Mammograms

PURPOSE: Genetic cancer risk assessment (GCRA) provides pathogenic variant (PV) carriers with the invaluable opportunity to undertake timely cancer risk-reducing (RR) measures and initiate cascade testing (CT). This study describes the uptake of these strategies and the related barriers among breast cancer-associated germline PV carriers in Mexico. METHODS: Carriers who were at least 6 months after disclosure of genetic test results at two GCRA referral centers were invited to answer a survey assessing sociodemographic characteristics, awareness of their carrier status and its implications, uptake of RR measures according to international guidelines by PV, CT initiation, and associated challenges...

Architectural distortion (AD) is one of the most common findings on mammograms, and it may represent not only cancer but also a lesion such as a radial scar that may have an associated cancer. AD accounts for 18-45% missed cancer, and the positive predictive value of AD is approximately 74.5%. Early detection of AD leads to early diagnosis and treatment of the cancer and improves the overall prognosis. However, detection of AD is a challenging task. In this work, we propose a new approach for detecting architectural distortion in mammography images by combining preprocessing methods and a novel structure fusion attention model...

Early detection and treatment of breast cancer can significantly reduce patient mortality, and mammogram is an effective method for early screening. Computer-aided diagnosis (CAD) of mammography based on deep learning can assist radiologists in making more objective and accurate judgments. However, existing methods often depend on datasets with manual segmentation annotations. In addition, due to the large image sizes and small lesion proportions, many methods that do not use region of interest (ROI) mostly rely on multi-scale and multi-feature fusion models...

BACKGROUND: Visual assessment of mammographic breast composition remains the most common worldwide, although subjective variability limits its reproducibility. This study aimed to investigate the inter- and intra-observer variability in qualitative visual assessment of mammographic breast composition through a multi-institutional observer performance study for the first time in Japan. METHODS: This study enrolled 10 Japanese physicians from five different institutions...

INTRODUCTION AND AIM: Breast cancer is one of the significant causes of mortality in India, ranking second only to cervical cancer among women. Annually, the country has witnessed the detection of 200,000 new cases, with 60% identified in the early stages. This study aimed to assess the effectiveness of a health education intervention program designed to enhance knowledge about breast cancer among women in rural Karnataka. MATERIALS AND METHODS: A descriptive study design was employed and a total of 320 women were selected through multi-stage sampling...

Generative models are used as an alternative data augmentation technique to alleviate the data scarcity problem faced in the medical imaging field. Diffusion models have gathered special attention due to their innovative generation approach, the high quality of the generated images, and their relatively less complex training process compared with Generative Adversarial Networks. Still, the implementation of such models in the medical domain remains at an early stage. In this work, we propose exploring the use of diffusion models for the generation of high-quality, full-field digital mammograms using state-of-the-art conditional diffusion pipelines...

PURPOSE: Nationally legislated dense breast notification (DBN) informs women of their breast density (BD) and the impact of BD on breast cancer risk and detection, but consequences for screening participation are unclear. We evaluated the association of DBN in New York State (NYS) with subsequent screening mammography in a largely Hispanic/Latina cohort. METHODS: Women aged 40-60 were surveyed in their preferred language (33% English, 67% Spanish) during screening mammography from 2016 to 2018...

PURPOSE: In this study, we aimed to assess the new trends in characteristics, molecular subtypes, and imaging findings of breast cancer in very young women. METHODS: We retrospectively reviewed the database of a primary breast cancer referral center in southern Iran in 342 cases of 30-year-old or younger women from 2001 to 2020. Pathologic data, including nuclear subtype and grade, tumor stage, presence of in situ cancer, imaging data including lesion type in mammogram and ultrasound, and treatment data were recorded...

OBJECTIVE: Patients who miss screening mammogram (SM) appointments without notifying the healthcare system (no-show) risk care delays. We investigate sociodemographic characteristics of patients who experience SM no-shows at a community health center and whether and when the missed exams are completed. METHODS: We included patients with SM appointments at a community health center between 1/1/2021-12/31/2021. Language, race, ethnicity, insurance type, residential ZIP code tabulation area (ZCTA) poverty, appointment outcome (no-show, same-day cancellation, completed), and dates of completed SMs after no-show appointments with ≥ 1-year follow-up were collected...

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence . This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To evaluate the ability of a semiautonomous artificial intelligence (AI) model to identify screening mammograms not suspicious for breast cancer and reduce the number of false-positive examinations...

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence . This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To explore the standalone breast cancer detection performance at different risk score thresholds of a commercially available artificial intelligence (AI) system...

Background Mammographic density measurements are used to identify patients who should undergo supplemental imaging for breast cancer detection, but artificial intelligence (AI) image analysis may be more effective. Purpose To assess whether AISmartDensity-an AI-based score integrating cancer signs, masking, and risk-surpasses measurements of mammographic density in identifying patients for supplemental breast imaging after a negative screening mammogram. Materials and Methods This retrospective study included randomly selected individuals who underwent screening mammography at Karolinska University Hospital between January 2008 and December 2015...

Breast Cancer is a significant global health challenge, particularly affecting women with higher mortality compared with other cancer types. Timely detection of such cancer types is crucial, and recent research, employing deep learning techniques, shows promise in earlier detection. The research focuses on the early detection of such tumors using mammogram images with deep-learning models. The paper utilized four public databases where a similar amount of 986 mammograms each for three classes (normal, benign, malignant) are taken for evaluation...

PURPOSE: Breast arterial calcifications (BAC) are incidentally observed on mammograms, yet their implications remain unclear. We investigated lifestyle, reproductive, and cardiovascular determinants of BAC in women undergoing mammography screening. Further, we investigated the relationship between BAC, coronary arterial calcifications (CAC) and estimated 10-year atherosclerotic cardiovascular (ASCVD) risk. METHODS: In this cross-sectional study, we obtained reproductive history and CVD risk factors from 215 women aged 18 or older who underwent mammography and cardiac computed tomographic angiography (CCTA) within a 2-year period between 2007 and 2017 at hospital...

OBJECTIVES: To evaluate whether the quantitative abnormality scores provided by artificial intelligence (AI)-based computer-aided detection/diagnosis (CAD) for mammography interpretation can be used to predict invasive upgrade in ductal carcinoma in situ (DCIS) diagnosed on percutaneous biopsy. METHODS: Four hundred forty DCIS in 420 women (mean age, 52.8 years) diagnosed via percutaneous biopsy from January 2015 to December 2019 were included. Mammographic characteristics were assessed based on imaging features (mammographically occult, mass/asymmetry/distortion, calcifications only, and combined mass/asymmetry/distortion with calcifications) and BI-RADS assessments...

Purpose To evaluate long-term trends in mammography screening rates and identify sociodemographic and breast cancer risk characteristics associated with return to screening after the COVID-19 pandemic. Materials and Methods In this retrospective study, statewide screening mammography data of 222 384 female individuals aged 40 years or older (mean age, 58.8 years ± 11.7 [SD]) from the Vermont Breast Cancer Surveillance System were evaluated to generate descriptive statistics and Joinpoint models to characterize screening patterns during 2000-2022...

BACKGROUND: Ductal Carcinoma In Situ (DCIS) can progress to invasive breast cancer, but most DCIS lesions never will. Therefore, four clinical trials (COMET, LORIS, LORETTA, AND LORD) test whether active surveillance for women with low-risk Ductal carcinoma In Situ is safe (E. S. Hwang et al., BMJ Open, 9: e026797, 2019, A. Francis et al., Eur J Cancer. 51: 2296-2303, 2015, Chizuko Kanbayashi et al. The international collaboration of active surveillance trials for low-risk DCIS (LORIS, LORD, COMET, LORETTA),  L...

The most fatal disease affecting women worldwide now is breast cancer. Early detection of breast cancer enhances the likelihood of a full recovery and lowers mortality. Based on medical imaging, researchers from all around the world are developing breast cancer screening technologies. Due to their rapid progress, deep learning algorithms have caught the interest of many in the field of medical imaging. This research proposes a novel method in mammogram image feature extraction with classification and optimization using machine learning in breast cancer detection...

OBJECTIVES: To evaluate the use of a commercial artificial intelligence (AI)-based mammography analysis software for improving the interpretations of breast ultrasound (US)-detected lesions. METHODS: A retrospective analysis was performed on 1109 breasts that underwent both mammography and US-guided breast biopsy. The AI software processed mammograms and provided an AI score ranging from 0 to 100 for each breast, indicating the likelihood of malignancy. The performance of the AI score in differentiating mammograms with benign outcomes from those revealing cancers following US-guided breast biopsy was evaluated...

Women with high mammographic density (MD) have an increased risk of breast cancer. They may be offered contrast-enhanced mammogram (CEM) to improve breast cancer screening performance. Using a cohort of women receiving CEM, we evaluated whether conventional and modified MD measures were associated with breast cancer. Sixty-six cases with newly diagnosed unilateral breast cancer were frequency-matched on age to 133 cancer-free controls. On low-energy cranio-caudal CEMs (equivalent to standard mammogram), we measured quantitative MD using CUMULUS software at the conventional intensity threshold ("Cumulus") and higher-than-conventional thresholds ("Altocumulus", "Cirrocumulus")...