean 28.8%). Comparing manual consensus Ki67 PIs with VDS Ki67 PIs revealed substantial correlation (r = 0.90), while manual consensus versus DL Ki67 PIs demonstrated high correlation (r = 0.95).Automated Ki67 hotspot detection and analysis correlated strongly with manual Ki67 assessment and provided higher PIs compared to manual assessment. The DL‐based algorithm outperformed the VDS‐based algorithm in clinical applicability, because it did not depend on virtual alignment of slides and correlated stronger with manual scores. Use of a DL‐based algorithm may allow clearer Ki67 PI cutoff values, thereby improving the clinical usability of Ki67. [ABSTRACT FROM AUTHOR]

Breast cancer detection at an early stage is crucial for improving patient survival rates. This work introduces an innovative thermal imaging prototype that incorporates compression techniques inspired by mammography equipment. The prototype offers a radiation-free and precise cancer diagnosis. By integrating compression and illumination methods, thermal picture quality has increased, and the accuracy of classification has improved. Essential components of the suggested thermography device include an equipment body, plates, motors, pressure sensors, light sources, and a thermal camera. We created a 3D model of the gadget using the SolidWorks software 2020 package. Furthermore, the classification research employed both cancer and normal images from the experimental results to validate the efficacy of the suggested system. We employed preprocessing and segmentation methods on the obtained dataset. We successfully categorized the thermal pictures using various classifiers and examined their performance. The logistic regression model showed excellent performance, achieving an accuracy of 0.976, F1 score of 0.977, precision of 1.000, and recall of 0.995. This indicates a high level of accuracy in correctly classifying thermal abnormalities associated with breast cancer. The proposed prototype serves as a highly effective tool for conducting initial investigations into breast cancer detection, offering potential advancements in early-stage diagnosis, and improving patient survival rates. [ABSTRACT FROM AUTHOR]

Purpose: This work aims to compare the interval cancer rate and interval cancer characteristics between women screened with digital breast tomosynthesis (DBT) + digital mammography (DM) and those screened with DM alone. Methods: The interval cancer rate and interval cancer characteristics of the study population included in the Córdoba Breast Tomosynthesis Screening Trial (CBTST) were compared to a contemporary control population screened with DM. The tumour characteristics of screen-detected and interval cancers were also compared. Contingency tables were used to compare interval cancer rates. The chi-square test and Fisher's exact test were used to compare the qualitative characteristics of the cancers whereas Student's t test and the Mann–Whitney U test were used to analyse quantitative features. Results: A total of 16,068 screening exams with DBT + DM were conducted within the CBTST (mean age 57.59 ± 5.9 [SD]) between January 2015 and December 2016 (study population). In parallel, 23,787 women (mean age 58.89 ± 5.9 standard deviation [SD]) were screened with DM (control population). The interval cancer rate was lower in the study population than in the control population (15 [0.93‰; 95% confidence interval (CI): 0.73, 1.14] vs 43 [1.8‰; 95% CI: 1.58, 2.04] respectively; p = 0.045). The difference in rate was more marked in women with dense breasts (0.95‰ in the study population vs 3.17‰ in the control population; p = 0.031). Interval cancers were smaller in the study population than in the control population (p = 0.031). Conclusions: The interval cancer rate was lower in women screened with DBT + DM compared to those screened with DM alone. These differences were more pronounced in women with dense breasts. Clinical relevance statement: Women screened using tomosynthesis and digital mammography had a lower rate of interval cancer than women screened with digital mammography, with the greatest difference in the interval cancer rate observed in women with dense breasts. Key Points: • The interval cancer rate was lower in the study population (digital breast tomosynthesis [DBT] + digital mammography [DM]) than in the control population (DM). • The difference in interval cancer rates was more pronounced in women with dense breasts. • Interval cancers were smaller in the study population (DBT + DM) than in the control population (DM). [ABSTRACT FROM AUTHOR]

Purpose: We present a comprehensive investigation into the organizational, social, and ethical impact of implementing digital breast tomosynthesis (DBT) as a primary test for breast cancer screening in Italy. The analyses aimed to assess the feasibility of DBT specifically for all women aged 45–74, women aged 45–49 only, or those with dense breasts only. Methods: Questions were framed according to the European Network of Health Technology Assessment (EuNetHTA) Screening Core Model to produce evidence for the resources, equity, acceptability, and feasibility domains of the Grading of Recommendations Assessment, Development and Evaluation (GRADE) decision framework. The study integrated evidence from the literature, the MAITA DBT trials, and Italian pilot programs. Structured interviews, surveys, and systematic reviews were conducted to gather data on organizational impact, acceptability among women, reading and acquisition times, and the technical requirements of DBT in screening. Results: Implementing DBT could significantly affect the screening program, primarily due to increased reading times and the need for additional human resources (radiologists and radiographers). Participation rates in DBT screening were similar, if not better, to those observed with standard digital mammography, indicating good acceptability among women. The study also highlighted the necessity for specific training for radiographers. The interviewed key persons unanimously considered feasible tailored screening strategies based on breast density or age, but they require effective communication with the target population. Conclusions: An increase in radiologists' and radiographers' workload limits the feasibility of DBT screening. Tailored screening strategies may maximize the benefits of DBT while mitigating potential challenges. [ABSTRACT FROM AUTHOR]

Upon surgical removal of solid tumors in patients with breast cancer, the sentinel lymph nodes are mostly concomitantly removed as this is the first site to which tumor cells metastasize. To identify the sentinel lymph nodes, superparamagnetic iron oxide (SPIO) particles can be employed, a method that has several advantages over the classical tracer with radioisotopes. However, one downside of SPIO particles is the formation of artifacts on magnetic resonance imaging (MRI) of the breast. In a previous study, we investigated if lowering the dose of these particles from 2 to 1 mL would prevent the occurrence of these artifacts. In a study of 14 patients, radiologists do not observe any artifacts on the full‐field digital mammography and contrast‐enhanced mammography images, contrary to breast MRI images, in which case artifacts are observed on all images. Hence, lowering the dose from 2 to 1 mL SPIO particles did not change the outcome of the breast MRI images. In this article, based on these results, we discuss the origins of these artifacts from a mathematical and physical point of view. [ABSTRACT FROM AUTHOR]

A drastic rise in the incidence of breast cancer in patients is witnessed as per the new roadmap launched by the World Health Organization in 2023. Premature breast cancer detection improves survival chances by allowing for more effective clinical treatments. The artificial intelligence-enabled digital mammography examination supports the early finding of breast cancer. However, detecting tumour shape and location variations is challenging because of mammogram images' low contrast and noise. The mammogram images are preprocessed in this work, and the features are extracted using Haar wavelet that is further applied to machine learning and deep learning prototypes. As the modified convolution neural network categorizes the local images based on handcrafted features and global features based on preprocessed statistics, the hybrid deep neural network effectively classifies the benign and malignant diagnosis. The hybrid deep neural network model achieved significant experimental results compared to machine learning techniques. The results are substantial and have an area under the curve of 0.92. [ABSTRACT FROM AUTHOR]

To explore the diagnostic efficacy of tomosynthesis spot compression (TSC) compared with conventional spot compression (CSC) for ambiguous findings on full-field digital mammography (FFDM). In this retrospective study, 122 patients (including 108 patients with dense breasts) with ambiguous FFDM findings were imaged with both CSC and TSC. Two radiologists independently reviewed the images and evaluated lesions using the Breast Imaging Reporting and Data System. Pathology or at least a 1-year follow-up imaging was used as the reference standard. Diagnostic efficacies of CSC and TSC were compared, including area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). The mean glandular dose was recorded and compared for TSC and CSC. Of the 122 patients, 63 had benign lesions and 59 had malignant lesions. For Reader 1, the following diagnostic efficacies of TSC were significantly higher than those of CSC: AUC (0.988 vs. 0.906, P = 0.001), accuracy (93.4% vs. 77.8%, P = 0.001), specificity (87.3% vs. 63.5%, P = 0.002), PPV (88.1% vs. 70.5%, P = 0.010), and NPV (100% vs. 90.9%, P = 0.029). For Reader 2, TSC showed higher AUC (0.949 vs. 0.909, P = 0.011) and accuracy (83.6% vs. 71.3%, P = 0.022) than CSC. The mean glandular dose of TSC was higher than that of CSC (1.85 ± 0.53 vs. 1.47 ± 0.58 mGy, P < 0.001) but remained within the safety limit. TSC provides better diagnostic efficacy with a slightly higher but tolerable radiation dose than CSC. Therefore, TSC may be a candidate modality for patients with ambiguous findings on FFDM. [ABSTRACT FROM AUTHOR]

Contrast-enhanced mammography (CEM) is a relatively new imaging technique that allows morphologic, anatomic and functional imaging of the breast. The aim of our study was to validate contrast-enhanced mammography (CEM) compared to mammography (MMG) and digital breast tomosynthesis (DBT) in daily clinical practice. This retrospective study included 316 consecutive patients who underwent MMG, DBT and CEM at the Centre for Prevention and Diagnosis of Chronic Diseases of Primorsko-goranska County. Two breast radiologists independently analyzed the image data, without available anamnestic information and without the possibility of comparison with previous images, to determine the presence of suspicious lesions and their morphological features according to the established criteria of the Breast Imaging Reporting and Data System (BI-RADS) lexicon. The diagnostic value of MMG, DBT and CEM was assessed by ROC analysis. The interobserver agreement was excellent. CEM showed higher diagnostic accuracy in terms of sensitivity and specificity compared to MMG and DBT, the reporting time for CEM was significantly shorter, and CEM findings resulted in a significantly lower proportion of equivocal findings (BI-RADS 0), suggesting fewer additional procedures. In conclusion, CEM achieves high diagnostic accuracy while maintaining simplicity, reproducibility and applicability in complex clinical settings. [ABSTRACT FROM AUTHOR]

Background: The effect of gender-affirming testosterone therapy (TT) on breast cancer risk is unclear. This study investigated the association between TT and breast tissue composition and breast tissue density in trans masculine individuals (TMIs). Methods: Of the 444 TMIs who underwent chest-contouring surgeries between 2013 and 2019, breast tissue composition was assessed in 425 TMIs by the pathologists (categories of lobular atrophy and stromal composition) and using our automated deep-learning algorithm (% epithelium, % fibrous stroma, and % fat). Forty-two out of 444 TMIs had mammography prior to surgery and their breast tissue density was read by a radiologist. Mammography digital files, available for 25/42 TMIs, were analyzed using the LIBRA software to obtain percent density, absolute dense area, and absolute non-dense area. Linear regression was used to describe the associations between duration of TT use and breast tissue composition or breast tissue density measures, while adjusting for potential confounders. Analyses stratified by body mass index were also conducted. Results: Longer duration of TT use was associated with increasing degrees of lobular atrophy (p < 0.001) but not fibrous content (p = 0.82). Every 6 months of TT was associated with decreasing amounts of epithelium (exp(β) = 0.97, 95% CI 0.95,0.98, adj p = 0.005) and fibrous stroma (exp(β) = 0.99, 95% CI 0.98,1.00, adj p = 0.05), but not fat (exp(β) = 1.01, 95%CI 0.98,1.05, adj p = 0.39). The effect of TT on breast epithelium was attenuated in overweight/obese TMIs (exp(β) = 0.98, 95% CI 0.95,1.01, adj p = 0.14). When comparing TT users versus non-users, TT users had 28% less epithelium (exp(β) = 0.72, 95% CI 0.58,0.90, adj p = 0.003). There was no association between TT and radiologist's breast density assessment (p = 0.58) or LIBRA measurements (p > 0.05). Conclusions: TT decreases breast epithelium, but this effect is attenuated in overweight/obese TMIs. TT has the potential to affect the breast cancer risk of TMIs. Further studies are warranted to elucidate the effect of TT on breast density and breast cancer risk. [ABSTRACT FROM AUTHOR]

Objective The use of artificial intelligence has potential in assisting many aspects of imaging interpretation. We undertook a prospective service evaluation from March to October 2022 of Mammography Intelligent Assessment (MIA) operating "silently" within our Breast Screening Service, with a view to establishing its performance in the local population and setting. This evaluation addressed the performance of standalone MIA vs conventional double human reading of mammograms. Methods MIA analyzed 8779 screening events over an 8-month period. The MIA outcome did not influence the decisions made on the clinical pathway. Cases were reviewed approximately 6 weeks after the screen reading decision when human reading and/or MIA indicated a recall. Results There were 146 women with positive concordance between human reading and MIA (human reader and MIA recalled) in whom 58 breast cancers were detected. There were 270 women with negative discordance (MIA no recall, human reader recall) for whom 19 breast cancers and 1 breast lymphoma were detected, with 1 cancer being an incidental finding at assessment. Six hundred and four women had positive discordance (MIA recall, human reader no recall) in whom 2 breast cancers were detected at review. The breast cancers demonstrated a wide spectrum of mammographic features, sites, sizes, and pathologies, with no statistically significant difference in features between the negative discordant and positive concordant cases. Conclusion Of 79 breast cancers identified by human readers, 18 were not identified by MIA, and these had no specific features or site to suggest a systematic error for MIA analysis of 2D screening mammograms. [ABSTRACT FROM AUTHOR]

Abstract Although digital breast tomosynthesis has higher sensitivity than digital mammography and at least as high specificity, digital mammography remains the most common method for conducting mammographic screening. At the same time, mammography systems are now delivered “DBT-ready” and can be used for either digital mammography or digital breast tomosynthesis. In this paper, we ask whether it is ethically permissible to use such equipment for digital mammography, given its lower sensitivity. We argue it is not, and that clinics are ethically required to use their DBT-ready equipment to screen with digital breast tomosynthesis whenever this is practically possible. Our argument relies on a comparison between digital breast tomosynthesis and a hypothesized improvement in the image quality of digital mammography. Critical relevance statement Women may lose out on the benefits of screening with digital breast tomosynthesis when DBT-ready equipment is used to screen with digital mammography; we argue that this practice is ethically problematic. Key Points Digital breast tomosynthesis finds more cases of breast cancer than digital mammography. Mammography equipment can often be used to screen with both digital breast tomosynthesis and digital mammography. When they can, clinics are ethically required to use existing equipment to screen with digital breast tomosynthesis instead of digital mammography.

Abstract Background Pathological nipple discharge (PND) commonly caused by benign diseases, but occasionally it signifies a major medical concern. Ultrasonography, in addition to mammography, is regarded as the standard imaging modality in the diagnosis of PND but their sensitivity in some cases are low, subsequently we used a contrast enhanced mammography (CEDM) as supplementary diagnostic modality in patients with PND. The purpose of our study was to investigate the diagnostic efficacy of CEDM in evaluating PND patients, added values of incorporating the CEDM in the diagnostic workup of patients with PND and to demonstrate its diagnostic significance as a predictor of malignancy in these patients as there have been few studies that have addressed the role of CEDM in the evaluation of PND. Results Forty seven patients with PND were enrolled in this prospective study and underwent CEDM. The CEDM had high specificity (83.2%) compared to the combined sonomammography (SM) (59.3%), as there was a decrease in the number of false positive cases detected by the CEDM (6 cases) compared to the combined SM (11 cases). Combined (SM) had a moderate degree of agreement (55%, P = 0.01) with the final diagnosis, whereas CEDM had a strong degree of agreement (75%, P

Abstract Background Mammography alone is an ineffective method for breast cancer surveillance and diagnosing cancer recurrence. The aim was to evaluate the ability of artificial intelligence (AI) to read digital mammograms as an additive tool to exclude recurrence in the operative bed of known breast cancer patients following the different surgical procedures. Methods We used a retrospective cohort study of post-surgery mammograms (n = 577). Imaging was performed within 6 months after the surgery or more. The AI solution used to read mammograms (AI-MMG) provided a targeted heat map of the operative bed, which was supported by a decision likelihood score percentage of cancer recurrence. The reference for suspicious or malignant-looking abnormalities (n = 62, 12.3%) was diagnosed by biopsy. A clear operative bed and benign-looking changes (n = 442) were confirmed by ultrasound characterization patterns and one year of intermittent follow-up. Results The AI scoring percentage for a clear operative bed ranged between 0 and 26%, with a mean of 15% ± 5.4%. Operative bed benign changes ranged from 10 to 88%, with a mean of 48.2% ± 21.2%, while malignancy recurrence ranged from 65 to 99%, with an average of 87.7% ± 10.5%. The “ROC: Receiver Operating Characteristic” curve for AI to predict cancer in the surgical bed on mammograms was 0.906. The optimum cutoff value to distinguish between benign postoperative alterations and malignancy recurrence was 56.5% (95%, CI 0.824–1.060, p value

Xulei Sui,1 Peipei Feng,1 Xiaoyuan Sun,1 Peng Liang,1 Ying Liu,2 Guowei Zhang3 1Department of Radiology, Yantaishan Hospital, Yantai City, 264001, People’s Republic of China; 2Department of Pathology, Yantaishan Hospital (East Courtyard), Yantai City, 264003, People’s Republic of China; 3Department of Radiology, Yantaishan Hospital (East Courtyard), Yantai City, 264003, People’s Republic of ChinaCorrespondence: Guowei Zhang, Department of Radiology, Yantaishan Hospital (East Courtyard), No. 10087 Keji Avenue, Laishan District, Yantai City, 264003, People’s Republic of China, Tel +86-0535-6602048, Email zhangguoweiyt@163.comPurpose: This study determined the digital mammography and ultrasonography imaging features of pure invasive micropapillary carcinoma of the breast (PIMPC) and the correlation with pathologic features.Patients Methods: Nineteen patients diagnosed with PIMPC at Yantaishan Hospital from October 2015 to February 2022 were included in the study group. Forty patients with breast masses diagnosed as nonspecific invasive ductal carcinoma of the breast (NIDC) from July to December 2021 were included in the control group. Digital mammography and ultrasonography features were compared between the two groups.Results: Patients with PIMPC had a younger age profile compared to patients with NIDC (P=0.017). Moreover, PIMPC masses were smaller than NIDC masses (P=0.040). Imaging features analysis revealed significant differences in age groups (< 45 years: χ²=5.971, P=0.044) and the presence of spiculations or the crab claw sign (χ²=8.583, P=0.004) between patients with PIMPC and NIDC. However, there were no statistically significant differences in the presence of calcifications, blood flow grading, pathologic molecular subtypes between the study and control groups. The Ki-67 proliferative index (χ²=1.052, P=0.389), vascular invasion (χ²=2.263, P=0.197), and lymph node metastasis (χ²=1.968, P=0.386) showed no significant differences between PIMPC and NIDC patients.Conclusion: PIMPC imaging features show specificity, such as tiny breast masses, spiculated edges, or crab claw-like patterns, and malignant signs appeared when the lesion was < 2 cm in diameter. PIMPC mainly occurs in middle-aged women 45– 59 y of age. Patients with PIMPC and NIDC of the breast are frequently associated with lymph node metastases and greater than one-half of the cases (74%) were shown to have a Ki-67 index > 30%, suggesting a significant risk of recurrence and metastasis. Early therapeutic care for these patients is crucial. These results warrant further validation with additional samples from several centers due to the limited single-center sample size in the current study.Keywords: pure invasive micropapillary breast carcinoma, digital mammography, ultrasonography, breast cancer, pathologic features

Background: Pathological nipple discharge (PND) commonly caused by benign diseases, but occasionally it signifies a major medical concern. Ultrasonography, in addition to mammography, is regarded as the standard imaging modality in the diagnosis of PND but their sensitivity in some cases are low, subsequently we used a contrast enhanced mammography (CEDM) as supplementary diagnostic modality in patients with PND. The purpose of our study was to investigate the diagnostic efficacy of CEDM in evaluating PND patients, added values of incorporating the CEDM in the diagnostic workup of patients with PND and to demonstrate its diagnostic significance as a predictor of malignancy in these patients as there have been few studies that have addressed the role of CEDM in the evaluation of PND. Results: Forty seven patients with PND were enrolled in this prospective study and underwent CEDM. The CEDM had high specificity (83.2%) compared to the combined sonomammography (SM) (59.3%), as there was a decrease in the number of false positive cases detected by the CEDM (6 cases) compared to the combined SM (11 cases). Combined (SM) had a moderate degree of agreement (55%, P = 0.01) with the final diagnosis, whereas CEDM had a strong degree of agreement (75%, P < 0.001). Additionally, the combined SM reported 76.6% accuracy with an area under the curve of 0.8, whereas the CEDM had 87.2% accuracy with an area under the curve of 0.89. Conclusions: CEDM had higher specificity, positive predictive value, and accuracy than SM in PND patients, along with its stronger agreement with the final pathology results, subsequently reduce the rate of false positive cases and the rate of recall back, making it a highly accurate malignancy predictor in those patients and can be an invaluable diagnostic imaging tool for identifying associated malignancies. [ABSTRACT FROM AUTHOR]

Regardless of many years of research work and conducting numerous clinical studies, breast cancer (BC) continues to grow among women, threatening their lives and future. Currently, it affects more than 10% of women globally. Therefore, researchers have recently redirected their endeavors toward detecting BC at an early stage, recognizing its substantial impact on survival rates, and acknowledging its immense potential for providing effective cancer treatments at this early diagnosed disease stage. The utilization of biomarkerbased diagnostic techniques significantly contributes to the advancement of early detection and precision. A wide range of biomarkers is linked to every newly diagnosed BC case. Traditionally, breast cancer was diagnosed using a variety of techniques such as mammography, molecular imaging, and ultrasonography. These techniques had several drawbacks, drawing the attention of many scientists to developing new methods and strategies. Among these strategies, electrochemical biosensors have proven to be excellent alternatives to more complex and traditional detection strategies in terms of performance, accuracy, robustness, and a tremendous range of applications. In this review, the use of conventional methods for detecting BC as well as their challenges and shortcomings have been stated. Moreover, a deep discussion was given to the newly developed nanocomposite-based electrochemical bio-sensing approaches that have been exploited for the effective detection and quantification of breast cancer biomarkers. [ABSTRACT FROM AUTHOR]

While precision medicine applications of radiomics analysis are promising, differences in image acquisition can cause "batch effects" that reduce reproducibility and affect downstream predictive analyses. Harmonization methods such as ComBat have been developed to correct these effects, but evaluation methods for quantifying batch effects are inconsistent. In this study, we propose the use of the multivariate statistical test PERMANOVA and the Robust Effect Size Index (RESI) to better quantify and characterize batch effects in radiomics data. We evaluate these methods in both simulated and real radiomics features extracted from full-field digital mammography (FFDM) data. PERMANOVA demonstrated higher power than standard univariate statistical testing, and RESI was able to interpretably quantify the effect size of site at extremely large sample sizes. These methods show promise as more powerful and interpretable methods for the detection and quantification of batch effects in radiomics studies. [ABSTRACT FROM AUTHOR]

Background/Objectives: Nephrolithiasis is a heterogeneous disease with a high prevalence and recurrence rate. Although there has been much progress regarding the surgical treatment of stones, a standardized follow-up, especially in recurrent stone formers (SFs), has yet to be decided. This fact leads to the overuse of computed tomography (CT) scans and many reoperations in patients, thus increasing their morbidity and the financial burden on the health systems. This review systematically searched the literature for original articles regarding imaging strategies and endoscopic treatment for patients with recurrent urolithiasis, aiming to identify optimal strategies to deal with these patients. Methods: We systematically searched the Medline database (accessed on 1 April 2024) for articles regarding imaging modalities and endoscopic treatment for patients with recurrent urinary tract lithiasis. Results: No specific follow-up or endoscopic treatment strategy exists for patients with recurrent urolithiasis. CT scan was the imaging modality most used in the studies, followed by X-ray, ultrasonography, and digital tomosynthesis. A transparent algorithm could not be identified. Percutaneous nephrolithotomy (PCNL), retrograde intrarenal surgery (RIRS), and ureteroscopy (URS) were used in the studies for endoscopic treatment. PCNL showed the best stone-free (SFr) rate and lowest hazard ratio (HR) for reoperation. RIRS showed superiority over extracorporeal shockwave lithotripsy for recurrent SFs, but fragments over 4 mm increased the recurrent rate. URS has an increased HR for reoperation for bilateral stones. Conclusions: The heterogeneity of urolithiasis leaves urologists without a standardized plan for recurrent SFs. Thus, each patient's follow-up should be planned individually and holistically. Pre-stenting is not to be avoided, especially in high-risk patients, and SFr status needs to be the aim. Finally, CT scans should not be generally overused but should be part of a patient's treatment plan. Prospective studies are required to define SFr status, the size of significant residual fragments, and the modalities of intervention and follow-up. [ABSTRACT FROM AUTHOR]

[Display omitted] An intraoperative diagnosis is critical for precise cancer surgery. However, traditional intraoperative assessments based on hematoxylin and eosin (H&E) histology, such as frozen section, are time-, resource-, and labor-intensive, and involve specimen-consuming concerns. Here, we report a near-real-time automated cancer diagnosis workflow for breast cancer that combines dynamic full-field optical coherence tomography (D-FFOCT), a label-free optical imaging method, and deep learning for bedside tumor diagnosis during surgery. To classify the benign and malignant breast tissues, we conducted a prospective cohort trial. In the modeling group (n = 182), D-FFOCT images were captured from April 26 to June 20, 2018, encompassing 48 benign lesions, 114 invasive ductal carcinoma (IDC), 10 invasive lobular carcinoma, 4 ductal carcinoma in situ (DCIS), and 6 rare tumors. Deep learning model was built up and fine-tuned in 10,357 D-FFOCT patches. Subsequently, from June 22 to August 17, 2018, independent tests (n = 42) were conducted on 10 benign lesions, 29 IDC, 1 DCIS, and 2 rare tumors. The model yielded excellent performance, with an accuracy of 97.62%, sensitivity of 96.88% and specificity of 100%; only one IDC was misclassified. Meanwhile, the acquisition of the D-FFOCT images was non-destructive and did not require any tissue preparation or staining procedures. In the simulated intraoperative margin evaluation procedure, the time required for our novel workflow (approximately 3 min) was significantly shorter than that required for traditional procedures (approximately 30 min). These findings indicate that the combination of D-FFOCT and deep learning algorithms can streamline intraoperative cancer diagnosis independently of traditional pathology laboratory procedures. [ABSTRACT FROM AUTHOR]

Key Points: Question: What are the benefits and harms of different screening mammography strategies? Findings: Six validated CISNET models found that, compared with no screening, biennial mammography screening with digital breast tomosynthesis from age 40 to 74 yielded a median of 8.2 breast cancer deaths averted per 1000 women screened, equal to a 30% reduction in breast cancer mortality, and 165 life-years gained, 1376 false-positive recalls, 201 benign biopsies, and 14 overdiagnosed cases per 1000 women screened. For each strategy, benefits were larger for Black women than for all women. Meaning: Biennial mammography from ages 40 to 74 years has favorable benefit-to-harm tradeoffs. Importance: The effects of breast cancer incidence changes and advances in screening and treatment on outcomes of different screening strategies are not well known. Objective: To estimate outcomes of various mammography screening strategies. Design, Setting, and Population: Comparison of outcomes using 6 Cancer Intervention and Surveillance Modeling Network (CISNET) models and national data on breast cancer incidence, mammography performance, treatment effects, and other-cause mortality in US women without previous cancer diagnoses. Exposures: Thirty-six screening strategies with varying start ages (40, 45, 50 years) and stop ages (74, 79 years) with digital mammography or digital breast tomosynthesis (DBT) annually, biennially, or a combination of intervals. Strategies were evaluated for all women and for Black women, assuming 100% screening adherence and "real-world" treatment. Main Outcomes and Measures: Estimated lifetime benefits (breast cancer deaths averted, percent reduction in breast cancer mortality, life-years gained), harms (false-positive recalls, benign biopsies, overdiagnosis), and number of mammograms per 1000 women. Results: Biennial screening with DBT starting at age 40, 45, or 50 years until age 74 years averted a median of 8.2, 7.5, or 6.7 breast cancer deaths per 1000 women screened, respectively, vs no screening. Biennial DBT screening at age 40 to 74 years (vs no screening) was associated with a 30.0% breast cancer mortality reduction, 1376 false-positive recalls, and 14 overdiagnosed cases per 1000 women screened. Digital mammography screening benefits were similar to those for DBT but had more false-positive recalls. Annual screening increased benefits but resulted in more false-positive recalls and overdiagnosed cases. Benefit-to-harm ratios of continuing screening until age 79 years were similar or superior to stopping at age 74. In all strategies, women with higher-than-average breast cancer risk, higher breast density, and lower comorbidity level experienced greater screening benefits than other groups. Annual screening of Black women from age 40 to 49 years with biennial screening thereafter reduced breast cancer mortality disparities while maintaining similar benefit-to-harm trade-offs as for all women. Conclusions: This modeling analysis suggests that biennial mammography screening starting at age 40 years reduces breast cancer mortality and increases life-years gained per mammogram. More intensive screening for women with greater risk of breast cancer diagnosis or death can maintain similar benefit-to-harm trade-offs and reduce mortality disparities. This modeling study uses Cancer Intervention and Surveillance Modeling Network models and national data on breast cancer incidence, mammography performance, treatment effects, and other-cause mortality in US women without previous cancer diagnoses to estimate outcomes of various mammography screening strategies. [ABSTRACT FROM AUTHOR]

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. This systematic review to support a 2024 US Preventive Services Task Force Recommendation Statement summarizes published evidence on the benefits and harms of screening for breast cancer in adult females. [ABSTRACT FROM AUTHOR]

Background: Locally advanced breast cancer (LABC) can be caused by a variety of breast neoplasms. The use of mammography with sonography to estimate pathological complete response (PCR) after neoadjuvant chemotherapy (NAC) has a good degree of precision. Aim of work: To evaluate the precision of breast sonomammography in determining LABC response to neoadjuvant chemotherapy. Subjects and methods: A total of 54 female patients with locally advanced breast cancer were included in the study, which was conducted prospectively from August 2019 to May 2020, they were sent to the Zagazig University Radiology Department and hospitals by the General Surgery and Oncology Departments. A thorough medical history (personal, past, present, and family) was collected, as well as a thorough physical examination. All subjects had bilateral digital mammography as well as a traditional B-mode ultrasonography examination. Before neoadjuvant treatment, a sonomammographic evaluation of the tumour size and morphometric criteria was performed. Biopsy processes were done for suspected breast masses. after finishing the NAC 1:4 weeks previous to surgery, a sonomammographic evaluation of the tumour size and morphological criteria was conducted. Results: There was significant association between post chemotherapy diameters and different breast subtypes in radiological assessment. HR-/HER2-were the most subtype to show radiological response followed by HR+/HER2-while none of HR+/HER2+ show response to NAC. Conclusion: In patients with LABC, mammography and breast ultrasonography are regarded more reliable techniques for assessing tumour size and nodal staging of breast cancer subtypes prior to or after treatment. [ABSTRACT FROM AUTHOR]

The evaluation of mammographic breast density, a critical indicator of breast cancer risk, is traditionally performed by radiologists via visual inspection of mammography images, utilizing the Breast Imaging-Reporting and Data System (BI-RADS) breast density categories. However, this method is subject to substantial interobserver variability, leading to inconsistencies and potential inaccuracies in density assessment and subsequent risk estimations. To address this, we present a deep learning-based automatic detection algorithm (DLAD) designed for the automated evaluation of breast density. Our multicentric, multi-reader study leverages a diverse dataset of 122 full-field digital mammography studies (488 images in CC and MLO projections) sourced from three institutions. We invited two experienced radiologists to conduct a retrospective analysis, establishing a ground truth for 72 mammography studies (BI-RADS class A: 18, BI-RADS class B: 43, BI-RADS class C: 7, BI-RADS class D: 4). The efficacy of the DLAD was then compared to the performance of five independent radiologists with varying levels of experience. The DLAD showed robust performance, achieving an accuracy of 0.819 (95% CI: 0.736–0.903), along with an F1 score of 0.798 (0.594–0.905), precision of 0.806 (0.596–0.896), recall of 0.830 (0.650–0.946), and a Cohen's Kappa (κ) of 0.708 (0.562–0.841). The algorithm achieved robust performance that matches and in four cases exceeds that of individual radiologists. The statistical analysis did not reveal a significant difference in accuracy between DLAD and the radiologists, underscoring the model's competitive diagnostic alignment with professional radiologist assessments. These results demonstrate that the deep learning-based automatic detection algorithm can enhance the accuracy and consistency of breast density assessments, offering a reliable tool for improving breast cancer screening outcomes. [ABSTRACT FROM AUTHOR]

The aim of this study was to compare the characteristics of breast microcalcification on digital mammography (DM) with the histological and molecular subtypes of breast cancer and to identify the predictive value of DM and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in assessing microcalcifications for radiologic–pathologic correlation. We relied on our prospectively maintained database of suspicious microcalcifications on DM, from which data were retrospectively collected between January 2020 and April 2023. We enrolled 158 patients, all of whom were subjected to biopsy. Additionally, 63 patients underwent breast DCE-MRI. Microcalcifications with a linear branched morphology were correlated with malignancies (p < 0.001), among which an association was highlighted between triple negatives (TNs) and segmental distribution (p < 0.001). Amorphous calcifications were correlated with atypical ductal hyperplasia (ADH) (p = 0.013), coarse heterogeneous (p < 0.001), and fine-pleomorphic (p = 0.008) with atypical lobular hyperplasia (ALH) and fine pleomorphic (p = 0.009) with flat epithelial atypia (FEA). Regarding DCE-MRI, no statistical significance was observed between non-mass lesions and ductal carcinoma in situ (DCIS). Concerning mass lesions, three were identified as DCIS and five as invasive ductal carcinoma (IDC). In conclusion, microcalcifications assessed in DM exhibit promising predictive characteristics concerning breast lesion subtypes, leading to a reduction in diagnostic times and further examination costs, thereby enhancing the clinical management of patients. [ABSTRACT FROM AUTHOR]

Objectives: To analyze discordant and false-negatives of double reading digital breast tomosynthesis (DBT) versus digital mammography (DM) including reading times in the Oslo Tomosynthesis Screening Trial (OTST), and reclassify these in a retrospective reader study as missed, minimal sign, or true-negatives. Methods: The prospective OTST comparing double reading DBT vs. DM had paired design with four parallel arms: DM, DM + computer aided detection, DBT + DM, and DBT + synthetic mammography. Eight radiologists interpreted images in batches using a 5-point scale. Reading time was automatically recorded. A retrospective reader study including four radiologists classified screen-detected cancers with at least one false-negative score and screening examinations of interval cancers as negative, non-specific minimal sign, significant minimal sign, and missed; the two latter groups are defined "actionable." Statistics included chi-square, Fisher's exact, McNemar's, and Mann–Whitney U tests. Results: Discordant rate (cancer missed by one reader) for screen-detected cancers was overall comparable (DBT (31% [71/227]) and DM (30% [52/175]), p =.81), significantly lower at DBT for spiculated cancers (DBT, 19% [20/106] vs. DM, 36% [38/106], p =.003), but high (28/49 = 57%, p = 0.001) for DBT-only detected spiculated cancers. Reading time and sensitivity varied among readers. False-negative DBT-only detected spiculated cancers had shorter reading time than true-negatives in 46% (13/28). Retrospective evaluation classified the following DBT exams "actionable": three missed by both readers, 95% (39/41) of discordant cancers detected by both modes, all 30 discordant DBT-only cancers, 25% (13/51) of interval cancers. Conclusions: Discordant rate was overall comparable for DBT and DM, significantly lower at DBT for spiculated cancers, but high for DBT-only detected spiculated lesions. Most false-negative screen-detected DBT were classified as "actionable." Clinical relevance statement: Retrospective evaluation of false-negative interpretations from the Oslo Tomosynthesis Screening Trial shows that most discordant and several interval cancers could have been detected at screening. This underlines the potential for modern AI-based reading aids and triage, as high-volume screening is a demanding task. Key Points: • Digital breast tomosynthesis (DBT) screening is more sensitive and has higher specificity compared to digital mammography screening, but high-volume DBT screening is a demanding task which can result in high discordance rate among readers. • Independent double reading DBT screening had overall comparable discordance rate as digital mammography, lower for spiculated masses seen on both modalities, and higher for small spiculated cancer seen only on DBT. • Almost all discordant digital breast tomosynthesis-detected cancers (72 of 74) and 25% (13 of 51) of the interval cancers in the Oslo Tomosynthesis Screening Trial were retrospectively classified as actionable and could have been detected by the readers. [ABSTRACT FROM AUTHOR]

Introduction: While digital breast tomosynthesis (DBT) has proven to enhance cancer detection and reduce recall rates (RR), its integration into BreastScreen Australia for screening has been limited, in part due to perceived cost implications. This study aims to assess the cost effectiveness of digital mammography (DM) compared with synthesized mammography and DBT (SM + DBT) in a first round screening context for short‐term outcomes. Methods: Clients recalled for nonspecific density (NSD) as a single lesion by both readers at the Northern Sydney Central Coast BreastScreen service in 2019 were included. Prior images were excluded to simulate first‐round screening. Eleven radiologists read DM and synthesized mammography with DBT (SM + DBT) images 4 weeks apart. Recall rates (RR), reading time, and diagnostic parameters were measured, and costs for screen reading and assessment were calculated. Result: Among 65 clients studied, 13 were diagnosed with cancer, with concordant cancer recalls. SM + DBT reduced recall rates (RR), increased reading time, maintained cancer detection sensitivity, and significantly improved other diagnostic parameters, particularly false positive rates. Benign biopsy recalls remained equivalent. While SM + DBT screen reading cost was significantly higher than DM (DM AU$890 ± 186 vs SM + DBT AU$1279 ± 265; P < 0.001), the assessment cost (DM AU$29,504 ± 9427 vs SM + DBT AU$18,021 ± 5606; P < 0.001), and combined screen reading and assessment costs were significantly lower (DM AU$30,394 ± 9508 vs SM + DBT AU$19,300 ± 5721; P = 0.001). SM + DBT screen reading and assessment of 65 patients resulted in noteworthy cost savings (AU$11,094), equivalent to assessing 12 additional clients. Conclusion: In first round screening, DBT yields significant cost savings by effectively reducing unnecessary recalls to assessment while maintaining diagnostic efficacy. [ABSTRACT FROM AUTHOR]

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Background: Mammography alone is an ineffective method for breast cancer surveillance and diagnosing cancer recurrence. The aim was to evaluate the ability of artificial intelligence (AI) to read digital mammograms as an additive tool to exclude recurrence in the operative bed of known breast cancer patients following the different surgical procedures. Methods: We used a retrospective cohort study of post-surgery mammograms (n = 577). Imaging was performed within 6 months after the surgery or more. The AI solution used to read mammograms (AI-MMG) provided a targeted heat map of the operative bed, which was supported by a decision likelihood score percentage of cancer recurrence. The reference for suspicious or malignant-looking abnormalities (n = 62, 12.3%) was diagnosed by biopsy. A clear operative bed and benign-looking changes (n = 442) were confirmed by ultrasound characterization patterns and one year of intermittent follow-up. Results: The AI scoring percentage for a clear operative bed ranged between 0 and 26%, with a mean of 15% ± 5.4%. Operative bed benign changes ranged from 10 to 88%, with a mean of 48.2% ± 21.2%, while malignancy recurrence ranged from 65 to 99%, with an average of 87.7% ± 10.5%. The "ROC: Receiver Operating Characteristic" curve for AI to predict cancer in the surgical bed on mammograms was 0.906. The optimum cutoff value to distinguish between benign postoperative alterations and malignancy recurrence was 56.5% (95%, CI 0.824–1.060, p value < 0.001). Excellent agreement between AI-MMG and pathology or ultrasound results was observed, and Kappa was 0.894, p value < 0.001. Conclusions: The use of artificial intelligence has enhanced the diagnostic performance of the postoperative mammograms to rule out recurrent malignancies in breast cancer surveillance. [ABSTRACT FROM AUTHOR]

Background: Patients with a Breast Imaging Reporting and Data System (BI-RADS) 4 mammogram are currently recommended for biopsy. However, 70–80% of the biopsies are negative/benign. In this study, we developed a deep learning classification algorithm on mammogram images to classify BI-RADS 4 suspicious lesions aiming to reduce unnecessary breast biopsies. Materials and methods: This retrospective study included 847 patients with a BI-RADS 4 breast lesion that underwent biopsy at a single institution and included 200 invasive breast cancers, 200 ductal carcinoma in-situ (DCIS), 198 pure atypias, 194 benign, and 55 atypias upstaged to malignancy after excisional biopsy. We employed convolutional neural networks to perform 4 binary classification tasks: (I) benign vs. all atypia + invasive + DCIS, aiming to identify the benign cases for whom biopsy may be avoided; (II) benign + pure atypia vs. atypia-upstaged + invasive + DCIS, aiming to reduce excision of atypia that is not upgraded to cancer at surgery; (III) benign vs. each of the other 3 classes individually (atypia, DCIS, invasive), aiming for a precise diagnosis; and (IV) pure atypia vs. atypia-upstaged, aiming to reduce unnecessary excisional biopsies on atypia patients. Results: A 95% sensitivity for the "higher stage disease" class was ensured for all tasks. The specificity value was 33% in Task I, and 25% in Task II, respectively. In Task III, the respective specificity value was 30% (vs. atypia), 30% (vs. DCIS), and 46% (vs. invasive tumor). In Task IV, the specificity was 35%. The AUC values for the 4 tasks were 0.72, 0.67, 0.70/0.73/0.72, and 0.67, respectively. Conclusion: Deep learning of digital mammograms containing BI-RADS 4 findings can identify lesions that may not need breast biopsy, leading to potential reduction of unnecessary procedures and the attendant costs and stress. [ABSTRACT FROM AUTHOR]

Breast cancer is a major health concern worldwide. Mammography, a cost-effective and accurate tool, is crucial in combating this issue. However, low contrast, noise, and artifacts can limit the diagnostic capabilities of radiologists. Computer-Aided Diagnosis (CAD) systems have been developed to overcome these challenges, with the accurate outlining of the breast being a critical step for further analysis. This study introduces the SAM-breast model, an adaptation of the Segment Anything Model (SAM) for segmenting the breast region in mammograms. This method enhances the delineation of the breast and the exclusion of the pectoral muscle in both medio lateral-oblique (MLO) and cranio-caudal (CC) views. We trained the models using a large, multi-center proprietary dataset of 2492 mammograms. The proposed SAM-breast model achieved the highest overall Dice Similarity Coefficient (DSC) of 99.22% ± 1.13 and Intersection over Union (IoU) 98.48% ± 2.10 over independent test images from five different datasets (two proprietary and three publicly available). The results are consistent across the different datasets, regardless of the vendor or image resolution. Compared with other baseline and deep learning-based methods, the proposed method exhibits enhanced performance. The SAM-breast model demonstrates the power of the SAM to adapt when it is tailored to specific tasks, in this case, the delineation of the breast in mammograms. Comprehensive evaluations across diverse datasets—both private and public—attest to the method's robustness, flexibility, and generalization capabilities. [ABSTRACT FROM AUTHOR]

Background: The assessment information of tumor margins is extremely important for the success of the breast cancer surgery and whether the patient undergoes a second operation. However, conducting surgical margin assessments is a time-consuming task that requires pathology-related skills and equipment, and often cannot be provided in a timely manner. To address this challenge, digital breast tomosynthesis technology was utilized to generate detailed cross-sectional images of the breast tissue and integrate deep learning algorithms for image segmentation, achieving an assessment of tumor margins during surgery. Methods: this study utilized post-operative tissue samples from 46 patients who underwent breast-conserving treatment, and generated image sets using digital breast tomosynthesis for the training and evaluation of deep learning models. Results: Deep learning algorithms effectively identifying the tumor area. They achieved a Mean Intersection over Union (MIoU) of 0.91, global accuracy of 99%, weighted IoU of 44%, precision of 98%, recall of 83%, F1 score of 89%, and dice coefficient of 93% on the training dataset; for the testing dataset, MIoU was at 83%, global accuracy at 97%, weighted IoU at 38%, precision at 87%, recall rate at 69%, F1 score at 76%, dice coefficient at 86%. Conclusions: The initial evaluation suggests that the deep learning-based image segmentation method is highly accurate in measuring breast tumor margins. This helps provide information related to tumor margins during surgery, and by using different datasets, this research method can also be applied to the surgical margin assessment of various types of tumors. [ABSTRACT FROM AUTHOR]

Background: Ultrasound strain imaging, which delineates mechanical properties to detect tissue abnormalities, involves estimating the time delay between two radio‐frequency (RF) frames collected before and after tissue deformation. The existing regularized optimization‐based time‐delay estimation (TDE) techniques suffer from at least one of the following drawbacks: (1) The regularizer is not aligned with the tissue deformation physics due to taking only the first‐order displacement derivative into account; (2) The L2$L2$‐norm of the displacement derivatives, which oversmooths the estimated time‐delay, is utilized as the regularizer; (3) The modulus function defined mathematically should be approximated by a smooth function to facilitate the optimization of L1$L1$‐norm. Purpose: Our purpose is to develop a novel TDE technique that resolves the aforementioned shortcomings of the existing algorithms. Methods: Herein, we propose employing the alternating direction method of multipliers (ADMM) for optimizing a novel cost function consisting of L2$L2$‐norm data fidelity term and L1$L1$‐norm first‐ and second‐order spatial continuity terms. ADMM empowers the proposed algorithm to use different techniques for optimizing different parts of the cost function and obtain high‐contrast strain images with smooth backgrounds and sharp boundaries. We name our technique ADMM for totaL variaTion RegUlarIzation in ultrasound STrain imaging (ALTRUIST). ALTRUIST's efficacy is quantified using absolute error (AE), Structural SIMilarity (SSIM), signal‐to‐noise ratio (SNR), contrast‐to‐noise ratio (CNR), and strain ratio (SR) with respect to GLUE, OVERWIND, and L1$L1$‐SOUL, three recently published energy‐based techniques, and UMEN‐Net, a state‐of‐the‐art deep learning‐based algorithm. Analysis of variance (ANOVA)‐led multiple comparison tests and paired t$t$‐tests at 5%$5\%$ overall significance level were conducted to assess the statistical significance of our findings. The Bonferroni correction was taken into account in all statistical tests. Two simulated layer phantoms, three simulated resolution phantoms, one hard‐inclusion simulated phantom, one multi‐inclusion simulated phantom, one experimental breast phantom, and three in vivo liver cancer datasets have been used for validation experiments. We have published the ALTRUIST code at http://code.sonography.ai. Results: ALTRUIST substantially outperforms the four state‐of‐the‐art benchmarks in all validation experiments, both qualitatively and quantitatively. ALTRUIST yields up to 573%∗${573\%}^{*}$, 41%∗${41\%}^{*}$, and 51%∗${51\%}^{*}$ SNR improvements and 443%∗${443\%}^{*}$, 53%∗${53\%}^{*}$, and 15%∗${15\%}^{*}$ CNR improvements over L1$L1$‐SOUL, its closest competitor, for simulated, phantom, and in vivo liver cancer datasets, respectively, where the asterisk (*) indicates statistical significance. In addition, ANOVA‐led multiple comparison tests and paired t$t$‐tests indicate that ALTRUIST generally achieves statistically significant improvements over GLUE, UMEN‐Net, OVERWIND, and L1$L1$‐SOUL in terms of AE, SSIM map, SNR, and CNR. Conclusions: A novel ultrasonic displacement tracking algorithm named ALTRUIST has been developed. The principal novelty of ALTRUIST is incorporating ADMM for optimizing an L1$L1$‐norm regularization‐based cost function. ALTRUIST exhibits promising performance in simulation, phantom, and in vivo experiments. [ABSTRACT FROM AUTHOR]

Objectives: To compare contrast-enhanced mammography (CEM) with low-energy image (LEI) alone and with magnetic resonance imaging (MRI) in the preoperative diagnosis of ductal carcinoma in situ (DCIS). Methods: In this single-center retrospective study, we reviewed 98 pure DCIS lesions in 96 patients who underwent CEM and MRI within 2 weeks preoperatively. The diagnostic performances of each imaging modality, lesion morphology, and extent were evaluated. Results: The sensitivity of CEM to DCIS was similar to that of MRI (92.9% vs. 93.9%, p = 0.77) and was significantly higher than that of LEI alone (76.5%, p = 0.002). The sensitivity of CEM to calcified DCIS (92.4%) was not significantly different from LEI alone (92.4%) and from MRI (93.9%, p = 1.00). However, CEM contributed to the simultaneous comparison of calcifications with enhancements. CEM had considerably higher sensitivity compared with LEI alone (93.8% vs. 43.8%, p < 0.001) and performed similarly to MRI (93.8%, p = 1.00) for noncalcified DCIS. All DCIS lesions were enhanced in MRI, whereas 94.9% (93/98) were enhanced in CEM. Non-mass enhancement was the most common presentation (CEM 63.4% and MRI 66.3%). The difference between the lesion size on each imaging modality and the histopathological size was smallest in MRI, followed by CEM, and largest in LEI. Conclusion: CEM was more sensitive than LEI alone and comparable to MRI in DCIS diagnosis. The enhanced morphology of DCIS in CEM was consistent with that in MRI. CEM was superior to LEI alone in size measurement of DCIS. Clinical relevance statement: This study investigated the value of CEM in the diagnosis and evaluation of DCIS, aiming to offer a reference for the selection of examination methods for DCIS and contribute to the early diagnosis and precise treatment of DCIS. Key Points: • DCIS is an important indication for breast surgery. Early and accurate diagnosis is crucial for DCIS treatment and prognosis. • CEM overcomes the deficiency of mammography in noncalcified DCIS diagnosis, exhibiting similar sensitivity to MRI; and CEM contributes to the comparison of calcification and enhancement of calcified DCIS, thereby outperforming MRI. • CEM is superior to LEI alone and slightly inferior to MRI in the size evaluation of DCIS. [ABSTRACT FROM AUTHOR]

Breast cancer is the most prevalent cancer in females in a large number of countries around the world. One in every twenty females gets confirmed to have breast cancer during their lifetime, though the number varies significantly by country. There are various conventional methods to spot the breast cancer cells, such as magnetic resonance imaging, X-ray mammography, biopsies, and ultrasound. The early identification of cancer cells helps to save many human lives. This paper investigates tumor detection in a breast phantom using a Photonic crystal (PhC) based hourglass terahertz patch antenna. This paper designed four different antenna structures: conventional hourglass patch antenna (with and without breast phantom) and PhC-based hourglass patch antenna (with and without breast phantom). The proposed PhC antenna models and the breast phantom are designed using the CST studio suite and analyzed the various antenna parameters. The observed S11 parameter for the proposed structures is − 21.50, − 25.34, − 33.78 and − 50.32 dB. The proposed antenna resonated in THz frequency, which does not affect human health and is safer due to non-ionizing characteristics. The suggested PhC structure is tiny, less expensive, lightweight, environmentally benign, and can be utilized as a primary screening technique for people with breast cancer, especially in resource-limited areas. [ABSTRACT FROM AUTHOR]

Breast cancer detection is considered a challenging task for the average experienced radiologist due to the variation of the lesions' size and shape, especially with the existence of high fibro-glandular tissues. The revolution of deep learning and computer vision contributes recently in introducing systems that can provide an automated diagnosis for breast cancer that can act as a second opinion for doctors/radiologists. The most of previously proposed deep learning-based Computer-Aided Diagnosis (CAD) systems mainly utilized Convolutional Neural Networks (CNN) that focuses on local features. Recently, vision transformers (ViT) have shown great potential in image classification tasks due to its ability in learning the local and global spatial features. This paper proposes a fully automated CAD framework based on YOLOv4 network and ViT transformers for mass detection and classification of Contrast Enhanced Spectral Mammography (CESM) images. CESM is an evolution type of Full Field Digital Mammography (FFDM) images that provides enhanced visualization for breast tissues. Different experiments were conducted to evaluate the proposed framework on two different datasets that are INbreast and CDD-CESM that provides both FFDM and CESM images. The model achieved at mass detection a mean Average Precision (mAP) score of 98.69%, 81.52%, and 71.65% and mass classification accuracy of 95.65%, 97.61%, and 80% for INbreast, CE-CESM, and DM-CESM, respectively. The proposed framework showed competitive results regarding the state-of-the-art models in INbreast. It outperformed the previous work in the literature in terms of the F1-score by almost 5% for mass detection in CESM. Moreover, the experiments showed that the CESM could provide more morphological features that can be more informative, especially with the highly dense breast tissues. [ABSTRACT FROM AUTHOR]

Background: Artificial intelligence (AI) algorithms for the independent assessment of screening mammograms have not been well established in a large screening cohort of Asian women. We compared the performance of screening digital mammography considering breast density, between radiologists and AI standalone detection among Korean women. Methods: We retrospectively included 89,855 Korean women who underwent their initial screening digital mammography from 2009 to 2020. Breast cancer within 12 months of the screening mammography was the reference standard, according to the National Cancer Registry. Lunit software was used to determine the probability of malignancy scores, with a cutoff of 10% for breast cancer detection. The AI's performance was compared with that of the final Breast Imaging Reporting and Data System category, as recorded by breast radiologists. Breast density was classified into four categories (A–D) based on the radiologist and AI-based assessments. The performance metrics (cancer detection rate [CDR], sensitivity, specificity, positive predictive value [PPV], recall rate, and area under the receiver operating characteristic curve [AUC]) were compared across breast density categories. Results: Mean participant age was 43.5 ± 8.7 years; 143 breast cancer cases were identified within 12 months. The CDRs (1.1/1000 examination) and sensitivity values showed no significant differences between radiologist and AI-based results (69.9% [95% confidence interval [CI], 61.7–77.3] vs. 67.1% [95% CI, 58.8–74.8]). However, the AI algorithm showed better specificity (93.0% [95% CI, 92.9–93.2] vs. 77.6% [95% CI, 61.7–77.9]), PPV (1.5% [95% CI, 1.2–1.9] vs. 0.5% [95% CI, 0.4–0.6]), recall rate (7.1% [95% CI, 6.9–7.2] vs. 22.5% [95% CI, 22.2–22.7]), and AUC values (0.8 [95% CI, 0.76–0.84] vs. 0.74 [95% CI, 0.7–0.78]) (all P < 0.05). Radiologist and AI-based results showed the best performance in the non-dense category; the CDR and sensitivity were higher for radiologists in the heterogeneously dense category (P = 0.059). However, the specificity, PPV, and recall rate consistently favored AI-based results across all categories, including the extremely dense category. Conclusions: AI-based software showed slightly lower sensitivity, although the difference was not statistically significant. However, it outperformed radiologists in recall rate, specificity, PPV, and AUC, with disparities most prominent in extremely dense breast tissue. [ABSTRACT FROM AUTHOR]

Background: Automatic exposure control (AEC) plays a crucial role in mammography by determining the exposure conditions needed to achieve specific image quality based on the absorption characteristics of compressed breasts. This study aimed to characterize the behavior of AEC for digital mammography (DM), digital breast tomosynthesis (DBT), and low-energy (LE) and high-energy (HE) acquisitions used in contrast-enhanced mammography (CEM) for three mammography systems from two manufacturers. Methods: Using phantoms simulating various breast thicknesses, 363 studies were acquired using all available AEC modes 165 DM, 132 DBT, and 66 LE-CEM and HE-CEM. AEC behaviors were compared across systems and modalities to assess the impact of different technical components and manufacturers' strategies on the resulting mean glandular doses (MGDs) and image quality metrics such as contrast-to-noise ratio (CNR). Results: For all systems and modalities, AEC increased MGD for increasing phantom thicknesses and decreased CNR. The median MGD values (interquartile ranges) were 1.135 mGy (0.772–1.668) for DM, 1.257 mGy (0.971–1.863) for DBT, 1.280 mGy (0.937–1.878) for LE-CEM, and 0.630 mGy (0.397–0.713) for HE-CEM. Medians CNRs were 14.2 (7.8–20.2) for DM, 4.91 (2.58–7.20) for a single projection in DBT, 11.9 (8.0–18.2) for LE-CEM, and 5.2 (3.6–9.2) for HE-CEM. AECs showed high repeatability, with variations lower than 5% for all modes in DM, DBT, and CEM. Conclusions: The study revealed substantial differences in AEC behavior between systems, modalities, and AEC modes, influenced by technical components and manufacturers' strategies, with potential implications in radiation dose and image quality in clinical settings. Relevance statement: The study emphasized the central role of automatic exposure control in DM, DBT, and CEM acquisitions and the great variability in dose and image quality among manufacturers and between modalities. Caution is needed when generalizing conclusions about differences across mammography modalities. Key points: • AEC plays a crucial role in DM, DBT, and CEM. • AEC determines the "optimal" exposure conditions needed to achieve specific image quality. • The study revealed substantial differences in AEC behavior, influenced by differences in technical components and strategies. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate whether the quantitative abnormality scores provided by artificial intelligence (AI)-based computer-aided detection/diagnosis (CAD) for mammography interpretation can be used to predict invasive upgrade in ductal carcinoma in situ (DCIS) diagnosed on percutaneous biopsy. Methods: Four hundred forty DCIS in 420 women (mean age, 52.8 years) diagnosed via percutaneous biopsy from January 2015 to December 2019 were included. Mammographic characteristics were assessed based on imaging features (mammographically occult, mass/asymmetry/distortion, calcifications only, and combined mass/asymmetry/distortion with calcifications) and BI-RADS assessments. Routine pre-biopsy 4-view digital mammograms were analyzed using AI-CAD to obtain abnormality scores (AI-CAD score, ranging 0–100%). Multivariable logistic regression was performed to identify independent predictive mammographic variables after adjusting for clinicopathological variables. A subgroup analysis was performed with mammographically detected DCIS. Results: Of the 440 DCIS, 117 (26.6%) were upgraded to invasive cancer. Three hundred forty-one (77.5%) DCIS were detected on mammography. The multivariable analysis showed that combined features (odds ratio (OR): 2.225, p = 0.033), BI-RADS 4c or 5 assessments (OR: 2.473, p = 0.023 and OR: 5.190, p < 0.001, respectively), higher AI-CAD score (OR: 1.009, p = 0.007), AI-CAD score ≥ 50% (OR: 1.960, p = 0.017), and AI-CAD score ≥ 75% (OR: 2.306, p = 0.009) were independent predictors of invasive upgrade. In mammographically detected DCIS, combined features (OR: 2.194, p = 0.035), and higher AI-CAD score (OR: 1.008, p = 0.047) were significant predictors of invasive upgrade. Conclusion: The AI-CAD score was an independent predictor of invasive upgrade for DCIS. Higher AI-CAD scores, especially in the highest quartile of ≥ 75%, can be used as an objective imaging biomarker to predict invasive upgrade in DCIS diagnosed with percutaneous biopsy. Critical relevance statement: Noninvasive imaging features including the quantitative results of AI-CAD for mammography interpretation were independent predictors of invasive upgrade in lesions initially diagnosed as ductal carcinoma in situ via percutaneous biopsy and therefore may help decide the direction of surgery before treatment. Key points: • Predicting ductal carcinoma in situ upgrade is important, yet there is a lack of conclusive non-invasive biomarkers. • AI-CAD scores—raw numbers, ≥ 50%, and ≥ 75%—predicted ductal carcinoma in situ upgrade independently. • Quantitative AI-CAD results may help predict ductal carcinoma in situ upgrade and guide patient management. [ABSTRACT FROM AUTHOR]

Contrast-enhanced mammography (CEM) is an emerging breast imaging technology with promise for breast cancer screening, diagnosis, and procedural guidance. However, best uses of CEM in comparison with other breast imaging modalities such as tomosynthesis, ultrasound, and MRI remain inconclusive in many clinical settings. This review article summarizes recent peer-reviewed literature, emphasizing retrospective reviews, prospective clinical trials, and meta-analyses published from 2020 to 2023. The intent of this article is to supplement prior comprehensive reviews and summarize the current state-of-the-art of CEM. [ABSTRACT FROM AUTHOR]

Objective To report the latest UK mammography dose survey results and to compare radiation doses from digital breast tomosynthesis (DBT) and full-field digital mammography (FFDM) in UK breast screening. Methods Anonymized exposure factors were collected for 111 152 screening cases and 5113 assessment cases from 405 x-ray sets across the United Kingdom using an online submission system linked to a national database of mammography quality control data. Output and beam quality measurements from each set were combined with exposure data to estimate mean glandular doses (MGD). Results FFDM doses increased by ∼10% compared to the 2016-2019 national survey but compressed breast thicknesses (CBT) remained similar. DBT doses were 34%-40% higher than FFDM overall and 34% higher than FFDM for breasts 50-60 mm thick. We found a possible overestimation of PMMA breast equivalent thicknesses at low CBTs, but the evidence was not conclusive. Conclusion Recent changes to the mix of x-ray models in use in UK breast screening have resulted in higher FFDM breast doses. DBT doses in the NHSBSP are on average higher than FFDM by ∼34%-40%. Advances in knowledge This is the first national study to report DBT and FFDM MGDs in UK breast screening. [ABSTRACT FROM AUTHOR]

BACKGROUND: According on the proportion of fibroglandular tissue to fatty tissue, the Breast Imaging Reporting and Data System (BI-RADS) divides breast density into 4 groups and offers radiologists standardized communication tools. OBJECTIVE: To determine the association of breast density with development of breast cancer in local Asian population. MATERIAL AND METHODS: Total 108 female patients were included in the cross-sectional study. A full-field digital mammography equipment was used for the mammograms. For bilateral breasts, four mediolateral oblique and craniocaudal images were acquired. The American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) was used to guide the interpretation of mammogram. The categories were recorded as follows: 1) Almost entirely fat (25% fibroglandular tissue); 2) Scattered fibroglandular (26%- 50%); 3) Heterogeneously dense (51%-75%); and 4) Extremely dense (>75%). Stratification was done and post stratification Chi square test or Fisher s exact test was applied. P-value 0.05 was considered statistically significant. RESULTS: The categories of BI-RADS were found as 16(14.8%) had category A, 48(44.4%) had category B, 38(35.2%) had category C and 6(5.6%) had category D. The breast cancer was noted in 5(31.3%) cases of category A, 24(50%) of cases of category B, 30(78.9%) of cases of category C and 5(83.3%) cases of category D, the breast cancer was significantly associated with the categories of BI-RADS (p=0.002). CONCLUSION: There is a significant association between the risk of breast cancer inour population with mammographic breast tissue density categorized by BI-RADS. [ABSTRACT FROM AUTHOR]

Breast cancer is a global health concern, emphasizing the need for early detection. However, current mammography struggles to effectively image dense breasts. Breast ultrasound can be an adjunctive method, but it is highly dependent on operator skill. Ultrasound computed tomography (USCT) reflection imaging provides high-quality 3D images, but often uses delay-and-sum (DAS) beamforming, which limits its image quality. This article proposes the integration of coherence-factor (CF) beamforming into ultrasound computed tomography (USCT) reflection imaging to enhance image quality. CF assesses the focus quality of beamforming by analyzing the signal coherence across different channels, assigning higher weights to high-quality focus points and thereby improving overall image quality. Numerical simulations and phantom experiments using our built USCT prototype were conducted to optimize the imaging parameters and assess and compare the image quality of CF and DAS beamforming. Numerical simulations demonstrated that CF beamforming can significantly enhance image quality. Phantom experiments with our prototype revealed that CF beamforming significantly improves image resolution (from 0.35 mm to 0.14 mm) and increases contrast ratio (from 24.54 dB to 63.28 dB). The integration of CF beamforming into USCT reflection imaging represents a substantial improvement in image quality, offering promise for enhanced breast cancer detection and imaging capabilities. [ABSTRACT FROM AUTHOR]

Objective: To develop a bimodal nomogram to reduce unnecessary biopsies in breast lesions with discordant ultrasound (US) and mammography (MG) Breast Imaging Reporting and Data System (BI-RADS) assessments. Methods: This retrospective study enrolled 706 women following opportunistic screening or diagnosis with discordant US and MG BI-RADS assessments (where one assessed a lesion as BI-RADS 4 or 5, while the other assessed the same lesion as BI-RADS 0, 2, or 3) from two medical centres between June 2019 and June 2021. Univariable and multivariable logistic regression analyses were used to develop the nomogram. DeLong's and McNemar's tests were used to assess the model's performance. Results: Age, MG features (margin, shape, and density in masses, suspicious calcifications, and architectural distortion), and US features (margin and shape in masses as well as calcifications) were independent risk factors for breast cancer. The nomogram obtained an area under the curve of 0.87 (95% confidence interval (CI), 0.83–0.91), 0.91 (95% CI, 0.87 – 0.96), and 0.92 (95% CI, 0.86–0.98) in the training, internal validation, and external testing samples, respectively, and demonstrated consistency in calibration curves. Coupling the nomogram with US reduced unnecessary biopsies from 74 to 44% and the missed malignancies rate from 13 to 2%. Similarly, coupling with MG reduced missed malignancies from 20 to 6%, and 63% of patients avoided unnecessary biopsies. Interobserver agreement between US and MG increased from – 0.708 (poor agreement) to 0.700 (substantial agreement) with the nomogram. Conclusion: When US and MG BI-RADS assessments are discordant, incorporating the nomogram may improve the diagnostic accuracy, avoid unnecessary breast biopsies, and minimise missed diagnoses. Clinical relevance statement: The nomogram developed in this study could be used as a computer program to assist radiologists with detecting breast cancer and ensuring more precise management and improved treatment decisions for breast lesions with discordant assessments in clinical practice. Key Points: • Coupling the nomogram with US and mammography improves the detection of breast cancers without the risk of unnecessary biopsy or missed malignancies. • The nomogram increases mammography and US interobserver agreement and enhances the consistency of decision-making. • The nomogram has the potential to be a computer program to assist radiologists in identifying breast cancer and making optimal decisions. [ABSTRACT FROM AUTHOR]