Search

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 FROM AUTHOR]

Objectives: It is uncertain what the effects of introducing digital breast tomosynthesis (DBT) in the Dutch breast cancer screening programme would be on detection, recall, and interval cancers (ICs), while reading times are expected to increase. Therefore, an investigation into the efficiency and cost-effectiveness of DBT screening while optimising reading is required.The Screening Tomosynthesis trial with advanced REAding Methods (STREAM) aims to include 17,275 women (age 50–72 years) eligible for breast cancer screening in the Netherlands for two biennial DBT screening rounds to determine the short-, medium-, and long-term effects and acceptability of DBT screening and identify an optimised strategy for reading DBT. The control group will consist of 86,400 women selected from the database of the Dutch breast cancer screening programme screened with digital mammography. The intervention group will undergo DBT examinations only. Four different reading strategies will be evaluated on a subset of first-round screening exams. These four strategies will also be evaluated combined with replacing one of the two readers with AI predictions. The Microsimulation Screening Analysis (MISCAN)-Breast model will be used to estimate the long-term outcomes of DBT screening assuming the best-performing reading method.The primary outcome measure is the IC and advanced cancer rate at the second round (combined endpoint) in the DBT group compared to the control group. Secondary outcome measures are participation, recall and detection rates, positive predictive value, acceptability, reading method with the best case-based area under the curve and reading time, predicted breast cancer mortality, number of cancers overdiagnosed, and cost-effectiveness.Question The short-, medium-, and long-term effects of digital breast tomosynthesis (DBT) imaging in the Dutch breast cancer screening programme are unknown, but essential to decide about implementation.Findings This protocol paper describes the primary endpoint of the STREAM trial: the combined interval and advanced cancer detection rate at the second DBT round.Clinical relevance The STREAM trial is a prospective, non-randomised, population-based study in the Dutch breast cancer screening programme, that aims to evaluate the effects and acceptability of two rounds of DBT screening to determine if DBT can enhance the programme’s outcomes.Materials and methods: It is uncertain what the effects of introducing digital breast tomosynthesis (DBT) in the Dutch breast cancer screening programme would be on detection, recall, and interval cancers (ICs), while reading times are expected to increase. Therefore, an investigation into the efficiency and cost-effectiveness of DBT screening while optimising reading is required.The Screening Tomosynthesis trial with advanced REAding Methods (STREAM) aims to include 17,275 women (age 50–72 years) eligible for breast cancer screening in the Netherlands for two biennial DBT screening rounds to determine the short-, medium-, and long-term effects and acceptability of DBT screening and identify an optimised strategy for reading DBT. The control group will consist of 86,400 women selected from the database of the Dutch breast cancer screening programme screened with digital mammography. The intervention group will undergo DBT examinations only. Four different reading strategies will be evaluated on a subset of first-round screening exams. These four strategies will also be evaluated combined with replacing one of the two readers with AI predictions. The Microsimulation Screening Analysis (MISCAN)-Breast model will be used to estimate the long-term outcomes of DBT screening assuming the best-performing reading method.The primary outcome measure is the IC and advanced cancer rate at the second round (combined endpoint) in the DBT group compared to the control group. Secondary outcome measures are participation, recall and detection rates, positive predictive value, acceptability, reading method with the best case-based area under the curve and reading time, predicted breast cancer mortality, number of cancers overdiagnosed, and cost-effectiveness.Question The short-, medium-, and long-term effects of digital breast tomosynthesis (DBT) imaging in the Dutch breast cancer screening programme are unknown, but essential to decide about implementation.Findings This protocol paper describes the primary endpoint of the STREAM trial: the combined interval and advanced cancer detection rate at the second DBT round.Clinical relevance The STREAM trial is a prospective, non-randomised, population-based study in the Dutch breast cancer screening programme, that aims to evaluate the effects and acceptability of two rounds of DBT screening to determine if DBT can enhance the programme’s outcomes.Results: It is uncertain what the effects of introducing digital breast tomosynthesis (DBT) in the Dutch breast cancer screening programme would be on detection, recall, and interval cancers (ICs), while reading times are expected to increase. Therefore, an investigation into the efficiency and cost-effectiveness of DBT screening while optimising reading is required.The Screening Tomosynthesis trial with advanced REAding Methods (STREAM) aims to include 17,275 women (age 50–72 years) eligible for breast cancer screening in the Netherlands for two biennial DBT screening rounds to determine the short-, medium-, and long-term effects and acceptability of DBT screening and identify an optimised strategy for reading DBT. The control group will consist of 86,400 women selected from the database of the Dutch breast cancer screening programme screened with digital mammography. The intervention group will undergo DBT examinations only. Four different reading strategies will be evaluated on a subset of first-round screening exams. These four strategies will also be evaluated combined with replacing one of the two readers with AI predictions. The Microsimulation Screening Analysis (MISCAN)-Breast model will be used to estimate the long-term outcomes of DBT screening assuming the best-performing reading method.The primary outcome measure is the IC and advanced cancer rate at the second round (combined endpoint) in the DBT group compared to the control group. Secondary outcome measures are participation, recall and detection rates, positive predictive value, acceptability, reading method with the best case-based area under the curve and reading time, predicted breast cancer mortality, number of cancers overdiagnosed, and cost-effectiveness.Question The short-, medium-, and long-term effects of digital breast tomosynthesis (DBT) imaging in the Dutch breast cancer screening programme are unknown, but essential to decide about implementation.Findings This protocol paper describes the primary endpoint of the STREAM trial: the combined interval and advanced cancer detection rate at the second DBT round.Clinical relevance The STREAM trial is a prospective, non-randomised, population-based study in the Dutch breast cancer screening programme, that aims to evaluate the effects and acceptability of two rounds of DBT screening to determine if DBT can enhance the programme’s outcomes.Key Points: It is uncertain what the effects of introducing digital breast tomosynthesis (DBT) in the Dutch breast cancer screening programme would be on detection, recall, and interval cancers (ICs), while reading times are expected to increase. Therefore, an investigation into the efficiency and cost-effectiveness of DBT screening while optimising reading is required.The Screening Tomosynthesis trial with advanced REAding Methods (STREAM) aims to include 17,275 women (age 50–72 years) eligible for breast cancer screening in the Netherlands for two biennial DBT screening rounds to determine the short-, medium-, and long-term effects and acceptability of DBT screening and identify an optimised strategy for reading DBT. The control group will consist of 86,400 women selected from the database of the Dutch breast cancer screening programme screened with digital mammography. The intervention group will undergo DBT examinations only. Four different reading strategies will be evaluated on a subset of first-round screening exams. These four strategies will also be evaluated combined with replacing one of the two readers with AI predictions. The Microsimulation Screening Analysis (MISCAN)-Breast model will be used to estimate the long-term outcomes of DBT screening assuming the best-performing reading method.The primary outcome measure is the IC and advanced cancer rate at the second round (combined endpoint) in the DBT group compared to the control group. Secondary outcome measures are participation, recall and detection rates, positive predictive value, acceptability, reading method with the best case-based area under the curve and reading time, predicted breast cancer mortality, number of cancers overdiagnosed, and cost-effectiveness.Question The short-, medium-, and long-term effects of digital breast tomosynthesis (DBT) imaging in the Dutch breast cancer screening programme are unknown, but essential to decide about implementation.Findings This protocol paper describes the primary endpoint of the STREAM trial: the combined interval and advanced cancer detection rate at the second DBT round.Clinical relevance The STREAM trial is a prospective, non-randomised, population-based study in the Dutch breast cancer screening programme, that aims to evaluate the effects and acceptability of two rounds of DBT screening to determine if DBT can enhance the programme’s outcomes.Graphical Abstract: It is uncertain what the effects of introducing digital breast tomosynthesis (DBT) in the Dutch breast cancer screening programme would be on detection, recall, and interval cancers (ICs), while reading times are expected to increase. Therefore, an investigation into the efficiency and cost-effectiveness of DBT screening while optimising reading is required.The Screening Tomosynthesis trial with advanced REAding Methods (STREAM) aims to include 17,275 women (age 50–72 years) eligible for breast cancer screening in the Netherlands for two biennial DBT screening rounds to determine the short-, medium-, and long-term effects and acceptability of DBT screening and identify an optimised strategy for reading DBT. The control group will consist of 86,400 women selected from the database of the Dutch breast cancer screening programme screened with digital mammography. The intervention group will undergo DBT examinations only. Four different reading strategies will be evaluated on a subset of first-round screening exams. These four strategies will also be evaluated combined with replacing one of the two readers with AI predictions. The Microsimulation Screening Analysis (MISCAN)-Breast model will be used to estimate the long-term outcomes of DBT screening assuming the best-performing reading method.The primary outcome measure is the IC and advanced cancer rate at the second round (combined endpoint) in the DBT group compared to the control group. Secondary outcome measures are participation, recall and detection rates, positive predictive value, acceptability, reading method with the best case-based area under the curve and reading time, predicted breast cancer mortality, number of cancers overdiagnosed, and cost-effectiveness.Question The short-, medium-, and long-term effects of digital breast tomosynthesis (DBT) imaging in the Dutch breast cancer screening programme are unknown, but essential to decide about implementation.Findings This protocol paper describes the primary endpoint of the STREAM trial: the combined interval and advanced cancer detection rate at the second DBT round.Clinical relevance The STREAM trial is a prospective, non-randomised, population-based study in the Dutch breast cancer screening programme, that aims to evaluate the effects and acceptability of two rounds of DBT screening to determine if DBT can enhance the programme’s outcomes. [ABSTRACT FROM AUTHOR]

A look back at Skeletal Radiology in 2016 reveals a sizable number of publications that significantly advanced the state of knowledge about diseases of the musculoskeletal system. This review summarizes the content of some of the most intriguing papers of the year. [ABSTRACT FROM AUTHOR]

Digital Breast Tomosynthesis (DBT) is a modern 3D Computed Tomography X-ray technique for the early detection of breast tumors, which is receiving growing interest in the medical and scientific community. Since DBT performs incomplete sampling of data, the image reconstruction approaches based on iterative methods are preferable to the classical analytic techniques, such as the Filtered Back Projection algorithm, providing fewer artifacts. In this work, we consider a Model-Based Iterative Reconstruction (MBIR) method well suited to describe the DBT data acquisition process and to include prior information on the reconstructed image. We propose a gradient-based solver named Scaled Gradient Projection (SGP) for the solution of the constrained optimization problem arising in the considered MBIR method. Even if the SGP algorithm exhibits fast convergence, the time required on a serial computer for the reconstruction of a real DBT data set is too long for the clinical needs. In this paper we propose a parallel SGP version designed to perform the most expensive computations of each iteration on Graphics Processing Unit (GPU). We apply the proposed parallel approach on three different GPU boards, with computational performance comparable with that of the boards usually installed in commercial DBT systems. The numerical results show that the proposed GPU-based MBIR method provides accurate reconstructions in a time suitable for clinical trials. [ABSTRACT FROM AUTHOR]

As a special case of tomography, digital breast tomosynthesis (DBT) can realize quasi-3D image reconstruction for breast lesion detection from few-view and limited-angle projection data. For DBT image reconstruction, iterative algorithms are needed to suppress artifacts due to undersampling, and adaptive regularizations are necessary for preserving the edges of masses and calcifications. This paper presents a novel reconstruction method by regularizing projection onto convex sets (POCS) with multiscale Tikhonov-total variation (MTTV). The regularization, known as adaptive multiscale anisotropic diffusion, is able to preserve edges to a considerable extent and selectively suppress noise without introducing artifacts. The proposed method is referred to as MTTV-POCS and is evaluated quantitatively using 3D numerical breast and Shepp-Logan phantoms as well as two clinical volume images acquired from an advanced DBT machine. Experimental results show that the proposed method has better performance in metrics of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) over two existing methods: adaptive-steepest-descent-POCS (ASD-POCS) and selective-diffusion regularized simultaneous algebraic reconstruction technique (SD-SART). As indicated by the results, the proposed method is applicable to DBT for high-quality image reconstruction. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to describe the safety and efficacy of 9G needle biopsy under tomosynthesis guidance with the patient in the prone position. Materials and methods: This is a retrospective observational study conducted on patients with non-palpable breast mass exclusively detectable through tomosynthesis, performed from the 1st January 2018 to the 1st August 2020. The procedures were performed by taking 12 tissue samples from each mass. The evaluated technical success was considered as a conclusive sample for histological diagnosis. We performed a comparison between the procedural data of interventions in patients who have a lesion < or = 10 mm and > 10 mm and between high-contrast and low-contrast masses. The histological data of the samples were analyzed. Results: Five hundred biopsies of the total 1500 performed from the 1st January 2018 to the 1st August 2020 were included in the study; repetitions for inadequate withdrawal occurred 0.4% (3/500). No major complications have ever been observed. Two cases (0.2%) of minor bleeding were observed with self-limited bleeding from the skin breach at 90 min without clinical sequelae in an asymptomatic patient. The biopsy samples showed carcinoma in 55.2% (276/500). Conclusion: Our study suggests that the 9G needle sampling biopsy procedure through tomosynthesis guide with prone patient is a safe and effective procedure for the characterization of indeterminate breast mass. [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]

Objectives: To evaluate the impact of using an artificial intelligence (AI) system as support for human double reading in a real-life scenario of a breast cancer screening program with digital mammography (DM) or digital breast tomosynthesis (DBT). Material and methods: We analyzed the performance of double reading screening with mammography and tomosynthesis after implementarion of AI as decision support. The study group consisted of a consecutive cohort of 1 year screening between March 2021 and March 2022 where double reading was performed with concurrent AI support that automatically detects and highlights lesions suspicious of breast cancer in mammography and tomosynthesis. Screening performance was measured as cancer detection rate (CDR), recall rate (RR), and positive predictive value (PPV) of recalls. Performance in the study group was compared using a McNemar test to a control group that included a screening cohort of the same size, recorded just prior to the implementation of AI. Results: A total of 11,998 women (mean age 57.59 years ± 5.8 [sd]) were included in the study group (5049 DM and 6949 DBT). Comparing global results (including DM and DBT) of double reading with vs. without AI support, we observed an increase in CDR, PPV, and RR by 3.2/‰ (5.8 vs. 9; p < 0.001), 4% (10.6 vs. 14.6; p < 0.001), and 0.7% (5.4 vs. 6.1; p < 0.001) respectively. Conclusion: AI used as support for human double reading in a real-life breast cancer screening program with DM and DBT increases CDR and PPV of the recalled women. Clinical relevance statement: Artificial intelligence as support for human double reading improves accuracy in a real-life breast cancer screening program both in digital mammography and digital breast tomosynthesis. Key Points: • AI systems based on deep learning technology offer potential for improving breast cancer screening programs. • Using artificial intelligence as support for reading improves radiologists' performance in breast cancer screening programs with mammography or tomosynthesis. • Artificial intelligence used concurrently with human reading in clinical screening practice increases breast cancer detection rate and positive predictive value of the recalled women. [ABSTRACT FROM AUTHOR]

Objectives: Response assessment to neoadjuvant systemic treatment (NAST) to guide individualized treatment in breast cancer is a clinical research priority. We aimed to develop an intelligent algorithm using multi-modal pretreatment ultrasound and tomosynthesis radiomics features in addition to clinical variables to predict pathologic complete response (pCR) prior to the initiation of therapy. Methods: We used retrospective data on patients who underwent ultrasound and tomosynthesis before starting NAST. We developed a support vector machine algorithm using pretreatment ultrasound and tomosynthesis radiomics features in addition to patient and tumor variables to predict pCR status (ypT0 and ypN0). Findings were compared to the histopathologic evaluation of the surgical specimen. The main outcome measures were area under the curve (AUC) and false-negative rate (FNR). Results: We included 720 patients, 504 in the development set and 216 in the validation set. Median age was 51.6 years and 33.6% (242 of 720) achieved pCR. The addition of radiomics features significantly improved the performance of the algorithm (AUC 0.72 to 0.81; p = 0.007). The FNR of the multi-modal radiomics and clinical algorithm was 6.7% (10 of 150 with missed residual cancer). Surface/volume ratio at tomosynthesis and peritumoral entropy characteristics at ultrasound were the most relevant radiomics. Hormonal receptors and HER-2 status were the most important clinical predictors. Conclusion: A multi-modal machine learning algorithm with pretreatment clinical, ultrasound, and tomosynthesis radiomics features may aid in predicting residual cancer after NAST. Pending prospective validation, this may facilitate individually tailored NAST regimens. Clinical relevance statement: Multi-modal radiomics using pretreatment ultrasound and tomosynthesis showed significant improvement in assessing response to NAST compared to an algorithm using clinical variables only. Further prospective validation of our findings seems warranted to enable individualized predictions of NAST outcomes. Key Points: • We proposed a multi-modal machine learning algorithm with pretreatment clinical, ultrasound, and tomosynthesis radiomics features to predict response to neoadjuvant breast cancer treatment. • Compared with the clinical algorithm, the AUC of this integrative algorithm is significantly higher. • Used prior to the initiative of therapy, our algorithm can identify patients who will experience pathologic complete response following neoadjuvant therapy with a high negative predictive value. [ABSTRACT FROM AUTHOR]

This document discusses the unintended consequences of using artificial intelligence (AI) in breast imaging. AI algorithms are being used to detect and diagnose breast lesions, providing probabilities of malignancy. While studies suggest that AI can improve accuracy and efficiency in interpreting mammograms, the true impact of AI on clinical practice and radiologist decision-making is still unknown. A study conducted with radiologists using an AI algorithm as a decision support tool found that sensitivity increased but specificity decreased when interpreting mammograms with a high cancer prevalence. The study also highlighted the phenomenon of automation bias, where radiologists may rely too heavily on AI-generated decisions. It is important to carefully consider the impact of AI on radiologist performance and the human-machine interaction to ensure safe deployment and enhanced diagnostic performance. [Extracted from the article]

Digital tomosynthesis is an imaging modality that allows for tomographic reconstructions using only a fraction of the images needed for CT reconstruction. Since it offers the advantages of tomographic images with a smaller imaging dose delivered to the patient, the technique offers much promise for use in patient positioning prior to radiation delivery. This paper describes a software environment developed to help in the creation of digital tomosynthesis image sets from digital portal images using three different reconstruction algorithms. The software then allows for use of the tomograms for patient positioning or for dose recalculation if shifts are not applied, possibly as part of an adaptive radiotherapy regimen. [ABSTRACT FROM AUTHOR]

Objectives: Digital breast tomosynthesis (DBT) can improve diagnostic accuracy compared to 2D mammography, but DBT reporting is time-consuming and potentially more fatiguing. Changes in diagnostic accuracy and subjective and objective fatigue were evaluated over a DBT reporting session, and the impact of taking a reporting break was assessed. Materials and methods: Forty-five National Health Service (NHS) mammography readers from 6 hospitals read a cancer-enriched set of 40 DBT cases whilst eye tracked in this prospective cohort study, from December 2020 to April 2022. Eye-blink metrics were assessed as objective fatigue measures. Twenty-one readers had a reporting break, 24 did not. Subjective fatigue questionnaires were completed before and after the session. Diagnostic accuracy and subjective and objective fatigue measures were compared between the cohorts using parametric and non-parametric significance testing. Results: Readers had on average 10 years post-training breast screening experience and took just under 2 h (105.8 min) to report all cases. Readers without a break reported greater levels of subjective fatigue (44% vs. 33%, p = 0.04), which related to greater objective fatigue: an increased average blink duration (296 ms vs. 286 ms, p < 0.001) and a reduced eye-opening velocity (76 mm/s vs. 82 mm/s, p < 0.001). Objective fatigue increased as the trial progressed for the no break cohort only (ps < 0.001). No difference was identified in diagnostic accuracy between the groups (accuracy: 87% vs. 87%, p = 0.92). Conclusions: Implementing a break during a 2-h DBT reporting session resulted in lower levels of subjective and objective fatigue. Breaks did not impact diagnostic accuracy, which may be related to the extensive experience of the readers. Clinical relevance statement: DBT is being incorporated into many mammography screening programmes. Recognising that reporting breaks are required when reading large volumes of DBT studies ensures this can be factored in when setting up reading sessions. Trial registration: Clinical trials registration number: NCT03733106 Key Points: • Use of digital breast tomosynthesis (DBT) in breast screening programmes can cause significant reader fatigue. • The effectiveness of incorporating reading breaks into DBT reporting sessions, to reduce mammography reader fatigue, was investigated using eye tracking. • Integrating breaks into DBT reporting sessions reduced reader fatigue; however, diagnostic accuracy was unaffected. [ABSTRACT FROM AUTHOR]

This document discusses the challenges and potential benefits of implementing artificial intelligence (AI) in breast imaging. The integration of AI in healthcare, specifically in breast imaging, has gained traction due to the increasing demand for imaging services and a predicted decrease in the workforce. The authors conducted a scoping review to identify the barriers and facilitators to the implementation of AI in breast imaging diagnosis. The major barriers include limitations in data availability and quality, the need for interpretable models, technical challenges, evaluation and validation issues, legal and ethical concerns, and the need for education. The facilitators include data initiatives, demonstrating clinical impact, utilizing multivariable data and algorithms, increasing accessibility of certified AI products, and the benefits of AI for radiologist education. The authors emphasize the importance of involving all stakeholders in the design, deployment, and evaluation of AI solutions. While progress has been made, there are still barriers that need to be overcome to fully harness the potential of AI in breast imaging. The findings align with other publications on the use of AI in healthcare, highlighting the need for high-quality data, algorithm development, user education, and ethical frameworks. Financial resources and IT-related difficulties are identified as major obstacles to widespread AI adoption in healthcare. Overall, the implementation of AI in breast imaging requires careful consideration and investment to turn it into a valuable tool. [Extracted from the article]

Purpose: This study aimed to establish a radiomics nomogram model based on digital breast tomosynthesis (DBT) images, to predict the status of axillary lymph nodes (ALN) in patients with breast carcinoma. Methods: The data of 120 patients with confirmed breast carcinoma, including 49 cases with axillary lymph node metastasis (ALNM), were retrospectively analyzed in this study. The dataset was randomly divided into a training group consisting of 84 patients (37 with ALNM) and a validation group comprising 36 patients (12 with ALNM). Clinical information was collected for all cases, and radiomics features were extracted from DBT images. Feature selection was performed to develop the Radscore model. Univariate and multivariate logistic regression analysis were employed to identify independent risk factors for constructing both the clinical model and nomogram model. To evaluate the performance of these models, receiver operating characteristic (ROC) curve analysis, calibration curve, decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discriminatory improvement (IDI) were conducted. Results: The clinical model identified tumor margin and DBT_reported_LNM as independent risk factors, while the Radscore model was constructed using 9 selected radiomics features. Incorporating tumor margin, DBT_reported_LNM, and Radscore, the radiomics nomogram model exhibited superior performance with AUC values of 0.933 and 0.920 in both datasets, respectively. The NRI and IDI showed a significant improvement, suggesting that the Radscore may serve as a useful biomarker for predicting ALN status. Conclusion: The radiomics nomogram based on DBT demonstrated effective preoperative prediction performance for ALNM in patients with breast cancer. [ABSTRACT FROM AUTHOR]

Contrast-enhanced mammography (CEM) is a promising technique for breast cancer detection and screening. It uses iodinated contrast agent and dual-energy mammography to enhance tumor visibility. CEM has shown potential advantages such as lower cost and slightly reduced sensitivity compared to breast MRI. Artificial intelligence (AI) models have been developed for CEM to assist with tasks like segmentation and classification of breast lesions. These AI models have achieved high accuracy in lesion classification and segmentation, demonstrating their potential clinical applicability. However, further research is needed to improve the accuracy for certain types of breast lesions. Overall, the development of AI and imaging technology for CEM has the potential to benefit patients with breast disease. [Extracted from the article]

Dental CBCT and panoramic images are important imaging modalities used in dental diagnosis and treatment planning. In order to acquire a panoramic image without an additional panoramic scan, in this study, we proposed a method of reconstructing a panoramic image by extracting panoramic projection data from dental CBCT projection data. After specifying the patient's dental arch from the patient's CBCT image, panoramic projection data are extracted from the CBCT projection data along the appropriate panoramic scan trajectory that fits the dental arch. A total of 40 clinical human datasets and one head phantom dataset were used to test the proposed method. The clinical human dataset used in this study includes cases in which it is difficult to reconstruct panoramic images from CBCT images, such as data with severe metal artifacts or data without teeth. As a result of applying the panoramic image reconstruction method proposed in this study, we were able to successfully acquire panoramic images from the CBCT projection data of various patients. The proposed method acquires a universally applicable panoramic image that is less affected by CBCT image quality and metal artifacts by extracting panoramic projection data from dental CBCT data and reconstructing a panoramic image. [ABSTRACT FROM AUTHOR]

In this study we demonstrate the first direct comparison between synchrotron x-ray propagation-based CT (PB-CT) and cone-beam breast-CT (CB-CT) on human mastectomy specimens (N = 12) including different benign and malignant lesions. The image quality and diagnostic power of the obtained data sets were compared and judged by two independent expert radiologists. Two cases are presented in detail in this paper including a comparison with the corresponding histological evaluation. Results indicate that with PB-CT it is possible to increase the level of contrast-to-noise ratio (CNR) keeping the same level of dose used for the CB-CT or achieve the same level of CNR reached by CB-CT at a lower level of dose. In other words, PB-CT can achieve a higher diagnostic potential compared to the commercial breast-CT system while also delivering a considerably lower mean glandular dose. Therefore, we believe that PB-CT technique, if translated to a clinical setting, could have a significant impact in improving breast cancer diagnosis. [ABSTRACT FROM AUTHOR]

Quality control measures in mammography image interpretation: radiologists' attitudes and preferences vs perceived obstacles and limitations 10.1007/s00330-022-09316-y. 36502459 4 Janusch-Roi A, Neamtiu L, Dimitrova N et al (2021) European Commission Initiative on Breast Cancer- Manual for Breast Cancer Services- European Quality Assurance Scheme for Breast Cancer Services, EUR 30750 EN, Publications Office of the European Union, Luxembourg, 2021, ISBN 978-92-76-39192-0, doi:https://doi.org/10.2760/155701, JRC125431. A survey by the European Society of Breast Imaging on radiologists' preferences regarding quality assurance measures of image interpretation in screening and diagnostic mammography. [Extracted from the article]

Population-based breast cancer screening programs using mammography have been effective in reducing breast cancer mortality. However, these programs have limitations, such as false negatives and false positives. Artificial intelligence (AI) algorithms based on deep learning have been developed to assist in breast cancer screening. These algorithms can identify lesions, assign a degree of suspicion to findings, and classify studies based on the likelihood of cancer. Retrospective studies have shown that AI can safely reduce the reading workload without compromising cancer detection or increasing false-positive recalls. Prospective studies have also supported the use of AI in breast cancer screening, although there are limitations. The application of AI in screening has the potential to extend screening to populations with a lack of breast radiologists, but ethical and legal issues must be considered. [Extracted from the article]

Accurate determination of resection margins in breast specimens is important as complete removal of malignancy is a prerequisite for patients' outcome. Mammography (DM) as 2D-technique provides only limited value in margin assessment. Therefore, we investigated whether cone-beam computed tomography (CBCT) or digital breast tomosynthesis (DBT) has incremental value in assessing margins to microcalcifications. Three independent readers investigated breast specimens for presence of microcalcifications and the smallest distance to margins. Histopathology served as gold standard. Microcalcifications were detected in 15 out of 21 included specimens (71%). Pooled sensitivity for DM, DBT and CBCT for microcalcifications compared to preoperative DM was 0.98 (CI 0.94–0.99), 0.83 (CI 0.73–0.94) and 0.94 (CI 0.87–0.99), pooled specificity was 0.99 (CI 0.99–0.99), 0.73 (CI 0.51–0.96) and 0.60 (CI 0.35–0.85). Mean measurement error for margin determination for DM, DBT and CBCT was 10 mm, 14 mm and 6 mm (p = 0.002) with significant difference between CBCT and the other devices (p < 0.03). Mean reading time required by the readers to analyze DM, DBT and CBCT, was 36, 43 and 54 s (p < 0.001). Although DM allows reliable detection of microcalcifications, measurement of resection margin was significantly more accurate with CBCT. Thus, a combination of methods or improved CBCT might provide a more accurate determination of disease-free margins in breast specimens. [ABSTRACT FROM AUTHOR]

This document is a commentary on an article titled "Contrast-enhanced breast computed tomography: can lymph node metastasis be predicted from primary tumor?" The commentary discusses the limitations of mammography and digital breast tomosynthesis for breast cancer screening and explores the potential of dedicated breast CT as an alternative. It highlights a study that investigates the use of radiomics analysis of primary tumors identified in breast CT to predict axillary lymph node metastasis. The study found that the radiomics model outperformed the model using clinical and histopathologic factors in predicting metastasis. However, the study has limitations and further validation is needed. The commentary suggests that future studies should compare the performance of different modalities and explore approaches that do not involve contrast administration. [Extracted from the article]

Objectives: This study was designed to compare the detection of subtle lesions (calcification clusters or masses) when using the combination of digital breast tomosynthesis (DBT) and synthetic mammography (SM) with digital mammography (DM) alone or combined with DBT. Methods: A set of 166 cases without cancer was acquired on a DBT mammography system. Realistic subtle calcification clusters and masses in the DM images and DBT planes were digitally inserted into 104 of the acquired cases. Three study arms were created: DM alone, DM with DBT and SM with DBT. Five mammographic readers located the centre of any lesion within the images that should be recalled for further investigation and graded their suspiciousness. A JAFROC figure of merit (FoM) and lesion detection fraction (LDF) were calculated for each study arm. The visibility of the lesions in the DBT images was compared with SM and DM images. Results: For calcification clusters, there were no significant differences (p > 0.075) in FoM or LDF. For masses, the FoM and LDF were significantly improved in the arms using DBT compared to DM alone (p < 0.001). On average, both calcification clusters and masses were more visible on DBT than on DM and SM images. Conclusions: This study demonstrated that masses were detected better with DBT than with DM alone and there was no significant difference (p = 0.075) in LDF between DM&DBT and SM&DBT for calcifications clusters. Our results support previous studies that it may be acceptable to not acquire digital mammography alongside tomosynthesis for subtle calcification clusters and ill-defined masses. Key Points: • The detection of masses was significantly better using DBT than with digital mammography alone. • The detection of calcification clusters was not significantly different between digital mammography and synthetic 2D images combined with tomosynthesis. • Our results support previous studies that it may be acceptable to not acquire digital mammography alongside tomosynthesis for subtle calcification clusters and ill-defined masses for the imaging technology used. [ABSTRACT FROM AUTHOR]

X-ray absorption of breast cancers and surrounding healthy tissue can be very similar, a situation that sometimes leads to missed cancers or false-positive diagnoses. To increase the accuracy of mammography and breast tomosynthesis, we describe dynamic X-ray elastography using a novel pulsed X-ray source. This new imaging modality provides both absorption and mechanical properties of the imaged material. We use a small acoustic speaker to vibrate the sample while a synchronously pulsed cold cathode X-ray source images the mechanical deformation. Using these stroboscopic images, we derive two-dimensional stiffness maps of the sample in addition to the conventional X-ray image. In a breast phantom composed of ZrO2 powder embedded in gel, dynamic elastography derived stiffness maps were able to discriminate a hard inclusion from surrounding material with a contrast-to-noise ratio (CNR) of 4.5. The CNR on the corresponding absorption image was 1.1. This demonstrates the feasibility of dynamic X-ray elastography with a synchronously pulsed X-ray source. [ABSTRACT FROM AUTHOR]

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

Objectives: To compare inter-reader agreement and diagnostic confidence in detecting sacroiliitis by the modified New York criteria (mNY) on digital radiography (DR) versus digital pelvic tomosynthesis assisted DR (DR+DPT), and to evaluate changes in the presence of axial spondyloarthritis (axSpA) according to the Assessment of Spondyloarthritis International Society (ASAS) criteria.Methods: One hundred and thirty-eight patients who underwent both DR and DPT with suspicious axSpA in our rheumatologic clinic were included from February 2017 to February 2018. Three radiologists independently graded sacroiliitis and confidence level on DR first and then re-graded them on DPT in a paired manner. Agreement, confidence, and diagnostic accuracy were evaluated for readers. Changes in the presence of disease by mNY and ASAS criteria were assessed between DR alone and DR+DPT.Results: On DR alone, 73 patients were assessed with radiographic sacroiliitis, and 85 were classified into axSpA by the ASAS criteria; however, 78 and 85, respectively, were classified on DR+DPT. With the assistance of DPT, 17 and 12 patients changed to the disease positivity according to the mNY and ASAS criteria, respectively; the negative results changed to positive in 11 and six patients, respectively. For all readers, agreement improved with DPT (0.79 to 0.89). DR+DPT achieved higher diagnostic accuracy (AUC, P < 0.05).Conclusion: The combination of DR and DPT achieved a higher diagnostic performance than that of DR alone, with better agreement. On DR+DPT, the diagnoses of 9.0% of patients with suspicious axSpA (12 of 134) were changed to the status of disease by the ASAS criteria. [ABSTRACT FROM AUTHOR]

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

Deep learning has shown tremendous potential in the task of object detection in images. However, a common challenge with this task is when only a limited number of images containing the object of interest are available. This is a particular issue in cancer screening, such as digital breast tomosynthesis (DBT), where less than 1% of cases contain cancer. In this study, we propose a method to train an inpainting generative adversarial network to be used for cancer detection using only images that do not contain cancer. During inference, we removed a part of the image and used the network to complete the removed part. A significant error in completing an image part was considered an indication that such location is unexpected and thus abnormal. A large dataset of DBT images used in this study was collected at Duke University. It consisted of 19,230 reconstructed volumes from 4348 patients. Cancerous masses and architectural distortions were marked with bounding boxes by radiologists. Our experiments showed that the locations containing cancer were associated with a notably higher completion error than the non-cancer locations (mean error ratio of 2.77). All data used in this study has been made publicly available by the authors. [ABSTRACT FROM AUTHOR]

This study objectively evaluates the similarity between standard full-field digital mammograms and two-dimensional synthesized digital mammograms (2DSM) in a cohort of women undergoing mammography. Under an institutional review board–approved data collection protocol, we retrospectively analyzed 407 women with digital breast tomosynthesis (DBT) and full-field digital mammography (FFDM) examinations performed from September 1, 2014, through February 29, 2016. Both FFDM and 2DSM images were used for the analysis, and 3216 available craniocaudal (CC) and mediolateral oblique (MLO) view mammograms altogether were included in the dataset. We analyzed the mammograms using a fully automated algorithm that computes 152 structural similarity, texture, and mammographic density–based features. We trained and developed two different global mammographic image feature analysis–based breast cancer detection schemes for 2DSM and FFDM images, respectively. The highest structural similarity features were obtained on the coarse Weber Local Descriptor differential excitation texture feature component computed on the CC view images (0.8770) and MLO view images (0.8889). Although the coarse structures are similar, the global mammographic image feature–based cancer detection scheme trained on 2DSM images outperformed the corresponding scheme trained on FFDM images, with area under a receiver operating characteristic curve (AUC) = 0.878 ± 0.034 and 0.756 ± 0.052, respectively. Consequently, further investigation is required to examine whether DBT can replace FFDM as a standalone technique, especially for the development of automated objective-based methods. [ABSTRACT FROM AUTHOR]

Objectives: To compare upgrade rates of ductal carcinoma in situ (DCIS) on digital mammography (DM) versus digital breast tomosynthesis (DBT) and identify patient, imaging, and pathological features associated with upgrade risk. Methods: A retrospective review was performed of 318 women (mean 59 years, range 37–89) with screening-detected DCIS from 2007 to 2011 (DM group) and from 2013 to 2016 (DBT group). Comparisons made between DM and DBT groups using the unpaired t test and chi-square test include detection rates of DCIS, upgrade rates to invasive cancer, and pathological features of DCIS and upgraded cases. Patient, imaging, and pathological features associated with upgrade were also determined. P values < 0.05 were considered significant. Results: There was no significant difference in detection rates of DCIS between DM and DBT groups (0.9 versus 1.0 per 1000 examinations, p = 0.45). Upgrade rates of DCIS to invasive cancer in DM and DBT groups were similar (17.3% versus 16.8%, p = 0.90), despite significant differences in pathological features of DCIS between DM and DBT groups (including nuclear grade, comedonecrosis, and progesterone receptor status [p ≤ 0.01]). Among upgraded cases, a higher proportion were high-grade invasive cancers with DBT (36.7% versus 9.5%, p = 0.03). In both groups, ultrasound-guided (versus stereotactic) biopsy was associated with higher upgrade risk (p ≤ 0.03). Conclusions: There was no significant difference in detection rates or upgrade rates of DCIS on DM versus DBT; however, upgraded cases were more likely to be high grade with DBT, suggesting possible differences in tumor biology between cancers with DM and DBT. In both DM and DBT groups, biopsy modality was associated with upgrade risk. Key Points: • Detection rates and upgrade rates of ductal carcinoma in situ (DCIS) on digital mammography (DM) versus digital breast tomosynthesis (DBT) are similar. • A higher proportion of upgraded cases were high-grade invasive cancers with DBT than DM, suggesting possible differences in tumor biology between cancers that are detected with DM and DBT. • With both DM and DBT, ultrasound-guided biopsy (versus stereotactic biopsy) was associated with a higher risk of upgrade. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate at which sensitivity digital breast tomosynthesis (DBT) would become cost-effective compared to digital mammography (DM) in a population breast cancer screening program, given a constant estimate of specificity. Methods: In a microsimulation model, the cost-effectiveness of biennial screening for women aged 50–75 was simulated for three scenarios: DBT for women with dense breasts and DM for women with fatty breasts (scenario 1), DBT for the whole population (scenario 2) or maintaining DM screening (reference). For DM, sensitivity was varied depending on breast density from 65 to 87%, and for DBT from 65 to 100%. The specificity was set at 96.5% for both DM and DBT. Direct medical costs were considered, including screening, biopsy and treatment costs. Scenarios were considered to be cost-effective if the incremental cost-effectiveness ratio (ICER) was below €20,000 per life year gain (LYG). Results: For both scenarios, the ICER was more favourable at increasing DBT sensitivity. Compared with DM screening, 0.8–10.2% more LYGs were found when DBT sensitivity was at least 75% for scenario 1, and 4.7–18.7% when DBT sensitivity was at least 80% for scenario 2. At €96 per DBT, scenario 1 was cost-effective at a DBT sensitivity of at least 90%, and at least 95% for scenario 2. At €80 per DBT, these values decreased to 80% and 90%, respectively. Conclusion: DBT is more likely to be a cost-effective alternative to mammography in women with dense breasts. Whether DBT could be cost-effective in a general population highly depends on DBT costs. Key Points: • DBT could be a cost-effective screening modality for women with dense breasts when its sensitivity is at least 90% at a maximum cost per screen of €96. • DBT has the potential to be cost-effective for screening all women when sensitivity is at least 90% at a maximum cost per screen of €80. • Whether DBT could be used as an alternative to mammography for screening all women is highly dependent on the cost of DBT per screen. [ABSTRACT FROM AUTHOR]

Image texture, the relative spatial arrangement of intensity values in an image, encodes valuable information about the scene. As it stands, much of this potential information remains untapped. Understanding how to decipher textural details would afford another method of extracting knowledge of the physical world from images. In this work, we attempt to bridge the gap in research between quantitative texture analysis and the visual perception of textures. The impact of changes in image texture on human observer's ability to perform signal detection and localization tasks in complex digital images is not understood. We examine this critical question by studying task-based human observer performance in detecting and localizing signals in tomographic breast images. We have also investigated how these changes impact the formation of second-order image texture. We used digital breast tomosynthesis (DBT) an FDA approved tomographic X-ray breast imaging method as the modality of choice to show our preliminary results. Our human observer studies involve localization ROC (LROC) studies for low contrast mass detection in DBT. Simulated images are used as they offer the benefit of known ground truth. Our results prove that changes in system geometry or processing leads to changes in image texture magnitudes. We show that the variations in several well-known texture features estimated in digital images correlate with human observer detection–localization performance for signals embedded in them. This insight can allow efficient and practical techniques to identify the best imaging system design and algorithms or filtering tools by examining the changes in these texture features. This concept linking texture feature estimates and task based image quality assessment can be extended to several other imaging modalities and applications as well. It can also offer feedback in system and algorithm designs with a goal to improve perceptual benefits. Broader impact can be in wide array of areas including imaging system design, image processing, data science, machine learning, computer vision, perceptual and vision science. Our results also point to the caution that must be exercised in using these texture features as image-based radiomic features or as predictive markers for risk assessment as they are sensitive to system or image processing changes. [ABSTRACT FROM AUTHOR]

In digital mammography, which is used for the early detection of breast tumors, oversight may occur due to overlap between normal tissues and lesions. However, since digital breast tomosynthesis can acquire three-dimensional images, tissue overlapping is reduced, and, therefore, the shape and distribution of the lesions can be easily identified. However, it is often difficult to distinguish between benign and malignant breast lesions on images, and the diagnostic accuracy can be reduced due to complications from radiological interpretations, owing to acquisition of a higher number of images. In this study, we developed an automated classification method for diagnosing breast lesions on digital breast tomosynthesis images using radiomics to comprehensively analyze the radiological images. We extracted an analysis area centered on the lesion and calculated 70 radiomic features, including the shape of the lesion, existence of spicula, and texture information. The accuracy was compared by inputting the obtained radiomic features to four classifiers (support vector machine, random forest, naïve Bayes, and multi-layer perceptron), and the final classification result was obtained as an output using a classifier with high accuracy. To confirm the effectiveness of the proposed method, we used 24 cases with confirmed pathological diagnosis on biopsy. We also compared the classification results based on the presence or absence of dimension reduction using least absolute shrinkage and a selection operator (LASSO). As a result, when the support vector machine was used as a classifier, the correct identification rate of the benign tumors was 55% and that of malignant tumors was 84%, with best results. These results indicate that the proposed method may help in more accurately diagnosing cases that are difficult to classify on images. [ABSTRACT FROM AUTHOR]

We summarise here the information to be provided to women and referring physicians about percutaneous breast biopsy and lesion localisation under imaging guidance. After explaining why a preoperative diagnosis with a percutaneous biopsy is preferred to surgical biopsy, we illustrate the criteria used by radiologists for choosing the most appropriate combination of device type for sampling and imaging technique for guidance. Then, we describe the commonly used devices, from fine-needle sampling to tissue biopsy with larger needles, namely core needle biopsy and vacuum-assisted biopsy, and how mammography, digital breast tomosynthesis, ultrasound, or magnetic resonance imaging work for targeting the lesion for sampling or localisation. The differences among the techniques available for localisation (carbon marking, metallic wire, radiotracer injection, radioactive seed, and magnetic seed localisation) are illustrated. Type and rate of possible complications are described and the issue of concomitant antiplatelet or anticoagulant therapy is also addressed. The importance of pathological-radiological correlation is highlighted: when evaluating the results of any needle sampling, the radiologist must check the concordance between the cytology/pathology report of the sample and the radiological appearance of the biopsied lesion. We recommend that special attention is paid to a proper and tactful approach when communicating to the woman the need for tissue sampling as well as the possibility of cancer diagnosis, repeat tissue sampling, and or even surgery when tissue sampling shows a lesion with uncertain malignant potential (also referred to as "high-risk" or B3 lesions). Finally, seven frequently asked questions are answered. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the impact of utilizing digital breast tomosynthesis (DBT) or/and full-field digital mammography (FFDM), and different transfer learning strategies on deep convolutional neural network (DCNN)-based mass classification for breast cancer.Methods: We retrospectively collected 441 patients with both DBT and FFDM on which regions of interest (ROIs) covering the malignant, benign and normal tissues were extracted for DCNN training and validation. Experiments were conducted for tasks in distinguishing malignant/benign/normal: (1) classification capabilities of DBT vs FFDM and the role of transfer learning were validated on 2D-DCNN; (2) different strategies of combining DBT and FFDM and the associated impacts on classification were explored; (3) 2D-DCNN and 3D-DCNN trained from scratch with volumetric DBT were compared.Results: 2D-DCNN with transfer learning outperformed that without for DBT in distinguishing malignant (ΔAUC = 0.059 ± 0.009, p < 0.001), benign (ΔAUC = 0.095 ± 0.010, p < 0.001) and normal tissue (ΔAUC = 0.042 ± 0.004, p < 0.001) (paired samples t test). 2D-DCNN trained on DBT (with transfer learning) achieved higher accuracy than those on FFDM (malignant: ΔAUC = 0.014 ± 0.014, p = 0.037; benign: ΔAUC = 0.031 ± 0.006, p < 0.001; normal: ΔAUC = 0.017 ± 0.004, p < 0.001) (independent samples t test). The 2D-DCNN employing both DBT and FFDM for training achieved better performances in benign (FFDM: ΔAUC = 0.010 ± 0.008, p < 0.001; DBT: ΔAUC = 0.009 ± 0.005, p < 0.001) and normal (FFDM: ΔAUC = 0.005 ± 0.003, p < 0.001; DBT: ΔAUC = 0.002 ± 0.002, p < 0.001) (related samples Friedman test). The 3D-DCNN and 2D-DCNN trained from scratch with DBT only produced moderate classification.Conclusions: Transfer learning facilitates mass classification for both DBT and FFDM, and DBT outperforms FFDM when equipped with transfer learning. Integrating DBT and FFDM in DCNN training enhances mass classification accuracy for breast cancer.Key Points: • Transfer learning facilitates mass classification for both DBT and FFDM, and the DBT-based DCNN outperforms the FFDM-based DCNN when equipped with transfer learning. • Integrating DBT and FFDM in DCNN training enhances breast mass classification accuracy. • 3D-DCNN/2D-DCNN trained from scratch with volumetric DBT but without transfer learning only produce moderate mass classification result. [ABSTRACT FROM AUTHOR]

Quantitative assessment of microcalcification (MC) cluster image quality is presented, in terms of cluster signal-difference-to-noise ratio (SDNR) intercomparison among digital breast tomosynthesis (DBT) and 2-dimensional (2D) and synthetic-2-dimensional (s2D) mammography. A phantom that provides realistic appearance of MC clusters located in uniform and nonuniform background was imaged in 2D and DBT, considering various scattering conditions. MC cluster SDNR differentiation is investigated with respect to MC particle size (uniform background) and surrounding parenchyma density (nonuniform background). An accurate MC cluster segmentation method was used to delineate individual MC particles and estimate MC cluster SDNR. Analysis of the uniform part of the phantom indicated higher performance of DBT and 2D over s2D for the smallest cluster size (106-177 μm), no difference among mammographic modes for the largest MC cluster (224-354 μm), and enhanced role of 2D for decreasing cluster size and increasing scattering. Analysis of the nonuniform part of the phantom indicated DBT performed better than 2D and s2D in case of dense parenchyma pattern, while 2D and s2D did not differ across parenchyma density patterns and scattering conditions. The presented MC cluster SDNR analysis was capable of revealing subtle differences among mammographic modes and suggests a methodology for clinical image quality assessment. Graphical abstract. [ABSTRACT FROM AUTHOR]

Objectives: To retrospectively investigate early performance measures of digital breast tomosynthesis (DBT) versus standard digital mammography (DM) for consecutive screening rounds.Methods: We included information about 35,736 women screened in BreastScreen Norway, 2008-2016, with at least two consecutive screening examinations. The pair of two consecutive screening examinations was the unit of analysis, and results from the subsequent examination were the measure of interest. Screening technique changed during the study period, resulting in four study groups: DM after DM, DBT after DM, DM after DBT, and DBT after DBT. We compared selected early performance measures between the study groups.Results: Recall for DM after DM was 3.6% and lower for all other study groups (p < 0.001). The rate of screen-detected breast cancer was 4.6/1000 for DM after DM; for DBT after DM and DBT after DBT, it was 9.9/1000 and 8.3/1000, respectively (p < 0.001 relative to DM after DM), and for DM after DBT 4.3/1000. The rate of tubular carcinoma was higher for DBT after DBT or after DM compared with DM after DM (p < 0.01). The rate of histologic grade 1 tumors was higher for DBT after DM compared with DM after DM (p < 0.001). We did not observe any statistical difference in the interval cancer rates.Conclusions: Lower recall and higher cancer detection rates for screening with DBT were sustainable over two consecutive screening rounds. Positive predictive values were higher for DBT than DM. There were no differences in the interval cancer rates between the study groups.Key Points: • There is limited knowledge about early performance measures for screening with digital breast tomosynthesis beyond one screening round. • A decline in recall rate and an incline in the rate of screen-detected breast cancer were observed for women screened with DBT compared with DM, irrespective of prior screening technique. The interval breast cancer rate did not differ statistically for women screened with DBT versus DM. • Tumor characteristics tended to be prognostic favorable for DBT compared with DM with no differences in rates of more advanced cancers. The clinical significance of increased cancer detection and the potential for future mortality reduction remain unknown. [ABSTRACT FROM AUTHOR]

Background: Several studies in Europe and the US have shown promising results favouring digital breast tomosynthesis compared to standard digital mammography (DM). However, the costs of implementing the technology in screening programmes are not yet known.Methods: A randomised controlled trial comparing the results from digital breast tomosynthesis including synthetic mammograms (DBT) vs. DM was performed in Bergen during 2016 and 2017 as a part of BreastScreen Norway. The trial included 29,453 women and allowed for a detailed comparison of procedure use and screening, recall and treatment costs estimated at the individual level.Results: The increased cost of equipment, examination and reading time with DBT vs. DM was €8.5 per screened woman (95% CI 8.4-8.6). Costs of DBT remained significantly higher after adding recall assessment costs, €6.2 (95% CI 4.6-7.9). Substantial reductions in either examination and reading times, price of DBT equipment or price of IT storage and connectivity did not change the conclusion. Adding treatment costs resulted in too wide confidence intervals to draw definitive conclusions (additional costs of tomosynthesis €9.8, 95% CI -56 to 74). Performing biopsy at recall, radiation therapy and chemotherapy was significantly more frequent among women screened with DBT.Conclusion: The results showed lower incremental costs of DBT vs. DM, compared to what is found in previous cost analyses of DBT and DM. However, the incremental costs were still higher for DBT compared with DM after including recall costs. Further studies with long-term treatment data are needed to understand the complete costs of implementing DBT in screening. [ABSTRACT FROM AUTHOR]

Objectives: Tomosynthesis (DBT) has proven to be more sensitive than digital mammography, but it requires longer reading time. We retrospectively compared accuracy and reading times of a simplified protocol with 1-cm-thick slabs versus a standard protocol of slabs + 1-mm-spaced planes, both integrated with synthetic 2D.Methods: We randomly selected 894 DBTs (including 12 cancers) from the experimental arm of the RETomo trial. DBTs were read by two radiologists to estimate specificity. A second set of 24 cancers (8 also present in the first set) mixed within 276 negative DBTs was read by two radiologists. In total, 28 cancers with 64 readings were used to estimate sensitivity. Radiologists read with both protocols separated by a 3-month washout. Only women that were positive at the screening reading were assessed. Variance was estimated taking into account repeated measures.Results: Sensitivity was 82.8% (53/64, 95% confidence interval (95% CI) 67.2-92.2) and 90.6% (95% CI 80.2-95.8) with simplified and standard protocols, respectively. In the random screening setting, specificity was 97.9% (1727/1764, 95% CI 97.1-98.5) and 96.3% (95% CI 95.3-97.1), respectively. Inter-reader agreement was 0.68 and 0.54 with simplified and standard protocols, respectively. Median reading times with simplified protocol were 20% to 30% shorter than with standard protocol.Conclusions: A simplified protocol reduced reading time and false positives but may have a negative impact on sensitivity.Key Points: • The adoption of digital breast tomosynthesis (DBT) in screening, more sensitive than mammography, could be limited by its potential effect on the radiologists' workload, i.e., increased reading time and fatigue. • A DBT simplified protocol with slab only, compared to a standard protocol (slab plus planes) both integrated with synthetic 2D, reduced time and false positives but had a negative impact on sensitivity. [ABSTRACT FROM AUTHOR]

Digital breast tomosynthesis (DBT) has been widely implemented in place of 2D mammography, although it is less effective in women with extremely dense breasts. Breast ultrasound detects additional early-stage, invasive breast cancers when combined with mammography; however, its relevant limitations, including the shortage of trained operators, operator dependence and small field of view, have limited its widespread implementation. Automated breast sonography (ABS) is a promising technique but the time to interpret and false-positive rates need to be improved. Supplemental screening with contrast-enhanced magnetic resonance imaging (MRI) in high-risk women reduces late-stage disease; abbreviated MRI protocols may reduce cost and increase accessibility to women of average risk with dense breasts. Contrast-enhanced digital mammography (CEDM) and molecular breast imaging improve cancer detection but require further validation for screening and direct biopsy guidance should be implemented for any screening modality. This article reviews the status of screening women with dense breasts. KEY POINTS: • The sensitivity of mammography is reduced in women with dense breasts. Supplemental screening with US detects early-stage, invasive breast cancers. • Tomosynthesis reduces recall rate and increases cancer detection rate but is less effective in women with extremely dense breasts. • Screening MRI improves early diagnosis of breast cancer more than ultrasound and is currently recommended for women at high risk. Risk assessment is needed, to include breast density, to ascertain who should start early annual MRI screening. [ABSTRACT FROM AUTHOR]

Purpose of Review: To review research on deep learning models and their potential application within breast screening.Recent Findings: The greatest issue in breast screening is a workforce crisis across the UK, much of Europe and even Japan. Traditional computer-aided detection (CAD) for mammography decision-support could not reach the level of an independent reader. Deep learning (DL) outperforms CAD and is close to surpassing human performance. DL is already capable of decision support and density assessment for 2D full-field digital mammography (FFDM), and is on the cusp of providing consistent, accurate and interpretable mammography reading as an independent reader.Summary: A bold vision for the future of breast cancer screening is required if programmes are to maintain double reading standards. DL provides the potential for single reading programmes, such as in the USA, to reach EU double reading accuracy, as well as providing practical support for adoption of the emerging modality of digital breast tomosynthesis. [ABSTRACT FROM AUTHOR]

Objectives: To compare software estimates of volumetric breast density (VBD) based on breast tomosynthesis (BT) projections to those based on digital mammography (DM) images in a large screening cohort, the Malmö Breast Tomosynthesis Screening Trial (MBTST).Methods: DM and BT images of 9909 women (enrolled 2010-2015) were retrospectively analysed with prototype software to estimate VBD. Software calculation is based on a physics model of the image acquisition process and incorporates the effect of masking in DM based on accumulated dense tissue areas. VBD (continuously and categorically) was compared between BT [central projection (mediolateral oblique view (MLO)] and two-view DM, and with radiologists' BI-RADS density 4th ed. scores. Agreement and correlation were investigated with weighted kappa (κ), Spearman's correlation coefficient (r), and Bland-Altman analysis.Results: There was a high correlation (r = 0.83) between VBD in DM and BT and substantial agreement between the software breast density categories [observed agreement, 61.3% and 84.8%; κ = 0.61 and ĸ = 0.69 for four (a/b/c/d) and two (fat involuted vs. dense) density categories, respectively]. There was moderate agreement between radiologists' BI-RADS scores and software density categories in DM (ĸ = 0.55) and BT (ĸ = 0.47).Conclusions: In a large public screening setting, we report a substantial agreement between VBD in DM and BT using software with special focus on masking effect. This automated and objective mode of measuring VBD may be of value to radiologists and women when BT is used as the primary breast cancer screening modality.Key Points: • There was a high correlation between continuous volumetric breast density in DM and BT. • There was substantial agreement between software breast density categories (four groups) in DM and BT; with clinically warranted binary software breast density categories, the agreement increased markedly. • There was moderate agreement between radiologists' BI-RADS scores and software breast density categories in DM and BT. [ABSTRACT FROM AUTHOR]

Purpose: To determine the frequency and characteristics of contralateral, non-recalled breast abnormalities following recall at screening mammography.Methods: We included a series of 130,338 screening mammograms performed between 1 January 2014 and 1 January 2016. During the 1-year follow-up, clinical data were collected for all recalls. Screening outcome was determined for recalled women with or without evaluation of contralateral breast abnormalities.Results: Of 3,995 recalls (recall rate 3.1%), 129 women (3.2%) underwent assessment of a contralateral, non-recalled breast abnormality. Most lesions were detected at clinical mammography and/or breast tomosynthesis (101 women, 78.3%). The biopsy rate was similar for recalled lesions and contralateral, non-recalled lesions, but the positive predictive value of biopsy was higher for recalled lesions (p = 0.01). A comparable proportion of the recalled lesions and contralateral, non-recalled lesions were malignant (p = 0.1). The proportion of ductal carcinoma in situ was similar for both groups, as well as invasive cancer characteristics and type of surgical treatment.Conclusions: About 3% of recalled women underwent evaluation of contralateral, non-recalled breast lesions. Evaluation of the contralateral breast after recall is important as we found that 15.5% of contralateral, non-recalled lesions were malignant. Contralateral cancers and screen-detected cancers show similar characteristics, stage and surgical treatment.Key Points: • 3% of recalled women underwent evaluation of contralateral, non-recalled lesions • One out of seven contralateral, non-recalled lesions was malignant • A contralateral cancer was diagnosed in 0.5% of recalls • Screen-detected cancers and non-recalled, contralateral cancers showed similar histological characteristics • Tumour stage and surgical treatment were similar for both groups. [ABSTRACT FROM AUTHOR]

Purpose: To determine the feasibility of a prototype device combining 3D-automated breast ultrasound (ABVS) and digital breast tomosynthesis in a single device to detect and characterize breast lesions.Methods: In this prospective feasibility study, the FUSION-X-US prototype was used to perform digital breast tomosynthesis and ABVS in 23 patients with an indication for tomosynthesis based on current guidelines after clinical examination and standard imaging. The ABVS and tomosynthesis images of the prototype were interpreted separately by two blinded experts. The study compares the detection and BI-RADS® scores of breast lesions using only the tomosynthesis and ABVS data from the FUSION-X-US prototype to the results of the complete diagnostic workup.Results: Image acquisition and processing by the prototype was fast and accurate, with some limitations in ultrasound coverage and image quality. In the diagnostic workup, 29 solid lesions (23 benign, including three cases with microcalcifications, and six malignant lesions) were identified. Using the prototype, all malignant lesions were detected and classified as malignant or suspicious by both investigators.Conclusion: Solid breast lesions can be localized accurately and fast by the Fusion-X-US system. Technical improvements of the ultrasound image quality and ultrasound coverage are needed to further study this new device.Key Points: The prototype combines tomosynthesis and automated 3D-ultrasound (ABVS) in one device. It allows accurate detection of malignant lesions, directly correlating tomosynthesis and ABVS data. The diagnostic evaluation of the prototype-acquired data was interpreter-independent. The prototype provides a time-efficient and technically reliable diagnostic procedure. The combination of tomosynthesis and ABVS is a promising diagnostic approach. [ABSTRACT FROM AUTHOR]

Purpose: To compare the performance of one-view digital breast tomosynthesis (1v-DBT) to that of three other protocols combining DBT and mammography (DM) for breast cancer detection.Materials and Methods: Six radiologists, three experienced with 1v-DBT in screening, retrospectively reviewed 181 cases (76 malignant, 50 benign, 55 normal) in two sessions. First, they scored sequentially: 1v-DBT (medio-lateral oblique, MLO), 1v-DBT (MLO) + 1v-DM (cranio-caudal, CC) and two-view DM + DBT (2v-DM+2v-DBT). The second session involved only 2v-DM. Lesions were scored using BI-RADS® and level of suspiciousness (1-10). Sensitivity, specificity, receiver operating characteristic (ROC) and jack-knife alternative free-response ROC (JAFROC) were computed.Results: On average, 1v-DBT was non-inferior to any of the other protocols in terms of JAFROC figure-of-merit, area under ROC curve, sensitivity or specificity (p>0.391). While readers inexperienced with 1v-DBT screening improved their sensitivity when adding more images (69-79 %, p=0.019), experienced readers showed similar sensitivity (76 %) and specificity (70 %) between 1v-DBT and 2v-DM+2v-DBT (p=0.482). Subanalysis by lesion type and breast density showed no difference among modalities.Conclusion: Detection performance with 1v-DBT is not statistically inferior to 2v-DM or to 2v-DM+2v-DBT; its use as a stand-alone modality might be sufficient for readers experienced with this protocol.Key Points: • One-view breast tomosynthesis is not inferior to two-view digital mammography. • One-view DBT is not inferior to 2-view DM plus 2-view DBT. • Training may lead to 1v-DBT being sufficient for screening. [ABSTRACT FROM AUTHOR]

An editorial is presented on full-field digital mammography (DM) being the standard technique today for breast cancer screening. Topics include diagnostic work-up including supplementary mammographic views, ultrasound, and digital breast tomosynthesis (DBT); and women often causing psychologic distress and transient anxiety to the health care.

Objectives: To compare radiation dose delivered by digital mammography (FFDM) and breast tomosynthesis (DBT) for a single view.Methods: 4,780 FFDM and 4,798 DBT images from 1,208 women enrolled in a screening trial were used to ground dose comparison. Raw images were processed by an automatic software to determine volumetric breast density (VBD) and were used together with exposure data to compute the mean glandular dose (MGD) according to Dance's model. DBT and FFDM were compared in terms of operation of the automatic exposure control (AEC) and MGD level.Results: Statistically significant differences were found between FFDM and DBT MGDs for all views (CC: MGDFFDM=1.366 mGy, MGDDBT=1.858 mGy; p<0.0001; MLO: MGDFFDM=1.374 mGy, MGDDBT=1.877 mGy; p<0.0001). Considering the 4,768 paired views, Bland-Altman analysis showed that the average increase of DBT dose compared to FFDM is 38 %, and a range between 0 % and 75 %.Conclusions: Our findings show a modest increase of radiation dose to the breast by tomosynthesis compared to FFDM. Given the emerging role of DBT, its use in conjunction with synthetic 2D images should not be deterred by concerns regarding radiation burden, and should draw on evidence of potential clinical benefit.Key Points: • Most studies compared tomosynthesis in combination with mammography vs. mammography alone. • There is some concern about the dose increase with tomosynthesis. • Clinical data show a small increase in radiation dose with tomosynthesis. • Synthetic 2D images from tomosynthesis at zero dose reduce potential harm. • The small dose increase should not be a barrier to use of tomosynthesis. [ABSTRACT FROM AUTHOR]

Objective: To compare six strategies using digital breast tomosynthesis in women with mammographically negative dense breasts.Materials and Methods: This is a substudy of the 'ASTOUND' trial. 163 women who underwent tomosynthesis with synthetically reconstructed projection images (S-2D) inclusive of 13 (7.9%) cases diagnosed with breast cancer at histopathology after surgery were evaluated. Accuracy measures and screen-reading time of six reading strategies were assessed: (A) Single reading of S-2D alone, (B) single reading of tomosynthesis alone, (C) single reading of joint interpretation of tomosynthesis + S-2D, (D) double-reading of S-2D alone, (E) double reading of tomosynthesis alone, (F) double reading of joint interpretation of tomosynthesis + S-2D.Results: The median age of the patients was 53 years (range, 36-88 years). The highest global accuracy was obtained with double reading of tomosynthesis + S2D (F) with an AUC of 0.979 (p<0.001) and a mean reading time of 154 s versus 34 s for the fastest strategy (single reading of S-2D alone). The AUCs for the other five strategies did not differ from each other.Conclusion: Double reading of tomosynthesis+ S2D had the best accuracy of six screen-reading strategies although it had the longest reading time.Key Points: • Tomosynthesis acquisitions are progressively implemented with reconstructed synthesized 2D images • Double reading using S-2D plus tomosynthesis had the highest global accuracy (p<0.001). • Double reading of S-2D plus tomosynthesis increased reading time. [ABSTRACT FROM AUTHOR]

Imaging plays a major role in the diagnosis, treatment, and follow-up of breast cancer. Findings that require further assessment will be detected both at screening and curative mammography. Most findings that are further worked up tend to yield benign diagnoses. Consequently, there is an ongoing search for new tools to reduce recalls and unnecessary biopsies while maintaining or improving cancer detection rates. The clinically most promising methods in this respect are described and discussed in this review. [ABSTRACT FROM AUTHOR]

Purpose : Tomosynthesis is attracting attention as a low-dose tomography technology compared with X-ray CT. However, conventional tomosynthesis imaging devices are large and stationary. Furthermore, there is a limitation in the working range of the X-ray source during image acquisition. We have previously proposed the use of a portable X-ray device for tomosynthesis that can be used for ward rounds and emergency medicine. The weight of this device can be reduced by using a flat panel detector (FPD), and flexibility is realized by the free placement of the X-ray source and FPD. Tomosynthesis using a portable X-ray device requires calibration of the geometry between the X-ray source and detector at each image acquisition. We propose a method for geometry calibration and demonstrate tomosynthesis image reconstruction by this method. Methods : An image processing-based calibration method using an asymmetric and multilayered calibration object (AMCO) is presented. Since the AMCO is always attached to the X-ray source housing for geometry calibration, the additional setting of a calibration object or marker around or on the patients is not required. The AMCO's multilayer structure improves the calibration accuracy, especially in the out-of-plane direction. Results : Two experiments were conducted. The first was performed to evaluate the calibration accuracy using an XY positioning stage and a gonio stage. As a result, an accuracy of approximately 1 mm was achieved both in the in-plane and out-of-plane directions. An angular accuracy of approximately $$0.5^{\circ }$$ was confirmed. The second experiment was conducted to evaluate the reconstructed image using a foot model phantom. Only the sagittal plane could be clearly observed with the proposed method. Conclusion : We proposed a tomosynthesis imaging system using a portable X-ray device. From the experimental results, the proposed method could provide sufficient calibration accuracy and a clear sagittal plane of the reconstructed tomosynthesis image. [ABSTRACT FROM AUTHOR]