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Simple Summary: A novel mammogram image-based BCa explainable AI (BCaXAI) model is proposed, with a deep learning-based framework, for precise, noninvasive, objective, and timely manner diagnosis of BCa. The proposed BCaXAI leverages the Inception-ResNet v2 architecture, where the integration of explainable AI components, such as Grad-CAM, provides radiologists with valuable visual insights into the model's decision-making process, fostering trust and confidence in the AI-based system. Breast cancer (BCa) poses a severe threat to women's health worldwide as it is the most frequently diagnosed type of cancer and the primary cause of death for female patients. The biopsy procedure remains the gold standard for accurate and effective diagnosis of BCa. However, its adverse effects, such as invasiveness, bleeding, infection, and reporting time, keep this procedure as a last resort for diagnosis. A mammogram is considered the routine noninvasive imaging-based procedure for diagnosing BCa, mitigating the need for biopsies; however, it might be prone to subjectivity depending on the radiologist's experience. Therefore, we propose a novel, mammogram image-based BCa explainable AI (BCaXAI) model with a deep learning-based framework for precise, noninvasive, objective, and timely manner diagnosis of BCa. The proposed BCaXAI leverages the Inception-ResNet V2 architecture, where the integration of explainable AI components, such as Grad-CAM, provides radiologists with valuable visual insights into the model's decision-making process, fostering trust and confidence in the AI-based system. Based on using the DDSM and CBIS-DDSM mammogram datasets, BCaXAI achieved exceptional performance, surpassing traditional models such as ResNet50 and VGG16. The model demonstrated superior accuracy (98.53%), recall (98.53%), precision (98.40%), F1-score (98.43%), and AUROC (0.9933), highlighting its effectiveness in distinguishing between benign and malignant cases. These promising results could alleviate the diagnostic subjectivity that might arise as a result of the experience-variability between different radiologists, as well as minimize the need for repetitive biopsy procedures. [ABSTRACT FROM AUTHOR]

Background: In the United States, breast cancer remains one of the most diagnosed cancers among females and remains the second leading cause of cancer death. In addition, breast cancer is most likely diagnosed at an advanced stage among Hispanic females in the United States due to lower mammogram utilization. Aims: The objective of this study was to determine the effectiveness of a multilevel, multicomponent community-based breast cancer screening intervention called the Breast Cancer Education Screening and NavigaTion (BEST) program. The primary outcome was the completion of a screening mammogram 4 months post-intervention. Method: We used a pragmatic approach for evaluation, utilizing a quasi-experimental delayed intervention design. We recruited women from the community aged between 50 and 75, uninsured or underinsured, and overdue for screening. Results: Six hundred participants were recruited (300 intervention and 300 control). Among completers, the screening rate was 97% in the intervention group and 4.4% in the control group (RR = 22.2, 95% CI: 12.5–39.7, p <.001). In multivariable analysis, age ≥ 65 (RR = 1.29, p =.047), perceived benefits (RR = 1.04, p =.026), curability (RR = 1.24, p <.001), subjective norms (RR = 1.14, p =.014), and fatalism (RR =.96, p =.004) remained significantly associated with screening outcome. Conclusion: A multicomponent, bilingual, and culturally tailored intervention effectively facilitated breast cancer screening completion in an underserved population of Hispanic women. Individuals with improved screening outcomes were more likely to have higher positive beliefs. Our study has important implications regarding using multicomponent interventions in increasing breast cancer screening completion in poorly screened populations. It also highlights differences in health belief motivation for breast cancer screening completion. [ABSTRACT FROM AUTHOR]

Aim: Early diagnosis and screening of breast cancer are of vital importance in the fight against the disease. It is crucial to identify the underlying barriers that prevent women from undergoing mammography and to develop necessary strategies to overcome these obstacles. The purpose of this study was to adapt "The Perceived Barriers of Mammography Scale (PBMS‐23)" into Turkish and to conduct a validity and reliability study. Methods: This study used a methodological approach. The study was conducted with 233 women admitted to the Gynecology Outpatient Clinic of a university hospital. Data were collected by using the Introductory Information Form, PBMS‐23, Champion's Health Belief Model Scale for Mammography Screening (CHBMS‐MS). Psychometric properties of the scale were tested with content validity, confirmatory factor analysis, convergent validity, and test–retest reliability coefficient. Results: Content validity confirmatory factor analysis resulted in 휒2/SD = 1.67; CFI = 0.97, NFI = 0.92, GFI = 0.86, RMSEA = 0.054. The scale had a Cronbach's alpha of 0.87, while the subscales had Cronbach's alpha values of 0.34 to 0.80. The intraclass correlation coefficient values calculated for the test–retest reliability were between 0.83 and 0.96 for the subscales and 0.96 for the overall scale. There is a positive and statistically significant relationship (p < 0.01) between the CHBMS‐MS barriers dimension and prejudices dimension scores and all subdimensions and the total of the PBMS‐23. Conclusion: The PBMS‐23 was accepted as a valid and reliable tool with 23 items and eight‐factor structure that can be useful in measuring Turkish women's barriers to mammography. [ABSTRACT FROM AUTHOR]

OBJECTIVES: To identify barriers to surveillance mammography adherence in Korean breast cancer survivors (BCSs), which is crucial for early detection of recurrence and new cancers. SAMPLE & SETTING: 195 BCSs were recruited from a breast cancer clinic and its support groups at a South Korean hospital. METHODS & VARIABLES: This descriptive study used a cross-sectional design. Participants completed a self-administered multi-instrument survey based on a comprehensive framework for adherence, including individual characteristics, symptoms, quality of life, cognitive appraisal, social support, and healthcare system factors. RESULTS: Having had a mammogram within the past year was considered adherent (n = 177), and no mammography within the past year was considered nonadherent (n = 18). Logistic regression revealed that longer time since diagnosis (p < 0.001), greater depression (p = 0.024), and higher health services utilization (p < 0.001) were predictors of lower mammography adherence (c² = 76.618, p < 0.001, R² = 58%). IMPLICATIONS FOR NURSING: This is the first study to identify depression as a barrier to surveillance mammography in BCSs, suggesting that depression screening and treatment may be important for increasing adherence. [ABSTRACT FROM AUTHOR]

Objective: To investigate the features and outcomes of breast cancer in high-risk subgroups. Materials and Methods: REB approved an observational study of women diagnosed with breast cancer from 2010 to 2019. Three radiologists, using the BI-RADS lexicon, blindly reviewed mammogram and MRI screenings without a washout period. Consensus was reached with 2 additional reviewers. Inter-rater agreement was measured by Fleiss Kappa. Statistical analysis included Mann-Whitney U, Chi-square tests for cohort analysis, and Kaplan-Meier for survival rates, with a Cox model for comparative analysis using gene mutation as a reference. Results: The study included 140 high-risk women, finding 155 malignant lesions. Significant age differences noted: chest radiation therapy (median age 44, IQR: 37.0-46.2), gene mutation (median age 49, IQR: 39.8-58.0), and familial risk (median age 51, IQR: 44.5-56.0) (P =.007). Gene mutation carriers had smaller (P =.01), higher-grade tumours (P =.002), and more triple-negative ER- (P =.02), PR- (P =.002), and HER2- (P =.02) cases. MRI outperformed mammography in all subgroups. Substantial to near-perfect inter-rater agreement observed. Over 10 years, no deaths occurred in chest radiation group, with no significant survival difference between gene mutation and familial risk groups, HR = 0.93 (95% CI: 0.27, 3.26), P =.92. Conclusion: The study highlights the importance of age and specific tumour characteristics in identifying high-risk breast cancer subgroups. MRI is confirmed as an effective screening tool. Despite the aggressive nature of cancers in gene mutation carriers, early detection is crucial for survival outcomes. These insights, while necessitating further validation with larger studies, advocate for a move toward personalized medical care, strengthening the existing healthcare guidelines. [ABSTRACT FROM AUTHOR]

Purpose: Breast cancer (BC) incidence is increasing globally. Age-specific BC incidence trend analyses are lacking for women under age 50 in Canada. In this study, we evaluate the incidence trends in breast cancer in women under age 50 in Canada and compare them with corresponding trends among women 50 to 54. Methods: BC case counts were obtained from the National Cancer Incidence Reporting System (1984-1991) and the Canadian Cancer Registry (1992-2019) both housed at Statistics Canada. Population data were also obtained from Statistics Canada. Annual female BC age-specific incidence rates from 1984 to 2019 were derived for the following age groups: 20 to 29, 30 to 39, 40 to 49, 40 to 44, 45 to 49, and 50 to 54. Changes in trends in age-specific BC incidence rates, if any, and annual percent changes (APCs) for each identified trend, were determined using JoinPoint. Results: Statistically significant increasing trends in BC incidence rates were noted for almost all age groups: since 2001 for 20 to 29 (APC = 3.06%, P <.001); since 2009 for 30 to 39 (APC = 1.25%, P =.007); since 1984 for both 40 to 49 (APC = 0.26%, P <.001) and 40 to 44 (APC = 0.19%, P =.011), increased since 2015 for 40 to 49 (APC = 0.77%, P =.047); and since 2005 for 50 to 54 (APC = 0.38%, P =.022). Among women 45 to 49 there was a non-significant increase since 2005 (APC = 0.24, P =.058). Statistically significant average annualized increases in BC incidence rates were observed for each age group studied. Conclusions: Examining age-specific incidence rates formed a more complete picture of BC time trends with significant increasing trends in the incidence of BC among women in their 20s, 30s, 40s, and early 50s. A greater awareness regarding the increasing number of cases of BC in women younger than 50 is critical to allow for earlier diagnosis with its resultant reduced mortality and morbidity. [ABSTRACT FROM AUTHOR]

Due to the diversity of image sources, content-based multi-source image fusion and retrieval have shown promising capabilities in computer vision tasks, and especially when applied in Computer-Aided Diagnosis (CAD) to automate and improve the accuracy of medical image analysis. The combination of computer vision and CAD systems has the potential to revolutionize healthcare by augmenting the expertise of clinicians, improving overall diagnostic accuracy and helping experts in the clinical decision-making process by classifying and retrieving similar annotated clinical images to a given query. In the context of multi-view mammography interpretation, the concept of multi-view feature fusion has recently been studied to improve retrieval performance while effectively guaranteeing the complementarity of both MLO and CC views. However, conventional multi-view feature fusion makes descriptors long and lacks to take into consideration the relationship between descriptors. To deal with this issue, we propose two hierarchical multi-view feature fusion methods, for multi-view mammogram retrieval, based on the Canonical Correlation Analysis (CCA), which is the most commonly used multivariate parametric test. In fact, we have adapted CCA to determine the relationship between two descriptors by processing latent correlation factors. Moreover, after extracting descriptors for each view, a comparative study of texture and shape fusion descriptors is proposed in order to identify the more discriminative features for multi-view mammogram retrieval. Then, a query-dependent distance metric preserving both visual resemblance and semantic similarity is carried out to dynamically determine the more appropriate distance measure for each query image. Extensive experiments on the challenging Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) have demonstrated the effectiveness of the proposed hierarchical multi-view feature fusion for mammogram retrieval, which outperforms the performance achieved either by conventional fused information or by single view information. To improve the transparency of our paper, the source code of the proposed method and the related dataset (including readme files) are publicly accessible through the following GitHub link: https://github.com/ABDERRAHIMMAR/Multi-View-Feature-Fusion-for-Mammogram-Retrieval. This open-access resource empowers researchers and practitioners to delve deeper into our methodology, fostering collaboration and advancements in the field of computer-aided diagnosis and medical image analysis. [ABSTRACT FROM AUTHOR]

Breast cancer is widespread across the globe. It's the primary cause of death in cancer fatalities. According to the Palestinian Ministry of Health annual report, it ranked as the third reported death of all reported cancer deaths in the West Bank. Mammogram screening is the most common technique to diagnose breast abnormalities, but there is a challenge in the lack of skilled experts able to accurately interpret mammograms. Machine learning plays an important role in medical image processing particularly in early detection when the treatment is less expensive and available. In this paper we proposed different convolutional neural network (CNN) models to detect breast abnormalities with promising results. Six CNN models were used in this research on a unique (first-hand) dataset collected from the Palestinian Ministry of Health. The models are VGG16, VGG19, DenseNet121, ResNet50, Xception, and EfficientNetB7. Consequently, DenseNet121 outperformed other models with 0.83 and 0.85 for testing accuracy and area under curve (AUC) respectively. As a future work, the outperformed model can be combined with other patient data like genetic information, medical history, and lifestyle factors to evaluate the risk of developing specific diseases. This would increase the survival rate and enable proactive measures. [ABSTRACT FROM AUTHOR]

Background & Objective: Breast cancer threatens the lives of many women around the world each year. Although mammography is considered the gold standard for early detection of early-stage breast cancer, different diagnostic methods such as the Breastlight device have been proposed for cancer screening in recent years. This study aimed to compare the diagnostic accuracy of Breastlight and mammography in women referred to clinics in Khorramabad, Iran in 2018. Materials & Methods: In this clinical trial, 252 women older than 40 years of age who were eligible for breast cancer screening or had breast complaints were included. The breast tissue was first assessed using a Breastlight device in a dark room. Then, all participants were re-examined by mammography. The results of Breastlight and mammography methods including true positive rate, true negative rate, false positive rate, and false negative rate were compared. The main bases of comparison were sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of mammography and Breastlight methods. The collected data were analyzed by SPSS software version 22. Results: Compared to mammography, the Breastlight method showed a sensitivity of 78.3%, specificity of 99.5%, PPV of 97%, NPV of 95%, and diagnostic accuracy of 95.6%. Conclusion: Although mammography is more effective than other common methods in diagnosing early-stage breast cancer, Breastlight is a simple and cost-effective device that can be considered a valid tool in breast cancer screening. [ABSTRACT FROM AUTHOR]

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Objectives: Given the inevitable trend of domestic imaging center mergers and the current lack of comprehensive imaging evaluation guidelines for non-mass breast lesions, we have developed a novel BI-RADS risk prediction and stratification system for non-mass breast lesions that integrates clinical characteristics with imaging features from ultrasound, mammography, and MRI, with the aim of assisting clinicians in interpreting imaging reports. Methods: This study enrolled 350 patients with non-mass breast lesions (NMLs), randomly assigning them to a training set of 245 cases (70%) and a test set of 105 cases (30%). Radiologists conducted comprehensive evaluations of the lesions using ultrasound, mammography, andMRI. Independent predictors were identified using LASSO logistic regression, and a predictive riskmodel was constructed using a nomogram generated with R software, with subsequent validation in both sets. Results: LASSO logistic regression identified a set of independent predictors, encompassing age, clinical palpation hardness, distribution and morphology of calcifications, peripheral blood supply as depicted by color Doppler imaging, maximum lesion diameter, patterns of internal enhancement, distribution of non-mass lesions, time-intensity curve (TIC), and apparent diffusion coefficient (ADC) values. The predictive model achieved area under the curve (AUC) values of 0.873 for the training group and 0.877 for the testing group. The model's positive predictive values were as follows: BI-RADS 2 = 0%, BI-RADS 3 = 0%, BI-RADS 4A = 6.25%, BI-RADS 4B = 26.13%, BI-RADS 4C = 80.84%, and BI-RADS 5 = 97.33%. Conclusion: The creation of a risk-predictive BI-RADS stratification, specifically designed for non-mass breast lesions and integrating clinical and imaging data from multiple modalities, significantly enhances the precision of diagnostic categorization for these lesions. [ABSTRACT FROM AUTHOR]

Effective techniques for detection and classification are necessary to enhance the results of patients with common fatal diseases like breast cancer. With benefits including increased effectiveness and precision, DL and ML methods are currently being used as effective tools in the breast cancer diagnosis process. However, the efficiency and stability constraints of current approaches highlight the necessity for additional study. Therefore, this work suggested a new model of modified U-Net and convolutional networks for Breast Cancer (BC) segmentation and classification with new texture descriptors and the proposed model is named “U-TexBCNet”. First, mammography images are preprocessed by enhancing the image using the CLAHE filter and after inputting the preprocessed image into the modified U-Net-based segmentation for the segmentation process, the modified U-Net segmentation is named as “HexpNet”. Once the segmentation process is completed, the feature extraction process takes place. Here, the features include shape feature, color feature, VGG-16 feature, Inception V3 feature, GoogLeNet and modified LGTP feature extracted from the segmented image. Then, the extracted features are inputted into the hybrid classification model including modified CNN and LSTM. Lastly, the outcome from the classifiers (Modified CNN and LSTM) is averaged together to get the outcome of the proposed model. Ultimately, the results of the simulation are evaluated using both positive and negative criteria by contrasting them with the conventional models. [ABSTRACT FROM AUTHOR]

Objective: To assess the efficacy of contrast-enhanced mammography (CEM) in differentiating benign from malignant breast lesions in Asian patients with bloody nipple discharge (BND). Methods: This retrospective study included 58 women with BND (mean age: 51.7 years) who underwent standardized CEM at institutions in Taiwan and Singapore. Lesion characteristics (size, enhancement, conspicuity, shape, margins) were evaluated on CEM by blinded radiologists. Non-enhanced mammography (MMG) and ultrasound (US) within a defined timeframe were compared for diagnostic accuracy. Benign or malignant status was confirmed by biopsy or 2-year imaging follow-up. Results: Malignancy was found in 29 of 58 lesions (50.0%), with ductal carcinoma in situ (DCIS) being the most common. CEM demonstrated a 100% negative predictive value (NPV) for non-enhancing lesions. Significant predictors of malignancy on multivariate analysis include enhancing lesions of size ≥ 1.5 cm (p-value 0.025) and suspicious morphological features (irregular/spiculated margins, irregular shape, segmental/linear NME distribution) (p-value < 0.001). CEM outperformed MMG (sensitivity: 58.6%) and US (sensitivity: 79.3%), achieving a sensitivity of 100% and the highest diagnostic accuracy at 81.3%. Additionally, a CEM size cut-off of 1.5 cm yielded a sensitivity of 73.5% and a specificity of 84.3%. Conclusions: CEM effectively differentiates benign from malignant lesions in patients with BND, improving diagnostic accuracy and potentially reducing unnecessary interventions. [ABSTRACT FROM AUTHOR]

Simple Summary: Radiologists often call back women whose screening mammograms show features that are suggestive of breast cancer for additional testing. Imaging modalities used for the additional testing of these women vary across countries and screening services; however, we do not know the imaging modality that best distinguishes between women who have and those who do not have breast cancer. We examined published studies to identify the best imaging modality that can be used for the testing of women who are recalled following mammography screening. We collected and analysed all the results in the studies published to date. Our analysis showed that contrast-enhanced mammography, digital breast tomosynthesis, magnetic resonance imaging, and ultrasound perform better than digital mammography in correctly identifying and classifying cancer lesions referred for additional testing; however, digital breast tomosynthesis and digital mammography perform better in correctly dismissing women who do not have cancer. There are variations in the assessment pathways for women recalled at screening, and the imaging assessment pathway with the best diagnostic outcome is poorly understood. This paper examines the efficacy of five imaging modalities for the assessment of screen-recalled breast lesions. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) strategy was employed to identify studies that assessed the efficacy of imaging modalities in the assessment of lesions recalled at screening from the following eight databases: Medline, Web of Science, Embase, Scopus, Science Direct, PubMed, CINAHL, and Global Health. Search terms included "Breast assessment" AND "Diagnostic Workup" OR "Mammography" AND "Digital Breast tomosynthesis" AND "contrast enhanced mammography and Magnetic Resonance imaging" AND "breast ultrasound". Studies that examined the performance of digital mammography (DM), digital breast tomosynthesis (DBT), handheld ultrasound (HHUS), contrast-enhanced mammography (CEM), and magnetic resonance imaging (MRI) in screen-recalled lesions were reviewed. Meta-analyses of these studies were conducted using the MetaDisc 2.0 software package. Results: Fifty-four studies met the inclusion criteria and examined between one and three imaging modalities. Pooled results of each imaging modality demonstrated that CEM has the highest sensitivity (95; 95% CI: 90–97) followed by MRI (93; 95% CI: 88–96), DBT (91; 95% CI: 87–94), HHUS (90; 95% CI: 86–93), and DM (85; 95% CI: 78–90). The DBT demonstrated the highest specificity (85; 95% CI: 75–91) followed by DM (77; 95% CI: 66–85), CEM (73; 95% CI: 63–81), MRI (69; 95% CI: 55–81), and HHUS (65; 95% CI: 46–80). Conclusions: The CEM, MRI, DBT, and HHUS demonstrate excellent performance in correctly identifying and classifying cancer lesions referred for diagnostic work-up, but HHUS, MRI, and CEM have a more limited ability to discriminate benign lesions than DBT and DM. [ABSTRACT FROM AUTHOR]

Simple Summary: The aim of this study was to assess the impact of various factors—such as age, age at menarche, age at first childbirth, number of births, body mass index (BMI), living environment (rural vs. urban), and other factors later discussed—on the likelihood of developing breast cancer. This article's original research was based on data gathered from 687 breast biopsies, which were analyzed through comparative analysis and regression analysis to identify possible predictors for malignancy and tumor size. The comparative analysis and regression models in this study provide deeper insights into the roles of various variables, particularly positive hormonal receptor status, in malignancy. Introduction: Breast cancer has become one of the most serious and widespread public health concerns globally, affecting an increasing number of women—and, in rare cases, men—across the world. It is the most common cancer among women across all countries. In this study, we aimed to evaluate the influence of demographic factors, medical and reproductive history, diagnostic techniques, and hormone receptor status on the development and progression of breast cancer. Materials and Methods: A total of 687 female patients from Romania underwent standard breast examination techniques, including clinical breast examination, mammography, ultrasonography, and, ultimately, breast biopsy. Statistical analysis was performed using the R programming language and RStudio software. The study included a comparative analysis and a prediction analysis for malignancy and tumor size (cumulative histological dimension) through logistic and linear regression models. Results: The comparative analysis identified several variables associated with malignancy: older age (p < 0.001), non-vulnerability (p = 0.04), no daily physical activity (p = 0.002), no re-biopsy (p < 0.001), immunohistochemistry use (p < 0.001), use of larger gauge needles (p < 0.001), ultrasound-guided biopsy (p < 0.001), and vacuum biopsy (p < 0.001). The hormone receptor statuses—estrogen receptor (ER), progesterone receptor (PR), and androgen receptor (AR)—showed statistically significant differences in distribution across breast cancer B classifications. Logistic regression analysis identified ER, PR, and age as significant predictors of malignancy. Linear regression analysis revealed histopathological results, living environment, geographical region, vulnerability, prior breast examination, and the number of histological fragments as significant predictors of cumulative histological dimension. Conclusions: Our predictive models demonstrate the impact of demographic factors, medical history, diagnostic techniques, and hormone receptor status on breast cancer development and progression, accounting for a significant portion of the variance in malignancy and cumulative histological dimension. [ABSTRACT FROM AUTHOR]

Background: Dual-energy subtraction (DES) imaging is critical in contrast-enhanced mammography (CEM), as the recombination of low-energy (LE) and high-energy (HE) images produces contrast enhancement while reducing anatomical noise. The study's purpose was to compare the performance of the DES algorithm among three different CEM systems using a commercial phantom. Methods: A CIRS Model 022 phantom, designed for CEM, was acquired using all available automatic exposure modes (AECs) with three CEM systems from three different manufacturers (CEM1, CEM2, and CEM3). Three studies were acquired for each system/AEC mode to measure both radiation dose and image quality metrics, including estimation of measurement error. The mean glandular dose (MGD) calculated over the three acquisitions was used as the dosimetry index, while contrast-to-noise ratio (CNR) was obtained from LE and HE images and DES images and used as an image quality metric. Results: On average, the CNR of LE images of CEM1 was 2.3 times higher than that of CEM2 and 2.7 times higher than that of CEM3. For HE images, the CNR of CEM1 was 2.7 and 3.5 times higher than that of CEM2 and CEM3, respectively. The CNR remained predominantly higher for CEM1 even when measured from DES images, followed by CEM2 and then CEM3. CEM1 delivered the lowest MGD (2.34 ± 0.03 mGy), followed by CEM3 (2.53 ± 0.02 mGy) in default AEC mode, and CEM2 (3.50 ± 0.05 mGy). The doses of CEM2 and CEM3 increased by 49.6% and 8.0% compared with CEM1, respectively. Conclusion: One system outperformed others in DES algorithms, providing higher CNR at lower doses. Relevance statement: This phantom study highlighted the variability in performance among the DES algorithms used by different CEM systems, showing that these differences can be translated in terms of variations in contrast enhancement and radiation dose. Key Points: DES images, obtained by recombining LE and HE images, have a major role in CEM. Differences in radiation dose among CEM systems were between 8.0% and 49.6%. One DES algorithm achieved superior technical performance, providing higher CNR values at a lower radiation dose. [ABSTRACT FROM AUTHOR]

Background: Numerous underlying causes can lead to inflammatory breast disorders. A wide range of non-specific symptoms may be presenting symptoms, which could cause a delay in diagnosis and thus improper therapy. Studies on artificial intelligence (AI) are rapidly developing and offer a wide range of possible uses in breast imaging. Artificial intelligence-based computer-assisted diagnosis (AI-CAD) holds promise in the field of mammography. It demonstrated diagnostic performances that are equivalent to or even better than those achieved by stand-alone methods. The current work aimed to identify whether AI can improve the performance of mammography in diagnosing inflammatory breast diseases and excluding the underlying malignancy in cases resistant to treatment that may reduce the need for interventional procedures such as biopsy. Methods: Our study was a retrograde one done on 34 patients with pathologically proven inflammatory breast lesions. Results: Suppurative breast lesions gave high false positive results. This was also the case with granulomatous mastitis; while simple inflammatory lesions gave true negative results on AI interrogation. Conclusions: Artificial intelligence can be of great value in diagnosing simple inflammatory breast lesions thus following up on such lesions can usually be sufficient without asking for unneeded biopsies. On the other hand, our study showed that AI had high false positive results in suppurative lesions and granulomatous mastitis. Consequently, ultrasonography can be more reliable in their diagnosis. [ABSTRACT FROM AUTHOR]

Breast cancer has a very high incidence rate worldwide, and effective screening and early diagnosis are particularly important. In this paper, two improved You Only Look Once version 8 (YOLOv8) models, the YOLOv8-GHOST and YOLOv8-P2 models, are proposed to address the difficulty of distinguishing lesions from normal tissues in mammography images. The YOLOv8-GHOST model incorporates GHOSTConv and C3GHOST modules into the original YOLOv8 model to capture richer feature information while using only 57% of the number of parameters required by the original model. The YOLOv8-P2 algorithm significantly reduces the number of necessary parameters by streamlining the number of channels in the feature map. This paper proposes the YOLOv8-GHOST-P2 model by combining the above two improvements. Experiments conducted on the MIAS and DDSM datasets show that the new models achieved significantly improved computational efficiency while maintaining high detection accuracy. Compared with the traditional YOLOv8 method, the three new models improved and achieved F1 scores of 98.38%, 98.8%, and 98.57%, while the number of parameters reduced by 42.9%, 46.64%, and 2.8%. These improvements provide a more efficient and accurate tool for clinical breast cancer screening and lay the foundation for subsequent studies. Future work will explore the potential applications of the developed models to other medical image analysis tasks. [ABSTRACT FROM AUTHOR]

Background: Current cancer screening guidelines for transgender individuals are guided primarily by expert opinion, and are extrapolated from guidelines for cisgender populations, despite the additional unique risks that transgender populations face in cancer risk and cancer care. Aims: We examined adherence to current recommended screening guidelines as well as drivers of cancer screening in 192 transgender and gender-nonbinary (TGNB) individuals participating in Project AFFIRM, a multi-site longitudinal cohort study of TGNB individuals. Methods: We used a chi-squared analysis to look for significant associations between predictors and adherence to breast, cervical, prostate and colon cancer screening. We analyzed predictors by 3 different categories: sex/gender identity, healthcare access, and socioeconomic status. Results: Screening rates were low for breast, cervical, prostate and colon cancer in TGNB populations compared to national rates for cisgender populations. Among several significant predictors, gender-affirming surgery (hysterectomy) (p-value = <0.0001) and telling others they are transgender at a younger age (< 18) (p-value = 0.0344) were associated with increased screening adherence, while having HIV was associated with decreased screening adherence (p-value = 0.0045). Discussion: Our results suggest that interacting with the healthcare system to obtain comprehensive cancer screening can be difficult to navigate among the other healthcare needs of TGNB individuals both on an individual and systems level. Future efforts to mitigate the barriers to screening adherence should be targeted at the healthcare system level. [ABSTRACT FROM AUTHOR]

Background: Carcinoma breast is the second most common malignancy among women which evolves silently and ends in devastation. Tumor cell death induces antinuclear antibody (ANA) production that can be measured by enzyme linked immunosorbent assay (ELISA). Hence this study was undertaken to determine the utility of ANA ELISA in early diagnosis of carcinoma breast. Methods: This cross-sectional study was performed among 3 groups of participants who were age and gender matched. Each group had 30. Group A with carcinoma breast, group B with carcinoma of body sites other than breast and group C had normal, healthy people. From each participant, 5ml venous blood was collected. ANA IgG ELISA test was performed for all the serum samples. Results: ANA IgG was detected in 14 (15.55%) patients. It was detected in 8 (26.66%) of group A, 3 (10%) in group B and 3 (10%) in group C. ANA positivity among carcinoma breast patients was statistically significant p <0.05 (0.0397). Conclusion: Carcinoma breast patients had a higher ANA seropositivity (26.66%) compared to other two groups. ANA tests for multiple autoantigens along with other tools such as mammography can diagnose carcinoma breast at an early stage. [ABSTRACT FROM AUTHOR]

Objectives: We evaluated the role of contrast-enhanced mammogram (CEM) in the characterization of circumscribed lesions detected in digital breast tomosynthesis (DBT) and correlated with histopathology. Methods: A retrospective study was done on 205 circumscribed breast masses detected with DBT and for whom CEM was done before core biopsy/excision biopsy. Morphology of lesion enhancement was noted at a 2-min CEM image and depending on the enhancement pattern, they were classified as benign, malignant, and indeterminate. Indeterminate lesions were further characterized by contrast kinetics at 8 min and divided into benign and malignant lesions. The results were correlated with histopathology reports. Results: Among the 205 lesions, 158 were benign and 47 were malignant by histopathology. All 47 malignant cases were diagnosed as malignant (sensitivity-100%) and 150 out of 158 benign lesions were diagnosed as benign by CEM (specificity-95%). Eight benign lesions were reported as malignant (false positive) by CEM. Positive predictive value was 85.4% (47/55); negative predictive value (NPV) was 100% (150/150); accuracy was 96% (197/205). Conclusions: The high sensitivity of CEM in our study suggests that CEM helps in the early diagnosis of benign-looking circumscribed breast malignancies. The high NPV of CEM helps to avoid unnecessary biopsies and interventions in benign lesions. Advances in knowledge: This study describes the contrast enhancement pattern of benign and malignant circumscribed breast lesions and thereby helps in the diagnosis of malignancy at an early stage. CEM is a promising adjunct tool since it offers functional imaging as a supplement to anatomical imaging by DBT. [ABSTRACT FROM AUTHOR]

Background: The aim of this study is to evaluate the correlation between background parenchymal enhancement (BPE) and various patient-related and technical factors in recombined contrast-enhanced spectral mammography (CESM) images. Material and Methods: We assessed CESM images from 62 female patients who underwent CESM between May 2017 and October 2019, focusing on factors influencing BPE. A total of 235 images, all acquired using the same mammography machine, were analyzed. A region of interest (ROI) with a standard size of 0.75 to 1 cm2 was used to evaluate the minimal, maximal, and average pixel intensity enhancement. Additionally, the images were qualitatively assessed on a scale from 1 (minimal BPE) to 4 (marked BPE). We examined correlations with body mass index (BMI), age, hematocrit, hemoglobin levels, cardiovascular conditions, and the amount of pressure applied during the examination. Results: Our study identified a significant correlation between the amount of pressure applied during the examination and the BPE (Spearman's ρ = 0.546). Additionally, a significant but weak correlation was observed between BPE and BMI (Spearman's ρ = 0.421). No significant associations were found between BPE and menopausal status, cardiovascular preconditions, hematocrit, hemoglobin levels, breast density, or age. Conclusions: Patient-related and procedural factors significantly influence BPE in CESM images. Specifically, increased applied pressure and BMI are associated with higher BPE. [ABSTRACT FROM AUTHOR]

Simple Summary: Idiopathic granulomatous mastitis (IGM) is a rare breast disease that can be mistaken for inflammatory breast cancer. It requires a tissue biopsy for an accurate diagnosis. Even though it is not cancerous, IGM can cause emotional distress because of severe pain and ensuing breast deformity. It is important to distinguish IGM from other breast inflammations caused by infections. IGM is mostly found in premenopausal women, often after pregnancy or breastfeeding. Smoking and contraceptive use might be risk factors, but the evidence is unclear. The treatment options include NSAIDs, steroids, immunosuppressants, surgery, prolactin suppressants, and antibiotics; many patients relapse, making treatment challenging. This review summarizes current information, which mostly comes from case reports and small studies, and presents GRAMAREG as a registry for IGM initiated by the EUBREAST Study Group, which aims to collect detailed data on IGM to improve the knowledge base for diagnosis and treatment. Background: Idiopathic granulomatous mastitis (IGM) is a rare, benign inflammatory breast condition often mistaken for inflammatory breast cancer and, therefore, requires a biopsy for accurate diagnosis. Although not cancerous, IGM can cause emotional distress because of severe pain and ensuing breast deformity. Differentiating IGM from other breast inflammations caused by infections is essential. IGM mostly affects premenopausal women and is potentially associated with recent pregnancies and breastfeeding. The risk factors, including smoking and contraceptive use, have inconsistent associations. Steroid responses suggest an autoimmune component, though specific markers are lacking. Methods: We performed a narrative review on potential risk factors, diagnostics, and therapy of IGM. Results: Diagnostics and clinical management of IGM are challenging. The treatment options include NSAIDs, steroids, surgery, antibiotics, immunosuppressants, prolactin suppressants, and observation, each with varying effectiveness and side effects. Conclusions: Current IGM treatment evidence is limited, based on case reports and small series. There is no consensus on the optimal management strategy for this disease. The GRAMAREG study by the EUBREAST Study Group aims to collect comprehensive data on IGM to improve diagnostic and treatment guidelines. By enrolling patients with confirmed IGM, the study seeks to develop evidence-based recommendations, enhancing patient care and understanding of this condition. [ABSTRACT FROM AUTHOR]

Screening mammography is a part of routine well-woman care. It is important to understand how receipt of a false-positive mammography result may influence a woman's decision regarding whether and when to return for routine screening. This cohort study evaluates the association between screening mammography results and the probability of subsequent screening. Visual Abstract. Association Between False-Positive Results and Return to Screening Mammography in the Breast Cancer Surveillance Consortium Cohort: Screening mammography is a part of routine well-woman care. It is important to understand how receipt of a false-positive mammography result may influence a woman's decision regarding whether and when to return for routine screening. This cohort study evaluates the association between screening mammography results and the probability of subsequent screening. Background: False-positive results on screening mammography may affect women's willingness to return for future screening. Objective: To evaluate the association between screening mammography results and the probability of subsequent screening. Design: Cohort study. Setting: 177 facilities participating in the Breast Cancer Surveillance Consortium (BCSC). Patients: 3 529 825 screening mammograms (3 184 482 true negatives and 345 343 false positives) performed from 2005 to 2017 among 1 053 672 women aged 40 to 73 years without a breast cancer diagnosis. Measurements: Mammography results (true-negative result or false-positive recall with a recommendation for immediate additional imaging only, short-interval follow-up, or biopsy) from 1 or 2 screening mammograms. Absolute differences in the probability of returning for screening within 9 to 30 months of false-positive versus true-negative screening results were estimated, adjusting for race, ethnicity, age, time since last mammogram, BCSC registry, and clustering within women and facilities. Results: Women were more likely to return after a true-negative result (76.9% [95% CI, 75.1% to 78.6%]) than after a false-positive recall for additional imaging only (adjusted absolute difference, −1.9 percentage points [CI, −3.1 to −0.7 percentage points]), short-interval follow-up (−15.9 percentage points [CI, −19.7 to −12.0 percentage points]), or biopsy (−10.0 percentage points [CI, −14.2 to −5.9 percentage points]). Asian and Hispanic/Latinx women had the largest decreases in the probability of returning after a false positive with a recommendation for short-interval follow-up (−20 to −25 percentage points) or biopsy (−13 to −14 percentage points) versus a true negative. Among women with 2 screening mammograms within 5 years, a false-positive result on the second was associated with a decreased probability of returning for a third regardless of the first screening result. Limitation: Women could receive care at non-BCSC facilities. Conclusion: Women were less likely to return to screening after false-positive mammography results, especially with recommendations for short-interval follow-up or biopsy, raising concerns about continued participation in routine screening among these women at increased breast cancer risk. Primary Funding Source: National Cancer Institute. [ABSTRACT FROM AUTHOR]

Background: Ethnic minority women in the United States continue to experience racial disparities. Immigrant Arab women (IAW) underutilize mammography screening (MS) even compared with other ethnic minorities. This population still has gaps in knowledge regarding breast cancer (BC) and screening methods and encounters social barriers, highlighting the imperative role of the male figure and the family, which hinders screening. Materials and Methods: A cross-sectional design with an exploratory and predictive approach was employed to examine knowledge gaps and perceived social barriers associated with MS and to identify predictive factors of MS among IAW in California. The analysis included 316 women that met the eligibility criteria. Results: Familiarity with MS and awareness of screening recommendations were the specific areas of knowledge significantly associated with mammography uptake (odds ratio [OR] 15.4, 95% confidence interval [CI]: 5.6–40.8, p < 0.001) and (OR 12.07, 95% CI: 7–21, p < 0.001), respectively. Logistic regression revealed three MS predictors, the two knowledge areas and age. Perceived social barriers were significantly associated with age [F (3,312) = 4.684, p = 0.003]. There was a significant difference in social barriers between women in their 40s and those in their 60s (p = 0.002) and between women in their 50s and those in their 60s (p = 0.003), suggesting lower barriers among women in their 60s (M = 14.3). Living status was significantly associated with perceived social barriers [F (1,314) = 8.83, p = 0.003]. Conclusions: This study offered valuable insights for health care professionals, policymakers, and community organizations working to improve BC early detection in immigrant and ethnic women to reduce social disparities and reinforce social justice. Knowledge-deficit areas and social barriers must be analyzed and incorporated in awareness programs to improve screening practices of IAW. [ABSTRACT FROM AUTHOR]

Background: Public health campaigns have often used persuasive techniques to promote healthy behaviors but the use of persuasion by doctors is controversial. We sought to examine older women's perspectives. Methods: We conducted semi‐structured interviews with 20 community‐dwelling older women from the Baltimore metropolitan area. We asked whether participants thought it was ethically appropriate for doctors to try to persuade patients and explored their rationales. We probed about commonly used persuasive techniques and two example decisional contexts—stopping mammograms and moving out of one's house after multiple falls. We used qualitative thematic analysis to code the transcripts and summarized results into major themes. Results: We found mixed views on the ethical appropriateness of persuasion (theme 1); supporters of persuasion were motivated by the potential benefit to patients' health, whereas opponents thought patients should be the ultimate decision‐makers. Perspectives depended on the persuasive technique (theme 2), where emotional appeals elicited the most negative reactions while use of facts and patient stories were viewed more positively. Perspectives also varied by the decisional context (theme 3), where higher severity and certainty of harm influenced participants to be more accepting of persuasion. Participants suggested alternative communication approaches to persuasion (theme 4) that emphasized respect for patients. Conclusions: Our findings suggest that the type of persuasive technique and the decisional context are important considerations in the ethical debate around the use of persuasion. Limiting the use of persuasion to high‐stakes decisions and using facts and patient stories rather than emotional appeals are likely more acceptable. [ABSTRACT FROM AUTHOR]

Nonmass lesions in breast ultrasound (US) are areas of altered echogenicity without definite margins or mass effect. However, these lesions may show calcifications, associated architectural distortion, or shadowing just like masses. They vary in their echogenicity, distribution, ductal or nonductal appearance and the associated features that can be seen in variety of benign and malignant pathologies. With no uniform definition or classification system, there is no standardized approach in further risk categorization and management strategies of these lesions. Malignant nonmass lesions are not uncommon and few sonographic features can help in differentiating benign and malignant pathologies. US-guided tissue sampling or lesion localization can be preferred in the nonmass lesions identified on second look US after magnetic resonance imaging or mammography. This article aims to describe various imaging patterns and attempts to provide an algorithmic approach to nonmass findings on breast US. [ABSTRACT FROM AUTHOR]

Granular cell tumors of breast are rare neoplasms, majority of which are benign. Their imaging appearances are often indistinguishable from breast cancer. They may demonstrate a few differentiating features and unlike breast cancer, typically follow a benign course. Histopathology and immunochemistry form the cornerstone of diagnosis of granular cell tumor. In this article, we presented a series of three cases of granular cell tumors with variable presentations. Our goal is to increase familiarity for these neoplasms and for the readers to be able to distinguish them from the more common entity of breast cancer, as their prognosis and management differ. [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]

Early detection is the most effective defense against breast cancer. Mammography is a well-established X-ray-based technique that is used for annual or biennial screening of women above age of 40. Since the dense breast tissue sometimes obscures the cancer in an X-ray image, about 10% of screened women are recalled and undergo additional adjunctive modalities, such as ultrasound, digital breast tomosynthesis, or magnetic resonance imaging. These modalities have drawbacks such as additional radiation dosage, overdiagnosis, and high cost. A new concurrent multispectral imaging approach was recently presented to eliminate the high recall rates by utilizing the breast surface temperature data with an inverse physics-informed neural network algorithm. This method utilizes the bioheat transfer modeling as the governing physics equations and conducted inverse heat transfer modeling using infrared temperatures to predict the presence of a tumor heat source. Validation of the predicted tumor size and location was conducted on a biopsy-proven breast cancer patient using infrared temperature data captured of the breast surface and pathology reports. A regression analysis between the predicted temperatures and infrared temperatures showed a coefficient of determination of 0.98. The absolute error in the predicted tumor size was 0.4 cm and the maximum absolute error in tumor location was 0.3 cm. The proposed approach shows promising results and performance. However, additional testing with more patients is required to quantify the standard deviation in the prediction and establish the sensitivity and specificity of the machine learning technique. [ABSTRACT FROM AUTHOR]

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. 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. 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. 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. [ABSTRACT FROM AUTHOR]

Purpose: To characterize distance traveled for breast cancer screening and to sites of service for breast cancer treatment, among rural and urban women served by a Washington State healthcare network. Methods: Data for this study came from one of the largest not-for-profit integrated healthcare delivery systems in Washington State. Generalized linear mixed models with gamma log link function were used to examine the associations between travel distance and sociodemographic and contextual characteristics of patients. Results: Median travel distance for breast cancer screening facilities, hematologist/oncologists, radiation oncologists, or surgeons was 11, 19, 23, or 11 miles, respectively. Travel distance to breast cancer screening or referral facilities was longer in non-core metropolitan ZIP codes compared to metropolitan ZIP codes. AI/AN and Hispanic women travelled longer distances to reach referral facilities compared to other racial and ethnic groups. Conclusion: Disparities exist in travel distance to breast cancer screening and treatment. Further research is needed to describe sociodemographic and system level characteristics that contribute to such disparities and to discover novel approaches to alleviate this burden. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the diagnostic performance of abbreviated breast MRI compared with mammography in women with a family history of breast cancer included in the Croatian National Breast Screening Program. Methods: 178 women with a family history of breast cancer aged 50 to 69 underwent abbreviated breast MRI and mammography. Radiological findings for each method were categorized according to the BI-RADS classification. The gold standard for assessing the diagnostic accuracy of breast MRI and mammography, in terms of suspicious BI-RADS 4 and BI-RADS 5 findings, was the histopathological diagnosis. Performance measures, including cancer detection rates, specificity, sensitivity, and positive and negative predictive values, were calculated for both imaging methods. Results: Twelve new cases of breast cancer were detected, with seven (58.3%) identified only by abbreviated breast MRI, four (33.3%) detected by both mammography and breast MRI, and one (8.3%) diagnosed only by mammography. Diagnostic accuracy parameters for abbreviated breast MRI were 91.67% sensitivity, 94.58% specificity, 55.0% positive predictive value (PPV), and 99.37% negative predictive value (NPV), while for mammography, the corresponding values were 41.67%, 96.39%, 45.46%, and 95.81%, respectively. Conclusions: Abbreviated breast MRI is a useful supplement to screening mammography in women with a family history of breast cancer. Considering the results of the conducted research, it is recommended to assess whether women with a family history of breast cancer have an increased risk and subsequently provide annual abbreviated breast MRI in addition to mammography for early detection of breast cancer. [ABSTRACT FROM AUTHOR]

PURPOSE: Black and White women undergo screening mammography at similar rates, but racial disparities in breast cancer outcomes persist. To assess potential contributors, we investigated delays in follow-up after abnormal imaging by race/ethnicity. METHODS: Women who underwent screening mammography at our urban academic center from January 2015 to February 2018 and received a Breast Imaging Reporting and Data System 0 assessment were included. Kaplan-Meier estimates described distributions of time between diagnostic events from (1) screening to diagnostic imaging and (2) diagnostic imaging to biopsy. Multivariable logistic regression models estimated the associations between race/ethnicity and receipt of follow-up within 15 and 30 days. RESULTS: Two thousand five hundred and fifty-four women were included (48.6% non-Hispanic [NH] Black, 38.2% NH White, 13.1% other/unknown). Median time between screening and diagnostic imaging varied by race/ethnicity (White: 7 days [IQR, 2-14]; Black: 12 days [IQR, 7-23]; other/unknown: 9 days [IQR, 5-21]). There were similar disparities in days between diagnostic imaging and biopsy (White: 12 [IQR, 7-24]; Black: 21 [IQR, 13-37]; other/unknown: 16 [IQR, 9-30]) and between screening and biopsy (White: 20 [IQR, 11-41]; Black: 35 [IQR, 22-63]; other/unknown: 27.5 [IQR, 17-42]). After adjustment, odds of diagnostic imaging follow-up within 15 days of screening were lower for Black versus White women (odds ratio, 0.59 [95% CI, 0.44 to 0.80]; P <.001). CONCLUSION: In this diverse cohort, disparities in timely diagnostic follow-up after abnormal breast screening were observed, with Black women waiting 1.75 times as long as White women to obtain a tissue diagnosis. National guidelines for time to diagnostic follow-up may facilitate more timely breast cancer care and potentially affect outcomes. Despite getting screening mmgs at the same rates as White women, Black women were 40% less likely to get diagnostic mmgs w/in 15d of an abnl screen & it took them almost 2x as long to get a biopsy. Multi-level interventions to redress these #disparities are key. @DrLolaFayanju [ABSTRACT FROM AUTHOR]

Extremely dense breasts can be an independent risk factor for breast cancer. A new FDA rule requires that patients be notified of their breast density and the possible benefits of additional imaging to screen for breast cancer. Clinicians should be cognizant of the data about breast cancer risk, breast density, and recommendations to change screening techniques if patients, particularly premenopausal females, have extremely dense breasts but no other known risk factors. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Imaging of the postoperative breast is a challenging issue for the interpreting physician with many variable findings that may require additional assessment through targeted ultrasound, more mammography views, or other investigations. Artificial intelligence (AI) is a fast-developing field with various applications in the breast imaging including the detection and classification of lesions, the prediction of therapy response, and the prediction of breast cancer risk. This study aimed to identify whether Artificial Intelligence improves the mammographic detection and diagnosis of breast post-operative changes and hence improves follow-up and diagnostic workflow and reduces the need for additional exposure to extra radiation or contrast material doses as in Contrast Enhanced Mammography, and the need for interventional procedures as biopsy. Methods: This cross-sectional analytic study included 66 female patients following breast-conserving surgeries coming with breast complaints or for follow-up, with mammographically diagnosed changes. Results: Mammography had a sensitivity of 91.7%, a specificity of 94.4%, a positive predictive value (PPV) of 78.6%, a negative predictive value (NPV) of 98.1%, and an accuracy of 93.9%, while the AI method indices were sensitivity 91.7%, specificity 92.6%, (PPV) 73.3%, (NPV) 98%, and accuracy 92.4%. The calculated cut-off point for the quantitative AI (probability of malignancy "POM" score) was 51.5%. There was a statistically significantly higher average in the percentage of POM in malignant cases (76.5 ± 27.3%) compared to benign cases (27.1 ± 19.7%). However, the final indices for the combined use of mammography and (AI) were sensitivity 100%, specificity 88.9%, (PPV) 66.7%, (NPV) 100%, and accuracy 90.9%. Conclusion: Applying the AI algorithm on mammograms showed positive impacts on the sensitivity of the post-operative breast assessment, with an excellent reduction of the mammographic missed cancers. [ABSTRACT FROM AUTHOR]

Aims: This evaluation combines clinical and non-clinical collaborative breast referral triage to gain an understanding relating to the value of triage, by identifying 'suspected cancer' and 'cancer not suspected' populations, improve the patient pathway, and facilitate optimised resource availability. Method: An iterative service improvement method was used, with distinct phases of the process outlined to facilitate testing of ideas. The evaluation ran for 13 weeks in 2022. Regular team member meetings were arranged to discuss and agree improvement aims and outcomes. Findings: A triage flowchart was developed collaboratively, and subsequently adopted by the non-clinical booking team. Bespoke clinics were established, demonstrating no evidence of increased risk to patients, and meeting 28-day Faster Diagnosis Standard (FDS) requirements. Conclusion: breast referral triage of 'suspected cancer' and 'cancer not suspected' patients, using a clinical and non-clinical collaborative approach facilitates improved service visibility, prioritisation, management, and measurement. This also supports delivery of the 2019 NHS Long Term Plan to enhance earlier and faster cancer diagnosis by optimising access to diagnostic resources where required. [ABSTRACT FROM AUTHOR]

Simple Summary: There is a current gap in understanding the costs and healthcare resources used related to breast cancer risk assessment and screening. Risk-stratified breast screening overcomes various limitations of age-based screening, and participants are classified based on personalized differences in breast cancer risk. The overall goal of the PERSPECTIVE I&I project was to inform the implementation of more effective strategies of risk assessment for risk-stratified screening and earlier detection of breast cancer. This study appears to be the first to examine the healthcare utilization and costs stratified by the three breast cancer risk levels, determining the economic burden and resource needs linked with different risk categories. This study demonstrated that despite higher screening-related costs for high-risk individuals, overall healthcare costs were comparable across risk categories. Background: A prospective cohort study was undertaken within the PERSPECTIVE I&I project to evaluate healthcare resource utilization and costs associated with breast cancer risk assessment and screening and overall costs stratified by risk level, in Ontario, Canada. Methods: From July 2019 to December 2022, 1997 females aged 50 to 70 years consented to risk assessment and received their breast cancer risk level and personalized screening action plan in Ontario. The mean costs for risk-stratified screening-related activities included risk assessment, screening and diagnostic costs. The GETCOST macro from the Institute of Clinical Evaluative Sciences (ICES) assessed the mean overall healthcare system costs. Results: For the 1997 participants, 83.3%, 14.4% and 2.3% were estimated to be average, higher than average, and high risk, respectively (median age (IQR): 60 [56–64] years). Stratification into the three risk levels was determined using the validated multifactorial CanRisk prediction tool that includes family history information, a polygenic risk score (PRS), breast density and established lifestyle/hormonal risk factors. The mean number of genetic counseling visits, mammograms and MRIs per individual increased with risk level. High-risk participants incurred the highest overall mean risk-stratified screening-related costs in 2022 CAD (±SD) at CAD 905 (±269) followed by CAD 580 (±192) and CAD 521 (±163) for higher-than-average and average-risk participants, respectively. Among the breast screening-related costs, the greatest cost burden across all risk groups was the risk assessment cost, followed by total diagnostic and screening costs. The mean overall healthcare cost per participant (±SD) was the highest for the average risk participants with CAD 6311 (±19,641), followed by higher than average risk with CAD 5391 (±8325) and high risk with CAD 5169 (±7676). Conclusion: Although high-risk participants incurred the highest risk-stratified screening-related costs, their costs for overall healthcare utilization costs were similar to other risk levels. Our study underscored the importance of integrating risk stratification as part of the screening pathway to support breast cancer detection at an earlier and more treatable stage, thereby reducing costs and the overall burden on the healthcare system. [ABSTRACT FROM AUTHOR]

Background: Mammographic density has been associated with breast cancer risk, and is modulated by established breast cancer risk factors, such as reproductive and hormonal history, as well as lifestyle. Recent epidemiological and biological findings underscore the recognized benefits of breastfeeding in reducing breast cancer risk, especially for aggressive subtypes. Current research exploring the association among mammographic density, breastfeeding, and breast cancer is sparse. Main findings: Changes occur in the breasts during pregnancy in preparation for lactation, characterized by the proliferation of mammary gland tissues and the development of mammary alveoli. During lactation, the alveoli fill with milk, and subsequent weaning triggers the involution and remodeling of these tissues. Breastfeeding influences the breast microenvironment, potentially altering mammographic density. When breastfeeding is not initiated after birth, or is abruptly discontinued shortly after, the breast tissue undergoes forced and abrupt involution. Conversely, when breastfeeding is sustained over an extended period and concludes gradually, the breast tissue undergoes slow remodeling process known as gradual involution. Breast tissue undergoing abrupt involution displays denser stroma, altered collagen composition, heightened inflammation and proliferation, along with increased expression of estrogen receptor α (ERα) and progesterone receptor. Furthermore, elevated levels of pregnancy-associated plasma protein-A (PAPP-A) surpass those of its inhibitors during abrupt involution, enhancing insulin-like growth factor (IGF) signaling and collagen deposition. Prolactin and small molecules in breast milk may also modulate DNA methylation levels. Drawing insights from contemporary epidemiological and molecular biology studies, our review sheds light on how breastfeeding impacts mammographic density and explores its role in influencing breast cancer. Conclusion: This review highlights a clear protective link between breastfeeding and reduced breast cancer risk via changes in mammographic density. Future research should investigate the effects of breastfeeding on mammographic density and breast cancer risk among various ethnic groups and elucidate the molecular mechanisms underlying these associations. Such comprehensive research will enhance our understanding and facilitate the development of targeted breast cancer prevention and treatment strategies. [ABSTRACT FROM AUTHOR]

Advancements in Machine Learning (ML) and Computer Vision have led to notable improvements in the detection of breast cancer. However, the accuracy of the classifier is limited due to imbalanced datasets that cause overfitting. Thus, additional images are needed to improve the classifier's performance. Generative Adversarial Networks (GAN) are used for image augmentation. Still, its limitations, such as the random use of different types of GAN, can lead to a too-good discriminator, causing generator training to fail due to vanishing gradients. Therefore, selecting the appropriate GAN model for the given scenario is crucial for optimal performance. This paper proposes a novel Smart Generative Adversarial Network (Smart GAN) architecture to develop an efficient and computational classification model for a limited imbalanced dataset. Smart GAN uses a three-fold approach. Different types of GAN augment the dataset in the first phase of experimental work, and their evaluation metrics are calculated. In the second phase, the metric scores are used as rewards for the Reinforcement learning model (Q-learning approach). The best augmentation is chosen based on the best Q-values for each metric score. It compares three different Convolutional Neural Networks (CNN) and selects the best-suited network to classify the augmented datasets. The proposed Smart GAN architecture outperforms other existing approaches by giving a better accuracy of 89.62% and 89.91% on Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM) augmented datasets, respectively, representing an approximately 10% increment in detection rate compared to non-augmented datasets. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusions The objective of this study was to evaluate the efficacy of an in‐person, small‐group mammography video discussion (SMVD) intervention on mammography uptake among nonadherent Chinese American immigrant women.Women (N = 956) were randomized into either an SMVD group, where Chinese‐speaking community health workers (CHWs) used an effective, culturally appropriate video to discuss mammography, or a video‐only group, which viewed the cultural video sent by mail. Outcomes were mammography uptake at 6 months and 21 months postintervention.Women in both groups increased mammography uptake, and an outcome analysis revealed no group differences (adjusted odds ratio [AOR], 1.18; 95% confidence interval [CI], .68–2.06). Overall, 61.2% of the SMVD group and 55.3% of the video‐only group had at least one mammogram during the 21‐month follow‐up period. When considering attendance to the SMVD, SMVD attendees had higher mammography uptake than the video‐only group (AOR, 1.51; 95% CI, 1.19–1.92), and SMVD nonattendees had lower mammography uptake than the video‐only group (AOR, .33; 95% CI, .22–.50).Both intervention strategies were associated with increased mammography uptake. The authors observed that the increase in use was greater among women who participated in the SMVD session compared with those who viewed the cultural video only. Future research may explore a virtual SMVD intervention for higher session attendance and increased mammography uptake (ClinicalTrials.gov identifier NCT01292200). [ABSTRACT FROM AUTHOR]

Background: Although artificial intelligence (AI) has potential in the field of screening of breast cancer, there are still issues. It is vital to make sure AI does not overlook cancer or cause needless recalls. The aim of this work was to investigate the effectiveness of indulging AI in combination with one radiologist in the routine double reading of mammography for breast cancer screening. The study prospectively analyzed 32,822 screening mammograms. Reading was performed in a blind-paired style by (i) two radiologists and (ii) one radiologist paired with AI. A heatmap and abnormality scoring percentage were provided by AI for abnormalities detected on mammograms. Negative mammograms and benign-looking lesions that were not biopsied were confirmed by a 2-year follow-up. Results: Double reading by the radiologist and AI detected 1324 cancers (6.4%); on the other side, reading by two radiologists revealed 1293 cancers (6.2%) and presented a relative proportion of 1·02 (p < 0·0001). At the recall stage, suspicion and biopsy recommendation were more presented by the AI plus one radiologist combination than by the two radiologists. The interpretation of the mammogram by AI plus only one radiologist showed a sensitivity of 94.03%, a specificity of 99.75%, a positive predictive value of 96.571%, a negative predictive value of 99.567%, and an accuracy of 99.369% (from 99.252 to 99.472%). The positive likelihood ratio was 387.260, negative likelihood ratio was 0.060, and AUC "area under the curve" was 0.969 (0.967–0.971). Conclusions: AI could be used as an initial reader for the evaluation of screening mammography in routine workflow. Implementation of AI enhanced the opportunity to reduce false negative cases and supported the decision to recall or biopsy. [ABSTRACT FROM AUTHOR]

Background: Taiwan implemented the Cancer Screening Quality Improvement Program (CAQIP) in 2010. The program sought to enhance mass breast cancer screening accessibility. This study aimed to examine socioeconomic disparities in outreach screening utilization pre-CAQIP (2005–2009) and post-CAQIP (2010–2014). Method: We conducted a nationwide population-based observational study in Taiwan, analyzing four population databases to evaluate socioeconomic disparities among women aged 50 to 69 years undergoing their first mammography screening pre-CAQIP. Multivariate logistic regression was used to examine changes in utilization of outreach screening pre- and post-CAQIP implementation, and to estimate the Slope Index of Inequity (SII) and Relative Index of Inequity (RII) values. Results: Utilization of outreach screening through mobile mammography units (MMUs) increased from 6.12 to 32.87% between the two periods. Following CAQIP, a higher proportion of screened women were older, less educated, and from suburban or rural areas. The SII and RII for age, income, and urbanization levels decreased post-CAQIP. However, regarding education level, SII was − 0.592 and RII was 0.392 in the pre-CAQIP period, increasing to -0.173 and 0.804 post-CAQIP, respectively. Conclusions: Our study observed that utilization of outreach screening through MMUs increased after CAQIP. The MMUs made outreach screening services more accessible in Taiwan. Expanding outreach screening services and educational programs to promote mammography uptake in local communities could help reduce the potential effect of socioeconomic disparities, and thus may enhance early detection of breast cancer. Further study could focus on the accessibility of outreach screening and breast cancer outcomes. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) readers of mammograms compare favourably to individual radiologists in detecting breast cancer. However, AI readers cannot perform at the level of multi-reader systems used by screening programs in countries such as Australia, Sweden, and the UK. Therefore, implementation demands human-AI collaboration. Here, we use a large, high-quality retrospective mammography dataset from Victoria, Australia to conduct detailed simulations of five potential AI-integrated screening pathways, and examine human-AI interaction effects to explore automation bias. Operating an AI reader as a second reader or as a high confidence filter improves current screening outcomes by 1.9–2.5% in sensitivity and up to 0.6% in specificity, achieving 4.6–10.9% reduction in assessments and 48–80.7% reduction in human reads. Automation bias degrades performance in multi-reader settings but improves it for single-readers. This study provides insight into feasible approaches for AI-integrated screening pathways and prospective studies necessary prior to clinical adoption. Successful human-AI collaboration could greatly contribute to breast cancer mammographic screening. Here, the authors use a large-scale retrospective mammography dataset to simulate and compare five plausible AI-integrated screening pathways, finding optimal ways in which human-AI collaboration could be implemented in real-world settings. [ABSTRACT FROM AUTHOR]

Computer Aided Detection (CAD) has been used to help readers find cancers in mammograms. Although these automated systems have been shown to help cancer detection when accurate, the presence of CAD also leads to an over-reliance effect where miss errors and false alarms increase when the CAD system fails. Previous research investigated CAD systems which overlayed salient exogenous cues onto the image to highlight suspicious areas. These salient cues capture attention which may exacerbate the over-reliance effect. Furthermore, overlaying CAD cues directly on the mammogram occludes sections of breast tissue which may disrupt global statistics useful for cancer detection. In this study we investigated whether an over-reliance effect occurred with a binary CAD system, which instead of overlaying a CAD cue onto the mammogram, reported a message alongside the mammogram indicating the possible presence of a cancer. We manipulated the certainty of the message and whether it was presented only to indicate the presence of a cancer, or whether a message was displayed on every mammogram to state whether a cancer was present or absent. The results showed that although an over-reliance effect still occurred with binary CAD systems miss errors were reduced when the CAD message was more definitive and only presented to alert readers of a possible cancer. [ABSTRACT FROM AUTHOR]

Aim: Impact analysis of the invitation women to screening mammography and strategies of the invitation on the effectiveness of breast cancer screening programme in the Czech Republic. Methods: Questionnaire survey including 73 certified breast cancer screening centers in the Czech Republic. Evaluation of the answers to the questions focused on the specific form of established invitation strategy, time periodicity and the method of selection of the target female population. Comparison of „the return of women to screening programme indicator“ between the centers using active invitation strategy and those that do not. Results: Out of the total number of participated screening centers, the return rate of the sent questionnaire surveys was 97% (71 respondents). Active invitation strategy of the target female population for screening mammography using various forms and information systems is practiced by about half of the certfied screening centers, in most cases after a two-year time period since the previous examination. Most centers also accepted a favorable response from the patients. The analysis of „the return of women to screening programme indicator“ confirmed a clearly positive impact of the active invitation strategy on the female participation in breast cancer screening programme. Centers, that invite their clients, achieve a higher participation of women in the next screening examination, by about 10 p.p. Conclusions: The use of education and active search of patients leads to further increase the effectiveness of established breast cancer screening programme. [ABSTRACT FROM AUTHOR]

BACKGROUND: Mammographic breast density (MBD) is a strong risk factor and an intermediate phenotype for breast cancer, yet there are limited studies on how environmental pollutants are associated with MBD. OBJECTIVE: We investigated associations of perfluorooctane sulfonate (PFOS), perfluorooctanoic acid (PFOA), and perfluorohexane sulfonate (PFHxS) levels with measures of MBD and evaluated if early life factors modified any associations. METHODS: Metabolon performed metabolomics analysis using ultrahigh-performance liquid chromatography/tandem accurate mass spectrometry in fasting blood from 705 premenopausal women completing their annual screening mammogram in St. Louis, Missouri. We calculated least square means (LSM) of mammographic volumetric percent density (VPD), dense volume (DV), and nondense volume (NDV) by quartiles (Q) of PFOS, PFOA, and PFHxS from multivariable linear regression modeling overall and stratified by recruitment period, race, age at menarche, and body shape at age 10. Models were adjusted for age, age at menarche, body fat percentage, race, family history of breast cancer, oral contraceptive use, alcohol consumption, parity/age at first birth, and body shape at age 10. RESULTS: PFOS, PFOA, and PFHxS were not significantly associated with VPD or NDV. PFHxS was significantly positively associated with DV (Q1=67.64 cm³, Q2=69.91 cm³, Q3=69.06 cm³, Q4=75.79 cm³; 푝-trend=0.03). PFOS was positively associated with DV (Q1=65.45 cm³, Q2=70.74 cm³, Q3=73.31 cm³, Q4=73.52 cm³; 푝-trend=0.06) with DV being 8.1%, 12%, and 12.3% higher in Q2, Q3, and Q4 compared to Q1. Among women who were underweight/normal weight at age 10, PFOS was positively associated with VPD (Q1=9.02%, Q2=9.11%, Q3=9.48%, Q4=9.92%; 푝-trend=0.04) while there was an inverse association among women who were overweight/obese at age 10 (Q1=7.46%, Q2=6.94%, Q3=6.78%, Q4=5.47%; 푝-trend=0.005) (푝-interaction=0.04). DISCUSSION: We report novel associations of PFHxS and PFOS with DV in premenopausal women. PFOS, PFOA, and PFHxS were not associated with VPD and NDV. In addition, body shape at age 10 may modify the associations of PFOS with MBD. Further studies are needed to validate our findings and to evaluate the associations of other per- and polyfluoroalkyl substances (PFAS), as well as mixtures of PFAS, with MBD. [ABSTRACT FROM AUTHOR]