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According to the survey of World Health Organization (WHO), in 2020 there are 2.3 million women found with breast cancer and 685,000 deaths in world wide. 81% women get affected with cancer over the age of 50 at the time of detection. Breast cancer is the world's number 2 cancer and number 1 cancer in India and 66% survival rate in India is very low if compare to 90% in U.S and 90.2% in Australia. However, treatment for this cancer has possibility of 90% or more. So that, it need to be detect the cancer at very early stage to overcome the death rate. Main objective of this research to design a Breast Cancer diagnose system using image processing and deep learning which can be helpful for radiologist and physician for treating the diagnosis. Basically, Deep learning is a fast-developing fashion inside the health care enterprise and facilitates medical experts to examine records and pick out trends. And image processing plays vital role for enhancing the quality of image by removing noise which is very helpful for better abnormality classification. Now a days Convolution Neural Networks (CNNs) are very popular due to its better performance. In this work, we have used transfer learning with pre-trained VGG16 model. At initial testing stage, the model shows the over-fitting and after that performance improved. Hence we achieved better results by using this approach on DDSM and UPMC data-sets for breast cancer classification. Classifier classify the images into four classes as asymmetry, calcification, carcinoma and mass. Initially 2276 images were taken and divided into 80%-20% ratio. The accuracy achieved by this approach varied from 92% to 95%. We have also used transfer learning with VGG19 and ResNet50 for comparison and found VGG16 much powerful among them. We found, transfer learning with VGG16 giving better results on DDSM and UPMC data-sets. However, breast cancer divided into different categories according to its type, grade or stage of abnormalities, severity of cancer, aggressiveness of cancerous cells, presence/absence of gene etc. Hence classification can be done basis on other types of abnormalities. [ABSTRACT FROM AUTHOR]

Mobile health apps are widely used for breast cancer detection using artificial intelligence algorithms, providing radiologists with second opinions and reducing false diagnoses. This study aims to develop an open-source mobile app named "BraNet" for 2D breast imaging segmentation and classification using deep learning algorithms. During the phase off-line, an SNGAN model was previously trained for synthetic image generation, and subsequently, these images were used to pre-trained SAM and ResNet18 segmentation and classification models. During phase online, the BraNet app was developed using the react native framework, offering a modular deep-learning pipeline for mammography (DM) and ultrasound (US) breast imaging classification. This application operates on a client–server architecture and was implemented in Python for iOS and Android devices. Then, two diagnostic radiologists were given a reading test of 290 total original RoI images to assign the perceived breast tissue type. The reader's agreement was assessed using the kappa coefficient. The BraNet App Mobil exhibited the highest accuracy in benign and malignant US images (94.7%/93.6%) classification compared to DM during training I (80.9%/76.9%) and training II (73.7/72.3%). The information contrasts with radiological experts' accuracy, with DM classification being 29%, concerning US 70% for both readers, because they achieved a higher accuracy in US ROI classification than DM images. The kappa value indicates a fair agreement (0.3) for DM images and moderate agreement (0.4) for US images in both readers. It means that not only the amount of data is essential in training deep learning algorithms. Also, it is vital to consider the variety of abnormalities, especially in the mammography data, where several BI-RADS categories are present (microcalcifications, nodules, mass, asymmetry, and dense breasts) and can affect the API accuracy model. [ABSTRACT FROM AUTHOR]

Objectives: To compare the location of AI markings on screening mammograms with cancer location on diagnostic mammograms, and to classify interval cancers with high AI score as false negative, minimal sign, or true negative. Methods: In a retrospective study from 2022, we compared the performance of an AI system with independent double reading according to cancer detection. We found 93% (880/949) of the screen-detected cancers, and 40% (122/305) of the interval cancers to have the highest AI risk score (AI score of 10). In this study, four breast radiologists reviewed mammograms from 126 randomly selected screen-detected cancers and all 120 interval cancers with an AI score of 10. The location of the AI marking was stated as correct/not correct in craniocaudal and mediolateral oblique view. Interval cancers with an AI score of 10 were classified as false negative, minimal sign significant/non-specific, or true negative. Results: All screen-detected cancers and 78% (93/120) of the interval cancers with an AI score of 10 were correctly located by the AI system. The AI markings matched in both views for 79% (100/126) of the screen-detected cancers and 22% (26/120) of the interval cancers. For interval cancers with an AI score of 10, 11% (13/120) were correctly located and classified as false negative, 10% (12/120) as minimal sign significant, 26% (31/120) as minimal sign non-specific, and 31% (37/120) as true negative. Conclusion: AI markings corresponded to cancer location for all screen-detected cancers and 78% of the interval cancers with high AI score, indicating a potential for reducing the number of interval cancers. However, it is uncertain whether interval cancers with subtle findings in only one view are actionable for recall in a true screening setting. Clinical relevance statement: In this study, AI markings corresponded to the location of the cancer in a high percentage of cases, indicating that the AI system accurately identifies the cancer location in mammograms with a high AI score. Key Points: • All screen-detected and 78% of the interval cancers with high AI risk score (AI score of 10) had AI markings in one or two views corresponding to the location of the cancer on diagnostic images. • Among all 120 interval cancers with an AI score of 10, 21% (25/120) were classified as a false negative or minimal sign significant and had AI markings matching the cancer location, suggesting they may be visible on prior screening. • Most of the correctly located interval cancers matched only in one view, and the majority were classified as either true negative or minimal sign non-specific, indicating low potential for being detected earlier in a real screening setting. [ABSTRACT FROM AUTHOR]

Objectives: We aimed to evaluate the early-detection capabilities of AI in a screening program over its duration, with a specific focus on the detection of interval cancers, the early detection of cancers with the assistance of AI from prior visits, and its impact on workload for various reading scenarios. Materials and methods: The study included 22,621 mammograms of 8825 women within a 10-year biennial two-reader screening program. The statistical analysis focused on 5136 mammograms from 4282 women due to data retrieval issues, among whom 105 were diagnosed with breast cancer. The AI software assigned scores from 1 to 100. Histopathology results determined the ground truth, and Youden's index was used to establish a threshold. Tumor characteristics were analyzed with ANOVA and chi-squared test, and different workflow scenarios were evaluated using bootstrapping. Results: The AI software achieved an AUC of 89.6% (86.1–93.2%, 95% CI). The optimal threshold was 30.44, yielding 72.38% sensitivity and 92.86% specificity. Initially, AI identified 57 screening-detected cancers (83.82%), 15 interval cancers (51.72%), and 4 missed cancers (50%). AI as a second reader could have led to earlier diagnosis in 24 patients (average 29.92 ± 19.67 months earlier). No significant differences were found in cancer-characteristics groups. A hybrid triage workflow scenario showed a potential 69.5% reduction in workload and a 30.5% increase in accuracy. Conclusion: This AI system exhibits high sensitivity and specificity in screening mammograms, effectively identifying interval and missed cancers and identifying 23% of cancers earlier in prior mammograms. Adopting AI as a triage mechanism has the potential to reduce workload by nearly 70%. Clinical relevance statement: The study proposes a more efficient method for screening programs, both in terms of workload and accuracy. Key Points: • Incorporating AI as a triage tool in screening workflow improves sensitivity (72.38%) and specificity (92.86%), enhancing detection rates for interval and missed cancers. • AI-assisted triaging is effective in differentiating low and high-risk cases, reduces radiologist workload, and potentially enables broader screening coverage. • AI has the potential to facilitate early diagnosis compared to human reading. [ABSTRACT FROM AUTHOR]

Objectives: We aimed to develop a multi-modality model to predict axillary lymph node (ALN) metastasis by combining clinical predictors with radiomic features from magnetic resonance imaging (MRI) and mammography (MMG) in breast cancer. This model might potentially eliminate unnecessary axillary surgery in cases without ALN metastasis, thereby minimizing surgery-related complications. Methods: We retrospectively enrolled 485 breast cancer patients from two hospitals and extracted radiomics features from tumor and lymph node regions on MRI and MMG images. After feature selection, three random forest models were built using the retained features, respectively. Significant clinical factors were integrated with these radiomics models to construct a multi-modality model. The multi-modality model was compared to radiologists' diagnoses on axillary ultrasound and MRI. It was also used to assist radiologists in making a secondary diagnosis on MRI. Results: The multi-modality model showed superior performance with AUCs of 0.964 in the training cohort, 0.916 in the internal validation cohort, and 0.892 in the external validation cohort. It surpassed single-modality models and radiologists' ALN diagnosis on MRI and axillary ultrasound in all validation cohorts. Additionally, the multi-modality model improved radiologists' MRI-based ALN diagnostic ability, increasing the average accuracy from 70.70 to 78.16% for radiologist A and from 75.42 to 81.38% for radiologist B. Conclusion: The multi-modality model can predict ALN metastasis of breast cancer accurately. Moreover, the artificial intelligence (AI) model also assisted the radiologists to improve their diagnostic ability on MRI. Clinical relevance statement: The multi-modality model based on both MRI and mammography images allows preoperative prediction of axillary lymph node metastasis in breast cancer patients. With the assistance of the model, the diagnostic efficacy of radiologists can be further improved. Key Points: • We developed a novel multi-modality model that combines MRI and mammography radiomics with clinical factors to accurately predict axillary lymph node (ALN) metastasis, which has not been previously reported. • Our multi-modality model outperformed both the radiologists' ALN diagnosis based on MRI and axillary ultrasound, as well as single-modality radiomics models based on MRI or mammography. • The multi-modality model can serve as a potential decision support tool to improve the radiologists' ALN diagnosis on MRI. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate if background parenchymal enhancement (BPE) on contrast-enhanced mammography (CEM), graded according to the 2022 CEM-dedicated Breast Imaging Reporting and Data System (BI-RADS) lexicon, is associated with breast density, menopausal status, and age. Methods: This bicentric retrospective analysis included CEM examinations performed for the work-up of suspicious mammographic findings. Three readers independently and blindly evaluated BPE on recombined CEM images and breast density on low-energy CEM images. Inter-reader reliability was estimated using Fleiss κ. Multivariable binary logistic regression was performed, dichotomising breast density and BPE as low (a/b BI-RADS categories, minimal/mild BPE) and high (c/d BI-RADS categories, moderate/marked BPE). Results: A total of 200 women (median age 56.8 years, interquartile range 50.5−65.6, 140/200 in menopause) were included. Breast density was classified as a in 27/200 patients (13.5%), as b in 110/200 (55.0%), as c in 52/200 (26.0%), and as d in 11/200 (5.5%), with moderate inter-reader reliability (κ = 0.536; 95% confidence interval [CI] 0.482–0.590). BPE was minimal in 95/200 patients (47.5%), mild in 64/200 (32.0%), moderate in 25/200 (12.5%), marked in 16/200 (8.0%), with substantial inter-reader reliability (κ = 0.634; 95% CI 0.581–0.686). At multivariable logistic regression, premenopausal status and breast density were significant positive predictors of high BPE, with adjusted odds ratios of 6.120 (95% CI 1.847–20.281, p = 0.003) and 2.416 (95% CI 1.095–5.332, p = 0.029) respectively. Conclusion: BPE on CEM is associated with well-established breast cancer risk factors, being higher in women with higher breast density and premenopausal status. [ABSTRACT FROM AUTHOR]

Purpose of Review: Ductal carcinoma in situ (DCIS) accounts for roughly 25% of all new breast cancer diagnoses. Mortality from DCIS is low and has not significantly changed despite modern, aggressive care. This review will highlight the multiple strategies which are being proposed to de-escalate care, including foregoing sentinel lymph node biopsy (SLNB). Recent Findings: Under 5% of patients undergoing SLNB for DCIS have a positive lymph node, therefore the use of SLNB has been questioned and may be able to be foregone. In addition, recent genomic assays evaluating the benefit of radiation (Oncotype DCIS®, DCISionRT®), have elucidated a group of patients who may not need radiotherapy after breast conservation for DCIS. Finally, the option of foregoing all local treatment and instead focusing on active surveillance is being evaluated in multiple randomized clinical trials including LORIS, LORD and COMET. Summary: Data regarding whether SLNB can be safely omitted and the outcomes of the growing utilization of genomic assays and "watchful waiting" clinical trials remain forthcoming. [ABSTRACT FROM AUTHOR]

Breast cancer remains a substantial public health challenge, marked by a rising prevalence. Accurate early detection is paramount for effective treatment and improved patient outcomes in breast cancer. The diversity of breast tumors and the complexity of their microenvironment present significant challenges. Establishing a reliable breast calcification and micro-calcification detection approach is an ongoing issue that researchers must continue to investigate. The goal is to develop an effective methodology that contributes to increased patient survival. Therefore, this paper introduces a novel approach for classifying breast calcifications in mammography, aiming to distinguish between benign and malignant cases. Aiming to address these challenges, we proposed our hybrid approach for breast calcification classification in mammogram images. The proposed approach starts with an image pre-processing phase that includes noise reduction and enhancement filters. Afterward, we proposed our hybrid classification architecture. It includes two branches: First, the vision transformer (ViT++) branch for contextual features. Secondly, a CNN branch based on transfer learning techniques for visual features. Using the CBIS-DDSM dataset, the application of our proposed ViT++ architecture reached the maximum accuracy of 96.12%. Further, the application of the VGG16 as a single feature extractor had a much lower accuracy of 61.96%. Meanwhile, the combination of these techniques in the same architecture improved the accuracy to 99.22%. Three different pre-trained feature extractors were applied in the CNN branch: Xception, VGG16, and RegNetX002. However, the best-obtained outcomes were from the combination of the ViT++ and the VGG16. The experimental findings indicate that the proposed strategy for breast calcification detection has the potential to surpass the performance of currently top-ranked methods, particularly in terms of classification accuracy. [ABSTRACT FROM AUTHOR]

In medical diagnosis, mammographic imaging is mainly concerned with the breast parenchymal patterns (counterbalance of glandular tissue and fatty tissue) by which an expert radiologist can easily determine the abnormalities in the breast of cancer patients and if the interpretation of mammogram and the quality of mammogram both are well provided. Accordingly, improved mammographic view via an efficient image processing algorithm plays a significant role in the medical diagnosis of mammograms. This study introduces a sharpening method based on the modified Laplacian filter (MLF) on compute unified device architecture (CUDA) to improve the visibility and detection of pernicious lesions in a mammogram. To process considerably large mammograms on CPU, the conventional Laplacian sharpening is more time-consuming due to the processing of all pixels with serial execution manner. Although this type of image sharpening is well established for improved image quality, its effect on a larger image for use in the GPU environment has not been extensively studied. The proposed framework is successfully devised and implemented in an efficient parallel execution manner on a computing platform of graphic processing units (GPU). To examine the impact of mammograms and filter size on performance along with the comparative processing time between serial execute on CPU and parallel computing on GPU (except data transfer time). To accelerate the performance of the proposed model, we adopt both global and shared memory in GPU to realize further improvements of the execution speed. The proposed framework applies a new nonlinear filter constraints module in the sharping stage while the Laplacian filter attenuate noise sensitivity and leads to achieving visually improved results in comparison with formal sharping. The proposed framework has been extensively compared with other recent baseline methods showing to improvement in the computational cost of the image sharping approach. Experimental results establish that the two proposed sharping methods outperform the state-of-the-art methods with respect to execution speed. [ABSTRACT FROM AUTHOR]

Diagnostic reference level (DRL) for mammography for 2015 and 2020 has been published by J-RIME. More new dose studies are needed to revise the next DRL. In preparation for the next revision of the DRL for mammography, this study investigated data from the Japan Central Organization on Quality Assurance of Breast Cancer Screening on the mean average glandular dose (AGD) for institutional image accreditation in 2019–2023 and the relationship between the average at eligible institutions to date and the type of breast X-ray system. The 95th percentile values of the AGD distributions for the Computed Radiography (CR) and Flat Panel Detector (FPD) systems were 2.5 mGy and 2.0 mGy, respectively. Moreover, it is assumed that AGD is decreasing due to the spread of FPD systems, and it is expected that the further spread of FPD systems and systems with W/Rh target/filter will reduce AGD in future. [ABSTRACT FROM AUTHOR]

Breast cancer remains a prevalent and serious health issue, leading to high mortality rates among women worldwide. Early detection of breast cancer is pivotal in improving patient outcomes. This study introduces an innovative approach for breast cancer segmentation by integrating Support Vector Machine (SVM) with Harris Hawks Optimization (HHO) and Cuckoo Search (CS) algorithms. HHO, a metaheuristic optimization algorithm inspired by the cooperative behavior of Harris Hawks, is employed for effective exploration and exploitation within the search space, thereby enhancing the accuracy of image segmentation. The CS algorithm, incorporating Cuckoo Search principles, ensures a balanced exploration of local and global search spaces, contributing to a comprehensive optimization strategy. The hybrid HHO-CS SVM algorithm is instrumental in fine-tuning hyperparameters, resulting in superior performance and improved segmentation outcomes for breast cancer detection. This innovative amalgamation of techniques significantly elevates the accuracy and efficiency of breast cancer detection through image segmentation. [ABSTRACT FROM AUTHOR]

Objectives: To preoperatively evaluate the human epidermal growth factor 2 (HER2) status in breast cancer using mammographic radiomics features and clinical characteristics on a multi-vendor and multi-center basis. Methods: This multi-center study included a cohort of 1512 Chinese female with invasive ductal carcinoma of no special type (IDC-NST) from two different hospitals and five devices (1332 from Institution A, used for training and testing the models, and 180 women from Institution B, as the external validation cohort). The Gradient Boosting Machine (GBM) was employed to establish radiomics and multiomics models. Model efficacy was evaluated by the area under the curve (AUC). Results: The number of HER2-positive patients in the training, testing, and external validation cohort were 245(26.3%), 105 (26.3.8%), and 51(28.3%), respectively, with no statistical differences among the three cohorts (p = 0.842, chi-square test). The radiomics model, based solely on the radiomics features, achieved an AUC of 0.814 (95% CI, 0.784–0.844) in the training cohort, 0.776 (95% CI, 0.727–0.825) in the testing cohort, and 0.702 (95% CI, 0.614–0.790) in the external validation cohort. The multiomics model, incorporated radiomics features with clinical characteristics, consistently outperformed the radiomics model with AUC values of 0.838 (95% CI, 0.810–0.866) in the training cohort, 0.788 (95% CI, 0.741–0.835) in the testing cohort, and 0.722 (95% CI, 0.637–0.811) in the external validation cohort. Conclusions: Our study demonstrates that a model based on radiomics features and clinical characteristics has the potential to accurately predict HER2 status of breast cancer patients across multiple devices and centers. Clinical relevance statement: By predicting the HER2 status of breast cancer reliably, the presented model built upon radiomics features and clinical characteristics on a multi-vendor and multi-center basis can help in bolstering the model's applicability and generalizability in real-world clinical scenarios. Key Points: • The mammographic presentation of breast cancer is closely associated with the status of human epidermal growth factor receptor 2 (HER2). • The radiomics model, based solely on radiomics features, exhibits sub-optimal performance in the external validation cohort. • By combining radiomics features and clinical characteristics, the multiomics model can improve the prediction ability in external data. [ABSTRACT FROM AUTHOR]

Objectives: To perform a survey among members of the European Society of Breast Imaging (EUSOBI) regarding the use of contrast-enhanced mammography (CEM). Methods: A panel of nine board-certified radiologists developed a 29-item online questionnaire, distributed to all EUSOBI members (inside and outside Europe) from January 25 to March 10, 2023. CEM implementation, examination protocols, reporting strategies, and current and future CEM indications were investigated. Replies were exploratively analyzed with descriptive and non-parametric statistics. Results: Among 434 respondents (74.9% from Europe), 50% (217/434) declared to use CEM, 155/217 (71.4%) seeing less than 200 CEMs per year. CEM use was associated with academic settings and high breast imaging workload (p < 0.001). The lack of CEM adoption was most commonly due to the perceived absence of a clinical need (65.0%) and the lack of resources to acquire CEM-capable systems (37.3%). CEM protocols varied widely, but most respondents (61.3%) had already adopted the 2022 ACR CEM BI-RADS® lexicon. CEM use in patients with contraindications to MRI was the most common current indication (80.6%), followed by preoperative staging (68.7%). Patients with MRI contraindications also represented the most commonly foreseen CEM indication (88.0%), followed by the work-up of inconclusive findings at non-contrast examinations (61.5%) and supplemental imaging in dense breasts (53.0%). Respondents declaring CEM use and higher CEM experience gave significantly more current (p = 0.004) and future indications (p < 0.001). Conclusions: Despite a trend towards academic high-workload settings and its prevalent use in patients with MRI contraindications, CEM use and progressive experience were associated with increased confidence in the technique. Clinical relevance statement: In this first survey on contrast-enhanced mammography (CEM) use and perspectives among the European Society of Breast Imaging (EUSOBI) members, the perceived absence of a clinical need chiefly drove the 50% CEM adoption rate. CEM adoption and progressive experience were associated with more extended current and future indications. Key Points: • Among the 434 members of the European Society of Breast Imaging who completed this survey, 50% declared to use contrast-enhanced mammography in clinical practice. • Due to the perceived absence of a clinical need, contrast-enhanced mammography (CEM) is still prevalently used as a replacement for MRI in patients with MRI contraindications. • The number of current and future CEM indications marked by respondents was associated with their degree of CEM experience. [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: The aim of our study was to examine how breast radiologists would be affected by high cancer prevalence and the use of artificial intelligence (AI) for decision support. Materials and method: This reader study was based on selection of screening mammograms, including the original radiologist assessment, acquired in 2010 to 2013 at the Karolinska University Hospital, with a ratio of 1:1 cancer versus healthy based on a 2-year follow-up. A commercial AI system generated an exam-level positive or negative read, and image markers. Double-reading and consensus discussions were first performed without AI and later with AI, with a 6-week wash-out period in between. The chi-squared test was used to test for differences in contingency tables. Results: Mammograms of 758 women were included, half with cancer and half healthy. 52% were 40–55 years; 48% were 56–75 years. In the original non-enriched screening setting, the sensitivity was 61% (232/379) at specificity 98% (323/379). In the reader study, the sensitivity without and with AI was 81% (307/379) and 75% (284/379) respectively (p < 0.001). The specificity without and with AI was 67% (255/379) and 86% (326/379) respectively (p < 0.001). The tendency to change assessment from positive to negative based on erroneous AI information differed between readers and was affected by type and number of image signs of malignancy. Conclusion: Breast radiologists reading a list with high cancer prevalence performed at considerably higher sensitivity and lower specificity than the original screen-readers. Adding AI information, calibrated to a screening setting, decreased sensitivity and increased specificity. Clinical relevance statement: Radiologist screening mammography assessments will be biased towards higher sensitivity and lower specificity by high-risk triaging and nudged towards the sensitivity and specificity setting of AI reads. After AI implementation in clinical practice, there is reason to carefully follow screening metrics to ensure the impact is desired. Key Points: • Breast radiologists' sensitivity and specificity will be affected by changes brought by artificial intelligence. • Reading in a high cancer prevalence setting markedly increased sensitivity and decreased specificity. • Reviewing the binary reads by AI, negative or positive, biased screening radiologists towards the sensitivity and specificity of the AI system. [ABSTRACT FROM AUTHOR]

To explore the diagnostic efficacy of tomosynthesis spot compression (TSC) compared with conventional spot compression (CSC) for ambiguous findings on full-field digital mammography (FFDM). In this retrospective study, 122 patients (including 108 patients with dense breasts) with ambiguous FFDM findings were imaged with both CSC and TSC. Two radiologists independently reviewed the images and evaluated lesions using the Breast Imaging Reporting and Data System. Pathology or at least a 1-year follow-up imaging was used as the reference standard. Diagnostic efficacies of CSC and TSC were compared, including area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). The mean glandular dose was recorded and compared for TSC and CSC. Of the 122 patients, 63 had benign lesions and 59 had malignant lesions. For Reader 1, the following diagnostic efficacies of TSC were significantly higher than those of CSC: AUC (0.988 vs. 0.906, P = 0.001), accuracy (93.4% vs. 77.8%, P = 0.001), specificity (87.3% vs. 63.5%, P = 0.002), PPV (88.1% vs. 70.5%, P = 0.010), and NPV (100% vs. 90.9%, P = 0.029). For Reader 2, TSC showed higher AUC (0.949 vs. 0.909, P = 0.011) and accuracy (83.6% vs. 71.3%, P = 0.022) than CSC. The mean glandular dose of TSC was higher than that of CSC (1.85 ± 0.53 vs. 1.47 ± 0.58 mGy, P < 0.001) but remained within the safety limit. TSC provides better diagnostic efficacy with a slightly higher but tolerable radiation dose than CSC. Therefore, TSC may be a candidate modality for patients with ambiguous findings on FFDM. [ABSTRACT FROM AUTHOR]

Introduction: Breast arterial calcifications (BAC) are common incidental findings on routine mammograms, which have been suggested as a sex-specific biomarker of cardiovascular disease (CVD) risk. Previous work showed the efficacy of a pretrained convolutional network (CNN), VCG16, for automatic BAC detection. In this study, we further tested the method by a comparative analysis with other ten CNNs. Material and methods: Four-view standard mammography exams from 1,493 women were included in this retrospective study and labeled as BAC or non-BAC by experts. The comparative study was conducted using eleven pretrained convolutional networks (CNNs) with varying depths from five architectures including Xception, VGG, ResNetV2, MobileNet, and DenseNet, fine-tuned for the binary BAC classification task. Performance evaluation involved area under the receiver operating characteristics curve (AUC-ROC) analysis, F1-score (harmonic mean of precision and recall), and generalized gradient-weighted class activation mapping (Grad-CAM++) for visual explanations. Results: The dataset exhibited a BAC prevalence of 194/1,493 women (13.0%) and 581/5,972 images (9.7%). Among the retrained models, VGG, MobileNet, and DenseNet demonstrated the most promising results, achieving AUC-ROCs > 0.70 in both training and independent testing subsets. In terms of testing F1-score, VGG16 ranked first, higher than MobileNet (0.51) and VGG19 (0.46). Qualitative analysis showed that the Grad-CAM++ heatmaps generated by VGG16 consistently outperformed those produced by others, offering a finer-grained and discriminative localization of calcified regions within images. Conclusion: Deep transfer learning showed promise in automated BAC detection on mammograms, where relatively shallow networks demonstrated superior performances requiring shorter training times and reduced resources. Relevance statement: Deep transfer learning is a promising approach to enhance reporting BAC on mammograms and facilitate developing efficient tools for cardiovascular risk stratification in women, leveraging large-scale mammographic screening programs. Key points: • We tested different pretrained convolutional networks (CNNs) for BAC detection on mammograms. • VGG and MobileNet demonstrated promising performances, outperforming their deeper, more complex counterparts. • Visual explanations using Grad-CAM++ highlighted VGG16's superior performance in localizing BAC. [ABSTRACT FROM AUTHOR]

Purpose: To assess the diagnostic performance of breast MRI for BI-RADS 4A microcalcifications on mammography and propose a potential clinical pathway to avoid unnecessary biopsies. Methods: Bibliometrics analysis of breast MRI and BI-RADS 4 was provided. A retrospective analysis was conducted on 139 women and 142 cases of BI-RADS 4A microcalcifications on mammography from Fudan University Shanghai Cancer Center. The mammographic BI-RADS level and the MRI reports were compared with the final pathological diagnosis. Results: Much attention has been given to breast MRI and BI-RADS 4 in the literature. However, studies on BI-RADS 4A are limited. Pathological results showed 117 cases (82.4%) were benign lesions, malignant cases of 25 (17.6%) in our study. The positive predictive values (PPV), specificity, sensitivity and negative predictive values (NPV) of MRI were 44.2% (23/52), 75.2% (88/117), 92.0% (23/25), and 97.8% (88/90), respectively. Therefore, 75.2% (88/117) of biopsies for benign lesions could potentially be avoided. There were 2.2% (2/90) malignant lesions missed. Logistic regression indicated that patients who are postmenopausal (HR = 2.655, p = 0.012), have a history of breast cancer (family history) (HR = 2.833, p = 0.029), and exhibit clustered microcalcifications (HR = 2.179, p = 0.046) are more likely to have a higher MRI BI-RADS level. Conclusions: Breast MRI has the potential to improve the diagnosis of BI-RADS 4A microcalcifications on mammography. We propose a potential clinical pathway that patients with BI-RADS 4A on mammography who are premenopausal, have no personal history of breast cancer (family history) or have non-clustered distribution of calcifications can undergo MRI to avoid unnecessary biopsies. [ABSTRACT FROM AUTHOR]

The most common and rapidly spreading disease in the world is breast cancer. Most cases of breast cancer are observed in females. Breast cancer is controlled with early detection. Therefore, early detection and categorization of breast cancer are essential to enable patients to take the right course of treatment. Early discovery helps to manage many cases and lower the death rate. In this study, a brand-new Spider Monkey-based Recurrent Neural System (SMbRNS) is created for predicting breast cancer cells in an early stage. Breast mammography images are used in this instance as the dataset for the system. The breast dataset is also analyzed using the established SMbRNS function to detect and segregate the breast cancer-afflicted region efficiently. The developed model aims to enhance the segmented breast cancer results using spider monkey fitness. The developed method computes the chance of breast cancer using the dataset; segmented images are used for monitoring. Additionally, the Python code used to perform this strategy allows for evaluating the created model parameters against earlier research. The experimental results are validated with other prevailing models regarding the accuracy, precision, sensitivity, specificity, and F1-score to prove the efficiency. The designed model gained 99.82% accuracy and 99.12% precision for segmenting breast cancer. The current study model produces mammography with better accuracy for the segmentation of breast cancer. [ABSTRACT FROM AUTHOR]

Objective: To improve breast radiographers' individual performance by using automated software to assess the correctness of breast positioning and compression in tomosynthesis screening. Materials and methods: In this retrospective longitudinal analysis of prospective cohorts, six breast radiographers with varying experience in the field were asked to use automated software to improve their performance in breast compression and positioning. The software tool automatically analyzes craniocaudal (CC) and mediolateral oblique (MLO) views for their positioning quality by scoring them according to PGMI classifications (perfect, good, moderate, inadequate) and checking whether the compression pressure is within the target range. The positioning and compression data from the studies acquired before the start of the project were used as individual baselines, while the data obtained after the training were used to test whether conscious use of the software could help the radiographers improve their performance. The percentage of views rated perfect or good and the percentage of views in target compression were used as overall metrics to assess changes in performance. Results: Following the use of the software, all radiographers significantly increased the percentage of images rated as perfect or good in both CCs and MLOs. Individual improvements ranged from 7 to 14% for CC and 10 to 16% for MLO views. Moreover, most radiographers exhibited improved compression performance in CCs, with improvements up to 16%. Conclusion: Active use of a software tool to automatically assess the correctness of breast compression and positioning in breast cancer screening can improve the performance of radiographers. Clinical relevance statement: This study suggests that the use of a software tool for automatically evaluating correctness of breast compression and positioning in breast cancer screening can improve the performance of radiographers on these metrics, which may ultimately lead to improved screening outcomes. Key Points: • Proper breast positioning and compression are critical in breast cancer screening to ensure accurate diagnosis. • Active use of the software increased the quality of craniocaudal and mediolateral oblique views acquired by all radiographers. • Improved performance of radiographers is expected to improve screening outcomes. [ABSTRACT FROM AUTHOR]

Purpose: To develop a classification tree via semiquantitative analysis for ultrasonographic breast composition assessment using routine breast ultrasonography examination images. Methods: This study retrospectively enrolled 100 consecutive normal women who underwent screening mammography and supplemental ultrasonography. Based on sonographic breast composition, the patients' breasts were classified as nondense or dense, which were correlated with mammographic breast composition. Ultrasonographic breast composition was classified based on the fibroglandular tissue (FGT) thickness-to-subcutaneous fat and retromammary fat (FAT) thickness ratio. In addition, the presence of a high glandular tissue component (GTC) in FGT or the presence of evident fat lobules in FGT was investigated. The cutoff point between the nondense and dense breasts was calculated from the area under the curve (AUC). Results: All cases with a high GTC were dense breasts, and all cases with evident fat lobules in the FGT were nondense breasts. The AUC of the FGT thickness-to-FAT ratio of all cases, the group without a high GTC, the group without evident fat lobules in the FGT, and the group without a high GTC or evident fat lobules in the FGT were 0.93, 0.94, 0.99, and 1, respectively. Conclusion: The presence of a high GTC indicated dense breasts, and the presence of evident fat lobules in the FGT represented nondense breasts. For the remaining cases, the cutoff point of the FGT thickness-to-FAT thickness ratio was 0.93 for ultrasonographic two-grade scale breast composition assessment with 100% accuracy. [ABSTRACT FROM AUTHOR]

Purpose: Multiple ecological levels influence racial inequities in the completion of diagnostic testing after receiving abnormal mammography results (diagnostic resolution). Yet, few studies examine more than two ecological levels. We investigated the contributions of county, imaging facility, and patient characteristics on our primary and secondary outcomes, the achievement of diagnostic resolution by (1)Black women and Latinas, and (2) the entire sample. We hypothesized that women of color would be less likely to achieve resolution than their White counterparts, and this relationship would be mediated by imaging facility features and moderated by county characteristics. Methods: Records for 25,144 women with abnormal mammograms between 2011 and 2019 from the Carolina Mammography Registry were merged with publicly available county data. Diagnostic resolution was operationalized as the percentage of women achieving resolution within 60 days of receiving abnormal results and overall time to resolution and examined using mixed effects logistic regression and Cox proportional hazard models, respectively. Results: Women of color with abnormal screening mammograms were less likely to achieve resolution within 60 days compared with White women (OR 0.83, CI 0.78–0.89; OR 0.74, CI.60–0.91, respectively) and displayed longer resolution times (HR 0.87, CI 0.84–0.91; HR 0.78, CI 0.68–0.89). Residential segregation had a moderating effect, with Black women in more segregated counties being less likely to achieve resolution by 60 days but lost statistical significance after adjustment. No mediators were discovered. Conclusion: More work is needed to understand how imaging center and community characteristics impact racial inequities in resolution and resolution in general. [ABSTRACT FROM AUTHOR]

Background: Visual assessment of mammographic breast composition remains the most common worldwide, although subjective variability limits its reproducibility. This study aimed to investigate the inter- and intra-observer variability in qualitative visual assessment of mammographic breast composition through a multi-institutional observer performance study for the first time in Japan. Methods: This study enrolled 10 Japanese physicians from five different institutions. They used the new Japanese breast-composition classification system 4th edition to subjectively evaluate the breast composition in 200 pairs of right and left normal mediolateral oblique mammograms (number determined using precise sample size calculations) twice, with a 1-month interval (median patient age: 59 years [range 40–69 years]). The primary endpoint of this study was the inter-observer variability using kappa (κ) value. Results: Inter-observer variability for the four and two classes of breast-composition assessment revealed moderate agreement (Fleiss' κ: first and second reading = 0.553 and 0.587, respectively) and substantial agreement (Fleiss' κ: first and second reading = 0.689 and 0.70, respectively). Intra-observer variability for the four and two classes of breast-composition assessment demonstrated substantial agreement (Cohen's κ, median = 0.758) and almost perfect agreement (Cohen's κ, median = 0.813). Assessments of consensus between the 10 physicians and the automated software Volpara® revealed slight agreement (Cohen's κ; first and second reading: 0.104 and 0.075, respectively). Conclusions: Qualitative visual assessment of mammographic breast composition using the new Japanese classification revealed excellent intra-observer reproducibility. However, persistent inter-observer variability, presenting a challenge in establishing it as the gold standard in Japan. [ABSTRACT FROM AUTHOR]

Breast cancer is the most frequently diagnosed cancer in women accounting for about 30% of all new cancer cases and the incidence is constantly increasing. Implementation of mammographic screening has contributed to a reduction in breast cancer mortality of at least 20% over the last 30 years. Screening programs usually include all women irrespective of their risk of developing breast cancer and with age being the only determining factor. This approach has some recognized limitations, including underdiagnosis, false positive cases, and overdiagnosis. Indeed, breast cancer remains a major cause of cancer-related deaths in women undergoing cancer screening. Supplemental imaging modalities, including digital breast tomosynthesis, ultrasound, breast MRI, and, more recently, contrast-enhanced mammography, are available and have already shown potential to further increase the diagnostic performances. Use of breast MRI is recommended in high-risk women and women with extremely dense breasts. Artificial intelligence has also shown promising results to support risk categorization and interval cancer reduction. The implementation of a risk-stratified approach instead of a "one-size-fits-all" approach may help to improve the benefit-to-harm ratio as well as the cost-effectiveness of breast cancer screening. Key Points: Regular mammography should still be considered the mainstay of the breast cancer screening. High-risk women and women with extremely dense breast tissue should use MRI for supplemental screening or US if MRI is not available. Women need to participate actively in the decision to undergo personalized screening. Key recommendations: Mammography is an effective imaging tool to diagnose breast cancer in an early stage and to reduce breast cancer mortality (evidence level I). Until more evidence is available to move to a personalized approach, regular mammography should be considered the mainstay of the breast cancer screening. High-risk women should start screening earlier; first with yearly breast MRI which can be supplemented by yearly or biennial mammography starting at 35–40 years old (evidence level I). Breast MRI screening should be also offered to women with extremely dense breasts (evidence level I). If MRI is not available, ultrasound can be performed as an alternative, although the added value of supplemental ultrasound regarding cancer detection remains limited. Individual screening recommendations should be made through a shared decision-making process between women and physicians. [ABSTRACT FROM AUTHOR]

Objectives: Evaluate microcalcification detectability in digital breast tomosynthesis (DBT) and synthetic 2D mammography (SM) for different acquisition setups using a virtual imaging trial (VIT) approach. Materials and methods: Medio-lateral oblique (MLO) DBT acquisitions on eight patients were performed at twice the automatic exposure controlled (AEC) dose. The noise was added to the projections to simulate a given dose trajectory. Virtual microcalcification models were added to a given projection set using an in-house VIT framework. Three setups were evaluated: (1) standard acquisition with 25 projections at AEC dose, (2) 25 projections with a convex dose distribution, and (3) sparse setup with 13 projections, every second one over the angular range. The total scan dose and angular range remained constant. DBT volume reconstruction and synthetic mammography image generation were performed using a Siemens prototype algorithm. Lesion detectability was assessed through a Jackknife-alternative free-response receiver operating characteristic (JAFROC) study with six observers. Results: For DBT, the area under the curve (AUC) was 0.97 ± 0.01 for the standard, 0.95 ± 0.02 for the convex, and 0.89 ± 0.03 for the sparse setup. There was no significant difference between standard and convex dose distributions (p = 0.309). Sparse projections significantly reduced detectability (p = 0.001). Synthetic images had a higher AUC with the convex setup, though not significantly (p = 0.435). DBT required four times more reading time than synthetic mammography. Discussion: A convex setup did not significantly improve detectability in DBT compared to the standard setup. Synthetic images exhibited a non-significant increase in detectability with the convex setup. Sparse setup significantly reduced detectability in both DBT and synthetic mammography. Clinical relevance statement: This virtual imaging trial study allowed the design and efficient testing of different dose distribution trajectories with real mammography images, using a dose-neutral protocol. Key Points: • In DBT, a convex dose distribution did not increase the detectability of microcalcifications compared to the current standard setup but increased detectability for the SM images. • A sparse setup decreased microcalcification detectability in both DBT and SM images compared to the convex and current clinical setups. • Optimal microcalcification cluster detection in the system studied was achieved using either the standard or convex dose setting, with the default number of projections. [ABSTRACT FROM AUTHOR]

Women with a personal history of breast cancer (PHBC) are at an increased risk of either a local recurrence or a new primary breast cancer. Thus, surveillance is essential for the detection of recurrent disease at the earliest possible stage, allowing for prompt treatment, and potentially improving overall survival. Nowadays, mammography follow-up is the only surveillance imaging technique recommended by international guidelines. Nevertheless, sensitivity of mammography is lower after breast cancer treatment, particularly during the first 5 years, due to increased density or post-treatment changes. Contrast-enhanced breast imaging techniques, such as MRI or contrast-enhanced mammography (CEM), are very sensitive to detect malignant enhancement, especially in dense breasts. This Special Report will provide arguments in favor of and against breast cancer follow-up with MRI or CEM, in a debate style between experts in Breast Imaging. Finally, the scientific points of pros and cons arguments will be summarized to help objectively decide the best follow-up strategy for women with a personal history of breast cancer. Clinical relevance statement: A personalized approach to follow-up imaging after conservative breast cancer treatment could optimize patient outcomes, using mammography as a baseline for most patients, and MRI or CEM selectively in patients with higher risks for a recurrence. Key Points: • Women with a personal history of breast cancer are at an increased risk of either a local recurrence or a new primary breast cancer. • Breast cancer survivors may benefit from additional imaging with MRI/CEM, in case of increased risk of a second breast cancer, with dense breasts or a cancer diagnosis before age 50 years. • As survival after local recurrence seems to depend on the initial stage at diagnosis, imaging should be more focused on detecting tumors in the earliest stages. [ABSTRACT FROM AUTHOR]

Objective: To explore the ability of artificial intelligence (AI) to classify breast cancer by mammographic density in an organized screening program. Materials and method: We included information about 99,489 examinations from 74,941 women who participated in BreastScreen Norway, 2013–2019. All examinations were analyzed with an AI system that assigned a malignancy risk score (AI score) from 1 (lowest) to 10 (highest) for each examination. Mammographic density was classified into Volpara density grade (VDG), VDG1–4; VDG1 indicated fatty and VDG4 extremely dense breasts. Screen-detected and interval cancers with an AI score of 1–10 were stratified by VDG. Results: We found 10,406 (10.5% of the total) examinations to have an AI risk score of 10, of which 6.7% (704/10,406) was breast cancer. The cancers represented 89.7% (617/688) of the screen-detected and 44.6% (87/195) of the interval cancers. 20.3% (20,178/99,489) of the examinations were classified as VDG1 and 6.1% (6047/99,489) as VDG4. For screen-detected cancers, 84.0% (68/81, 95% CI, 74.1–91.2) had an AI score of 10 for VDG1, 88.9% (328/369, 95% CI, 85.2–91.9) for VDG2, 92.5% (185/200, 95% CI, 87.9–95.7) for VDG3, and 94.7% (36/38, 95% CI, 82.3–99.4) for VDG4. For interval cancers, the percentages with an AI score of 10 were 33.3% (3/9, 95% CI, 7.5–70.1) for VDG1 and 48.0% (12/25, 95% CI, 27.8–68.7) for VDG4. Conclusion: The tested AI system performed well according to cancer detection across all density categories, especially for extremely dense breasts. The highest proportion of screen-detected cancers with an AI score of 10 was observed for women classified as VDG4. Clinical relevance statement: Our study demonstrates that AI can correctly classify the majority of screen-detected and about half of the interval breast cancers, regardless of breast density. Key Points: • Mammographic density is important to consider in the evaluation of artificial intelligence in mammographic screening. • Given a threshold representing about 10% of those with the highest malignancy risk score by an AI system, we found an increasing percentage of cancers with increasing mammographic density. • Artificial intelligence risk score and mammographic density combined may help triage examinations to reduce workload for radiologists. [ABSTRACT FROM AUTHOR]

Covid-19 infection influenced the screening test rate of breast cancer worldwide due to the quarantine measures, routine procedures reduction, and delay of early diagnosis, causing high mortality risk and severity of the disease. X-ray mammography is the gold standard for diagnosing early signs of breast cancer, and Artificial Intelligence enables the detection of suspicious lesions and classifying them in terms of malignancy. This paper aimed to investigate mass detection and classification in a large-scale OPTIMAM dataset with 6000 cases and extracted 3524 images with masses in the mammograms of the Hologic manufacturer. The methodology of the detection step is to train the RetinaNet architecture of ResNet50, ResNet101, and ResNet152 backbones with three types of initializations by ImageNet and COCO weights and from scratch. The dataset was pre-processed to generate two types of input with entire mammograms and patches, which are stated as the first and the second approaches. The results show that in the first approach, RetinaNet of ResNet50 backbone with ImageNet and COCO weights and ResNet152 with the same weights performed 0.91 True Positive Rate at 0.78 False Positive Per Image, respectively. In contrast, in the second approach, ResNet152 with ImageNet weights reached 0.88 TPR at 0.78 FPPI. In the classification step, the Transfer Learning approach was applied with fine-tuning by adding L2-regularization and class weights to balance class distribution in the datasets. [ABSTRACT FROM AUTHOR]

Breast cancer mortality reduction progress has halted in recent years. The mortality rate was rising, and breast cancer was the leading cause of death among women. Early diagnosis is critical in treatment since it can prevent complications and heavy pathologic therapy. Many Computer-Aided Diagnosis (CAD) systems were developed for this purpose. However, to produce more accurate findings, it must continue to be enhanced by adopting new methodologies. To efficiently handle semantic segmentation in a predicted image, we propose a novel Fully Convolutional Network (FCN) called DEES-Breast that presents an End-to-End system for an early breast cancer detection from mammographic scans. The DEES-Breast uses an encoder-decoder architecture to identify relevant features from scans at several scales and upsample them to generate the best segmentation results. The main advantage of the proposed architecture is the skip connection mode within the decoder and encoder layers, which merges high-level features encoded with low-level features decoded from the decoder. The CNN used at the encoder tries to admit relevant studies having similar contrast values using thirteen convolutional layers and three fully connected layers. Various complex preprocessing methods were carefully used to enhance the model's performance. These methods included various procedures, such as image cropping, CLAHE enhancement, artifact removal, etc., and allowed us to create a well-prepared dataset for training and testing. Geometric data augmentations were carefully integrated into the pipeline to improve generalization capabilities and reduce overfitting. CBIS-DDSM images and a private database were used to test our suggested architecture comprehensively. Quantitative criteria for evaluating segmentation outcomes, such as Dice coefficient, precision, and recall, are all above 90%, demonstrating that the proposed architecture system can differentiate functional tissues in breast mammogram images. As a result, our proposed architecture has the potential to offer the classification required to aid in the clinical detection of breast cancer while also improving imaging in other modalities of medical mammography. [ABSTRACT FROM AUTHOR]

Purpose: Recruit and sequence breast cancer subjects in Guatemalan and US Hispanic populations. Identify optimum strategies to recruit Latin American and Hispanic women into genetic studies of breast cancer. Methods: We used targeted gene sequencing to identify pathogenic variants in 19 familial breast cancer susceptibility genes in DNA from unselected Hispanic breast cancer cases in the US and Guatemala. Recruitment across the US was achieved through community-based strategies. In addition, we obtained patients receiving cancer treatment at major hospitals in Texas and Guatemala. Results: We recruited 287 Hispanic US women, 38 (13%) from community-based and 249 (87%) from hospital-based strategies. In addition, we ascertained 801 Guatemalan women using hospital-based recruitment. In our experience, a hospital-based approach was more efficient than community-based recruitment. In this study, we sequenced 103 US and 137 Guatemalan women and found 11 and 10 pathogenic variants, respectively. The most frequently mutated genes were BRCA1, BRCA2, CHEK2, and ATM. In addition, an analysis of 287 US Hispanic patients with pathology reports showed a significantly higher percentage of triple-negative disease in patients with pathogenic variants (41% vs. 15%). Finally, an analysis of mammography usage in 801 Guatemalan patients found reduced screening in women with a lower socioeconomic status (p < 0.001). Conclusion: Guatemalan and US Hispanic women have rates of hereditary breast cancer pathogenic variants similar to other populations and are more likely to have early age at diagnosis, a family history, and a more aggressive disease. Patient recruitment was higher using hospital-based versus community enrollment. This data supports genetic testing in breast cancer patients to reduce breast cancer mortality in Hispanic women. [ABSTRACT FROM AUTHOR]

Purpose: Mitigating false negative imaging studies remains an important issue given its association with worse morbidity and mortality in patients with breast cancer. We aimed to identify risk factors that predispose to false negative breast imaging exams. Methods: In an IRB-approved, HIPAA compliant retrospective study, we identified all patients who were diagnosed with breast cancer within 365 days of a negative imaging study assessed as BI-RADS 1-3 between January 1, 2014 and January 31, 2020. A matched cohort based on mammographic breast density was created from randomly selected studies with BI-RADS 4-5 designation that yielded breast cancer at pathology within the same time frame. Patient and cancer characteristics, prior personal history of breast cancer and gene mutation status were collected from patient charts. Pearson chi-squared and Student's t-test on two independent groups with significance at < 0.05 was used for statistical analysis. Results: We identified 155 false negative studies of 129 missed cancers and 128 breast density matched true positive cancers. False negative studies were screening mammograms in 57.42% (89/155), diagnostic mammograms in 29.68% (46/155), ultrasounds in 6.45% (10/155) and MRIs in 6.45% (10/155). Rates of personal (41.09% vs. 18.75%, p < 0.001) and family history of breast cancer (68.22% vs. 49.21%, p = 0.002) were higher in the false negative cohort and remained significant when asymptomatic MRI-detected cancers were removed. Conclusion: Our findings suggest that supplemental screening may be useful in breast cancer survivors. [ABSTRACT FROM AUTHOR]

Ethnic minority women experience disparities in mammography screening utilisation and breast cancer outcomes. This systematic review and meta-analysis synthesised multidomain and multilevel factors that intersect to influence the utilisation of mammography among ethnic minorities. A literature search was conducted in five databases (PubMed, Ovid MEDLINE, EMBASE, CINAHL, and PsycINFO) from inception to May 2022. Guided by the National Institute on Minority Health and Health Disparity research framework, the retrieved data were synthesised using narrative summaries and meta-analyses. Among the 27 studies, most (n = 24) reported individual, interpersonal, and community factors in the health care system domain. In the sociocultural domain, interpersonal and societal (n = 8) factors, such as modesty and karma beliefs, were less frequently identified than individual (n = 20) factors in relation to acculturation. Only individual-level factors were reported for the biological and physical/built environment (e.g., rural residence) domains. In the behavioural domain, cancer screening behaviours had a high combined prediction ability (odds ratio = 18.23; I2 = 23%), whereas interpersonal (e.g., family obligations) and community (e.g., neighbourhood violence) factors discouraged mammography screening. Special focus should be given to ethnic minority women, especially those living in rural areas, those with considerable family obligations, and those who have suffered from violence and other life pressures, to increase their access to mammography services. Multidomain and multilevel efforts, culturally appropriate strategies, and equity-advancing policies such as geographic access and insurance coverage would help to mitigate the ethnic disparities in mammography screening. [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: To validate an AI system for standalone breast cancer detection on an entire screening population in comparison to first-reading breast radiologists. Materials and methods: All mammography screenings performed between August 4, 2014, and August 15, 2018, in the Region of Southern Denmark with follow-up within 24 months were eligible. Screenings were assessed as normal or abnormal by breast radiologists through double reading with arbitration. For an AI decision of normal or abnormal, two AI-score cut-off points were applied by matching at mean sensitivity (AIsens) and specificity (AIspec) of first readers. Accuracy measures were sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and recall rate (RR). Results: The sample included 249,402 screenings (149,495 women) and 2033 breast cancers (72.6% screen-detected cancers, 27.4% interval cancers). AIsens had lower specificity (97.5% vs 97.7%; p < 0.0001) and PPV (17.5% vs 18.7%; p = 0.01) and a higher RR (3.0% vs 2.8%; p < 0.0001) than first readers. AIspec was comparable to first readers in terms of all accuracy measures. Both AIsens and AIspec detected significantly fewer screen-detected cancers (1166 (AIsens), 1156 (AIspec) vs 1252; p < 0.0001) but found more interval cancers compared to first readers (126 (AIsens), 117 (AIspec) vs 39; p < 0.0001) with varying types of cancers detected across multiple subgroups. Conclusion: Standalone AI can detect breast cancer at an accuracy level equivalent to the standard of first readers when the AI threshold point was matched at first reader specificity. However, AI and first readers detected a different composition of cancers. Clinical relevance statement: Replacing first readers with AI with an appropriate cut-off score could be feasible. AI-detected cancers not detected by radiologists suggest a potential increase in the number of cancers detected if AI is implemented to support double reading within screening, although the clinicopathological characteristics of detected cancers would not change significantly. Key Points: • Standalone AI cancer detection was compared to first readers in a double-read mammography screening population. • Standalone AI matched at first reader specificity showed no statistically significant difference in overall accuracy but detected different cancers. • With an appropriate threshold, AI-integrated screening can increase the number of detected cancers with similar clinicopathological characteristics. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the improvement of mammography interpretation for novice and experienced radiologists assisted by two commercial AI software. Methods: We compared the performance of two AI software (AI-1 and AI-2) in two experienced and two novice readers for 200 mammographic examinations (80 cancer cases). Two reading sessions were conducted within 4 weeks. The readers rated the likelihood of malignancy (range, 1–7) and the percentage probability of malignancy (range, 0–100%), with and without AI assistance. Differences in AUROC, sensitivity, and specificity were analyzed. Results: Mean AUROC increased in both novice (0.86 to 0.90 with AI-1 [p = 0.005]; 0.91 with AI-2 [p < 0.001]) and experienced readers (0.87 to 0.92 with AI-1 [p < 0.001]; 0.90 with AI-2 [p = 0.004]). Sensitivities increased from 81.3 to 88.8% with AI-1 (p = 0.027) and to 91.3% with AI-2 (p = 0.005) in novice readers, and from 81.9 to 90.6% with AI-1 (p = 0.001) and to 87.5% with AI-2 (p = 0.016) in experienced readers. Specificity did not decrease significantly in both novice (p > 0.999, both) and experienced readers (p > 0.999 with AI-1 and 0.282 with AI-2). There was no significant difference in the performance change depending on the type of AI software (p > 0.999). Conclusion: Commercial AI software improved the diagnostic performance of both novice and experienced readers. The type of AI software used did not significantly impact performance changes. Further validation with a larger number of cases and readers is needed. Clinical relevance statement: Commercial AI software effectively aided mammography interpretation irrespective of the experience level of human readers. Key Points: • Mammography interpretation remains challenging and is subject to a wide range of interobserver variability. • In this multi-reader study, two commercial AI software improved the sensitivity of mammography interpretation by both novice and experienced readers. The type of AI software used did not significantly impact performance changes. • Commercial AI software may effectively support mammography interpretation irrespective of the experience level of human readers. [ABSTRACT FROM AUTHOR]

Objectives: To assess the performance of an artificial intelligence (AI) algorithm in the Australian mammography screening program which routinely uses two independent readers with arbitration of discordant results. Methods: A total of 7533 prevalent round mammograms from 2017 were available for analysis. The AI program classified mammograms into deciles on the basis of breast cancer (BC) risk. BC diagnoses, including invasive BC (IBC) and ductal carcinoma in situ (DCIS), included those from the prevalent round, interval cancers, and cancers identified in the subsequent screening round two years later. Performance was assessed by sensitivity, specificity, positive and negative predictive values, and the proportion of women recalled by the radiologists and identified as higher risk by AI. Results: Radiologists identified 54 women with IBC and 13 with DCIS with a recall rate of 9.7%. In contrast, 51 of 54 of the IBCs and 12/13 cases of DCIS were within the higher AI score group (score 10), a recall equivalent of 10.6% (a difference of 0.9% (CI −0.03 to 1.89%, p = 0.06). When IBCs were identified in the 2017 round, interval cancers classified as false negatives or with minimal signs in 2017, and cancers from the 2019 round were combined, the radiologists identified 54/67 and 59/67 were in the highest risk AI category (sensitivity 80.6% and 88.06 % respectively, a difference that was not different statistically). Conclusions: As the performance of AI was comparable to that of expert radiologists, future AI roles in screening could include replacing one reader and supporting arbitration, reducing workload and false positive results. Clinical relevance statement: AI analysis of consecutive prevalent screening mammograms from the Australian BreastScreen program demonstrated the algorithm's ability to match the cancer detection of experienced radiologists, additionally identifying five interval cancers (false negatives), and the majority of the false positive recalls. Key Points: • The AI program was almost as sensitive as the radiologists in terms of identifying prevalent lesions (51/54 for invasive breast cancer, 63/67 when including ductal carcinoma in situ). • If selected interval cancers and cancers identified in the subsequent screening round were included, the AI program identified more cancers than the radiologists (59/67 compared with 54/67, sensitivity 88.06 % and 80.6% respectively p = 0.24). • The high negative predictive value of a score of 1–9 would indicate a role for AI as a triage tool to reduce the recall rate (specifically false positives). [ABSTRACT FROM AUTHOR]

Purpose of Review: Since its approval by the Unites States Food and Drug Administration (FDA) in 2015, cone-beam breast computed tomography (CBBCT) has gained acceptance among radiologists for breast cancer imaging. This review aims to highlight the advancements and benefits of CBBCT in the diagnostic workup of breast disease. It showcases how CBBCT, including both non-contrast (NC-CBBCT) and contrast-enhanced (CE-CBBCT) protocols, complements and often surpasses the performance of more traditional breast imaging modalities such as mammography and magnetic resonance imaging (MRI). Recent Findings: Studies in clinical settings have shown CBBCT's efficacy in detecting and characterizing breast lesions of differing morphologies, including non-mass enhancement and calcifications—tasks that previously required the use of multiple modalities. In addition, CBBCT significantly enhances patient comfort and efficiency, offering quick acquisition times without the discomfort of breast compression. The technology can be utilized for guiding biopsies, planning surgical interventions, and assessing breast density and tumor characteristics, evidence supporting its integration into clinical practice. Summary: CBBCT holds the potential to shift the imaging paradigm in breast cancer care, indicating a promising future for the modality in terms of enhancing diagnostic accuracy, improving patient experience, and influencing treatment outcomes. [ABSTRACT FROM AUTHOR]

Purpose of Review: Breast cancer is the most common cancer among women. Accurate clinical staging is important as it guides management and treatment. The radiologist plays an important role in the evaluation of breast cancer because imaging is integral to clinical staging. This review aims to highlight the various aspects of staging breast cancer with an emphasis on the appropriate use of imaging. Recent Findings: The 8th edition of the American Joint Commission on Cancer (AJCC) Staging Manual incorporates tumor markers and other prognostic factors, in addition to the traditional anatomic staging. This allows a more precise estimation of disease extent, allowing appropriate treatment and management of patients. Imaging plays an important role in accurately staging patients clinically. Different breast imaging modalities and systemic imaging modalities complement each other to give the most accurate clinical stage, which is then combined with biological and prognostic factors to guide patient management. Summary: Understanding the staging of breast cancer is essential for the radiologist and allows for individualized and prognostic-based management of breast cancer patients. [ABSTRACT FROM AUTHOR]

Objectives: We explored associations between mammographic features and risk of breast cancer death among women with small (<15 mm) and large (≥15 mm) invasive screen-detected breast cancer. Methods: We included data from 17,614 women diagnosed with invasive breast cancer as a result of participation in BreastScreen Norway, 1996–2020. Data on mammographic features (mass, spiculated mass, architectural distortion, asymmetric density, density with calcification and calcification alone), tumour diameter and cause of death was obtained from the Cancer Registry of Norway. Cox regression was used to estimate hazard ratios (HR) with 95% confidence intervals (CI) for breast cancer death by mammographic features using spiculated mass as reference, adjusting for age, tumour diameter and lymph node status. All analyses were dichotomised by tumour diameter (small versus large). Results: Mean age at diagnosis was 60.8 (standard deviation, SD=5.8) for 10,160 women with small tumours and 60.0 (SD=5.8) years for 7454 women with large tumours. The number of breast cancer deaths was 299 and 634, respectively. Mean time from diagnosis to death was 8.7 (SD=5.0) years for women with small tumours and 7.2 (4.6) years for women with large tumours. Using spiculated mass as reference, adjusted HR for breast cancer death among women with small tumours was 2.48 (95% CI 1.67–3.68) for calcification alone, while HR for women with large tumours was 1.30 (95% CI 1.02–1.66) for density with calcification. Conclusions: Small screen-detected invasive cancers presenting as calcification and large screen-detected cancers presenting as density with calcification were associated with the highest risk of breast cancer death. Clinical relevance statement: Small tumours (<15 mm) presented as calcification alone and large tumours (≥ 15 mm) presented as density with calcification were associated with the highest risk of breast cancer death among women with screen-detected invasive breast cancer diagnosed 1996–2020. Key Points: • Women diagnosed with invasive screen-detected breast cancer 1996–2020 were analysed. • Small screen-detected cancers presenting as calcification alone resulted in the highest risk of breast cancer death. • Large screen-detected cancers presenting as density with calcification resulted in the highest risk of breast cancer death. [ABSTRACT FROM AUTHOR]

Objectives: To compare contrast-enhanced mammography (CEM) with low-energy image (LEI) alone and with magnetic resonance imaging (MRI) in the preoperative diagnosis of ductal carcinoma in situ (DCIS). Methods: In this single-center retrospective study, we reviewed 98 pure DCIS lesions in 96 patients who underwent CEM and MRI within 2 weeks preoperatively. The diagnostic performances of each imaging modality, lesion morphology, and extent were evaluated. Results: The sensitivity of CEM to DCIS was similar to that of MRI (92.9% vs. 93.9%, p = 0.77) and was significantly higher than that of LEI alone (76.5%, p = 0.002). The sensitivity of CEM to calcified DCIS (92.4%) was not significantly different from LEI alone (92.4%) and from MRI (93.9%, p = 1.00). However, CEM contributed to the simultaneous comparison of calcifications with enhancements. CEM had considerably higher sensitivity compared with LEI alone (93.8% vs. 43.8%, p < 0.001) and performed similarly to MRI (93.8%, p = 1.00) for noncalcified DCIS. All DCIS lesions were enhanced in MRI, whereas 94.9% (93/98) were enhanced in CEM. Non-mass enhancement was the most common presentation (CEM 63.4% and MRI 66.3%). The difference between the lesion size on each imaging modality and the histopathological size was smallest in MRI, followed by CEM, and largest in LEI. Conclusion: CEM was more sensitive than LEI alone and comparable to MRI in DCIS diagnosis. The enhanced morphology of DCIS in CEM was consistent with that in MRI. CEM was superior to LEI alone in size measurement of DCIS. Clinical relevance statement: This study investigated the value of CEM in the diagnosis and evaluation of DCIS, aiming to offer a reference for the selection of examination methods for DCIS and contribute to the early diagnosis and precise treatment of DCIS. Key Points: • DCIS is an important indication for breast surgery. Early and accurate diagnosis is crucial for DCIS treatment and prognosis. • CEM overcomes the deficiency of mammography in noncalcified DCIS diagnosis, exhibiting similar sensitivity to MRI; and CEM contributes to the comparison of calcification and enhancement of calcified DCIS, thereby outperforming MRI. • CEM is superior to LEI alone and slightly inferior to MRI in the size evaluation of DCIS. [ABSTRACT FROM AUTHOR]

Purpose: Population screening programs have contributed to reduced breast cancer mortality, but disadvantaged or vulnerable groups may not have shared these improvements. In North American and European studies, women living with mental health conditions have reduced breast screening rates. There are no current Australasian data to support health system planning and improvement strategies. Methods: The New South Wales (NSW) BreastScreen program offers free screening to NSW women aged 50–74. We compared 2-year breast screening rates for mental health service users (n = 33,951) and other NSW women (n = 1,051,495) in this target age range, after standardisation for age, socioeconomic status and region of residence. Mental health service contacts were identified through linkage to hospital and community mental health data. Results: Only 30.3% of mental health service users participated in breast screening, compared with 52.7% of other NSW women (crude incidence rate ratio 0.57, 95% CI 0.56–0.59). Standardisation for age, socioeconomic disadvantage or rural residence did not alter this screening gap. Around 7000 fewer women received screening than would be expected from comparable population rates. Screening gaps were largest in women over 60 and in socioeconomically advantaged areas. Women with severe or persistent mental illness had slightly higher screening rates than other mental health service users. Conclusions: Low breast cancer screening participation rates for NSW mental health service users suggest significant risk of later detection, possibly leading to more extensive treatment and premature mortality. Focussed strategies are needed to support greater breast screening participation for NSW women who use mental health services. [ABSTRACT FROM AUTHOR]

Purpose: Screening history influences stage at detection, but regular preventive care may also influence breast tumor diagnostic characteristics. Few studies have evaluated healthcare utilization (both screening and primary care) in racially diverse screening-eligible populations. Methods: This analysis included 2,058 women age 45–74 (49% Black) from the Carolina Breast Cancer Study, a population-based cohort of women diagnosed with invasive breast cancer between 2008 and 2013. Screening history (threshold 0.5 mammograms per year) and pre-diagnostic healthcare utilization (i.e. regular care, based on responses to "During the past ten years, who did you usually see when you were sick or needed advice about your health?") were assessed as binary exposures. The relationship between healthcare utilization and tumor characteristics were evaluated overall and race-stratified. Results: Among those lacking screening, Black participants had larger tumors (5 + cm) (frequency 19.6% vs 11.5%, relative frequency difference (RFD) = 8.1%, 95% CI 2.8–13.5), but race differences were attenuated among screening-adherent participants (10.2% vs 7.0%, RFD = 3.2%, 0.2–6.2). Similar trends were observed for tumor stage and mode of detection (mammogram vs lump). Among all participants, those lacking both screening and regular care had larger tumors (21% vs 8%, RR = 2.51, 1.76–3.56) and advanced (3B +) stage (19% vs 6%, RR = 3.15, 2.15–4.63) compared to the referent category (screening-adherent and regular care). Under-use of regular care and screening was more prevalent in socioeconomically disadvantaged areas of North Carolina. Conclusions: Access to regular care is an important safeguard for earlier detection. Our data suggest that health equity interventions should prioritize both primary care and screening. [ABSTRACT FROM AUTHOR]

Purpose: To ensure high-quality screening programmes and effective utilization of resources, it is important to monitor how cancer detection is affected by different strategies performed at recall assessment. This study aimed to describe procedures performed at recall assessment and compare and evaluate the performance of the assessment in Denmark, Norway, and Spain in terms of screen-detected cancer (SDC) and interval cancer (IC) rates. Methods: We included women aged 50–69 years from Denmark, Norway, and Spain, who were recalled for assessment after screening mammography, and recorded all procedures performed during six months after diagnosis, and the timing of the procedures. Women were followed for two years and screen-detected and interval cancer, and sensitivity of recall was calculated and compared. Results: In total, data from 24,645 Danish, 30,050 Norwegian, and 41,809 Spanish women were included in the study. Most of the women had some assessment within 2 months in all three countries. SDC rates were higher in Denmark (0.57) and Norway (0.60) compared to Spain (0.38), as were the IC rates, i.e. 0.25 and 0.18 vs. 0.12, respectively. The sensitivity of the diagnostic follow-up was somewhat higher in Denmark (98.3%) and Norway (98.2%), compared to Spain (95.4%), but when excluding non-invasive assessment pathways, the sensitivities were comparable. Conclusion: This comparison study showed variation in the assessment procedures used in the three countries as well as the SDC and IC rates and the sensitivity of recall. [ABSTRACT FROM AUTHOR]

Purpose: To compare machine learning (ML) models with logistic regression model in order to identify the optimal factors associated with mammography-occult (i.e. false-negative mammographic findings) magnetic resonance imaging (MRI)-detected newly diagnosed breast cancer (BC). Material and methods: The present single-centre retrospective study included consecutive women with BC who underwent mammography and MRI (no more than 45 days apart) for breast cancer between January 2018 and May 2023. Various ML algorithms and binary logistic regression analysis were utilized to extract features linked to mammography-occult BC. These features were subsequently employed to create different models. The predictive value of these models was assessed using receiver operating characteristic curve analysis. Results: This study included 1957 malignant lesions from 1914 patients, with an average age of 51.64 ± 9.92 years and a range of 20–86 years. Among these lesions, there were 485 mammography-occult BCs. The optimal features of mammography-occult BC included calcification status, tumour size, mammographic density, age, lesion enhancement type on MRI, and histological type. Among the different ML models (ANN, L1-LR, RF, and SVM) and the LR-based combined model, the ANN model with RF features was found to be the optimal model. It demonstrated the best discriminative performance in predicting mammography false- negative findings, with an AUC of 0.912, an accuracy of 86.90%, a sensitivity of 85.85%, and a specificity of 84.18%. Conclusion: Mammography-occult MRI-detected breast cancers have features that should be considered when performing breast MRI to improve the detection rate for breast cancer and aid in clinician management. [ABSTRACT FROM AUTHOR]

Background: Automatic exposure control (AEC) plays a crucial role in mammography by determining the exposure conditions needed to achieve specific image quality based on the absorption characteristics of compressed breasts. This study aimed to characterize the behavior of AEC for digital mammography (DM), digital breast tomosynthesis (DBT), and low-energy (LE) and high-energy (HE) acquisitions used in contrast-enhanced mammography (CEM) for three mammography systems from two manufacturers. Methods: Using phantoms simulating various breast thicknesses, 363 studies were acquired using all available AEC modes 165 DM, 132 DBT, and 66 LE-CEM and HE-CEM. AEC behaviors were compared across systems and modalities to assess the impact of different technical components and manufacturers' strategies on the resulting mean glandular doses (MGDs) and image quality metrics such as contrast-to-noise ratio (CNR). Results: For all systems and modalities, AEC increased MGD for increasing phantom thicknesses and decreased CNR. The median MGD values (interquartile ranges) were 1.135 mGy (0.772–1.668) for DM, 1.257 mGy (0.971–1.863) for DBT, 1.280 mGy (0.937–1.878) for LE-CEM, and 0.630 mGy (0.397–0.713) for HE-CEM. Medians CNRs were 14.2 (7.8–20.2) for DM, 4.91 (2.58–7.20) for a single projection in DBT, 11.9 (8.0–18.2) for LE-CEM, and 5.2 (3.6–9.2) for HE-CEM. AECs showed high repeatability, with variations lower than 5% for all modes in DM, DBT, and CEM. Conclusions: The study revealed substantial differences in AEC behavior between systems, modalities, and AEC modes, influenced by technical components and manufacturers' strategies, with potential implications in radiation dose and image quality in clinical settings. Relevance statement: The study emphasized the central role of automatic exposure control in DM, DBT, and CEM acquisitions and the great variability in dose and image quality among manufacturers and between modalities. Caution is needed when generalizing conclusions about differences across mammography modalities. Key points: • AEC plays a crucial role in DM, DBT, and CEM. • AEC determines the "optimal" exposure conditions needed to achieve specific image quality. • The study revealed substantial differences in AEC behavior, influenced by differences in technical components and strategies. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate whether the quantitative abnormality scores provided by artificial intelligence (AI)-based computer-aided detection/diagnosis (CAD) for mammography interpretation can be used to predict invasive upgrade in ductal carcinoma in situ (DCIS) diagnosed on percutaneous biopsy. Methods: Four hundred forty DCIS in 420 women (mean age, 52.8 years) diagnosed via percutaneous biopsy from January 2015 to December 2019 were included. Mammographic characteristics were assessed based on imaging features (mammographically occult, mass/asymmetry/distortion, calcifications only, and combined mass/asymmetry/distortion with calcifications) and BI-RADS assessments. Routine pre-biopsy 4-view digital mammograms were analyzed using AI-CAD to obtain abnormality scores (AI-CAD score, ranging 0–100%). Multivariable logistic regression was performed to identify independent predictive mammographic variables after adjusting for clinicopathological variables. A subgroup analysis was performed with mammographically detected DCIS. Results: Of the 440 DCIS, 117 (26.6%) were upgraded to invasive cancer. Three hundred forty-one (77.5%) DCIS were detected on mammography. The multivariable analysis showed that combined features (odds ratio (OR): 2.225, p = 0.033), BI-RADS 4c or 5 assessments (OR: 2.473, p = 0.023 and OR: 5.190, p < 0.001, respectively), higher AI-CAD score (OR: 1.009, p = 0.007), AI-CAD score ≥ 50% (OR: 1.960, p = 0.017), and AI-CAD score ≥ 75% (OR: 2.306, p = 0.009) were independent predictors of invasive upgrade. In mammographically detected DCIS, combined features (OR: 2.194, p = 0.035), and higher AI-CAD score (OR: 1.008, p = 0.047) were significant predictors of invasive upgrade. Conclusion: The AI-CAD score was an independent predictor of invasive upgrade for DCIS. Higher AI-CAD scores, especially in the highest quartile of ≥ 75%, can be used as an objective imaging biomarker to predict invasive upgrade in DCIS diagnosed with percutaneous biopsy. Critical relevance statement: Noninvasive imaging features including the quantitative results of AI-CAD for mammography interpretation were independent predictors of invasive upgrade in lesions initially diagnosed as ductal carcinoma in situ via percutaneous biopsy and therefore may help decide the direction of surgery before treatment. Key points: • Predicting ductal carcinoma in situ upgrade is important, yet there is a lack of conclusive non-invasive biomarkers. • AI-CAD scores—raw numbers, ≥ 50%, and ≥ 75%—predicted ductal carcinoma in situ upgrade independently. • Quantitative AI-CAD results may help predict ductal carcinoma in situ upgrade and guide patient management. [ABSTRACT FROM AUTHOR]

Objectives: To develop a multitask deep learning (DL) algorithm to automatically classify mammography imaging findings and predict the existence of extensive intraductal component (EIC) in invasive breast cancer. Methods: Mammograms with invasive breast cancers from 2010 to 2019 were downloaded for two radiologists performing image segmentation and imaging findings annotation. Images were randomly split into training, validation, and test datasets. A multitask approach was performed on the EfficientNet-B0 neural network mainly to predict EIC and classify imaging findings. Three more models were trained for comparison, including a single-task model (predicting EIC), a two-task model (predicting EIC and cell receptor status), and a three-task model (combining the abovementioned tasks). Additionally, these models were trained in a subgroup of invasive ductal carcinoma. The DeLong test was used to examine the difference in model performance. Results: This study enrolled 1459 breast cancers on 3076 images. The EIC-positive rate was 29.0%. The three-task model was the best DL model with an area under the curve (AUC) of EIC prediction of 0.758 and 0.775 at the image and breast (patient) levels, respectively. Mass was the most accurately classified imaging finding (AUC = 0.915), followed by calcifications and mass with calcifications (AUC = 0.878 and 0.824, respectively). Cell receptor status prediction was less accurate (AUC = 0.625–0.653). The multitask approach improves the model training compared to the single-task model, but without significant effects. Conclusions: A mammography-based multitask DL model can perform simultaneous imaging finding classification and EIC prediction. Clinical relevance statement: The study results demonstrated the potential of deep learning to extract more information from mammography for clinical decision-making. Key Points: • Extensive intraductal component (EIC) is an independent risk factor of local tumor recurrence after breast-conserving surgery. • A mammography-based deep learning model was trained to predict extensive intraductal component close to radiologists' reading. • The developed multitask deep learning model could perform simultaneous imaging finding classification and extensive intraductal component prediction. [ABSTRACT FROM AUTHOR]

Purpose: There are differences in the distributions of breast cancer incidence and risk factors by race and ethnicity. Given the strong association between breast density and breast cancer, it is of interest describe racial and ethnic variation in the determinants of breast density. Methods: We characterized racial and ethnic variation in reproductive history and several measures of breast density for Hispanic (n = 286), non-Hispanic Black (n = 255), and non-Hispanic White (n = 1694) women imaged at a single hospital. We quantified associations between reproductive factors and percent volumetric density (PVD), dense volume (DV), non-dense volume (NDV), and a novel measure of pixel intensity variation (V) using multivariable-adjusted linear regression, and tested for statistical heterogeneity by race and ethnicity. Results: Reproductive factors most strongly associated with breast density were age at menarche, parity, and oral contraceptive use. Variation by race and ethnicity was most evident for the associations between reproductive factors and NDV (minimum p-heterogeneity:0.008) and V (minimum p-heterogeneity:0.004) and least evident for PVD (minimum p-heterogeneity:0.042) and DV (minimum p-heterogeneity:0.041). Conclusion: Reproductive choices, particularly those related to childbearing and oral contraceptive use, may contribute to racial and ethnic variation in breast density. [ABSTRACT FROM AUTHOR]

Deep neural networks have demonstrated promising performance in screening mammography with recent studies reporting performance at or above the level of trained radiologists on internal datasets. However, it remains unclear whether the performance of these trained models is robust and replicates across external datasets. In this study, we evaluate four state-of-the-art publicly available models using four publicly available mammography datasets (CBIS-DDSM, INbreast, CMMD, OMI-DB). Where test data was available, published results were replicated. The best-performing model, which achieved an area under the ROC curve (AUC) of 0.88 on internal data from NYU, achieved here an AUC of 0.9 on the external CMMD dataset (N = 826 exams). On the larger OMI-DB dataset (N = 11,440 exams), it achieved an AUC of 0.84 but did not match the performance of individual radiologists (at a specificity of 0.92, the sensitivity was 0.97 for the radiologist and 0.53 for the network for a 1-year follow-up). The network showed higher performance for in situ cancers, as opposed to invasive cancers. Among invasive cancers, it was relatively weaker at identifying asymmetries and was relatively stronger at identifying masses. The three other trained models that we evaluated all performed poorly on external datasets. Independent validation of trained models is an essential step to ensure safe and reliable use. Future progress in AI for mammography may depend on a concerted effort to make larger datasets publicly available that span multiple clinical sites. [ABSTRACT FROM AUTHOR]

In the mid-1990s, the identification of BRCA1/2 genes for breast cancer susceptibility led to testing breast MRI accuracy in screening women at increased risk. From 2000 onwards, ten intraindividual comparative studies showed the marked superiority of MRI: the sensitivity ranged 25−58% for mammography, 33−52% for ultrasound, 48−67% for mammography plus ultrasound, and 71−100% for MRI; specificity 93–100%, 91–98%, 89–98%, and 81–98%, respectively. Based on the available evidence, in 2006–2007, the UK National Institute for Clinical Excellence and the American Cancer Society recommended MRI screening of high-risk women, followed by other international guidelines. Despite evidence-based medicine ideally requiring randomised controlled trials (RCTs) for policy changes regarding screening procedures, breast MRI for high-risk screening was adopted in many countries worldwide. In 2019, the results of the "DENSE" RCT were published in favour of breast MRI screening of women with extremely dense breasts compared to mammography alone, showing a reduction of more than 80% of the interval cancer rate in women who attended MRI screening. Even though international recommendations in favour of this practice were issued, substantial obstacles still prevent health systems from adopting breast MRI for screening women with extremely dense breasts. A paradox is evident: we adopted a screening procedure without evidence from RCTs, and now that we have this level-1 evidence for the same procedure, we fail to do so. This critical review tries to explain the differences between the two cases, as examples of the complex pathways of translating radiological research into everyday practice. Critical relevance statement The high-level evidence in favour of breast MRI screening of women with extremely dense breasts is failing to persuade policy makers to translate this into clinical practice. Key points • Breast MRI screening of high-risk women was adopted on basis of the evidence provided by test accuracy comparative studies showing an MRI performance greatly superior to that of mammography. • Breast MRI screening of women with extremely dense breasts has not been adopted although the evidence of a large reduction in interval cancer rate from a RCT. • We illustrate the differences between the two cases, as an example of the complex ways of translation of radiological research in clinical practice according to the EBM theory. [ABSTRACT FROM AUTHOR]