Showing papers on "Digital mammography published in 2021"
TL;DR: The aim of this review is to provide an overview of the basic concepts and developments in the field AI for breast cancer detection in digital mammography and digital breast tomosynthesis.
90 citations
TL;DR: Contrastenhanced mammography (CEM) has emerged as a viable alternative to contrast-enhanced breast MRI, and it may increase access to vascular imaging while reducing examination cost as discussed by the authors.
Abstract: Contrast-enhanced mammography (CEM) has emerged as a viable alternative to contrast-enhanced breast MRI, and it may increase access to vascular imaging while reducing examination cost. Intravenous iodinated contrast materials are used in CEM to enhance the visualization of tumor neovascularity. After injection, imaging is performed with dual-energy digital mammography, which helps provide a low-energy image and a recombined or iodine image that depict enhancing lesions in the breast. CEM has been demonstrated to help improve accuracy compared with digital mammography and US in women with abnormal screening mammographic findings or symptoms of breast cancer. It has also been demonstrated to approach the accuracy of breast MRI in preoperative staging of patients with breast cancer and in monitoring response after neoadjuvant chemotherapy. There are early encouraging results from trials evaluating CEM in the screening of women who are at an increased risk of breast cancer. Although CEM is a promising tool, it slightly increases radiation dose and carries a small risk of adverse reactions to contrast materials. This review details the CEM technique, diagnostic and screening uses, and future applications, including artificial intelligence and radiomics.
75 citations
TL;DR: In this article, the authors proposed a digital mammography and digital breast tomosynthesis screening strategy based on artificial intelligence systems, which could reduce workload up to 70% without reducing sensitivity by 5% or more.
Abstract: Digital mammography and digital breast tomosynthesis screening strategies based on artificial intelligence systems could reduce workload up to 70% without reducing sensitivity by 5% or more.
52 citations
Abstract: Screening with one-view digital breast tomosynthesis in a prospective trial reduced the interval cancer rate compared with a contemporary control group screened with digital mammography
33 citations
TL;DR: In this paper, an automatic Diverse Features based Breast Cancer Detection (DFeBCD) system is proposed to classify a mammogram as normal or abnormal using four sets of distinct feature types.
30 citations
TL;DR: MRI provides the greatest increase in cancer detection and decreases interval cancers and late-stage disease; abbreviated techniques will reduce cost and improve availability.
Abstract: Screening mammography reduces breast cancer mortality; however, when used to examine women with dense breasts, its performance and resulting benefits are reduced. Increased breast density is an independent risk factor for breast cancer. Digital breast tomosynthesis (DBT), ultrasound (US), molecular breast imaging (MBI), MRI, and contrast-enhanced mammography (CEM) each have shown improved cancer detection in dense breasts when compared with 2D digital mammography (DM). DBT is the preferred mammographic technique for producing a simultaneous reduction in recalls (i.e., additional imaging). US further increases cancer detection after DM or DBT and reduces interval cancers (cancers detected in the interval between recommended screening examinations), but it also produces substantial additional false-positive findings. MBI improves cancer detection with an effective radiation dose that is approximately fourfold that of DM or DBT but is still within accepted limits. MRI provides the greatest increase in cancer detection and reduces interval cancers and late-stage disease; abbreviated techniques will reduce cost and improve availability. CEM appears to offer performance similar to that of MRI, but further validation is needed. Dense breast notification will soon be a national standard; therefore, understanding the performance of mammography and supplemental modalities is necessary to optimize screening for women with dense breasts.
28 citations
TL;DR: An artificial intelligence (AI) method to estimate breast density from digital mammograms using two convolutional neural network architectures to accurately segment the breast area suggests a strong agreement of breast density estimates between Deep-LIBRA and gold-standard assessment by an expert reader, as well as improved performance in breast cancer risk assessment over state-of-the-art open-source and commercial methods.
28 citations
TL;DR: It is shown that MC-GPU is able to simulate x-ray projections that incorporate many of the sources of variability found in clinical images, and that the simulated results are robust to some uncertainty in the input parameters.
25 citations
TL;DR: The current status of computational radiology is reviewed, with a focus on deep neural networks used in breast cancer screening and diagnosis, as well as leveraging AI-driven tools to efficiently interpret digital mammograms, and breast tomosynthesis imaging.
Abstract: Breast cancer screening has been shown to significantly reduce mortality in women. The increased utilization of screening examinations has led to growing demands for rapid and accurate diagnostic reporting. In modern breast imaging centers, full-field digital mammography (FFDM) has replaced traditional analog mammography, and this has opened new opportunities for developing computational frameworks to automate detection and diagnosis. Artificial intelligence (AI), and its subdomain of deep learning, is showing promising results and improvements on diagnostic accuracy, compared to previous computer-based methods, known as computer-aided detection and diagnosis.In this commentary, we review the current status of computational radiology, with a focus on deep neural networks used in breast cancer screening and diagnosis. Recent studies are developing a new generation of computer-aided detection and diagnosis systems, as well as leveraging AI-driven tools to efficiently interpret digital mammograms, and breast tomosynthesis imaging. The use of AI in computational radiology necessitates transparency and rigorous testing. However, the overall impact of AI to radiology workflows will potentially yield more efficient and standardized processes as well as improve the level of care to patients with high diagnostic accuracy.
24 citations
TL;DR: The benefits of DBT are well established as mentioned in this paper, however, the extent of these benefits may vary by practice environment and by geographic location, and whether DBT improves breast cancer-specific mortality remains a key question that requires further investigation.
Abstract: Digital breast tomosynthesis (DBT) has been widely adopted in breast imaging in both screening and diagnostic settings. The benefits of DBT are well established. Compared with two-dimensional digital mammography (DM), DBT preferentially increases detection of invasive cancers without increased detection of in-situ cancers, maximizing identification of biologically significant disease, while mitigating overdiagnosis. The higher sensitivity of DBT for architectural distortion allows increased diagnosis of invasive cancers overall and particularly improves the visibility of invasive lobular cancers. Implementation of DBT has decreased the number of recalls for false-positive findings at screening, contributing to improved specificity at diagnostic evaluation. Integration of DBT in diagnostic examinations has also resulted in an increased percentage of biopsies with positive results, improving diagnostic confidence. Although individual DBT examinations have a longer interpretation time compared with that for DM, DBT has streamlined the diagnostic workflow and minimized the need for short-term follow-up examinations, redistributing much-needed time resources to screening. Yet DBT has limitations. Although improvements in cancer detection and recall rates are seen for patients in a large spectrum of age groups and breast density categories, these benefits are minimal in women with extremely dense breast tissue, and the extent of these benefits may vary by practice environment and by geographic location. Although DBT allows detection of more invasive cancers than does DM, its incremental yield is lower than that of US and MRI. Current understanding of the biologic profile of DBT-detected cancers is limited. Whether DBT improves breast cancer-specific mortality remains a key question that requires further investigation. ©RSNA, 2021.
22 citations
TL;DR: The transition from film to digital mammography did not result in health benefits for screened women and reinforces the need to carefully evaluate effects of future changes in technology, such as tomosynthesis, to ensure new technology leads to improved health outcomes and beyond technical gains.
Abstract: Background Breast screening programs replaced film mammography with digital mammography, and the effects of this practice shift in population screening on health outcomes can be measured through examination of cancer detection and interval cancer rates Methods A systematic review and random effects meta-analysis were undertaken Seven databases were searched for publications that compared film with digital mammography within the same population of asymptomatic women and reported cancer detection and/or interval cancer rates Results The analysis included 24 studies with 16 583 743 screening examinations (10 968 843 film and 5 614 900 digital) The pooled difference in the cancer detection rate showed an increase of 051 per 1000 screens (95% confidence interval [CI] = 019 to 083), greater relative increase for ductal carcinoma in situ (252%, 95% CI = 174% to 335%) than invasive (4%, 95% CI = -3% to 13%), and a recall rate increase of 695 (95% CI = 347 to 1042) per 1000 screens after the transition from film to digital mammography Seven studies (808% of screens) reported interval cancers: the pooled difference showed no change in the interval cancer rate with -002 per 1000 screens (95% CI = -006 to 003) Restricting analysis to studies at low risk of bias resulted in findings consistent with the overall pooled results for all outcomes Conclusions The increase in cancer detection following the practice shift to digital mammography did not translate into a reduction in the interval cancer rate Recall rates were increased These results suggest the transition from film to digital mammography did not result in health benefits for screened women This analysis reinforces the need to carefully evaluate effects of future changes in technology, such as tomosynthesis, to ensure new technology leads to improved health outcomes and beyond technical gains
TL;DR: Wang et al. as discussed by the authors proposed to use a deep convolutional neural network (DCNN) to learn the transformation to generate synthetic digital mammography (SDM) using current DBT/FFDM combo images.
Abstract: Synthetic digital mammography (SDM), a 2D image generated from digital breast tomosynthesis (DBT), is used as a potential substitute for full-field digital mammography (FFDM) in clinic to reduce the radiation dose for breast cancer screening. Previous studies exploited projection geometry and fused projection data and DBT volume, with different post-processing techniques applied on re-projection data which may generate different image appearance compared to FFDM. To alleviate this issue, one possible solution to generate an SDM image is using a learning-based method to model the transformation from the DBT volume to the FFDM image using current DBT/FFDM combo images. In this study, we proposed to use a deep convolutional neural network (DCNN) to learn the transformation to generate SDM using current DBT/FFDM combo images. Gradient guided conditional generative adversarial networks (GGGAN) objective function was designed to preserve subtle MCs and the perceptual loss was exploited to improve the performance of the proposed DCNN on perceptual quality. We used various image quality criteria for evaluation, including preserving masses and MCs which are important in mammogram. Experiment results demonstrated progressive performance improvement of network using different objective functions in terms of those image quality criteria. The methodology we exploited in the SDM generation task to analyze and progressively improve image quality by designing objective functions may be helpful to other image generation tasks.
TL;DR: In this article, the authors investigated the value of full-field digital mammography-based deep learning (DL) in predicting malignancy of Breast Imaging Reporting and Data System (BI-RADS) 4 microcalcifications.
Abstract: To investigate the value of full-field digital mammography-based deep learning (DL) in predicting malignancy of Breast Imaging Reporting and Data System (BI-RADS) 4 microcalcifications. A total of 384 patients with 414 pathologically confirmed microcalcifications (221 malignant and 193 benign) were randomly allocated into the training, validation, and testing datasets (272/71/71 lesions) in this retrospective study. A combined DL model was developed incorporating mammography and clinical variables. Model performance was evaluated by using areas under the receiver operating characteristic curve (AUC) and compared with the clinical model, stand-alone DL image model, and BI-RADS approach. The predictive performance for malignancy was also compared between the combined model and human readers (2 juniors and 2 seniors). The combined DL model demonstrated favorable AUC, sensitivity, and specificity of 0.910, 85.3%, and 91.9% in predicting BI-RADS 4 malignant microcalcifications in the testing dataset, which outperformed the clinical model, DL image model, and BI-RADS with AUCs of 0.799, 0.841, and 0.804, respectively. The combined model achieved non-inferior performance as senior radiologists (p = 0.860, p = 0.800) and outperformed junior radiologists (p = 0.155, p = 0.029). The diagnostic performance of two junior radiologists was improved after artificial intelligence assistance with AUCs increased to 0.854 and 0.901 from 0.816 (p = 0.556) and 0.773 (p = 0.046), while the interobserver agreement was improved with a kappa value increased to 0.843 from 0.331. The combined deep learning model can improve the malignancy prediction of BI-RADS 4 microcalcifications in screening mammography and assist junior radiologists to achieve better performance, which can facilitate clinical decision-making. • The combined deep learning model demonstrated high diagnostic power, sensitivity, and specificity for predicting malignant BI-RADS 4 mammographic microcalcifications. • The combined model achieved similar performance with senior breast radiologists, while it outperformed junior breast radiologists. • Deep learning could improve the diagnostic performance of junior radiologists and facilitate clinical decision-making.
TL;DR: The findings support the implementation of SM in place of standard DM for mammographic imaging of the breasts and may lead to an overall reduction in radiation exposure.
Abstract: Please see the Author Video associated with this article. BACKGROUND. The use of synthetic 2D mammography (SM) with digital breast tomosynthesis (DBT) in place of standard 2D digital mammography (D...
TL;DR: In this article, the role of contrastenhanced digital mammography (CEDM) as a screening tool in women at intermediate risk for developing breast cancer due to a personal history of lobular neoplasia without additional risk factors was assessed.
Abstract: OBJECTIVE The objective of this study was to assess to the role of contrast-enhanced digital mammography (CEDM) as a screening tool in women at intermediate risk for developing breast cancer due to a personal history of lobular neoplasia without additional risk factors MATERIALS AND METHODS In this institutional review board-approved, observational, retrospective study, we reviewed our radiology department database to identify patients with a personal history of breast biopsy yielding lobular neoplasia who underwent screening CEDM at our institution between December 2012 and February 2019 A total of 132 women who underwent 306 CEDM examinations were included All CEDM examinations were interpreted by dedicated breast imaging radiologists in conjunction with a review of the patient's clinical history and available prior breast imaging In statistical analysis, sensitivity, specificity, NPV, positive likelihood ratio, and accuracy of CEDM in detecting cancer were determined, with pathology or 12-month imaging follow-up serving as the reference standard RESULTS CEDM detected cancer in six patients and showed an overall sensitivity of 100%, specificity of 88% (95% CI, 84-92%), NPV of 100%, and accuracy of 88% (95% CI, 84-92%) The positive likelihood ratio of 833 suggested that CEDM findings are 83 times more likely to be positive in an individual with breast cancer when compared with an individual without the disease CONCLUSION CEDM shows promise as a breast cancer screening examination in patients with a personal history of lobular neoplasia Continued investigation with a larger patient population is needed to determine the true sensitivity and positive predictive value of CEDM for these patients
TL;DR: CEDM allowed a significantly higher number of breast cancer detection than the s2D MG, DBT alone and DBT supplemented with ultrasonography in females with dense breast and may be considered as an alternative modality to DBT and ultrasound for the diagnostic evaluation of dense breasts.
Abstract: Objective:To assess the diagnostic efficacy of contrast-enhanced digital mammography (CEDM) in breast cancer detection in comparison to synthetic two-dimensional mammography (s2D MG), digital breas...
TL;DR: In this paper, the authors provide a practical guide for the recognition of Radial Scar (RS) on imaging, illustrating radiological findings according to the most recent literature, and delineate the management strategies that follow.
Abstract: Radial scar (RS) or complex sclerosing lesions (CSL) if > 10 mm is a benign lesion with an increasing incidence of diagnosis (ranging from 0.6 to 3.7%) and represents a challenge both for radiologists and for pathologists. The digital mammography and digital breast tomosynthesis appearances of RS are well documented, according to the literature. On ultrasound, variable aspects can be detected. Magnetic resonance imaging contribution to differential diagnosis with carcinoma is growing. As for the management, a vacuum-assisted biopsy (VAB) with large core is recommended after a percutaneous diagnosis of RS due to potential sampling error. According to the recent International Consensus Conference, a RS/CSL lesion, which is visible on imaging, should undergo therapeutic excision with VAB. Thereafter, surveillance is justified. The aim of this review is to provide a practical guide for the recognition of RS on imaging, illustrating radiological findings according to the most recent literature, and to delineate the management strategies that follow.
TL;DR: A systematic review and meta-analysis focusing on screening performance outcomes in women screened with digital breast tomosynthesis (DBT) plus digital mammography (DM) compared to DM alone was conducted in this article.
Abstract: Objectives Digital breast tomosynthesis (DBT) plus digital mammography (DM) in screening is problematic due to increased radiation by the double exposure. Synthesised two-dimensional mammography (s2D) calculated from DBT datasets at no additional dose appears a sensible alternative compared to adding DM. This systematic review and meta-analysis focuses on screening performance outcomes in women screened with DBT plus s2D compared to DM alone. Methods PubMed was searched from January 1, 2010, to September 2, 2020. Studies comparing DBT plus s2D to DM alone in breast cancer screening were included. Pooled risk ratios (RR) were estimated for cancer detection rates (CDR), recall rates, interval cancer rates (ICR), biopsy rates, and positive predictive values for recalls (PPV-1), for biopsies recommended (PPV-2), and for biopsies performed (PPV-3). Sensitivity analyses were performed using the leave-one-out approach. Risk of bias (RoB) was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS)-2 tool. Results Twelve papers covering 414,281 women were included from 766 records identified. CDR is increased ([RR, 95% CI] 1.35, 1.20-1.52), recall rates are decreased (0.79, 0.64-0.98), and PPV-1 is increased (1.69, 1.45-1.96) when using DBT plus s2D compared to DM alone. ICR and biopsy rates did not differ, but PPV-2 respectively PPV-3 increased with DBT plus s2D (1.57, 1.08-2.28 respectively 1.36, 1.17-1.58). Overall RoB of studies was assessed to be low. Conclusion Results show improved diagnostic outcomes with DBT plus s2D compared to DM alone and underline the value of DBT in combination with s2D in breast cancer screening. Key points • DBT plus s2D is associated with higher CDR, lower recall rates, and a higher PPV-1 compared to DM alone in breast cancer screening. • No differences in biopsy rates were found between screening modalities, but PPV-2 and PPV-3 were higher in women screened with DBT plus s2D compared to DM alone. • We identified inconsistent results of ICR in two studies comparing DBT plus s2D to DM alone-resulting in no differences when pooling ICR in meta-analysis.
TL;DR: In a large breast cancer screening study, the benefits of prevalent digital breast tomosynthesis did not come at the expense of higher rates or more aggressive subsequent round screen-detected cancers.
Abstract: In a large breast cancer screening study, the benefits of prevalent digital breast tomosynthesis did not come at the expense of higher rates or more aggressive subsequent round screen-detected cancers.
TL;DR: Among breast cancer survivors, digital breast tomosynthesis had a lower abnormal interpretation rate and higher specificity than traditional breast cancer diagnosis as discussed by the authors, compared to traditional mammography and mammography.
Abstract: Among breast cancer survivors, digital breast tomosynthesis had a lower abnormal interpretation rate and higher specificity.
TL;DR: In this article, the authors presented a dataset of computational digital breast phantoms derived from high-resolution 3D clinical breast images for the use in virtual clinical trials in 2D and 3D x-ray breast imaging.
Abstract: PURPOSE To present a dataset of computational digital breast phantoms derived from high-resolution three-dimensional (3D) clinical breast images for the use in virtual clinical trials in two-dimensional (2D) and 3D x-ray breast imaging. ACQUISITION AND VALIDATION METHODS Uncompressed computational breast phantoms for investigations in dedicated breast CT (BCT) were derived from 150 clinical 3D breast images acquired via a BCT scanner at UC Davis (California, USA). Each image voxel was classified in one out of the four main materials presented in the field of view: fibroglandular tissue, adipose tissue, skin tissue, and air. For the image classification, a semi-automatic software was developed. The semi-automatic classification was compared via manual glandular classification performed by two researchers. A total of 60 compressed computational phantoms for virtual clinical trials in digital mammography (DM) and digital breast tomosynthesis (DBT) were obtained from the corresponding uncompressed phantoms via a software algorithm simulating the compression and the elastic deformation of the breast, using the tissue's elastic coefficient. This process was evaluated in terms of glandular fraction modification introduced by the compression procedure. The generated cohort of 150 uncompressed computational breast phantoms presented a mean value of the glandular fraction by mass of 12.3%; the average diameter of the breast evaluated at the center of mass was 105 mm. Despite the slight differences between the two manual segmentations, the resulting glandular tissue segmentation did not consistently differ from that obtained via the semi-automatic classification. The difference between the glandular fraction by mass before and after the compression was 2.1% on average. The 60 compressed phantoms presented an average glandular fraction by mass of 12.1% and an average compressed thickness of 61 mm. DATA FORMAT AND ACCESS The generated digital breast phantoms are stored in DICOM files. Image voxels can present one out of four values representing the different classified materials: 0 for the air, 1 for the adipose tissue, 2 for the glandular tissue, and 3 for the skin tissue. The generated computational phantoms datasets were stored in the Zenodo public repository for research purposes (http://doi.org/10.5281/zenodo.4529852, http://doi.org/10.5281/zenodo.4515360). POTENTIAL APPLICATIONS The dataset developed within the INFN AGATA project will be used for developing a platform for virtual clinical trials in x-ray breast imaging and dosimetry. In addition, they will represent a valid support for introducing new breast models for dose estimates in 2D and 3D x-ray breast imaging and as models for manufacturing anthropomorphic physical phantoms.
TL;DR: Yaffe et al. as mentioned in this paper compared associations of breast density estimates from digital breast tomosynthesis (DBT) and digital mammography (DM) images with breast cancer risk assessment.
Abstract: Background While digital breast tomosynthesis (DBT) is rapidly replacing digital mammography (DM) in breast cancer screening, the potential of DBT density measures for breast cancer risk assessment remains largely unexplored. Purpose To compare associations of breast density estimates from DBT and DM with breast cancer. Materials and Methods This retrospective case-control study used contralateral DM/DBT studies from women with unilateral breast cancer and age- and ethnicity-matched controls (September 19, 2011-January 6, 2015). Volumetric percent density (VPD%) was estimated from DBT using previously validated software. For comparison, the publicly available Laboratory for Individualized Breast Radiodensity Assessment software package, or LIBRA, was used to estimate area-based percent density (APD%) from raw and processed DM images. The commercial Quantra and Volpara software packages were applied to raw DM images to estimate VPD% with use of physics-based models. Density measures were compared by using Spearman correlation coefficients (r), and conditional logistic regression was performed to examine density associations (odds ratios [OR]) with breast cancer, adjusting for age and body mass index. Results A total of 132 women diagnosed with breast cancer (mean age ± standard deviation [SD], 60 years ± 11) and 528 controls (mean age, 60 years ± 11) were included. Moderate correlations between DBT and DM density measures (r = 0.32-0.75; all P < .001) were observed. Volumetric density estimates calculated from DBT (OR, 2.3 [95% CI: 1.6, 3.4] per SD for VPD%DBT) were more strongly associated with breast cancer than DM-derived density for both APD% (OR, 1.3 [95% CI: 0.9, 1.9] [P < .001] and 1.7 [95% CI: 1.2, 2.3] [P = .004] per SD for LIBRA raw and processed data, respectively) and VPD% (OR, 1.6 [95% CI: 1.1, 2.4] [P = .01] and 1.7 [95% CI: 1.2, 2.6] [P = .04] per SD for Volpara and Quantra, respectively). Conclusion The associations between quantitative breast density estimates and breast cancer risk are stronger for digital breast tomosynthesis compared with digital mammography. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Yaffe in this issue.
TL;DR: In this paper, the authors compared preoperative contrastenhanced spectral mammography (CEM) versus digital mammography plus digital breast tomosynthesis (DM+DBT) in detecting breast cancer (BC) and assessing its size.
Abstract: To compare preoperative contrast-enhanced spectral mammography (CEM) versus digital mammography plus digital breast tomosynthesis (DM + DBT) in detecting breast cancer (BC) and assessing its size. We retrospectively included 78 patients with histological diagnosis of BC who underwent preoperative DM, DBT, and CEM over one year. Four readers, blinded to pathology and clinical information, independently evaluated DM + DBT versus CEM to detect BC and measure its size. Readers' experience ranged 3–10 years. We calculated the per-lesion cancer detection rate (CDR) and the complement of positive predictive value (1-PPV) of both methods, stratifying analysis on the total of lesions, index lesions, and additional lesions. The agreement in assessing cancer size versus pathology was assessed with Bland–Altman analysis. 100 invasive BCs (78 index lesions and 22 additional lesions) were analyzed. Compared to DM + DBT, CEM showed higher overall CDR in less experienced readers (range 0.85–0.90 vs. 0.95–0.96), and higher CDR for additional lesions, regardless of the reader (range 0.54–0.68 vs. 0.77–0.86). CEM increased the detection of additional disease in dense breasts in all readers and non-dense breasts in less experienced readers only. The 1-PPV of CEM (range 0.10–0.18) was comparable to that of DM + DBT (range 0.09–0.19). At Bland–Altman analysis, DM + DBT and CEM showed comparable mean differences and limits of agreement in respect of pathologic cancer size. Preoperative CEM improved the detection of additional cancer lesions compared to DM + DBT, particularly in dense breasts. CEM and DM + DBT achieved comparable performance in cancer size assessment.
TL;DR: At repeat screening, digital breast tomosynthesis plus synthetic mammography depicted more cancers than full-field digital mammography (FFDM) and found a lower number of stage II cancers compared with FFDM.
Abstract: Digital breast tomosynthesis at first round and at repeat screening depicted a higher proportion of stage I cancers than screening with digital mammography.
TL;DR: In this article, the authors consider the combination of contrast enhanced mammography (CEM) and US as a single appointment imaging strategy for preoperative staging of breast cancer, and conclude that there is only limited room for an additional benefit of breast MRI.
TL;DR: In this article, the authors investigated racial differences in the utilization and performance of screening modality and found that a lower proportion of screenings for Black women were performed using DBT plus DM (referred to as DBT) (44% for Black, 48% for other, 63% for Asian and 61% for White).
Abstract: PURPOSE Digital breast tomosynthesis (DBT) in conjunction with digital mammography (DM) is becoming the preferred imaging modality for breast cancer screening compared with DM alone, on the basis of improved recall rates (RR) and cancer detection rates (CDRs). The aim of this study was to investigate racial differences in the utilization and performance of screening modality. METHODS Retrospective data from 63 US breast imaging facilities from 2015 to 2019 were reviewed. Screening outcomes were linked to cancer registries. RR, CDR per 1,000 examinations, and positive predictive value for recall (cancers/recalled patients) were compared. RESULTS A total of 385,503 women contributed 542,945 DBT and 261,359 DM screens. A lower proportion of screenings for Black women were performed using DBT plus DM (referred to as DBT) (44% for Black, 48% for other, 63% for Asian, and 61% for White). Non-White women were less likely to undergo more than one mammographic examination. RRs were lower for DBT among all women (8.74 versus 10.06, P < .05) and lower across all races and within age categories. RRs were significantly higher for women with only one mammogram. CDRs were similar or higher in women undergoing DBT compared with DM, overall (4.73 versus 4.60, adjusted P = .0005) and by age and race. Positive predictive value for recall was greater for DBT overall (5.29 versus 4.45, adjusted P < .0001) and by age, race, and screening frequency. CONCLUSIONS All racial groups had improved outcomes with DBT screening, but disparities were observed in DBT utilization. These data suggest that reducing inequities in DBT utilization may improve the effectiveness of breast cancer screening.
TL;DR: The potential role of mammography-based radiomics appears promising but more efforts are required to further evaluate its reliability as a routine clinical tool as discussed by the authors, and the role of integrating radiomics with other information is discussed.
TL;DR: The optimal B-CT acquisition parameters are described, which provide diagnostic image quality for various breast sizes and densities, while keeping the average dose at a level similar to digital mammography.
Abstract: To investigate the dependence of signal-to-noise ratio (SNR) and calculated average dose per volume of spiral breast-CT (B-CT) on breast size and breast density and to provide a guideline for choosing the optimal tube current for each B-CT examination. Three representative B-CT datasets (small, medium, large breast size) were chosen to create 3D-printed breast phantoms. The phantoms were filled with four different agarose-oil-emulsions mimicking differences in breast densities. Phantoms were scanned in a B-CT system with systematic variation of the tube current (6, 12.5, 25, 32, 40, 50, 64, 80, 100, 125 mA). Evaluation of SNR and the average dose per volume using Monte Carlo simulations were performed for high (HR) and standard (STD) spatial resolution. SNR and average dose per volume increased with increasing tube current. Artifacts had negligible influence on image evaluation. SNR values ≥ 35 (HR) and ≥ 100 (STD) offer sufficient image quality for clinical evaluation with SNR being more dependent on breast density than on breast size. For an average absorbed dose limit of 6.5 mGy for the medium and large phantoms and 7 mGy for the small phantom, optimal tube currents were either 25 or 32 mA. B-CT offers the possibility to vary the X-ray tube current, allowing image quality optimization based on individual patient’s characteristics such as breast size and density. This study describes the optimal B-CT acquisition parameters, which provide diagnostic image quality for various breast sizes and densities, while keeping the average dose at a level similar to digital mammography. • Image quality optimization based on breast size and density varying the tube current using spiral B-CT.
TL;DR: In this paper, the agreement between artificial intelligence software (AI) and radiographers in assessing breast positioning criteria for mammograms from standard digital mammography and standard mammography was determined, and the agreement was confirmed.
Abstract: ObjectivesTo determine the agreement between artificial intelligence software (AI) and radiographers in assessing breast positioning criteria for mammograms from standard digital mammography and di...