Breast cancer is a prevalent global health concern, necessitating accurate diagnostic tools for effective management. Diagnostic imaging plays a pivotal role in breast cancer diagnosis, staging, treatment planning, and outcome evaluation. Radiomics is an emerging field of study in medical imaging that contains a broad set of computational methods to extract quantitative features from radiographic images. This can be utilized to guide diagnosis, treatment response, and prognosis in clinical settings.  A systematic review was performed in concordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and the Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy. Quality was assessed using the radiomics quality score. Diagnostic sensitivity and specificity of radiomics analysis, with 95% confidence intervals (CIs), were included for meta-analysis. The area under the curve analysis was recorded. An extensive statistical analysis was performed following the Cochrane guidelines. Statistical significance was determined if p-values were less than 0.05. Statistical analyses were conducted using Review Manager (RevMan), Version 5.4.1. A total of 31 manuscripts involving 8,773 patients were included, with 17 contributing to the meta-analysis. The cohort comprised 56.2% malignant breast cancers and 43.8% benign breast lesions. MRI demonstrated a sensitivity of 0.91 (95% CI: 0.89-0.92) and a specificity of 0.84 (95% CI: 0.82-0.86) in differentiating between benign and malignant breast cancers. Mammography-based radiomic features predicted breast cancer subtype with a sensitivity of 0.79 (95% CI: 0.76-0.82) and a specificity of 0.81 (95% CI: 0.79-0.84). Ultrasound-based analysis yielded a sensitivity of 0.92 (95% CI: 0.90-0.94) and a specificity of 0.85 (95% CI: 0.83-0.88). Only one study reported the results of radiomic evaluation from CT, which had a sensitivity of 0.95 (95% CI: 0.88-0.99) and a specificity of 0.56 (95% CI: 0.45-0.67).  Across different imaging modalities, radiomics exhibited robust diagnostic accuracy in differentiating benign and malignant breast lesions. The results underscore the potential of radiomic assessment as a minimally invasive alternative or adjunctive diagnostic tool for breast cancer. This is pioneering data that reports on a novel diagnostic approach that is understudied and underreported. However, due to study limitations, the complexity of this technology, and the need for future development, biopsy still remains the current gold standard method of determining breast cancer type.

Calciphylaxis, also called calcific uremic arteriolopathy, is a rare benign cutaneous manifestation. Although little is known about its pathogenesis, it is thought to be a result of vascular wall calcification leading to soft tissue necrosis, and it is usually encountered in patients with end-stage kidney disease (ESKD) on long-term renal dialysis. Breast calciphylaxis is a rare entity that may present as a breast mass or necrotic ulcers, and it is common for it to be initially mistaken for a malignant breast pathology. In this article, we present a case of bilateral breast calciphylaxis in a 66-year-old female with ESKD receiving long-term dialysis.

This paper reviews the potential use of fuzzy c-means clustering (FCM) and explores modifications to the distance function and centroid initialization methods to enhance image segmentation. The application of interest in the paper is the segmentation of breast tumours in mammograms. Breast cancer is the second leading cause of cancer deaths in Canadian women. Early detection reduces treatment costs and offers a favourable prognosis for patients. Classical methods, like mammograms, rely on radiologists to detect cancerous tumours, which introduces the potential for human error in cancer detection. Classical methods are labour-intensive, and, hence, expensive in terms of healthcare resources. Recent research supplements classical methods with automated mammogram analysis. The basic FCM method relies upon the Euclidean distance, which is not optimal for measuring non-spherical structures. To address these limitations, we review the implementation of a Mahalanobis-distance-based FCM (FCM-M). The three objectives of the paper are: (1) review FCM, FCM-M, and three centroid initialization algorithms in the literature, (2) illustrate the effectiveness of these algorithms in image segmentation, and (3) develop a Python package with the optimized algorithms to upload onto GitHub. Image analysis of the algorithms shows that using one of the three centroid initialization algorithms enhances the performance of FCM. FCM-M produced higher clustering accuracy and outlined the tumour structure better than basic FCM.

Breast cancer disproportionately impacts Native Hawaiian and Pacific Islander communities in Hawai\'i, as exemplified by high breast cancer prevalence and mortality rates. Breast cancer disparities are linked to socio-cultural determinants of health, signifying the importance of culturally-based interventions. This paper systematically reviewed breast cancer studies conducted in Hawai\'i. The literature search yielded 813 published studies, with a final total of 13 peer-reviewed studies that met this paper\'s inclusion criteria. All but 1 study incorporated cultural components. By evaluating key intervention components and assessing the quality of each study, the research team aimed to analyze the importance of cultural values in health interventions. Family and spirituality in coping with a cancer diagnosis were key themes in patients\' lived experiences. Other culturally-based components in these studies included community-engaged research and cultural training for health professionals. The collective findings suggest that breast cancer health interventions that incorporate cultural strengths, values, and worldviews may play a central role in reducing the overall breast cancer burden among these communities. The present review advocates for future research to take a more culturally-based strategy in addressing breast cancer health disparities among Native Hawaiian and Pacific Islanders in Hawai\'i.

Bariatric surgery is one of the most effective methods for treating obesity. It can effectively reduce body weight and reduce the incidence of obesity-related breast cancer. However, there are different conclusions about how bariatric surgery changes breast density. The purpose of this study was to clarify the changes in breast density from before to after bariatric surgery.
The relevant literature was searched through PubMed and Embase to screen for studies. Meta-analysis was used to clarify the changes in breast density from before to after bariatric surgery.
A total of seven studies were included in this systematic review and meta-analysis, including a total of 535 people. The average body mass index decreased from 45.3 kg/m2 before surgery to 34.4 kg/m2 after surgery. By the Breast Imaging Reporting and Data System score, the proportion of grade A breast density from before to after bariatric surgery decreased by 3.83% (183 vs. 176), grade B (248 vs. 263) increased by 6.05%, grade C (94 vs. 89) decreased by 5.32%, and grade D (1 vs. 4) increased by 300%. There was no significant change in breast density from before to after bariatric surgery (OR=1.27, 95% confidence interval (CI) [0.74, 2.20], P=0.38). By the Volpara density grade score, postoperative volumetric breast density increased (standardized mean difference = -0.68, 95% CI [-1.08, -0.27], P = 0.001).
Breast density increased significantly after bariatric surgery, but this depended on the method of detecting breast density. Further randomized controlled studies are needed to validate our conclusions.

BACKGROUND: The usage of artificial intelligence in medical image analysis has significantly surpassed that of earlier related technologies. This paper aimed to investigate the diagnostic accuracy of Artificial Intelligence (AI) based-deep learning models for breast cancer detection.
METHODS: We used the PICO (Patient/Population/Problem, Intervention, Comparison, Outcome) scheme to formulate the research question and construct our search terms. Studies were systematically examined from the available literature using the constructed search terms from PubMed, and ScienceDirect according to the PRISMA guidelines. The quality of the included studies was assessed using the QUADAS-2 checklist. The characteristics of each included study such as the study design, population, index test, and reference standard, were extracted. The sensitivity, specificity, and AUC for each study were also reported.
RESULTS: In this systematic review, 14 studies were analyzed. Eight studies showed that AI was more accurate than radiologists in evaluating mammographic images, while one comprehensive study found AI to be less precise. Studies that reported sensitivity and specificity without radiologist intervention showed performance scores ranging from 16.0% to 89.71%. With radiologist intervention, sensitivity was between 62% to 86%. Only three studies reported a specificity of 73.5% to 79%. The AUC of the studies was between 0.79 and 0.95. Thirteen studies were retrospective, and one was prospective.
CONCLUSIONS: There\'s inadequate evidence on the effectiveness of AI-based deep learning for breast cancer screening in clinical settings. More research is needed, including studies evaluating accuracy, RCTs, and large-scale cohort studies. This systematic review found that AI-based deep learning improves radiologists\' accuracy, especially for novice radiologists. Younger, tech-savvy clinicians may be more accepting of AI. Although it can\'t replace radiologists, the encouraging results suggest it will play a significant role in identifying breast cancer in the future.

The advent of artificial intelligence (AI) represents a real game changer in today\'s landscape of breast cancer imaging. Several innovative AI-based tools have been developed and validated in recent years that promise to accelerate the goal of real patient-tailored management. Numerous studies confirm that proper integration of AI into existing clinical workflows could bring significant benefits to women, radiologists, and healthcare systems. The AI-based approach has proved particularly useful for developing new risk prediction models that integrate multi-data streams for planning individualized screening protocols. Furthermore, AI models could help radiologists in the pre-screening and lesion detection phase, increasing diagnostic accuracy, while reducing workload and complications related to overdiagnosis. Radiomics and radiogenomics approaches could extrapolate the so-called imaging signature of the tumor to plan a targeted treatment. The main challenges to the development of AI tools are the huge amounts of high-quality data required to train and validate these models and the need for a multidisciplinary team with solid machine-learning skills. The purpose of this article is to present a summary of the most important AI applications in breast cancer imaging, analyzing possible challenges and new perspectives related to the widespread adoption of these new tools.

Artificial intelligence (AI), a rousing advancement disrupting a wide spectrum of applications with remarkable betterment, has continued to gain momentum over the past decades. Within breast imaging, AI, especially machine learning and deep learning, honed with unlimited cross-data/case referencing, has found great utility encompassing four facets: screening and detection, diagnosis, disease monitoring, and data management as a whole. Over the years, breast cancer has been the apex of the cancer cumulative risk ranking for women across the six continents, existing in variegated forms and offering a complicated context in medical decisions. Realizing the ever-increasing demand for quality healthcare, contemporary AI has been envisioned to make great strides in clinical data management and perception, with the capability to detect indeterminate significance, predict prognostication, and correlate available data into a meaningful clinical endpoint. Here, the authors captured the review works over the past decades, focusing on AI in breast imaging, and systematized the included works into one usable document, which is termed an umbrella review. The present study aims to provide a panoramic view of how AI is poised to enhance breast imaging procedures. Evidence-based scientometric analysis was performed in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guideline, resulting in 71 included review works. This study aims to synthesize, collate, and correlate the included review works, thereby identifying the patterns, trends, quality, and types of the included works, captured by the structured search strategy. The present study is intended to serve as a \"one-stop center\" synthesis and provide a holistic bird\'s eye view to readers, ranging from newcomers to existing researchers and relevant stakeholders, on the topic of interest.

To systematically assess the effectiveness of breast cancer (BC) interventions in improving breast self-examination (BSE), clinical breast examination (CBE), mammogram screening rates, and preventive activities in Muslim refugee and immigrant women.
Guided by the Health Belief Model, a mixed method systematic review and meta-analysis was performed using a sequential design.
This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis statement (PRISMA), the Critical Appraisal Skill Program Checklists, and the Joanna Briggs Institute (JBI) methodology for systematic review and meta-analysis. A systematic search of English-language peer-reviewed articles was undertaken in multiple health and social sciences databases from January 1, 2015, to March 31, 2022. Randomized clinical trials and quasi-experimental studies focused on the uptake of BSE, CBE, and mammograms were selected.
Fourteen articles were included in the review. Most of the studies relied on quasi-experimental designs and were carried out in the United States of America. The qualitative analysis of BC screening interventions generated three themes: (1) education, (2) access-focused, and (3) cultural and faith-based. The meta-analysis included three randomized control trials and two quasi-experimental studies. The meta-analysis demonstrates the effectiveness of community-led cultural and faith-based interventions in facilitating the completion of CBE and mammography screening. Education on BC and patient navigator interventions are more effectively used in conjunction than standalone interventions, yet community-based cultural and faith-based interventions are the most effective.
This systematic and meta-analysis review provides evidence on the effectiveness of access-focused and cultural and faith-based interventions in improving BC screening in Muslim refugee and immigrant women. Future research should focus on designing and measuring the effectiveness of cultural and faith-based interventions to increase Muslim refugee and immigrant women\'s BC screening knowledge and practices.
This systematic and meta-analysis review demonstrates the need to explore Muslim refugee and immigrant women\'s cultural contexts for developing culturally sensitive BC screening interventions. Knowledge and practice of BC and religiosity intersect with financial, geographic, and linguistic barriers to decrease participation in screening and preventive activities in Muslim refugee and immigrant women.

This narrative review identifies barriers and facilitators influencing breast cancer screening among Arab Muslim immigrants and refugees. Low participation rates create health inequities in breast cancer screening among this population.
A systematic search of peer-reviewed empirical articles was performed. PRISMA, CASP, and MMAT checklists were used to appraise the studies.
Results include 3 themes: individual, health care system and health providers, and cultural factors.
Lack of fluency in the new country\'s language, lack of knowledge, and poor exposure to breast cancer screening may contribute to the Arab Muslim women\'s vulnerability to undiagnosed or delayed breast cancer diagnosis.