Insights into the Refusal of Free Screening Mammograms: Exploring Contributing Factors.

Background: Despite the availability of free screening mammograms (SMG) through the Breast Cancer Early Detection (BCED) Program in the Qassim region of Saudi Arabia, a notable gap exists between program implementation and the actual uptake of SMG. This study aims to assess the refusal rate, identify barriers to participation, and shed light on the factors influencing women's decisions regarding SMG. Methods: A cross-sectional study was conducted among consecutive women aged 40-69 participating anonymously in the BCED program in September 2023. The participants were administered a validated Arabic language survey encompassing breast cancer screening backgrounds and knowledge, reasons for refusal, and factors influencing SMG reconsideration. Logistic regression was employed to identify factors linked with SMG refusal using SPSS version 28. Results: Of the 2446 eligible women in the study, 576 (23.6%) declined to undergo SMG. The median age of participants was 49 years, primarily married (91.5%) and residing in central cities (60.3%). Previous mammogram history was reported by 21.4%, with only 12.9% performing regular SMGs every 1-2 years. Married women had a 31% lower refusal likelihood to SMG compared to widowed/divorced women (Adjusted Odds Ratio [aOR] = 0.69, p = 0.02). Women residing in peripheral areas showed approximately 1.45 times higher odds of refusal compared to those in central cities (aOR = 1.45, p < 0.001), and women without prior history of SMG had 2.13 times higher odds of refusal (aOR = 2.14, p < 0.001). Conclusion: The refusal rate for SMG in the Qassim region aligns closely with rates observed in developed countries. Despite this progress, significant barriers to SMG uptake persist, and tailored interventions targeting specific demographic groups and addressing these barriers are essential to improving screening participation, promoting a culture of proactive screening behavior, and ensuring equitable access to screening services for all eligible women.

Optimizing Radiological Education: The Role of Learning Spacing via Test sets in Enhancing Diagnostic Proficiency in Breast Screening Readers.

RATIONALE AND OBJECTIVES: Integrating learning spacing in medicine has shown promise in enhancing knowledge retention and diagnostic proficiency. While studies demonstrate the effectiveness of spaced learning in various fields, limited research exists on its application in radiological training. This study aims to investigate the impact of intervals in spaced training on radiologists' and trainees' diagnostic performance via mammogram test sets. METHODS: 54 radiologists and 101 radiology trainees completed 207 and 458 first-time readings of 9 mammogram test sets between 2019 and 2023. Each test set comprised of 60 mammograms (20 cancer and 40 normal), sourced retrospectively from BreastScreen Australia. Each radiologist evaluated mammograms using the BIRADS lexicon. Readers' performance was compared with truth data and evaluated in terms of specificity, case sensitivity, lesion sensitivity, ROC AUC and JAFROC FOM. The progress of readers' performances in following test sets after the first one was analyzed using Wilcoxon Signed Rank test. The association of participants' performances and the intervals among test sets' completions was investigated using Pearson's test. RESULTS: Significant positive correlations were found between intervals and radiologists' improvement in specificity and JAFROC FOM (P < 0.05). The separation of 4 to 10 days showed the most improvement among radiologists across all metrics, while intervals exceeding 90 days related to highest increase in case sensitivity (5.15%), lesion sensitivity (6.55%), ROC AUC (3.05%) and JAFROC FOM (6.3%). Trainees completing test sets in one day showed positive correlations with their ROC AUC (R=0.45; P = 0.008) and JAFROC FOM (R=0.43; P = 0.02), while those taking a longer time to complete showed negative impacts on case sensitivity (P = 0.009) and ROC AUC (P = 0.02). Remarkable progress in trainees was found in case sensitivity (6.15%), lesion sensitivity (11.6%), ROC AUC (3.5%) and JAFROC FOM (4.35%) with test set intervals of 31-90 days. CONCLUSIONS: Radiologists demonstrated superior performance when the training test sets were spaced over longer intervals, whereas trainees exhibited proficiency with shorter time separations. By optimizing the spacing of reviewing and practicing radiological concepts, mammogram readers can bolster memory retention and diagnostic decision-making skills.

Intelligent breast cancer diagnosis with two-stage using mammogram images.

Breast cancer (BC) significantly contributes to cancer-related mortality in women, underscoring the criticality of early detection for optimal patient outcomes. Mammography is a key tool for identifying and diagnosing breast abnormalities; however, accurately distinguishing malignant mass lesions remains challenging. To address this issue, we propose a novel deep learning approach for BC screening utilizing mammography images. Our proposed model comprises three distinct stages: data collection from established benchmark sources, image segmentation employing an Atrous Convolution-based Attentive and Adaptive Trans-Res-UNet (ACA-ATRUNet) architecture, and BC identification via an Atrous Convolution-based Attentive and Adaptive Multi-scale DenseNet (ACA-AMDN) model. The hyperparameters within the ACA-ATRUNet and ACA-AMDN models are optimized using the Modified Mussel Length-based Eurasian Oystercatcher Optimization (MML-EOO) algorithm. The performance is evaluated using a variety of metrics, and a comparative analysis against conventional methods is presented. Our experimental results reveal that the proposed BC detection framework attains superior precision rates in early disease detection, demonstrating its potential to enhance mammography-based screening methodologies.

Breast Cancer Diagnosis Method Based on Cross-Mammogram Four-View Interactive Learning.

Computer-aided diagnosis systems play a crucial role in the diagnosis and early detection of breast cancer. However, most current methods focus primarily on the dual-view analysis of a single breast, thereby neglecting the potentially valuable information between bilateral mammograms. In this paper, we propose a Four-View Correlation and Contrastive Joint Learning Network (FV-Net) for the classification of bilateral mammogram images. Specifically, FV-Net focuses on extracting and matching features across the four views of bilateral mammograms while maximizing both their similarities and dissimilarities. Through the Cross-Mammogram Dual-Pathway Attention Module, feature matching between bilateral mammogram views is achieved, capturing the consistency and complementary features across mammograms and effectively reducing feature misalignment. In the reconstituted feature maps derived from bilateral mammograms, the Bilateral-Mammogram Contrastive Joint Learning module performs associative contrastive learning on positive and negative sample pairs within each local region. This aims to maximize the correlation between similar local features and enhance the differentiation between dissimilar features across the bilateral mammogram representations. Our experimental results on a test set comprising 20% of the combined Mini-DDSM and Vindr-mamo datasets, as well as on the INbreast dataset, show that our model exhibits superior performance in breast cancer classification compared to competing methods.

Co-design of an intervention to optimize mammographic screening participation in women with obesity and/or physical disabilities.

BACKGROUND: Mammographic breast screening/rescreening rates are suboptimal for women with obesity and/or physical disabilities. This study describes development of an intervention framework targeting obesity- and disability-related barriers to improve participation. METHODS: Mixed methods combined a systematic review with first-person perspectives to optimise screening engagement among women with obesity and/or physical disabilities. Phase 1 (systematic review) was conducted following the PRISMA framework. Phase 2 involved in-depth interviews with n = 8 women with lived experience of obesity and/or physical disabilities. An inductive coding approach was applied to the data which was then combined with Phase 1 results to develop the intervention framework. RESULTS: Six studies were included in the systematic review. Tailored education based on individual risk increased willingness to undergo mammographic screening. Recommendations to improve the screening experience included partnerships with consumers, targeted messaging, and enhanced professional development for breast screening staff. Participants also identified strategies to improve the uptake of screening and the experience itself. CONCLUSION: Development and evaluation of interventions informed by frameworks like the one developed in this study are needed to improve engagement in screening to promote regular participation among women with physical disabilities and/or obesity. IMPLICATIONS FOR PRACTICE: Successful implementation of practice interventions co-designed by women with obesity and/or physical disabilities are likely to improve their breast screening participation. Enhanced training of radiographers aimed at upskilling in empathetic communication around required manoeuvring and potentially longer screening times for clients with obesity and/or physical disabilities may encourage more positive client practitioner interactions. Client information aimed at women with obesity should include information on how to prepare for the appointment and explain there may be equipment limitations compromising imaging which may not be completed at an initial appointment.

Breast Adiposity: Menopausal Status Impact and its Influence on Glycemic and Anthropometric Metabolic Parameters.

CONTEXT: Ectopic fat depots are related to the deregulation of energy homeostasis, leading to diseases related to obesity and metabolic syndrome (MetS). Despite significant changes in body composition over women's lifespans, little is known about the role of breast adipose tissue (BrAT) and its possible utilization as an ectopic fat depot in women of different menopausal statuses. OBJECTIVE: We aimed to assess the relationship between BrAT and metabolic glycemic and lipid profiles and body composition parameters in adult women. METHODS: In this cross-sectional study, we enrolled adult women undergoing routine mammograms and performed history and physical examination, body composition assessment, semi-automated assessment of breast adiposity (BA) from mammograms, and fasting blood collection for biochemical analysis. Correlations and multivariate regression analysis were used to examine associations of BA with metabolic and body composition parameters. RESULTS: Of the 101 participants included in the final analysis, 76.2% were in menopause, and 23.8% were in premenopause. The BA was positively related with fasting plasma glucose, glycated hemoglobin, homeostasis model assessment of insulin resistance, body mass index, waist circumference, body fat percentage, and abdominal visceral and subcutaneous fat when adjusted for age among women in postmenopause. Also, the BA was an independent predictor of hyperglycemia and MetS. These associations were not present among women in premenopause. CONCLUSION: The BA was related to different adverse body composition and metabolic factors in women in postmenopause. The results suggest that there might be a relevant BrAT endocrine role during menopause, with mechanisms yet to be clarified, thus opening up research perspectives on the subject and potential clinical implications.

GravityNet for end-to-end small lesion detection.

This paper introduces a novel one-stage end-to-end detector specifically designed to detect small lesions in medical images. Precise localization of small lesions presents challenges due to their appearance and the diverse contextual backgrounds in which they are found. To address this, our approach introduces a new type of pixel-based anchor that dynamically moves towards the targeted lesion for detection. We refer to this new architecture as GravityNet, and the novel anchors as gravity points since they appear to be "attracted" by the lesions. We conducted experiments on two well-established medical problems involving small lesions to evaluate the performance of the proposed approach: microcalcifications detection in digital mammograms and microaneurysms detection in digital fundus images. Our method demonstrates promising results in effectively detecting small lesions in these medical imaging tasks.

Computer-extracted global radiomic features can predict the radiologists' first impression about the abnormality of a screening mammogram.

OBJECTIVE: Radiologists can detect the gist of abnormal based on their rapid initial impression on a mammogram (ie, global gist signal [GGS]). This study explores (1) whether global radiomic (ie, computer-extracted) features can predict the GGS; and if so, (ii) what features are the most important drivers of the signals. METHODS: The GGS of cases in two extreme conditions was considered: when observers detect a very strong gist (high-gist) and when the gist of abnormal was not/poorly perceived (low-gist). Gist signals/scores from 13 observers reading 4191 craniocaudal mammograms were collected. As gist is a noisy signal, the gist scores from all observers were averaged and assigned to each image. The high-gist and low-gist categories contained all images in the fourth and first quartiles, respectively. One hundred thirty handcrafted global radiomic features (GRFs) per mammogram were extracted and utilized to construct eight separate machine learning random forest classifiers (All, Normal, Cancer, Prior-1, Prior-2, Missed, Prior-Visible, and Prior-Invisible) for characterizing high-gist from low-gist images. The models were trained and validated using the 10-fold cross-validation approach. The models' performances were evaluated by the area under receiver operating characteristic curve (AUC). Important features for each model were identified through a scree test. RESULTS: The Prior-Visible model achieved the highest AUC of 0.84 followed by the Prior-Invisible (0.83), Normal (0.82), Prior-1 (0.81), All (0.79), Prior-2 (0.77), Missed (0.75), and Cancer model (0.69). Cluster shade, standard deviation, skewness, kurtosis, and range were identified to be the most important features. CONCLUSIONS: Our findings suggest that GRFs can accurately classify high- from low-gist images. ADVANCES IN KNOWLEDGE: Global mammographic radiomic features can accurately predict high- from low-gist images with five features identified to be valuable in describing high-gist images. These are critical in providing better understanding of the mammographic image characteristics that drive the strength of the GGSs which could be exploited to advance breast cancer (BC) screening and risk prediction, enabling early detection and treatment of BC thereby further reducing BC-related deaths.

Mammogram mass segmentation and classification based on cross-view VAE and spatial hidden factor disentanglement.

Breast masses are the most important clinical findings of breast carcinomas. The mass segmentation and classification in mammograms remain a crucial yet challenging topic in computer-aided diagnosis systems, as the masses show their irregularities in shape, size and texture. In this paper, we propose a new framework for mammogram mass classification and segmentation. Specifically, to utilize the complementary information within the mammographic cross-views, cranio caudal and mediolateral oblique, a cross-view based variational autoencoder (CV-VAE) combined with a spatial hidden factor disentanglement module is presented, where the two views can be reconstructed from each other through two explicitly disentangled hidden factors: class related (specified) and background common (unspecified). Then, the specified factor is not only divided into two categories: benign and malignant by a new introduced feature pyramid networks based mass classifier, but also used to predict the mass mask label based on a U-Net-like decoder. By integrating the two complementary modules, more discriminative morphological and semantic features can be learned to solve the mass classification and segmentation problems simultaneously. The proposed method is evaluated on two most used public mammography datasets, CBIS-DDSM and INbreast, achieving the Dice similarity coefficient (DSC) of 92.46% and 93.70% for segmentation and the area under receiver operating characteristic curve (AUC) of 93.20% and 95.01% for classification, respectively. Compared with other state-of-the-art approaches, it gives competitive results.

Can Artificial Intelligence Beat Humans in Detecting Breast Malignancy on Mammograms?

BACKGROUND: The study was aimed at identifying how useful Computer-Aided Detection (CAD) could be in reducing false-negative reporting in mammography and early detection of breast cancer at an early stage as the best protection is early detection. MATERIALS AND METHODS: This retrospective study was conducted in a tertiary care setup of Atomic Energy Cancer Hospital, Nuclear Medicine, Oncology and Radiotherapy Institute (AECH-NORI), where 33 patients with suspicious findings on mammography and subsequent biopsy-proven malignancy were included. The findings of mammography including the lesion type, breast parenchymal density, and sensitivity of CAD detection, as well as the final biopsy results, were recorded. A second group of 40 normal screening mammograms was also included who had no symptoms, had Breast Imaging-Reporting and Data System category I(BI-RADS I) mammograms, and had no pathology identified on correlative sonomammography as well. RESULTS: A total of 35 masses, 11 pleomorphic clusters of microcalcification, five clustered foci of macrocalcification, and nine lesions with pleomorphic clusters of microcalcification and two with pleomorphic clusters of microcalcification only were included. The CAD system was able to identify 26 masses (74%), eight lesions with pleomorphic clusters of microcalcification (72%), five foci of macrocalcification (100%), six lesions with pleomorphic clusters of microcalcification (66%), and two pleomorphic clusters of microcalcification without formed mass (100%). The overall sensitivity of the CAD system was 75.8%. CAD was able to identify 13 out of 16 masses with invasive ductal carcinoma (81.3%), eight out of nine lesions proven as invasive ductal carcinoma with ductal carcinoma in situ (DCIS) (88.9%), two out of five masses with invasive lobular carcinoma (40%), four out of four masses with invasive mammary carcinoma (100%), and zero out of one lesion identified as medullary carcinoma (0%). There was 100% detection for pleomorphic clusters of microcalcification without formed mass with CAD marking two out of two mammograms. CONCLUSION: CAD performed better with combined lesions, accurately marked pleomorphic clusters of microcalcification, and identified small lesions in predominant fibrofatty parenchymal density but was not reliable in dense breast, areas of asymmetric increased density, summation artifacts, edematous breast parenchyma, and retroareolar lesions. It also performed poorly with ill-defined lesions of invasive lobular carcinoma. Human intelligence hence beats CAD for the diagnosis of breast malignancy in mammograms as per our experience.

Breast Cancer Detection with an Ensemble of Deep Learning Networks Using a Consensus-Adaptive Weighting Method.

Breast cancer's high mortality rate is often linked to late diagnosis, with mammograms as key but sometimes limited tools in early detection. To enhance diagnostic accuracy and speed, this study introduces a novel computer-aided detection (CAD) ensemble system. This system incorporates advanced deep learning networks-EfficientNet, Xception, MobileNetV2, InceptionV3, and Resnet50-integrated via our innovative consensus-adaptive weighting (CAW) method. This method permits the dynamic adjustment of multiple deep networks, bolstering the system's detection capabilities. Our approach also addresses a major challenge in pixel-level data annotation of faster R-CNNs, highlighted in a prominent previous study. Evaluations on various datasets, including the cropped DDSM (Digital Database for Screening Mammography), DDSM, and INbreast, demonstrated the system's superior performance. In particular, our CAD system showed marked improvement on the cropped DDSM dataset, enhancing detection rates by approximately 1.59% and achieving an accuracy of 95.48%. This innovative system represents a significant advancement in early breast cancer detection, offering the potential for more precise and timely diagnosis, ultimately fostering improved patient outcomes.

Assessing patterns in cancer screening use by race and ethnicity during the coronavirus pandemic using electronic health record data.

BACKGROUND: Efforts to prevent the spread of the coronavirus led to dramatic reductions in nonemergency medical care services during the first several months of the COVID-19 pandemic. Delayed or missed screenings can lead to more advanced stage cancer diagnoses with potentially worse health outcomes and exacerbate preexisting racial and ethnic disparities. The objective of this analysis was to examine how the pandemic affected rates of breast and colorectal cancer screenings by race and ethnicity. METHODS: We analyzed panels of providers that placed orders in 2019-2020 for mammogram and colonoscopy cancer screenings using electronic health record (EHR) data. We used a difference-in-differences design to examine the extent to which changes in provider-level mammogram and colonoscopy orders declined over the first year of the pandemic and whether these changes differed across race and ethnicity groups. RESULTS: We found considerable declines in both types of screenings from March through May 2020, relative to the same months in 2019, for all racial and ethnic groups. Some rebound in screenings occurred in June through December 2020, particularly among White and Black patients; however, use among other groups was still lower than expected. CONCLUSIONS: This research suggests that many patients experienced missed or delayed screenings during the first few months of the pandemic, which could lead to detrimental health outcomes. Our findings also underscore the importance of having high-quality data on race and ethnicity to document and understand racial and ethnic disparities in access to care.

Cancer Screening Experiences of Black Breast and Ovarian Cancer Patients and Family Members.

Black women experience disproportionate rates of advanced breast cancer diagnoses and mortality. Mammography is a proven and effective tool in early breast cancer detection and impacts patient outcomes. We interviewed Black women with a personal or family history of breast and/or ovarian cancer to understand their screening experiences and views. N = 61 individuals completed an interview. Interview transcripts were qualitatively analyzed for themes regarding clinical experiences, guideline adherence, and family sharing specific to Black women and their families. Most participants were college educated with active health insurance. Women in this cohort were knowledgeable about the benefits of mammography and described few barriers to adhering to annual mammogram guidelines. Some with first-degree family history were frustrated at insurance barriers to mammography before the age of 40. Participants were generally comfortable encouraging family and friends to receive mammograms and expressed a desire for a similar screening tool for ovarian cancer. However, they expressed concern that factors such as screening awareness and education, lack of insurance coverage, and other systematic barriers might prevent other Black women from receiving regular screening. Black women in this cohort reported high adherence to mammography guidelines, but expressed concern about cultural and financial barriers that may impact cancer screening access in the population more generally and contribute to disparities. Participants noted the importance of frank and open discussions of breast cancer screening in their families and community as a means of improving awareness.

Mammography Datasets for Neural Networks-Survey.

Deep neural networks have gained popularity in the field of mammography. Data play an integral role in training these models, as training algorithms requires a large amount of data to capture the general relationship between the model's input and output. Open-access databases are the most accessible source of mammography data for training neural networks. Our work focuses on conducting a comprehensive survey of mammography databases that contain images with defined abnormal areas of interest. The survey includes databases such as INbreast, the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM), the OPTIMAM Medical Image Database (OMI-DB), and The Mammographic Image Analysis Society Digital Mammogram Database (MIAS). Additionally, we surveyed recent studies that have utilized these databases in conjunction with neural networks and the results they have achieved. From these databases, it is possible to obtain at least 3801 unique images with 4125 described findings from approximately 1842 patients. The number of patients with important findings can be increased to approximately 14,474, depending on the type of agreement with the OPTIMAM team. Furthermore, we provide a description of the annotation process for mammography images to enhance the understanding of the information gained from these datasets.

Visual hindsight bias for abnormal mammograms in radiologists.

Purpose: Hindsight bias-where people falsely believe they can accurately predict something once they know about it-is a pervasive decision-making phenomenon, including in the interpretation of radiological images. Evidence suggests it is not only a decision-making phenomenon but also a visual perception one, where prior information about an image enhances our visual perception of the contents of that image. The current experiment investigates to what extent expert radiologists perceive mammograms with visual abnormalities differently when they know what the abnormality is (a visual hindsight bias), above and beyond being biased at a decision level. Approach: N=40 experienced mammography readers were presented with a series of unilateral abnormal mammograms. After each case, they were asked to rate their confidence on a 6-point scale that ranged from confident mass to confident calcification. We used the random image structure evolution method, where the images repeated in an unpredictable order and with varied noise, to ensure any biases were visual, not cognitive. Results: Radiologists who first saw an original image with no noise were more accurate in the max noise level condition [area under the curve (AUC)=0.60] than those who first saw the degraded images (AUC=0.55; difference: p=0.005), suggesting that radiologists' visual perception of medical images is enhanced by prior visual experience with the abnormality. Conclusions: Overall, these results provide evidence that expert radiologists experience not only decision level but also visual hindsight bias, and have potential implications for negligence lawsuits.

DeepMiCa: Automatic segmentation and classification of breast MIcroCAlcifications from mammograms.

BACKGROUND AND OBJECTIVE: Breast cancer is the world's most prevalent form of cancer. The survival rates have increased in the last years mainly due to factors such as screening programs for early detection, new insights on the disease mechanisms as well as personalised treatments. Microcalcifications are the only first detectable sign of breast cancer and diagnosis timing is strongly related to the chances of survival. Nevertheless microcalcifications detection and classification as benign or malignant lesions is still a challenging clinical task and their malignancy can only be proven after a biopsy procedure. We propose DeepMiCa, a fully automated and visually explainable deep-learning based pipeline for the analysis of raw mammograms with microcalcifications. Our aim is to propose a reliable decision support system able to guide the diagnosis and help the clinicians to better inspect borderline difficult cases. METHODS: DeepMiCa is composed by three main steps: (1) Preprocessing of the raw scans (2) Automatic patch-based Semantic Segmentation using a UNet based network with a custom loss function appositely designed to deal with extremely small lesions (3) Classification of the detected lesions with a deep transfer-learning approach. Finally, state-of-the-art explainable AI methods are used to produce maps for a visual interpretation of the classification results. Each step of DeepMiCa is designed to address the main limitations of the previous proposed works resulting in a novel automated and accurate pipeline easily customisable to meet radiologists' needs. RESULTS: The proposed segmentation and classification algorithms achieve an area under the ROC curve of 0.95 and 0.89 respectively. Compared to previously proposed works, this method does not require high performance computational resources and provides a visual explanation of the final classification results. CONCLUSION: To conclude, we designed a novel fully automated pipeline for detection and classification of breast microcalcifications. We believe that the proposed system has the potential to provide a second opinion in the diagnosis process giving the clinicians the opportunity to quickly visualise and inspect relevant imaging characteristics. In the clinical practice the proposed decision support system could help reduce the rate of misclassified lesions and consequently the number of unnecessary biopsies.

The FEDRA Longitudinal Study: Repeated Volumetric Breast Density Measures and Breast Cancer Risk.

Mammographic breast density (MBD) is a strong independent risk factor for breast cancer (BC). We investigated the association between volumetric MBD measures, their changes over time, and BC risk in a cohort of women participating in the FEDRA (Florence-EPIC Digital mammographic density and breast cancer Risk Assessment) study. The study was carried out among 6148 women with repeated MBD measures from full-field digital mammograms and repeated information on lifestyle habits, reproductive history, and anthropometry. The association between MBD measures (modeled as time-dependent covariates), their relative annual changes, and BC risk were evaluated by adjusted Cox models. During an average of 7.8 years of follow-up, 262 BC cases were identified. BC risk was directly associated with standard deviation increments of volumetric percent density (VPD, HR 1.37, 95%CI 1.22-1.54) and dense volume (DV, HR 1.29, 95%CI 1.18-1.41). An inverse association emerged with non-dense volume (NDV, HR 0.82, 95%CI 0.69-0.98). No significant associations emerged between annual changes in VPD, DV, NDV, and BC risk. Higher values of MBD measures, modeled as time-dependent covariates, were positively associated with increased BC risk, while an inverse association was evident for increasing NDV. No effect of annual changes in MBD emerged.

Associations of Breast Arterial Calcifications with Cardiovascular Disease.

Cardiovascular diseases (CVD), including coronary artery disease (CAD), continue to be the leading cause of global mortality among women. While traditional CVD/CAD prevention tools play a significant role in reducing morbidity and mortality among both men and women, current tools for preventing CVD/CAD rely on traditional risk factor-based algorithms that often underestimate CVD/CAD risk in women compared with men. In recent years, some studies have suggested that breast arterial calcifications (BAC), which are benign calcifications seen in mammograms, may be linked to CVD/CAD. Considering that millions of women older than 40 years undergo annual screening mammography for breast cancer as a regular activity, innovative risk prediction factors for CVD/CAD involving mammographic data could offer a gender-specific and convenient solution. Such factors that may be independent of, or complementary to, current risk models without extra cost or radiation exposure are worthy of detailed investigation. This review aims to discuss relevant studies examining the association between BAC and CVD/CAD and highlights some of the issues related to previous studies' design such as sample size, population types, method of assessing BAC and CVD/CAD, definition of cardiovascular events, and other confounding factors. The work may also offer insights for future CVD risk prediction research directions using routine mammograms and radiomic features other than BAC such as breast density and macrocalcifications.

TrEnD: A transformer-based encoder-decoder model with adaptive patch embedding for mass segmentation in mammograms.

BACKGROUND: Breast cancer is one of the most prevalent malignancies diagnosed in women. Mammogram inspection in the search and delineation of breast tumors is an essential prerequisite for a reliable diagnosis. However, analyzing mammograms by radiologists is time-consuming and prone to errors. Therefore, the development of computer-aided diagnostic (CAD) systems to automate the mass segmentation procedure is greatly expected. PURPOSE: Accurate breast mass segmentation in mammograms remains challenging in CAD systems due to the low contrast, various shapes, and fuzzy boundaries of masses. In this paper, we propose a fully automatic and effective mass segmentation model based on deep learning for improving segmentation performance. METHODS: We propose an effective transformer-based encoder-decoder model (TrEnD). Firstly, we introduce a lightweight method for adaptive patch embedding (APE) of the transformer, which utilizes superpixels to adaptively adjust the size and position of each patch. Secondly, we introduce a hierarchical transformer-encoder and attention-gated-decoder structure, which is beneficial for progressively suppressing interference feature activations in irrelevant background areas. Thirdly, a dual-branch design is employed to extract and fuse globally coarse and locally fine features in parallel, which could capture the global contextual information and ensure the relevance and integrity of local information. The model is evaluated on two public datasets CBIS-DDSM and INbreast. To further demonstrate the robustness of TrEnD, different cropping strategies are applied to these datasets, termed tight, loose, maximal, and mix-frame. Finally, ablation analysis is performed to assess the individual contribution of each module to the model performance. RESULTS: The proposed segmentation model provides a high Dice coefficient and Intersection over Union (IoU) of 92.20% and 85.81% on the mix-frame CBIS-DDSM, while 91.83% and 85.29% for the mix-frame INbreast, respectively. The segmentation performance outperforms the current state-of-the-art approaches. By adding the APE and attention-gated module, the Dice and IoU have improved by 6.54% and 10.07%. CONCLUSION: According to extensive qualitative and quantitative assessments, the proposed network is effective for automatic breast mass segmentation, and has adequate potential to offer technical assistance for subsequent clinical diagnoses.