BACKGROUND: Despite its excellent screening effectiveness and sensitivity for breast cancer (BC), digital breast tomosynthesis (DBT) is controversial due to its high radiation exposure and long reading time. This study examines the diagnostic accuracy of DBT and digital mammography (DM) for BC screening and diagnosis in women with dense or non-dense breast tissue.
METHODS: PRISMA-compliant searches were performed on Medline, Embase, PubMed, Web of Science, and the Cochrane databases for articles comparing DBT and DM for BC screening until March 2023. Meta-analysis was performed using RevMan sofware, and the Cochrane Risk of Bias Assessment Tool was employed to assess study quality.
RESULTS: This meta-analysis included 11 trials with a total of 2,124,018 individuals. Screening with DBT resulted in a greater cancer detection rate, as demonstrated by a risk ratio (RR) of 1.27 (95% confidence interval (95% CI): 1.14-1.41). Digital breast tomosynthesis also had a reduced recall rate, with a RR of 0.88 (95% CI: 0.78-0.99), higher sensitivity and specificity values (pooled sensitivity of 0.91 (95% CI: 0.59-0.99)) and pooled specificity of 0.90 (95% CI: 0.42-1.0)) than DM (pooled sensitivity of 0.86 (95% CI: 0.52-1.0) and pooled specificity of 0.81 (95% CI: 0.12-1.0)). All acquired data exhibited reliability, lack of bias and statistical significance (p < 0.05).
CONCLUSIONS: Digital breast tomosynthesis is a more effective screening and diagnostic assessment tool for women with dense or non-dense breasts than DM in terms of incremental cancer detection, sensitivity and recall rate.

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.

UNASSIGNED: To construct and validate radiomics models that utilize ultrasound (US) and digital breast tomosynthesis (DBT) images independently and in combination to non-invasively predict the Ki-67 status in breast cancer.
UNASSIGNED: 149 breast cancer women who underwent DBT and US scans were retrospectively enrolled from June 2018 to August 2023 in total. Radiomics features were acquired from both the DBT and US images, then selected and reduced in dimensionality using several screening approaches. Establish radiomics models based on DBT, and US separately and combined. The area under the receiver operating characteristic curve (AUC), accuracy, specificity, and sensitivity were utilized to validate the predictive ability of the models. The decision curve analysis (DCA) was used to evaluate the clinical applicability of the models. The output of the classifier with the best AUC performance was converted into Rad-score and was regarded as Rad-Score model. A nomogram was constructed using the logistic regression method, integrating the Rad-Score and clinical factors. The model\'s stability was assessed through AUC, calibration curves, and DCA.
UNASSIGNED: Support vector machine (SVM), logistic regression (LR), and random forest (RF) were trained to establish radiomics models with the selected features, with SVM showing optimal results. The AUC values for three models (US_SVM, DBT_SVM, and merge_SVM) were 0.668, 0.704, and 0.800 respectively. The DeLong test indicated a notable disparity in the area under the curve (AUC) between merge_SVM and US_SVM (p = 0.048), while there was no substantial variability between merge_SVM and DBT_SVM (p = 0.149). The DCA curve indicates that merge_SVM is superior to unimodal models in predicting high Ki-67 level, showing more clinical values. The nomogram integrating Rad-Score with tumor size obtained the better performance in test set (AUC: 0.818) and had more clinical net.
UNASSIGNED: The fusion radiomics model performed better in predicting the Ki-67 expression level of breast carcinoma, but the gain effect is limited; thus, DBT is preferred as a preoperative diagnosis mode when resources are limited. Nomogram offers predictive advantages over other methods and can be a valuable tool for predicting Ki-67 levels in BC.

UNASSIGNED: Accurate detection of microcalcifications ( μ Calcs ) is crucial for the early detection of breast cancer. Some clinical studies have indicated that digital breast tomosynthesis (DBT) systems with a wide angular range have inferior μ Calc detectability compared with those with a narrow angular range. This study aims to (1) provide guidance for optimizing wide-angle (WA) DBT for improving μ Calcs detectability and (2) prioritize key optimization factors.
UNASSIGNED: An in-silico DBT pipeline was constructed to evaluate μ Calc detectability of a WA DBT system under various imaging conditions: focal spot motion (FSM), angular dose distribution (ADS), detector pixel pitch, and detector electronic noise (EN). Images were simulated using a digital anthropomorphic breast phantom inserted with 120   μ m μ Calc clusters. Evaluation metrics included the signal-to-noise ratio (SNR) of the filtered channel observer and the area under the receiver operator curve (AUC) of multiple-reader multiple-case analysis.
UNASSIGNED: Results showed that FSM degraded μ Calcs sharpness and decreased the SNR and AUC by 5.2% and 1.8%, respectively. Non-uniform ADS increased the SNR by 62.8% and the AUC by 10.2% for filtered backprojection reconstruction with a typical clinical filter setting. When EN decreased from 2000 to 200 electrons, the SNR and AUC increased by 21.6% and 5.0%, respectively. Decreasing the detector pixel pitch from 85 to 50    μ m improved the SNR and AUC by 55.6% and 7.5%, respectively. The combined improvement of a 50   μ m pixel pitch and EN200 was 89.2% in the SNR and 12.8% in the AUC.
UNASSIGNED: Based on the magnitude of impact, the priority for enhancing μ Calc detectability in WA DBT is as follows: (1) utilizing detectors with a small pixel pitch and low EN level, (2) allocating a higher dose to central projections, and (3) reducing FSM. The results from this study can potentially provide guidance for DBT system optimization in the future.

UNASSIGNED: Use of mechanical imaging (MI) as complementary to digital mammography (DM), or in simultaneous digital breast tomosynthesis (DBT) and MI - DBTMI, has demonstrated the potential to increase the specificity of breast cancer screening and reduce unnecessary biopsies compared with DM. The aim of this study is to investigate the increase in the radiation dose due to the presence of an MI sensor during simultaneous image acquisition when automatic exposure control is used.
UNASSIGNED: A radiation dose study was conducted on clinically available breast imaging systems with and without an MI sensor present. Our estimations were based on three approaches. In the first approach, exposure values were compared in paired clinical DBT and DBTMI acquisitions in 97 women. In the second approach polymethyl methacrylate (PMMA) phantoms of various thicknesses were used, and the average glandular dose (AGD) values were compared. Finally, a rectangular PMMA phantom with a 45 mm thickness was used, and the AGD values were estimated based on air kerma measurements with an electronic dosemeter.
UNASSIGNED: The relative increase in exposure estimated from digital imaging and communications in medicine headers when using an MI sensor in clinical DBTMI was 11.9 % ± 10.4 . For the phantom measurements of various thicknesses of PMMA, the relative increases in the AGD for DM and DBT measurements were, on average, 10.7 % ± 3.1 and 11.4 % ± 3.0 , respectively. The relative increase in the AGD using the electronic dosemeter was 11.2 % ± < 0.001 in DM and 12.2 % ± < 0.001 in DBT. The average difference in dose between the methods was 11.5 % ± 3.3 .
UNASSIGNED: Our measurements suggest that the use of simultaneous breast radiography and MI increases the AGD by an average of 11.5 % ± 3.3 . The increase in dose is within the acceptable values for mammography screening recommended by European guidelines.

OBJECTIVE: To assess performance endpoints of a combination of digital breast tomosynthesis (DBT) and full-field digital mammography (FFDM) compared with FFDM only in breast cancer screening.
METHODS: This was a prospective population-based screening study, including eligible (50-69 years) women attending the Capital Region Mammography Screening Program in Denmark. All attending women were offered FFDM. A subgroup was consecutively allocated to a screening room with DBT. All FFDM and DBT underwent independent double reading, and all women were followed up for 2 years after screening date or until next screening date, whichever came first.
RESULTS: 6353 DBT + FFDM and 395 835 FFDM were included in the analysis and were undertaken in 196 267 women in the period from 1 November 2012 to 12 December 2018. Addition of DBT increased sensitivity: 89.9% (95% confidence interval (CI): 81.0-95.5) for DBT + FFDM and 70.1% (95% CI: 68.6-71.6) for FFDM only, p < 0.001. Specificity remained similar: 98.2% (95% CI: 97.9-98.5) for DBT + FFDM and 98.3% (95% CI: 98.2-98.3) for FFDM only, p = 0.9. Screen-detected cancer rate increased statistically significantly: 11.18/1000 for DBT + FFDM and 6.49/1000 for FFDM only, p < 0.001. False-positive rate was unchanged: 1.75% for DBT + FFDM and 1.73% for FFDM only, p = 0.9. Positive predictive value for recall was 39.0% (95% CI: 31.9-46.5) for DBT + FFDM and 27.3% (95% CI: 26.4-28.2), for FFDM only, p < 0.0005. The interval cancer rate decreased: 1.26/1000 for DBT + FFDM and 2.76/1000 for FFDM only, p = 0.02.
CONCLUSIONS: DBT + FFDM yielded a statistically significant increase in cancer detection and program sensitivity.

OBJECTIVE: 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.

OBJECTIVE: The randomized TOmosynthesis plus SYnthesized MAmmography (TOSYMA) screening trial has shown that digital breast tomosynthesis plus synthesized mammography (DBT + SM) is superior to digital mammography (DM) in invasive breast cancer detection varying with breast density. On the other hand, the overall average glandular dose (AGD) of DBT is higher than that of DM. Comparing the DBT + SM and DM trial arm, we analyzed here the mean AGD and their determinants per breast density category and related them to the respective invasive cancer detection rates (iCDR).
METHODS: TOSYMA screened 99,689 women aged 50 to 69 years. Compression force, resulting breast thickness, the calculated AGD obtained from each mammography device, and previously published iCDR were used for comparisons across breast density categories in the two trial arms.
RESULTS: There were 196,622 exposures of 49,227 women (DBT + SM) and 197,037 exposures of 49,132 women (DM) available for analyses. Mean breast thicknesses declined from breast density category A (fatty) to D (extremely dense) in both trial arms. However, while the mean AGD in the DBT + SM arm declined concomitantly from category A (2.41 mGy) to D (1.89 mGy), it remained almost unchanged in the DM arm (1.46 and 1.51 mGy, respectively). In relative terms, the AGD elevation in the DBT + SM arm (64.4% (A), by 44.5% (B), 27.8% (C), and 26.0% (D)) was lowest in dense breasts where, however, the highest iCDR were observed.
CONCLUSIONS: Women with dense breasts may specifically benefit from DBT + SM screening as high cancer detection is achieved with only moderate AGD elevations.
CONCLUSIONS: TOSYMA suggests a favorable constellation for screening with digital breast tomosynthesis plus synthesized mammography (DBT + SM) in dense breasts when weighing average glandular dose elevation against raised invasive breast cancer detection rates. There is potential for density-, i.e., risk-adapted population-wide breast cancer screening with DBT + SM.
CONCLUSIONS: Breast thickness declines with visually increasing density in digital mammography (DM) and digital breast tomosynthesis (DBT). Average glandular doses of DBT decrease with increasing density; digital mammography shows lower and more constant values. With the smallest average glandular dose difference in dense breasts, DBT plus SM had the highest difference in invasive breast cancer detection rates.

Purpose To compare two deep learning-based commercially available artificial intelligence (AI) systems for mammography with digital breast tomosynthesis (DBT) and benchmark them against the performance of radiologists. Materials and Methods This retrospective study included consecutive asymptomatic patients who underwent mammography with DBT (2019-2020). Two AI systems (Transpara 1.7.0 and ProFound AI 3.0) were used to evaluate the DBT examinations. The systems were compared using receiver operating characteristic (ROC) analysis to calculate the area under the ROC curve (AUC) for detecting malignancy overall and within subgroups based on mammographic breast density. Breast Imaging Reporting and Data System results obtained from standard-of-care human double-reading were compared against AI results with use of the DeLong test. Results Of 419 female patients (median age, 60 years [IQR, 52-70 years]) included, 58 had histologically proven breast cancer. The AUC was 0.86 (95% CI: 0.85, 0.91), 0.93 (95% CI: 0.90, 0.95), and 0.98 (95% CI: 0.96, 0.99) for Transpara, ProFound AI, and human double-reading, respectively. For Transpara, a rule-out criterion of score 7 or lower yielded 100% (95% CI: 94.2, 100.0) sensitivity and 60.9% (95% CI: 55.7, 66.0) specificity. The rule-in criterion of higher than score 9 yielded 96.6% sensitivity (95% CI: 88.1, 99.6) and 78.1% specificity (95% CI: 73.8, 82.5). For ProFound AI, a rule-out criterion of lower than score 51 yielded 100% sensitivity (95% CI: 93.8, 100) and 67.0% specificity (95% CI: 62.2, 72.1). The rule-in criterion of higher than score 69 yielded 93.1% (95% CI: 83.3, 98.1) sensitivity and 82.0% (95% CI: 77.9, 86.1) specificity. Conclusion Both AI systems showed high performance in breast cancer detection but lower performance compared with human double-reading. Keywords: Mammography, Breast, Oncology, Artificial Intelligence, Deep Learning, Digital Breast Tomosynthesis © RSNA, 2024.

In this study, we investigate the performance of advanced 2D acquisition geometries - Pentagon and T-shaped - in digital breast tomosynthesis (DBT) and compare them against the conventional 1D geometry. Unlike the conventional approach, our proposed 2D geometries also incorporate anterior projections away from the chest wall. Implemented on the Next-Generation Tomosynthesis (NGT) prototype developed by X-ray Physics Lab (XPL), UPenn, we utilized various phantoms to compare three geometries: a Defrise slab phantom with alternating plastic slabs to study low-frequency modulation; a Checkerboard breast phantom (a 2D adaptation of the Defrise phantom design) to study the ability to reconstruct the fine features of the checkerboard squares; and the 360° Star-pattern phantom to assess aliasing and compute the Fourier-spectral distortion (FSD) metric that assesses spectral leakage and the contrast transfer function. We find that both Pentagon and T-shaped scans provide greater modulation amplitude of the Defrise phantom slabs and better resolve the squares of the Checkerboard phantom against the conventional scan. Notably, the Pentagon geometry exhibited a significant reduction in aliasing of spatial frequencies oriented in the right-left (RL) medio-lateral direction, which was corroborated by a near complete elimination of spectral leakage in the FSD plot. Conversely T-shaped scan redistributes the aliasing between both posteroanterior (PA) and RL directions thus maintaining non-inferiority against the conventional scan which is predominantly affected by PA aliasing. The results of this study underscore the potential of incorporating advanced 2D geometries in DBT systems, offering marked improvements in imaging performance over the conventional 1D approach.