A directional total variation minimization algorithm for isotropic resolution in digital breast tomosynthesis.

An optimization-based image reconstruction algorithm is developed for contrast enhanced digital breast tomosynthesis (DBT) using dual-energy scanning. The algorithm minimizes directional total variation (TV) with a data discrepancy and non-negativity constraints. Iodinated contrast agent (ICA) imaging is performed by reconstructing images from dual-energy DBT data followed by weighted subtraction. Physical DBT data is acquired with a Siemens Mammomat scanner of a structured breast phantom with ICA inserts. Results are shown for both directional TV minimization and filtered back-projection for reference. It is seen that directional TV is able to substantially reduce depth blur for the ICA objects.

Optimizing angular range in digital breast tomosynthesis: A phantom study investigating lesion detection across varied breast density and thickness.

PURPOSE: To determine the optimal angular range (AR) for digital breast tomosynthesis (DBT) systems that provides highest lesion visibility across various breast densities and thicknesses. METHOD: A modular DBT phantom, consisting of tissue-equivalent adipose and glandular modules, along with a module embedded with test objects (speckles, masses, fibers), was used to create combinations simulating different breast thicknesses, densities, and lesion locations. A prototype DBT system operated at four ARs (AR±7.5°, AR±12.5°, AR±19°, and AR±25°) to acquire 11 projection images for each combination, with separate fixed doses for thin and thick combinations. Three blinded radiologists independently assessed lesion visibility in reconstructed images; assessments were averaged and compared using linear mixed models. RESULTS: Speckle visibility was highest with AR±7.5° or AR±12.5°, decreasing with wider ARs in all density and thickness combinations. The difference between AR±7.5° and AR±12.5° was not statistically significant, except for the tube-side speckles in thin-fatty combinations (5.83 [AR±7.5°] vs. 5.39 [AR±12.5°], P = 0.019). Mass visibility was not affected by AR in thick combinations, while AR±12.5° exhibited the highest mass visibility for both thin-fatty and thin-dense combinations (P = 0.032 and 0.007, respectively). Different ARs provided highest fiber visibility for different combinations; however, AR±12.5° consistently provided highest or comparable visibility. AR±12.5° showed highest overall lesion visibility for all density and thickness combinations. CONCLUSIONS: AR±12.5° exhibited the highest overall lesion visibility across various phantom thicknesses and densities using a projection number of 11.

ACR Appropriateness Criteria® Female Breast Cancer Screening: 2023 Update.

Early detection of breast cancer from regular screening substantially reduces breast cancer mortality and morbidity. Multiple different imaging modalities may be used to screen for breast cancer. Screening recommendations differ based on an individual's risk of developing breast cancer. Numerous factors contribute to breast cancer risk, which is frequently divided into three major categories: average, intermediate, and high risk. For patients assigned female at birth with native breast tissue, mammography and digital breast tomosynthesis are the recommended method for breast cancer screening in all risk categories. In addition to the recommendation of mammography and digital breast tomosynthesis in high-risk patients, screening with breast MRI is recommended. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

Limited impact of adding digital breast tomosynthesis to full field digital mammography in an elevated breast cancer risk population.

PURPOSE: To investigate the impact of adding digital breast tomosynthesis (DBT) to full field digital mammography (FFDM) in screening asymptomatic women with an elevated breast cancer life time risk (BCLTR) but without known genetic mutation. METHODS: This IRB-approved single-institution multi-reader study on prospectively acquired FFDM + DBT images included 429 asymptomatic women (39-69y) with an elevated BC risk on their request form. The BCLTR was calculated for each patient using the IBISrisk calculator v8.0b. The screening protocol and reader study consisted of 4-view FFDM + DBT, which were read by four independent radiologists using the BI-RADS lexicon. Standard of care (SOC) included ultrasound (US) and magnetic resonance imaging (MRI) for women with > 30 % BCLTR. Breast cancer detection rate (BCDR), sensitivity and positive predictive value were assessed for FFDM and FFDM + DBT and detection outcomes were compared with McNemar-test. RESULTS: In total 7/429 women in this clinically elevated breast cancer risk group were diagnosed with BC using SOC (BCDR 16.3/1000) of which 4 were detected with FFDM. Supplemental DBT did not detect additional cancers and BCDR was the same for FFDM vs FFDM + DBT (9.3/1000, McNemar p = 1). Moderate inter-reader agreement for diagnostic BI-RADS score was found for both study arms (ICC for FFDM and FFDM + DBT was 0.43, resp. 0.46). CONCLUSION: In this single institution study, supplemental screening with DBT in addition to standard FFDM did not increase BCDR in this higher-than-average BC risk group, objectively documented using the IBISrisk calculator.

How long does it take to read a mammogram? Investigating the reading time of digital breast tomosynthesis and digital mammography.

PURPOSE: To analyse digital breast tomosynthesis (DBT) reading times in the screening setting, compared to 2D full-field digital mammography (FFDM), and investigate the impact of reader experience and professional group on interpretation times. METHOD: Reading time data were recorded in the PROSPECTS Trial, a prospective randomised trial comparing DBT plus FFDM or synthetic 2D mammography (S2D) to FFDM alone, in the National Health Service (NHS) breast screening programme, from January 2019-February 2023. Time to read DBT+FFDM or DBT+S2D and FFDM alone was calculated per case and reading times were compared between modalities using dependent T-tests. Reading times were compared between readers from different professional groups (radiologists and radiographer readers) and experience levels using independent T-tests. The learning curve effect of using DBT in screening on reading time was investigated using a Kruskal-Wallis test. RESULTS: Forty-eight readers interpreted 1,242 FFDM batches (34,210 FFDM cases) and 973 DBT batches (13,983 DBT cases). DBT reading time was doubled compared to FFDM (2.09 ± 0.64 min vs. 0.98 ± 0.30 min; p < 0.001), and DBT+S2D reading was longer than DBT + FFDM (2.24 ± 0.62 min vs. 2.04 ± 0.46 min; p = 0.006). No difference was identified in reading time between radiologists and radiographers (2.06 ± 0.71 min vs. 2.14 ± 0.46 min, respectively; p = 0.71). Readers with five or more years of experience reading DBT were quicker than those with less experience (1.86 ± 0.56 min vs. 2.37 ± 0.65 min; p = 0.008), and DBT reading time decreased after less than 9 months accrued screening experience (p = 0.01). CONCLUSIONS: DBT reading times were double those of FFDM in the screening setting, but there was a short learning curve effect with readers showing significant improvements in reading times within the first nine months of DBT experience. CLINICALTRIALS: gov Identifier: NCT03733106.

Cost-Effectiveness Analysis of Digital Breast Tomosynthesis and Mammography in Breast Cancer Screening: A Markov Modeling Study.

BACKGROUND: Mammography (MG) has demonstrated its effectiveness in diminishing mortality and advanced-stage breast cancer incidences in breast screening initiatives. Notably, research has accentuated the superior diagnostic efficacy and cost-effectiveness of digital breast tomosynthesis (DBT). However, the scope of evidence validating the cost-effectiveness of DBT remains limited, prompting a requisite for more comprehensive investigation. The present study aimed to rigorously evaluate the cost-effectiveness of DBT plus MG (DBT-MG) compared to MG alone within the framework of Taiwan's National Health Insurance program. METHODS: All parameters for the Markov decision tree model, encompassing event probabilities, costs, and utilities (quality-adjusted life years, QALYs), were sourced from reputable literature, expert opinions, and official records. With 10,000 iterations, a 2-year cycle length, a 30-year time horizon, and a 2% annual discount rate, the analysis determined the incremental cost-effectiveness ratio (ICER) to compare the cost-effectiveness of the two screening methods. Probabilistic and one-way sensitivity analyses were also conducted to demonstrate the robustness of findings. RESULTS: The ICER of DBT-MG compared to MG was US$5971.5764/QALYs. At a willingness-to-pay (WTP) threshold of US$33,004 (Gross Domestic Product of Taiwan in 2021) per QALY, more than 98% of the probabilistic simulations favored adopting DBT-MG versus MG. The one-way sensitivity analysis also shows that the ICER depended heavily on recall rates, biopsy rates, and positive predictive value (PPV2). CONCLUSION: DBT-MG shows enhanced diagnostic efficacy, potentially diminishing recall costs. While exhibiting a higher biopsy rate, DBT-MG aids in the detection of early-stage breast cancers, reduces recall rates, and exhibits notably superior cost-effectiveness.

Functional hemodynamic imaging markers for the prediction of pathological outcomes in breast cancer patients treated with neoadjuvant chemotherapy.

Significance: Achieving pathologic complete response (pCR) after neoadjuvant chemotherapy (NACT) is a significant predictor of increased likelihood of survival in breast cancer patients. Early prediction of pCR is of high clinical value as it could allow personalized adjustment of treatment regimens in non-responding patients for improved outcomes. Aim: We aim to assess the association between hemoglobin-based functional imaging biomarkers derived from diffuse optical tomography (DOT) and the pathological outcome represented by pCR at different timepoints along the course of NACT. Approach: Twenty-two breast cancer patients undergoing NACT were enrolled in a multimodal DOT and X-ray digital breast tomosynthesis (DBT) imaging study in which their breasts were imaged at different compression levels. Logistic regressions were used to study the associations between DOT-derived imaging markers evaluated after the first and second cycles of chemotherapy, respectively, with pCR status determined after the conclusion of NACT at the time of surgery. Receiver operating characteristic curve analysis was also used to explore the predictive performance of selected DOT-derived markers. Results: Normalized tumor HbT under half compression was significantly lower in the pCR group compared to the non-pCR group after two chemotherapy cycles (p=0.042). In addition, the change in normalized tumor StO2 upon reducing compression from full to half mammographic force was identified as another potential indicator of pCR at an earlier time point, i.e., after the first chemo cycle (p=0.038). Exploratory predictive assessments showed that AUCs using DOT-derived functional imaging markers as predictors reach as high as 0.75 and 0.71, respectively, after the first and second chemo cycle, compared to AUCs of 0.50 and 0.53 using changes in tumor size measured on DBT and MRI. Conclusions: These findings suggest that breast DOT could be used to assist response assessment in women undergoing NACT, a critical but unmet clinical need, and potentially enable personalized adjustments of treatment regimens.

Modulation transfer function of digital breast tomosynthesis: a comparison of various edge devices.

The modulation transfer function (MTF) is a fundamental tool for assessing the sharpness of digital breast tomosynthesis (DBT) systems and is primarily measured using edge devices. We compared the MTF of a Senographe Pristina DBT system using four-edge devices. These devices were composed of stainless steel with a thickness of 0.6, 0.8, and 1.0 mm, and 1.0 mm tungsten, based on different international guidelines. We evaluated spatial frequencies at MTFs of 0.5 (MTF50%) and 0.1 (MTF10%). The collimator-equipped and non-collimator configurations of the DBT were compared. We found no appreciable differences between scan and chest wall-nipple directions. Both MTF50% (2.90-2.99 cycles/mm) and MTF10% (6.69-6.94 cycles/mm) demonstrated minimal variation across the different edge devices. The collimator-equipped system exhibited an MTF50% that was approximately 5% higher than that of the non-collimator configuration. The choice of the edge device did not appreciably impact the MTF.

Feasibility study comparing synthesized mammography with digital breast tomosynthesis and digital mammography for simulated first round screening in a single BreastScreen NSW centre.

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.

Revolutionizing Breast Cancer Detection With Artificial Intelligence (AI) in Radiology and Radiation Oncology: A Systematic Review.

The number one cause of cancer in women worldwide is breast cancer. Over the last three decades, the use of traditional screen-film mammography has increased, but in recent years, digital mammography and 3D tomosynthesis have become standard procedures for breast cancer screening. With the advancement of technology, the interpretation of images using automated algorithms has become a subject of interest. Initially, computer-aided detection (CAD) was introduced; however, it did not show any long-term benefit in clinical practice. With recent advances in artificial intelligence (AI) methods, these technologies are showing promising potential for more accurate and efficient automated breast cancer detection and treatment. While AI promises widespread integration in breast cancer detection and treatment, challenges such as data quality, regulatory, ethical implications, and algorithm validation are crucial. Addressing these is essential for fully realizing AI's potential in enhancing early diagnosis and improving patient outcomes in breast cancer management. In this review article, we aim to provide an overview of the latest developments and applications of AI in breast cancer screening and treatment. While the existing literature primarily consists of retrospective studies, ongoing and future prospective research is poised to offer deeper insights. Artificial intelligence is on the verge of widespread integration into breast cancer detection and treatment, holding the potential to enhance early diagnosis and improve patient outcomes.

Digital Breast Tomosynthesis for Upgraded BIRADS Scoring towards the True Pathology of Lesions Detected by Contrast-Enhanced Mammography.

OBJECTIVE: To determine the added value of digital breast tomosynthesis (DBT) in the assessment of lesions detected by contrast-enhanced mammography (CEM). MATERIAL AND METHODS: A retrospective study was conducted in a tertiary university medical center. All CEM studies including DBT performed between January 2016 and December 2020 were included. Lesions were categorized and scored by four dedicated breast radiologists according to the recent CEM and DBT supplements to the Breast Imaging Reporting and Data System (BIRADS) lexicon. Changes in the BIRADS score of CEM-detected lesions with the addition of DBT were evaluated according to the pathology results and 1-year follow-up imaging study. RESULTS: BIRADS scores of CEM-detected lesions were upgraded toward the lesion's pathology with the addition of DBT (p > 0.0001), overall and for each reader. The difference in BIRADS scores before and after the addition of DBT was more significant for readers who were less experienced. The reason for changes in the BIRADS score was better lesion margin visibility. The main BIRADS descriptors applied in the malignant lesions were spiculations, calcifications, architectural distortion, and sharp or obscured margins. CONCLUSIONS: The addition of DBT to CEM provides valuable information on the enhancing lesion, leading to a more accurate BIRADS score.

X-ray source motion blur modeling and deblurring with generative diffusion for digital breast tomosynthesis.

Objective. Digital breast tomosynthesis (DBT) has significantly improved the diagnosis of breast cancer due to its high sensitivity and specificity in detecting breast lesions compared to two-dimensional mammography. However, one of the primary challenges in DBT is the image blur resulting from x-ray source motion, particularly in DBT systems with a source in continuous-motion mode. This motion-induced blur can degrade the spatial resolution of DBT images, potentially affecting the visibility of subtle lesions such as microcalcifications.Approach. We addressed this issue by deriving an analytical in-plane source blur kernel for DBT images based on imaging geometry and proposing a post-processing image deblurring method with a generative diffusion model as an image prior.Main results. We showed that the source blur could be approximated by a shift-invariant kernel over the DBT slice at a given height above the detector, and we validated the accuracy of our blur kernel modeling through simulation. We also demonstrated the ability of the diffusion model to generate realistic DBT images. The proposed deblurring method successfully enhanced spatial resolution when applied to DBT images reconstructed with detector blur and correlated noise modeling.Significance. Our study demonstrated the advantages of modeling the imaging system components such as source motion blur for improving DBT image quality.

Impact of AI for Digital Breast Tomosynthesis on Breast Cancer Detection and Interpretation Time.

Purpose To develop an artificial intelligence (AI) model for the diagnosis of breast cancer on digital breast tomosynthesis (DBT) images and to investigate whether it could improve diagnostic accuracy and reduce radiologist reading time. Materials and Methods A deep learning AI algorithm was developed and validated for DBT with retrospectively collected examinations (January 2010 to December 2021) from 14 institutions in the United States and South Korea. A multicenter reader study was performed to compare the performance of 15 radiologists (seven breast specialists, eight general radiologists) in interpreting DBT examinations in 258 women (mean age, 56 years ± 13.41 [SD]), including 65 cancer cases, with and without the use of AI. Area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and reading time were evaluated. Results The AUC for stand-alone AI performance was 0.93 (95% CI: 0.92, 0.94). With AI, radiologists' AUC improved from 0.90 (95% CI: 0.86, 0.93) to 0.92 (95% CI: 0.88, 0.96) (P = .003) in the reader study. AI showed higher specificity (89.64% [95% CI: 85.34%, 93.94%]) than radiologists (77.34% [95% CI: 75.82%, 78.87%]) (P < .001). When reading with AI, radiologists' sensitivity increased from 85.44% (95% CI: 83.22%, 87.65%) to 87.69% (95% CI: 85.63%, 89.75%) (P = .04), with no evidence of a difference in specificity. Reading time decreased from 54.41 seconds (95% CI: 52.56, 56.27) without AI to 48.52 seconds (95% CI: 46.79, 50.25) with AI (P < .001). Interreader agreement measured by Fleiss κ increased from 0.59 to 0.62. Conclusion The AI model showed better diagnostic accuracy than radiologists in breast cancer detection, as well as reduced reading times. The concurrent use of AI in DBT interpretation could improve both accuracy and efficiency. Keywords: Breast, Computer-Aided Diagnosis (CAD), Tomosynthesis, Artificial Intelligence, Digital Breast Tomosynthesis, Breast Cancer, Computer-Aided Detection, Screening Supplemental material is available for this article. © RSNA, 2024 See also the commentary by Bae in this issue.

An Image-Rich Educational Review of Breast Pain.

Breast pain is extremely common, occurring in 70% to 80% of women. Most cases of breast pain are from physiologic or benign causes, and patients should be reassured and offered treatment strategies to alleviate symptoms, often without diagnostic imaging. A complete clinical history and physical examination is key for distinguishing intrinsic breast pain from extramammary pain. Breast pain without other suspicious symptoms and with a negative history and physical examination result is rarely associated with malignancy, although it is a common reason for women to undergo diagnostic imaging. When breast imaging is indicated, guidelines according to the American College of Radiology Appropriateness Criteria should be followed as to whether mammography, US, or both are recommended. This review article summarizes the initial clinical evaluation of breast pain and evidence-based guidelines for imaging. Additionally, the article reviews cyclical and noncyclical breast pain and provides an image-rich discussion of the imaging presentation and management of benign and malignant breast pain etiologies.

Can artificial intelligence detect type 2 diabetes in women by evaluating the pectoral muscle on tomosynthesis: diagnostic study.

OBJECTIVES: This retrospective single-center analysis aimed to evaluate whether artificial intelligence can detect type 2 diabetes mellitus by evaluating the pectoral muscle on digital breast tomosynthesis (DBT). MATERIAL METHOD: An analysis of 11,594 DBT images of 287 consecutive female patients (mean age 60, range 40-77 years) was conducted using convolutional neural networks (EfficientNetB5). The inclusion criterion was left-sided screening images with unsuspicious interpretation who also had a current glycosylated hemoglobin A1c (HBA1c) % value. The exclusion criteria were inadequate imaging, history of breast cancer, and/or diabetes mellitus. HbA1c values between 5.6 and 6.4% were categorized as prediabetic, and those with values ≥ 6.5% were categorized as diabetic. A recorded HbA1c ≤ 5.5% served as the control group. Each group was divided into 3 subgroups according to age. Images were subjected to pattern analysis parameters then cropped and resized in a format to contain only pectoral muscle. The dataset was split into 85% for training and 15% for testing the model's performance. The accuracy rate and F1-score were selected as performance indicators. RESULTS: The training process was concluded in the 15th epoch, each comprising 1000 steps, with an accuracy rate of 92% and a loss of only 0.22. The average specificity and sensitivity for all 3 groups were 95%. The F1-score was 0.95. AUC-ROC was 0.995. PPV was 94%, and NPV was 98%. CONCLUSION: Our study presented a pioneering approach, applying deep learning for the detection of diabetes mellitus status in women using pectoral muscle images and was found to function with an accuracy rate of 92%. CRITICAL RELEVANCE STATEMENT: AI can differentiate pathological changes within pectoral muscle tissue by assessing radiological images and maybe a potential diagnostic tool for detecting diabetes mellitus and other diseases that affect muscle tissues. KEY POINTS: • AI may have an opportunistic use as a screening exam for diabetes during digital breast tomosynthesis. • This technique allows for early and non-invasive detection of diabetes mellitus by AI. • AI may have broad applications in detecting pathological changes within muscle tissue.

AMS-U-Net: automatic mass segmentation in digital breast tomosynthesis via U-Net.

Purpose: The objective of this study was to develop a fully automatic mass segmentation method called AMS-U-Net for digital breast tomosynthesis (DBT), a popular breast cancer screening imaging modality. The aim was to address the challenges posed by the increasing number of slices in DBT, which leads to higher mass contouring workload and decreased treatment efficiency. Approach: The study used 50 slices from different DBT volumes for evaluation. The AMS-U-Net approach consisted of four stages: image pre-processing, AMS-U-Net training, image segmentation, and post-processing. The model performance was evaluated by calculating the true positive ratio (TPR), false positive ratio (FPR), F-score, intersection over union (IoU), and 95% Hausdorff distance (pixels) as they are appropriate for datasets with class imbalance. Results: The model achieved 0.911, 0.003, 0.911, 0.900, 5.82 for TPR, FPR, F-score, IoU, and 95% Hausdorff distance, respectively. Conclusions: The AMS-U-Net model demonstrated impressive visual and quantitative results, achieving high accuracy in mass segmentation without the need for human interaction. This capability has the potential to significantly increase clinical efficiency and workflow in DBT for breast cancer screening.

The risk-based breast screening (RIBBS) study protocol: a personalized screening model for young women.

The optimal mammography screening strategy for women aged 45-49 years is a matter of debate. We present the RIBBS study protocol, a quasi-experimental, prospective, population-based study comparing a risk- and breast density-stratified screening model (interventional cohort) with annual digital mammography (DM) screening (observational control cohort) in a real-world setting. The interventional cohort consists of 10,269 women aged 45 years enrolled between 2020 and 2021 from two provinces of the Veneto Region (northen Italy). At baseline, participants underwent two-view digital breast tomosynthesis (DBT) and completed the Tyrer-Cuzick risk prediction model. Volumetric breast density (VBD) was calculated from DBT and the lifetime risk (LTR) was estimated by including VBD among the risk factors. Based on VBD and LTR, women were classified into five subgroups with specific screening protocols for subsequent screening rounds: (1) LTR ≤ 17% and nondense breast: biennial DBT; (2) LTR ≤ 17% and dense breast: biennial DBT and ultrasound; (3) LTR 17-30% or LTR > 30% without family history of BC, and nondense breast: annual DBT; (4) LTR 17-30% or > 30% without family history of BC, and dense breast: annual DBT and ultrasound; and (5) LTR > 30% and family history of BC: annual DBT and breast MRI. The interventional cohort is still ongoing. An observational, nonequivalent control cohort of 43,000 women aged 45 years participating in an annual DM screening programme was recruited in three provinces of the neighbouring Emilia-Romagna Region. Cumulative incidence rates of advanced BC at three, five, and ten years between the two cohorts will be compared, adjusting for the incidence difference at baseline.Trial registration This study is registered on Clinicaltrials.gov (NCT05675085).

[Structured screening for sporadic breast cancer]. / Strukturierte Früherkennung des sporadischen Mammakarzinoms.

BACKGROUND: The aim of secondary prevention of breast cancer is to detect the disease at the earliest curable stage and thus to reduce breast cancer-specific mortality. To this end, the nationwide population-based mammography screening program (MSP) was set up in Germany in 2005 in addition to an interdisciplinary prevention project for high-risk groups. OBJECTIVE: Overview of the current state of the MSP, the upcoming age expansion, and potential further developments. MATERIAL AND METHODS: Narrative review article with topic-guided literature and data search. RESULTS: Approximately 50% of the 70,500 new cases of breast cancer that occur each year are related to the age group of the MSP. 10 years after introduction of the MSP, the incidence of advanced breast cancer stages and breast cancer-related mortality of the screening target group have steadily decreased by about one quarter, while no relevant trends were seen in the neighboring age groups at the population level. CONCLUSION: The MSP has effectively contributed to a reduction of breast cancer mortality. With the expansion of the age groups to 45-75 years, more women have access to structured, quality assured screening. With the use of advanced stratifications and diagnostics as well as artificial intelligence, the MSP could be further optimized.

Image Quality Enhancement for Digital Breast Tomosynthesis: High-Density Object Artifact Reduction.

Breast cancer has a high incidence and mortality rate among women, early diagnosis is essential as it gives insight regarding the most appropriate therapeutic strategy for each case. Among all imaging diagnostic methods, digital breast tomosynthesis (DBT) is effective for early breast cancer detection. In DBT images, high-density object artifacts are generated when imaging objects with high X-ray absorptivity, which include metal artifacts, ripple artifacts, and deformation artifacts. In this study, we analyze the causes of these artifacts and propose a set of high-density object reconstruction methods based on iterative algorithms. Our method includes a reprojection-based high-density object projection data segmentation algorithm and an iterative reconstruction algorithm based on volume expansion. The experiments on simulation data and the human breast data with artificial surgical needles prove the effectiveness of our method. By using our algorithm, the problem of distorting the shape, size, and position of high-density objects during DBT reconstruction is raised, the influence of these artifacts is reduced, and the quality of the DBT reconstructed image is improved. We hope that our algorithm might contribute to promoting the usage of DBT.

Radial Scars on Screening Digital Breast Tomosynthesis: Upstaging Rates and Management Strategies.

BACKGROUND. Radial scars are more commonly identified on digital breast tomosynthesis (DBT) than on digital mammography (DM). Nonetheless, universal guidelines for radial scar management in the current era of DBT are lacking. OBJECTIVE. The purpose of this study was to determine the upstaging rates of screening DBT-detected radial scars with and without atypia and to identify features related to upstaging risk. METHODS. This retrospective study included patients who underwent core needle biopsy (CNB) showing a radial scar after screening DBT and DM from January 1, 2013, to December 31, 2020. Patients without surgical excision or at least 2 years of imaging follow-up after CNB were excluded. Rates of upstaging to breast cancer (ductal carcinoma in situ [DCIS] or invasive disease) were compared between radial scars with and without atypia at CNB. Associations of upstaging with patient, imaging, and pathologic variables were explored using standard statistical tests. RESULTS. Of 165 women with 171 radial scars, the final study sample included 153 women (mean age, 56 years; range, 33-83 years) with 159 radial scars that underwent surgical excision (80.5%, 128/159) or at least 2 years of imaging follow-up (19.5%, 31/159). Seven radial scars were upstaged to DCIS and one to invasive disease. Therefore, the up-staging rate of radial scars to cancer was 5.0% (8/159). The upstaging rate of radial scars without atypia at CNB was 1.6% (2/129) and that of radial scars with atypia was 20.0% (6/30) (p < .001). On multivariable analysis, features associated with higher upstaging risk included a prior breast cancer diagnosis (62.5% vs 4.8%; p = .01) and the presence of atypia at CNB (75.0% vs 15.9%; p = .02). The upstaging rate according to mammographic finding type was 7.1% (1/14) for asymmetries, 12.5% (2/16) for masses, 5.3% (5/95) for architectural distortion, and 0.0% (0/34) for calcifications. CONCLUSION. Screening-detected radial scars without atypia at CNB have a low upstaging rate to breast cancer of 1.6%. CLINICAL IMPACT. Imaging surveillance rather than surgery is a reasonable approach for radial scars without atypia, particularly for those presenting as calcifications.