Digital Breast Tomosynthesis Research Articles

Can Radiologists Replace Digital 2D Mammography with Synthetic 2D Mammography in Breast Cancer Screening and Diagnosis, or Are Both Still Needed?

Background/Objectives: Digital mammography (DM) has long been the standard for breast cancer screening, while digital breast tomosynthesis (DBT) offers an advanced 3D imaging modality capable of generating 2D Synthetic Mammography (SM) images. Despite SM's potential to reduce radiation exposure, many clinics favor DM, with DBT and SM, due to its perceived diagnostic reliability. This study investigates whether radiologists can replace DM with SM in breast cancer screening and diagnosis or if both modalities are necessary. Methods: We retrospectively analyzed DM and SM images from 375 women aged 40-65 who underwent DM with DBT at King Khaled University Hospital from 2020-2022. Three radiologists evaluated the images using ACR BI-RADS, assessing diagnostic accuracy via the area under the receiver operating characteristic (ROC) curve (AUC). The agreement in cancer conspicuity, breast density, size, and calcifications were measured using weighted kappa (κ). Results: Among 57 confirmed cancer cases and 290 cancer-free cases, DM demonstrated higher sensitivity (82.5% vs. 78.9%) and diagnostic accuracy (AUC 0.800 vs. 0.783, p < 0.05) compared to SM. However, SM detected more suspicious calcifications in cancer cases (75.6% vs. 51.2%, p < 0.05). Agreement was fair for conspicuity (κ = 0.288) and calcifications (κ = 0.409), moderate for density (κ = 0.591), and poor for size (κ = 0.254). Conclusions: while SM demonstrates enhanced effectiveness in detecting microcalcifications, DM still proves superior in overall diagnostic accuracy and image clarity. Therefore, although SM offers certain advantages, it remains slightly inferior to DM and cannot yet replace DM in breast cancer screening.

Changes in Reader Performance During Sequential Reading of Breast Cancer Screening Digital Breast Tomosynthesis Examinations.

Background Studies suggest that readers experience perceptual adaptation when interpreting batched screening mammograms, which may serve as a mechanism for improved performance. Purpose To analyze clinical digital breast tomosynthesis (DBT) screening data to evaluate changes in reader performance during sequential batch reading. Materials and Methods This observational retrospective study used data from the radiology information system collected for screening DBT examinations performed from January 2018 to December 2019. The reference standard was established based on pathology results, if applicable, or imaging findings at 1-year follow-up. Examinations were aggregated into batches, defined as a sequence of cases for a given reader with differences in interexamination time of 10 minutes or less. Mixed-effect models were used to evaluate performance and timing across batch positions. Statistical adjustments accounted for potential confounders, including patient characteristics (age, breast density), reading factors (day of week, time of day), and between-reader heterogeneity. Results The dataset included 121 652 examinations (median patient age, 61 years [IQR, 53-69 years]), including 1081 cancers interpreted by 15 radiologists. Unadjusted false-positive rates decreased from an averaged 15.5% at the first within-batch examination to 10.5% after three sequentially read examinations (P < .001), without a significant change in sensitivity (82.6% vs 84.2%; P = .15). The interpretation time consistently decreased, achieving a substantial reduction in longer batches (average 2.8 to 2.2 minutes for noncancer examinations; P < .001). Adjustment for sampling bias and confounders (patient age, breast density, day of week, time of day; all P < .005) reduced the effect on false-positive rates (11.5% to 9.4% after three examinations; P < .001) and interpretation time (3.2 to 2.7 minutes for noncancer examinations; P < .001). Conclusion Radiologists showed improved recall rates and interpretation times without change in sensitivity over the course of batch reading of DBT screening examinations. © RSNA, 2024 See also the editorial by Iima and Satake in this issue.

Artificial Intelligence-based Software for Breast Arterial Calcification Detection on Mammograms.

The performance of a commercially available artificial intelligence (AI)-based software that detects breast arterial calcifications (BACs) on mammograms is presented. This retrospective study was exempt from IRB approval and adhered to the HIPAA regulations. Breast arterial calcification detection using AI was assessed in 253 patients who underwent 314 digital mammography (DM) examinations and 143 patients who underwent 277 digital breast tomosynthesis (DBT) examinations between October 2004 and September 2022. Artificial intelligence performance for binary BAC detection was compared with ground truth (GT) determined by the majority consensus of breast imaging radiologists. Area under the receiver operating curve (AUC), sensitivity, specificity, positive predictive value and negative predictive value (NPV), accuracy, and BAC prevalence rates of the AI algorithm were compared. The case-level AUCs of AI were 0.96 (0.93-0.98) for DM and 0.95 (0.92-0.98) for DBT. Sensitivity, specificity, and accuracy were 87% (79%-93%), 92% (88%-96%), and 91% (87%-94%) for DM and 88% (80%-94%), 90% (84%-94%), and 89% (85%-92%) for DBT. Positive predictive value and NPV were 82% (72%-89%) and 95% (92%-97%) for DM and 84% (76%-90%) and 92% (88%-96%) for DBT, respectively. Results are 95% confidence intervals. Breast arterial calcification prevalence was similar for both AI and GT assessments. Breast AI software for detection of BAC presence on mammograms showed promising performance for both DM and DBT examinations. Artificial intelligence has potential to aid radiologists in detection and reporting of BAC on mammograms, which is a known cardiovascular risk marker specific to women.

Automated breast density assessment for full-field digital mammography and digital breast tomosynthesis.

Mammographic density is a strong risk factor for breast cancer (BC) and is reported clinically as part of Breast Imaging Reporting and Data System (BI-RADS) results issued by radiologists. Automated assessment of density is needed that can be used for both full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT) as both types of exams are acquired in standard clinical practice. We trained a deep learning model to automate the estimation of BI-RADS density from a prospective Washington University (WashU) clinic-based cohort of 9,714 women, entering into the cohort in 2013 with follow-up through, October 31, 2020. The cohort included 27% non-Hispanic Black women. The trained algorithm was assessed in an external validation cohort that included 18,360 women screened at Emory from January 1, 2013 and followed through December 31, 2020 that included 42% non-Hispanic Black women. Our model-estimated BI-RADS density demonstrated substantial agreement with the density as assessed by radiologist. In the external validation, the agreement with radiologists for category B 81% and C 77% for FFDM and B 83% and C 74% for DBT show important distinction for separation of women with dense breast. We obtained a Cohen's κ of 0.72 (95% CI, 0.71, 0.73) in FFDM and 0.71 (95% CI 0.69, 0.73) in DBT. We provided a consistent and fully automated BI-RADS estimation for both FFDM and DBT using a deep learning model. The software can be easily implemented anywhere for clinical use and risk prediction.

Our journey toward implementation of digital breast tomosynthesis in breast cancer screening: the Malmö Breast Tomosynthesis Screening Project.

The purpose is to describe the Malmö Breast Tomosynthesis Screening Project from the beginning to where we are now, and thoughts for the future. In two acts, we describe the efforts made by our research group to improve breast cancer screening by introducing digital breast tomosynthesis (DBT), all the way from initial studies to a large prospective population-based screening trial and beyond. Our studies have shown that DBT has significant advantages over digital mammography (DM), the current gold standard method for breast cancer screening in Europe, in many aspects except a major one-the increased radiologist workload introduced with DBT compared with DM. It is foreseen that AI could be a viable solution to overcome this problem. We have proved that one-view DBT is a highly efficient screening approach with respect to diagnostic performance.

Imaging phenotype evaluation from digital breast tomosynthesis data: A preliminary study

Background:Digital breast tomosynthesis (DBT) has been widely adopted as a supplemental imaging modality for diagnostic evaluation of breast cancer and confirmation studies. In this study, a deep learning-based method for characterizing breast tissue patterns in DBT data is presented. Methods:A set of 5388 2D image patches was produced from 230 right mediolateral oblique, 259 left mediolateral oblique, 18 right craniocaudal, and 15 left craniocaudal single-breast DBT studies, using slice-wise annotations of abnormalities and normal tissue. We implemented a patch classifier to predict samples according to two differing scenarios and train it using the patch dataset. First, tissue samples were classified into the following classes: malignant, benign, and normal breast tissue. Second, tissue samples were classified into the following classes: malignant mass, benign mass, benign architectural distortion, malignant architectural distortion, and normal breast tissue. We employed transfer learning and initialized the model base layers with existing pre-trained weights obtained from Globally-Aware Multiple Instance Classifier. Results:High class-wise recall values of 0.8906, 0.8541 and 0.7345 and specificities 0.9558, 0.9575 and 0.8830 were obtained for normal, benign, and malignant classification, respectively. More intricate classification yielded class-wise recall values of 0.8708, 0.8299, 0.9444 and 0.5723 and specificities 0.9406, 0.9833, 0.8943 and 0.9652 for benign mass, normal, malignant architectural distortion, and malignant mass, respectively. However, benign architectural distortion was confused with benign mass and malignant architectural distortion. Conclusions:Combining the proposed phenotype classifier with the commonly used malignant-benign-normal classification enables a more detailed assessment of digital breast tomosynthesis images.

Diagnostic Efficacy of Five Different Imaging Modalities in the Assessment of Women Recalled at Breast Screening-A Systematic Review and Meta-Analysis.

There are variations in the assessment pathways for women recalled at screening, and the imaging assessment pathway with the best diagnostic outcome is poorly understood. This paper examines the efficacy of five imaging modalities for the assessment of screen-recalled breast lesions. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) strategy was employed to identify studies that assessed the efficacy of imaging modalities in the assessment of lesions recalled at screening from the following eight databases: Medline, Web of Science, Embase, Scopus, Science Direct, PubMed, CINAHL, and Global Health. Search terms included "Breast assessment" AND "Diagnostic Workup" OR "Mammography" AND "Digital Breast tomosynthesis" AND "contrast enhanced mammography and Magnetic Resonance imaging" AND "breast ultrasound". Studies that examined the performance of digital mammography (DM), digital breast tomosynthesis (DBT), handheld ultrasound (HHUS), contrast-enhanced mammography (CEM), and magnetic resonance imaging (MRI) in screen-recalled lesions were reviewed. Meta-analyses of these studies were conducted using the MetaDisc 2.0 software package. Results: Fifty-four studies met the inclusion criteria and examined between one and three imaging modalities. Pooled results of each imaging modality demonstrated that CEM has the highest sensitivity (95; 95% CI: 90-97) followed by MRI (93; 95% CI: 88-96), DBT (91; 95% CI: 87-94), HHUS (90; 95% CI: 86-93), and DM (85; 95% CI: 78-90). The DBT demonstrated the highest specificity (85; 95% CI: 75-91) followed by DM (77; 95% CI: 66-85), CEM (73; 95% CI: 63-81), MRI (69; 95% CI: 55-81), and HHUS (65; 95% CI: 46-80). Conclusions: The CEM, MRI, DBT, and HHUS demonstrate excellent performance in correctly identifying and classifying cancer lesions referred for diagnostic work-up, but HHUS, MRI, and CEM have a more limited ability to discriminate benign lesions than DBT and DM.

Cost-Effectiveness of an Ultrasound-First Strategy in the Diagnostic Evaluation of Noncalcified Lesions Recalled From Screening Digital Breast Tomosynthesis.

BACKGROUND. Ultrasound may be sufficient in the diagnostic evaluation of many noncalcified lesions recalled from screening digital breast tomosynthesis (DBT). In some scenarios, omission of diagnostic mammography can save health care costs. OBJECTIVE. The purpose of this study was to evaluate the cost-effectiveness of a strategy of performing ultrasound first versus diagnostic mammography first in the diagnostic evaluation of noncalcified lesions recalled from screening DBT. METHODS. Decision tree analysis was performed to compare ultrasound first versus diagnostic mammography first in the diagnostic evaluation of DBT-recalled noncalcified lesions from a U.S. health care system perspective with a 40-year horizon. The analysis used probabilities and prevalence information from published single-institution prospective data, additional literature-derived estimates of diagnostic test performance, and Medicare-allowable reimbursement rates. Health states were represented in a Markov chain model. For each strategy, the total cost and effectiveness (expressed in quality-adjusted life-years [QALYs]) were estimated. Cost-effectiveness was assessed through incremental cost-effectiveness ratios (ICERs) and incremental net monetary benefit, with use of a willingness-to-pay (WTP) threshold of US$100,000 per QALY gained. Deterministic sensitivity analyses were performed to estimate the impact of different input parameters, and probabilistic sensitivity analysis with Monte Carlo simulations was conducted to estimate the impact of combined uncertainty across parameters. RESULTS. In the base-case scenario, for diagnostic evaluation of DBT-recalled noncalcified lesions, a strategy of performing ultrasound first versus diagnostic mammography first resulted in more cost savings (total cost, US$17,672 vs US$18,323) and greater cost-effectiveness (QALYs, 23.1309 vs 23.1306) over the 40-year horizon. The ultrasound-first strategy resulted in an ICER of -2,170,000 (expressed as U.S. dollars per QALY) and an incremental net monetary benefit of US$681 versus the diagnostic mammography-first strategy. Therefore, performing ultrasound first was deemed the more cost-effective strategy at the WTP threshold. In deterministic sensitivity analyses, the most important driver of cost-effectiveness was lost utility from delayed diagnosis, followed by the relative sensitivities of ultrasound and diagnostic mammography. In probabilistic sensitivity analysis, ultrasound first was the better strategy in 93.0% of iterations. CONCLUSION. A strategy of performing ultrasound first, with or without diagnostic mammography, is more cost-effective than a traditional strategy of conducting diagnostic mammography first. CLINICAL IMPACT. This cost-effectiveness analysis supports the growing prioritization of ultrasound as the primary method for evaluating DBT-recalled noncalcified lesions.

Value of automated breast ultrasound in screening: Standalone and as a supplemental to digital breast tomosynthesis.

We aimed to determine the value of standalone and supplemental automated breast ultrasound (ABUS) in detecting cancers in an opportunistic screening setting with digital breast tomosynthesis (DBT) and compare this combined screening method to DBT and ABUS alone in women older than 39 years with BI-RADS B-D density categories. In this prospective opportunistic screening study, 3466 women aged 39 or older with BI-RADS B-D density categories and with a mean age of 50 were included. The screening protocol consisted of DBT mediolateral-oblique views, 2D craniocaudal views, and ABUS with three projections for both breasts. ABUS was evaluated blinded to mammography findings. Statistical analysis evaluated diagnostic performance for DBT, ABUS, and combined workflows. Twenty-nine cancers were screen-detected. ABUS and DBT exhibited the same cancer detection rates (CDR) at 7.5/1000 whereas DBT + ABUS showed 8.4/1000, with ABUS contributing an additional CDR of 0.9/1000. Standalone ABUS outperformed DBT in detecting 12.5% more invasive cancers. DBT displayed better accuracy (95%) compared to ABUS (88%) and combined approach (86%). Sensitivities for DBT and ABUS were the same (84%), with DBT + ABUS showing a higher rate (94%). DBT outperformed ABUS in specificity (95% vs. 88%). DBT + ABUS exhibited a higher recall rate (14.89%) compared to ABUS (12.38%) and DBT (6.03%) (p < .001). Standalone ABUS detected more invasive cancers compared to DBT, with a higher recall rate. The combined approach showed a higher CDR by detecting one additional cancer per thousand.

Automated assessment of task-based performance of digital mammography and tomosynthesis systems using an anthropomorphic breast phantom and deep learning-based scoring.

Conventional metrics used for assessing digital mammography (DM) and digital breast tomosynthesis (DBT) image quality, including noise, spatial resolution, and detective quantum efficiency, do not necessarily predict how well the system will perform in a clinical task. A number of existing phantom-based methods have their own limitations, such as unrealistic uniform backgrounds, subjective scoring using humans, and regular signal patterns unrepresentative of common clinical findings. We attempted to address this problem with a realistic breast phantom with random hydroxyapatite microcalcifications and semi-automated deep learning-based image scoring. Our goal was to develop a methodology for objective task-based assessment of image quality for tomosynthesis and DM systems, which includes an anthropomorphic phantom, a detection task (microcalcification clusters), and automated performance evaluation using a convolutional neural network. Experimental 2D and pseudo-3D mammograms of an anthropomorphic inkjet-printed breast phantom with inserted microcalcification clusters were collected on clinical mammography systems to train a signal-present/signal-absent image classifier based on Resnet-18 architecture. In a separate validation study using simulations, this Resnet-18 classifier was shown to approach the performance of an ideal observer. Microcalcification detection performance was evaluated as a function of four dose levels using receiver operating characteristic (ROC) analysis [i.e., area under the ROC curve (AUC)]. To demonstrate the use of this evaluation approach for assessing different technologies, the method was applied to two different mammography systems, as well as to mammograms with re-binned pixels emulating a lower-resolution X-ray detector. Microcalcification detectability, as assessed by the deep learning classifier, was observed to vary with the exposure incident on the breast phantom for both DM and tomosynthesis. At full dose, experimental AUC was 0.96 (for DM) and 0.95 (for DBT), whereas at half dose, it dropped to 0.85 and 0.71, respectively. AUC performance on DM was significantly decreased with an effective larger pixel size obtained with re-binning. The task-based assessment approach also showed the superiority of a newer mammography system compared with an older system. An objective task-based methodology for assessing the image quality of mammography and tomosynthesis systems is proposed. Possible uses for this tool could be quality control, acceptance, and constancy testing, assessing the safety and effectiveness of new technology for regulatory submissions, and system optimization. The results from this study showed that the proposed evaluation method using a deep learning model observer can track differences in microcalcification signal detectability with varied exposure conditions.

External Validation of a Commercial Artificial Intelligence Algorithm on a Diverse Population for Detection of False Negative Breast Cancers.

There are limited data on the application of artificial intelligence (AI) on nonenriched, real-world screening mammograms. This work aims to evaluate the ability of AI to detect false negative cancers not detected at the time of screening when reviewed by the radiologist alone. A commercially available AI algorithm was retrospectively applied to patients undergoing screening full-field digital mammography (FFDM) or digital breast tomosynthesis (DBT) at a single institution from 2010 to 2019. Ground truth was established based on 1-year follow-up data. Descriptive statistics were performed with attention focused on AI detection of false negative cancers within these subsets. A total of 26 694 FFDM and 3183 DBT examinations were analyzed. Artificial intelligence was able to detect 7/13 false negative cancers (54%) in the FFDM cohort and 4/10 (40%) in the DBT cohort on the preceding screening mammogram that was interpreted as negative by the radiologist. Of these, 4 in the FFDM cohort and 4 in the DBT cohort were identified in breast densities of C or greater. False negative cancers detected by AI were predominantly luminal A invasive malignancies (9/11, 82%). Artificial intelligence was able to detect these false negative cancers a median time of 272 days sooner in the FFDM cohort and 248 days sooner in the DBT cohort compared to the radiologist. Artificial intelligence was able to detect cancers at the time of screening that were missed by the radiologist. Prospective studies are needed to evaluate the synergy of AI and the radiologist in real-world settings, especially on DBT examinations.

Diagnostic performance of tomosynthesis plus synthetic mammography versus full-field digital mammography with or without tomosynthesis in breast cancer screening: A systematic review and meta-analysis.

Digital breast tomosynthesis (DBT) with full-field digital mammography (FFDM) exposes women to a higher radiation dose. A synthetic 2D mammogram (S2D) is a two-dimensional image constructed from DBT. We aim to evaluate the S2D performance when used alone or combined with DBT compared to FFDM alone or with DBT. Studies were included if they recruited screening participants and reported on S2D performance. Studies were excluded if they included symptomatic patients, imaging was for diagnostic purposes, or if participants had a breast cancer history. Meta-analyses for cancer detection rates (CDR) and Specificities were conducted where available. Differences in the performance of imaging modalities were calculated within individual studies, and these were pooled by meta-analysis. Out of 3241 records identified, 17 studies were included in the review and 13 in the meta-analysis. The estimated combined difference in CDRs per thousand among individual studies that reported on DBT plus S2D vs. FFDM and those reporting on DBT plus S2D versus DBT plus FFDM was 2.03 (95% CI 0.81-3.25) and - 0.15 (95% CI -1.17 to 0.86), respectively. The estimated difference in percent specificities was 1.13 (95% CI -0.06 to 2.31) in studies comparing DBT plus S2D and FFDM. In studies comparing DBT plus S2D and DBT plus FFDM, the estimated difference in specificities was 1.08 (95% CI 0.59-1.56). DBT plus S2D showed comparable accuracy to FFDM plus DPT and improved cancer detection to FFDM alone. Integrating S2D with DBT in breast cancer screening is safe and preserves performance.

Mutual information-based radiomic feature selection with SHAP explainability for breast cancer diagnosis

Breast cancer is a prevalent concern for women globally, with misdiagnosis potentially leading to detrimental outcomes. Early detection is crucial, often reliant on medical imaging analysis. Digital Breast Tomosynthesis (DBT) is a promising modality, addressing limitations of traditional mammograms. However, diagnosing breast cancer involves subjective visual examination, leading to inaccuracies. Radiomics, applied in various imaging modalities such as MRI, and digital mammography, remains underutilized in DBT. This study introduces a Mutual Information-based Radiomic Feature Selection (MIRFS) framework for DBT breast cancer evaluation followed by SHAP explanations. Selected features were assessed using machine learning algorithms, with Random Forest achieving 92% accuracy in lesion classification. MIRFS demonstrates significant performance improvements, addressing subjectivity and enhancing diagnostic accuracy through explainability. SHAP methodology elucidates feature importance, aiding model interpretation. Compared to deep learning methods, MIRFS outperforms both deep learning and existing machine learning approaches, promising advancements in breast cancer diagnosis and treatment.

Contrast-Enhanced Digital Mammography for the Diagnosis and Determination of Extent of Disease in Invasive Lobular Carcinoma: Our Experience and Literature Review.

Contrast-enhanced imaging, including magnetic resonance imaging and, more recently, contrast-enhanced digital mammography (CEM), is indicated for the precise diagnosis of invasive lobular carcinoma (ILC). The aim of our study was to further validate the use of CEM for evaluation of extent of disease in ILC cases, with digital breast tomosynthesis (DBT) as an adjunct. A retrospective, institutional review board approved study was conducted in a tertiary medical center. All CEM examinations performed on ILC patients between 2017 and 2023 were reread by 2 dedicated breast radiologists. Clinical data and pathology reports were retrieved from electronic medical records. The longest diameter of the enhancing lesion was correlated to pathology findings. In addition, for each case, the readers provided brief commentary on the added value of DBT. Twenty-four CEM examinations were evaluated. The subjects in the study cohort were on average older than expected for ILC patients (74 vs 63 years) and were unable to undergo breast magnetic resonance imaging due to advanced age and comorbidities. Three subjects were treated with neoadjuvant therapy and thus were excluded from the correlation to pathology analysis. Enhancing lesions, ranging from 4-75 mm, strongly correlated to pathology results, with statistical significance. This was demonstrated for mass and nonmass lesions (r = 0.94, P < 0.001 and r = 0.99, P = 0.002, respectively). For most lesions (17/24, 71%), readers remarked that the addition of DBT allowed for improved characterization of lesion margins, mainly detecting architectural distortion. When compared with the pathology findings, ILC was accurately diagnosed and assessed using CEM. The addition of DBT was reported by the interpreting radiologists as a valuable adjunct for margin analysis.

A multiple x-ray-source array (MXA) system with a planar two-dimensional source distribution for digital breast tomosynthesis.

Digital breast tomosynthesis (DBT) has outpaced digital mammography in clinical adoption in the United States; however, substantial technological limitations remain to image quality in DBT, including undersampling from a one-dimensional (1D) scan geometry, x-ray source motion during acquisition, and patient motion artifacts from long exam times. A thermionic cathode x-ray system employing two-dimensional (2D, planar) multiple x-ray-source arrays (MXA) is proposed to improve DBT image quality. A 1D MXA, consisting of a linear array of thermionic cathodes was used to simulate a 2D MXA. The 1D MXA included 11 focal spots separated by a distance of =23mm. The 11 cathodes were paired with 11 molybdenum 50mm diameter anode disks, mounted on a rotating shaft within a single vacuum enclosure. Image quality was investigated as a function of MXA configuration by integrating the 1D MXA with a 200 × 250 mm2 flat panel detector at a source-to-detector distance of 630mm, resulting in a 20° tomographic arc. To simulate a 2D MXA, the detector (with phantom) was translated orthogonally to the linear array by a distance ( ) ranging from =0mm (conventional 1D) to =57mm. All sources operated at 30kV with 80mA and 4.5 mAs/pulse, yielding ∼100 mAs per DBT dataset. DBT reconstructions involved 22 projections and used filtered backprojection with a ramp and Hann apodization filter. Volumetric reconstructions for each source were weighted by sampling differences between sources, and averaged. Image quality was assessed in terms of contrast-to-noise ratio (CNR), background clutter noise and power spectrum, and slice sensitivity profile (SSP) using a set of physical phantoms, including: (i) contrast-detail signals coupled to spherical clutter (PMMA in air); (ii) an SSP phantom; (iii) a commercial "breast" phantom (CIRS BR3D, Sun Nuclear, Norfolk, VA); and (iv) bovine muscle. Background clutter noise amplitude reduced monotonically from the 1D MXA (σclutter=5.9 A.U., =0mm) and 2D MXA arrays with increasing , with statistical significance between the 1D MXA and 2D MXA with =57mm (σclutter=5.0 A.U., p<0.001). The contrast-detail/clutter phantom demonstrated CNR from the 2D MXA (δ=57mm) outperforming the 1D MXA in all combinations of contrast and detail. 2D power spectrum analysis of clutter demonstrated a pronounced Fourier domain null cone for the 1D MXA in the anterior field-of-view (away from the 1D MXA position), whereas the 2D MXA geometry (δ=57mm) did not exhibit the null cone. The SSP was 15%-50% narrower (FWHM) for the 2D versus the 1D geometry, across all reconstruction setups. The advantages of a 2D source geometry for DBT imaging were demonstrated quantitatively compared to a conventional 1D line of x-ray sources. The improvement in the 2D geometry was attributed both to improved Fourier domain sampling and reduced SSP. We conclude that 2D MXA sources have the potential to substantially improve DBT imaging in comparison to existing commercial DBT systems.

Hybrid segmentation and 3D Imaging: Comprehensive framework for breast cancer patient segmentation and classification based on digital breast tomosynthesis

Breast cancer is among the deadliest conditions and the second-leading reason for cancer mortality in women. Breast cells begin to develop into malignant, cancerous tumors, which is how breast cancer develops. Self-tests and routine professional examinations aid in early diagnosis, which increases the likelihood of surviving. A major difficulty for researchers is the categorization of breast cancer as a medical procedure. The prevailing aim of this research is the earlier recognition of breast cancer using 3D images and AI monitors. Initially, the mammogram images are collected and preprocessed to enhance the image quality. Noise removal is done using a Multistage Selective Convolution Filter (MSCF), contrast enhancement is done using an Enhanced Contrast Limited Adaptive Histogram (ECLAH), and an Improved Canny Operator (ICO) is employed for edge detection. Segmentation is done using the DENSE Squeeze Excitation Network (DENSE SE- Net). The preprocessed images are converted to 3D images using Digital Breast Tomosynthesis (DBT). From the hybrid segmented and 3D images, the objects are identified utilizing the You Only Look Once v7 (YOLO v7), and the images are classified into mild, medium, severe, and unaffected women using semi-supervised CNN (SS CNN). Finally, an AI monitor is used to monitor the patients. The simulation findings reveal that the suggested technique yields higher accuracy, precision, recall, and F1 score and better ROC characteristics than the existing approaches.

Rare benign breast neoplasm masquerading as malignancy on anatomical and functional imaging with contrast-enhanced mammography

We discuss a unique case of a benign breast neoplasm that initially appeared to be an intracystic papillary carcinoma on digital breast tomosynthesis, ultrasound, and contrast-enhanced mammogram. However, subsequent histopathological examination (HPE) revealed it to be a benign adenomyoepithelioma, an uncommon neoplasm in the breast. Understanding this rare condition and confirming it pre-operatively through HPE can prevent unnecessary aggressive treatment.

Assessment of local diagnostic reference levels (LDRLS) for full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT) mammography in Tamil Nadu, India.

The purpose of this study was to assess local diagnostic reference levels (LDRLs) for full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT) mammography in India. Data from 1500 women were collected from five different mammography facilities in major cities in Tamil Nadu, India. The mean of mean glandular dose were used to arrive at an LDRL. The noted mean compressed breast thickness was 55.26±3.4. The recorded mean MGDs for the five centres were 3.1±0.1 and 3.8±0.2mGy for FFDM and DBT, respectively. The 75th percentile value for all five centers is 3.3 and 4.0mGy for FFDM and DBT, respectively. The LDRLs found in the current study were also compared with those from earlier studies conducted in other nations, such as the United Kingdom, Malaysia, Morocco, and Ghana. The present study is the first of its kind to determine the LDRL for the FFDM and DBT scanners operating in the Tamil Nadu region, India, and is proposed as a starting point that will allow professionals to evaluate and optimize their practice. Furthermore, similar studies in other regions of India are necessary in order to establish National DRLs.

Digital breast tomosynthesis for early detection of malignant breast microcalcification: A hospital-based cross-sectional study

Background: Breast cancer is the leading cause of cancer-related deaths in women. Early detection by screening is crucial for improved survival. Digital breast tomosynthesis (DBT) may offer better visibility than conventional screening. This study aims to assess the diagnostic accuracy of DBT in detecting malignant breast microcalcifications compared to histopathological findings. Methods: This cross-sectional study was done at the Department of Radiology and Imaging, Bangabandhu Sheikh Mujib Medical University, Dhaka, from July 2022 to June 2023 and enrolled 35 women suspected of having breast cancer. Each patient underwent DBT and histopathology tests. The diagnostic accuracy of DBT in detecting malignant breast microcalcifications was assessed compared to the gold standard of histopathology. Results: The average age of the participants was 48.6 years. Palpable breast lumps (91.4%) were the most common clinical presentation. Among them, 80% had malignant microcalcifications diagnosed by DBT, primarily with linear (40.0%) and segmental (28.6%) distribution. Histopathology identified 80% of cases as malignant, with ductal carcinoma in situ (34.3%) being the most prevalent type. DBT demonstrated high sensitivity (100%) and specificity (85.7%) in detecting malignant microcalcifications, resulting in an overall accuracy of 97.1%. Conclusions: DBT has high diagnostic accuracy for detecting malignant breast microcalcifications and can be considered a valid tool for the early detection of breast microcalcifications, particularly in the context of diagnosing breast cancer.

Interval breast cancer rates for tomosynthesis vs mammography population screening: a systematic review and meta-analysis of prospective studies.

We aimed to synthesise evidence from prospective studies of digital breast tomosynthesis (DBT) screening to assess its effectiveness compared to digital mammography (DM). Specifically, we examined whether DBT reduces interval cancer rates (ICRs) in population breast cancer screening. We performed a systematic review and meta-analysis of DBT screening studies (identified from January 2013 to March 2024). We included both RCTs and non-randomised prospective studies that used an independent comparison for our primary outcome ICRs. The risk of bias was assessed with QUADAS-2. We compared the ICR, cancer detection rate (CDR), and recall rate of DBT and DM screening using random effects meta-analysis models. Subgroup analyses estimated outcomes by study design. Sensitivity analyses estimated absolute effects from relative effects. Ten prospective studies (three RCTs, seven non-randomised) were eligible; all had a low risk of bias. There were 205,245 DBT-screened and 306,476 DM-screened participants with follow-up for interval cancer data. The pooled absolute ICR did not significantly differ between DBT and DM: -2.92 per 10,000 screens (95% CI: -6.39 to 0.54); however subsequent subgroup analysis indicated certain study designs may have biased this ICR estimate. Pooled ICR from studies that only sampled groups from the same time and region indicated DBT led to 5.50 less IC per 10,000 screens (95% CI: -9.47 to -1.54). Estimates from subgroup analysis that compared randomised and non-randomised trials did not significantly differ. This meta-analysis provides suggestive evidence that DBT decreases ICR relative to DM screening; further evidence is needed to reduce uncertainty regarding ICR differences between DBT and DM. Question Does DBT have long-term benefits over standard DM? Finding We find suggestive evidence in our primary analysis and stronger evidence in a follow-up analysis that DBT reduces interval cancers. Clinical relevance This meta-analysis provides the first indication that DBT may detect additional cancers that are clinically meaningful, based on suggestive evidence of a reduction in ICR. This finding does not preclude the simultaneous possibility of overdiagnosis.