Diagnosis Of Breast Cancer Research Articles

Magnetic resonance imaging-guided single-fraction preoperative radiotherapy for early-stage breast cancer (the RICE trial): feasibility study.

Over the past decade, the adoption of screening programs, digital mammography, and magnetic resonance imaging (MRI) has increased early-stage breast cancer diagnosis rates. Mortality rates have decreased due to early detection and improved treatments, including personalized therapies. Accelerated partial-breast irradiation (APBI) is emerging as a convenient and effective treatment for some patients, with studies exploring its preoperative use. Preoperative APBI, especially with MRI guidance, offers improved tumor targeting and potentiallyreduced side effects. Magnetic Resonance Imaging-Guided Single-Fraction Pre-Operative Radiotherapy for Early-Stage Breast Cancer (RICE trial) aims to assess the feasibility and efficacy of MRI-guided single-dose radiotherapy (RT) for early-stage breast cancer. The RICE study is a prospective, single-arm study evaluating single-fraction preoperative, APBI treatment for patients with early-stage breast cancer using a magnetic resonance imaging linear accelerator (MRI linac). Eligible patients enrolled in this study will have a core biopsy to confirm estrogen receptor-positive and HER2-negative sub-type. RT planning will use a planning computed tomography (CT) co-registered with a MRI with the patient in either the supine or prone position. For the diagnostic workup, [18F] fluorodeoxyglucose positron emission tomography/CT ([18F] FDG PET/CT) and [18F] fluoroestradiol positron emission tomography/CT ([18F] FES PET/CT) will be performed prior to treatment. Thirty patients will receive a single ablative RT dose of 21 Gray to the tumor. Pre-treatment and post-treatment MRI scans will be acquired at baseline and 5weeks post-RT respectively. Breast-conserving surgery will be scheduled for 6weeks after APBI treatment using the MRI linac. The primary study endpoint is the successful administration of a single fraction of preoperative breast RT under the guidance of an MRI linac. Secondary endpoints include evaluating the utility of MRI, [18F] FDG PET/CT, and [18F] FES PET/CT as a non-invasive method for assessing treatment response in patients undergoing single-fraction preoperative APBI. The RICE trial represents a significant step in breast cancer treatment, offering insights that could lead to treatment protocols with minimized RT appointments and enhanced patient outcomes. This trial is registered with the Australian New Zealand Clinical Trials Registry (ANZCTR). Registered 31st of May 2021. ACTRN12621000659808 .

Statin prescription disparities in patients with breast cancer and diabetes for primary cardiovascular disease prevention

BackgroundThis brief report examines statin prescription trends for primary cardiovascular disease (CVD) prevention in breast cancer (BC) survivors with diabetes, a large population at particularly high CVD risk.MethodsA population-based, retrospective cohort study was conducted using Surveillance, Epidemiology, and End Results (SEER) cancer registry data linked to Medicare claims. We identified women with preexisting diabetes who were diagnosed with stage 0–III primary BC between 2008 and 2017 without preexisting CVD. We assessed statin prescription rates over time and also examined differences in statin prescription rates according to patient sociodemographic characteristics. Using a multivariate logistic regression adjusted for sociodemographic and clinical variables, independent predictors of statin prescription were identified.ResultsOf 8,423 BC patients with diabetes without preexisting CVD, 5,698 (68%) had a statin prescription. Statin prescriptions increased over time (BC diagnosis year 2008–2009: 65%, 2010–2011: 67%, 2012–2013: 66%, 2014–2015: 69%, 2016–2017: 70%; p = 0.01) and differed by age (66–69: 66%, 70–74: 70%, 75–79: 69%, ≥80: 65%; p < 0.01) and race (White: 68%, Black: 62%, Latina: 66%, Other: 72%; p < 0.01). In a multivariate analysis, race (Black vs. White: OR 0.80, 95% CI: 0.68–0.95) remained a predictor of statin prescription.ConclusionIn older early-stage BC survivors, statin prescriptions increased over time and varied by age, race, and BC stage. These findings can potentially inform strategies to improve guideline-concordant statin prescriptions in a group at high risk for CVD and reduce disparities.

Culture, community, and cancer: understandings of breast cancer from a non-lived experience among women living in Soweto.

Individual perceptions, socio-cultural beliefs and health system factors are key determinants of people's health seeking behavior and are widely cited as the causes of delayed breast cancer diagnosis among women from structurally vulnerable settings. Asking: "how do women with a non-lived experience of cancer understand the disease and, what informs their health seeking behaviors?", we qualitatively explored, individual, sociocultural and health system elements from a conceptual model derived from the Socioecological, Health Belief and Cancer Stigma Frameworks, to understand perspectives of breast cancer in a South African urban community setting. Using a deductive approach and allowing new themes to emerge inductively, we investigated phenomenologically, breast cancer perceptions among 34 women from Soweto, Johannesburg (aged 35-74years) in 6 Focus Group Discussions. We then conducted 20 follow-up semi-structured in-depth interviews to explore novel themes and suggestions for increasing breast cancer screening. Findings revealed some awareness of breast and other cancers, but confusion and gaps in understanding of the disease, resulting in socio-culturally influenced misperceptions of risks, causes, and outcomes following treatment of breast cancer. This fueled perceptions of profound fear and stigma against people with breast and other cancers. These findings together with participant perceptions of primary healthcare providers being unwelcoming, under-resourced, and insufficiently trained to deal with breast cancer, resulted in women reporting being reluctant to participating in screening/early detection care seeking behavior. Women only accessed primary care when experiencing extreme pain or ill-health. Participants suggested as solutions for future interventions, the need for sustained community engagement, harnessing existing clinic and community stakeholders and resources to provide clear and understandable breast cancer information and encouragement for screening uptake. Health literacy gaps surrounding breast cancer fuels socio-culturally influenced misperceptions, fear, stigma, and fatalism among women from Soweto. Women perceive primary care providers of having insufficient knowledge, skills, and resources to provide effective breast cancer screening services. Participants suggested the need for greater community engagement involving primary clinics and existing community stakeholders working collaboratively. Clear, understandable, and consistent information about breast cancer must be regularly disseminated and communities must be regularly encouraged to utilize breast cancer screening services.

Machine Learning Based Decision Support System for the Diagnosis of Breast Cancer

Breast cancer is among the most prevalent diseases encountered among women worldwide. Early diagnosis of breast cancer is crucial for the treatment of the disease. Detecting the disease at an early stage prevents deaths resulting from the condition. Recently, computer-aided systems have been developed to ensure early-stage diagnosis and accuracy of breast cancer. Computer-aided systems developed with machine learning approaches significantly contribute to the process of diagnosing breast cancer. The aim of this study is to propose a new classification system based on machine learning algorithms developed for the diagnosis of breast cancer. In this study, sub-data sets were created by reducing features, and data cleaning processes were applied. After these procedures, stages such as feature selection and feature extraction were applied. In this study, classification processes such as Ensemble, k- Nearest Neighbors (kNN), Support Vector Machines (SVMs), and Hybrid Artificial Intelligence were used in line with machine learning. With the obtained results, a Breast Cancer diagnosis algorithm was created. Performance evaluation criteria such as accuracy rate, specificity, sensitivity, kappa number and F-Measure were applied to the created algorithms. In the results obtained in this study, the highest accuracy rate was found to be 99.3% with the Ensemble method, the highest specificity rate was 98.7% with the Ensemble method, and the highest sensitivity rate was found to be 100% with many methods. In light of these results, it was observed that the machine learning algorithms used in this study, implemented in the Matlab environment, were effective. Consequently, it was proven that higher accuracy, specificity, and sensitivity rates can be found with different machine learning techniques. This also demonstrates that the study in our article is a reliable one in detecting diseased and healthy individuals in the diagnosis of breast cancer, showing that it is a more applicable and feasible study in the healthcare field.

An Integrated Multimodal-Based CAD System for Breast Cancer Diagnosis

Breast cancer has been one of the main causes of death among women recently, and it has been the focus of attention of many specialists and researchers in the health field. Because of its seriousness and spread speed, breast cancer-resisting methods, early diagnosis, diagnosis, and treatment have been the points of research discussion. Many computers-aided diagnosis (CAD) systems have been proposed to reduce the load on physicians and increase the accuracy of breast tumor diagnosis. To the best of our knowledge, combining patient information, including medical history, breast density, age, and other factors, with mammogram features from both breasts in craniocaudal (CC) and mediolateral oblique (MLO) views has not been previously investigated for breast tumor classification. In this paper, we investigated the effectiveness of using those inputs by comparing two combination approaches. The soft voting approach, produced from statistical information-based models (decision tree, random forest, K-nearest neighbor, Gaussian naive Bayes, gradient boosting, and MLP) and an image-based model (CNN), achieved 90% accuracy. Additionally, concatenating statistical and image-based features in a deep learning model achieved 93% accuracy. We found that it produced promising results that would enhance the CAD systems. As a result, this study finds that using both sides of mammograms outperformed the result of using only the infected side. In addition, integrating the mammogram features with statistical information enhanced the accuracy of the tumor classification. Our findings, based on a novel dataset, incorporate both patient information and four-view mammogram images, covering multiple classes: normal, benign, and malignant.

Breast cancer in women with previous gestational diabetes: a nationwide register-based cohort study

BackgroundGestational diabetes mellitus (GDM) is a common pregnancy complication characterized by insulin resistance. A link has been suggested between insulin resistance and breast cancer, which is the most common cancer in women. Hence, women with previous GDM may be at increased risk of developing breast cancer, yet, the existing evidence is conflicting. This study explored the association between GDM and incident breast cancer, including age at cancer diagnosis. Additionally, we investigated the potential impact of severity of insulin resistance during pregnancy and of subsequent diabetes development on the breast cancer risk.MethodsWe conducted a nationwide, register-based cohort study including all women giving birth in Denmark from 1997 to 2018. We defined GDM and breast cancer based on ICD-10 codes. Premenopausal and postmenopausal breast cancer was pragmatically defined as age at outcome < 50 years and ≥ 50 years, respectively. A proxy for severity of insulin resistance during pregnancy was based on insulin treatment; subsequent diabetes was defined as presence of ICD-10 codes and/or antidiabetic medication after pregnancy. The statistical analyses included Cox regression, logistic regression and t-test.ResultsOf 708,121 women, 3.4% had GDM. The median follow-up period was 11.9 years (range 0-21.9). The overall breast cancer risk was comparable in women with and without previous GDM (adjusted hazard ratio 0.96 [95% CI 0.83–1.12]). Premenopausal and postmenopausal breast cancer risk also did not differ; however, women with previous GDM had a breast cancer diagnosis at younger age (42.6 vs. 43.5 years, p-value 0.01). All-cause mortality was similar regardless of GDM history. Severity of insulin resistance during pregnancy and subsequent diabetes did not affect breast cancer risk.ConclusionsThis large, population-based cohort study showed no higher risk of incident breast cancer in women with previous GDM compared to women without previous GDM after a median of almost 12 years of follow-up. This was evident irrespective of menopausal state. The breast cancer risk was not influenced by the severity of insulin resistance during pregnancy and by subsequent diabetes development. Regardless of GDM history, attention towards prevention, early detection and treatment of breast cancer should be prioritized.

Identification of a Novel Biomarker Panel for Breast Cancer Screening

Breast cancer remains a major public health concern, and early detection is crucial for improving survival rates. Metabolomics offers the potential to develop non-invasive screening and diagnostic tools based on metabolic biomarkers. However, the inherent complexity of metabolomic datasets and the high dimensionality of biomarkers complicates the identification of diagnostically relevant features, with multiple studies demonstrating limited consensus on the specific metabolites involved. Unlike previous studies that rely on singular feature selection techniques such as Partial Least Square (PLS) or LASSO regression, this research combines supervised and unsupervised machine learning methods with random sampling strategies, offering a more robust and interpretable approach to feature selection. This study aimed to identify a parsimonious and robust set of biomarkers for breast cancer diagnosis using metabolomics data. Plasma samples from 185 breast cancer patients and 53 controls (from the Cooperative Human Tissue Network, USA) were analyzed. This study also overcomes the common issue of dataset imbalance by using propensity score matching (PSM), which ensures reliable comparisons between cancer and control groups. We employed Univariate Naïve Bayes, L2-regularized Support Vector Classifier (SVC), Principal Component Analysis (PCA), and feature engineering techniques to refine and select the most informative features. Our best-performing feature set comprised 11 biomarkers, including 9 metabolites (SM(OH) C22:2, SM C18:0, C0, C3OH, C14:2OH, C16:2OH, LysoPC a C18:1, PC aa C36:0 and Asparagine), a metabolite ratio (Kynurenine-to-Tryptophan), and 1 demographic variable (Age), achieving an area under the ROC curve (AUC) of 98%. These results demonstrate the potential for a robust, cost-effective, and non-invasive breast cancer screening and diagnostic tool, offering significant clinical value for early detection and personalized patient management.

Untargeted metabolomic profiling of small extracellular vesicles reveals potential new biomarkers for triple negative breast cancer.

Breast Cancer (BC) is one of the most diagnosed malignancies among women and the second leading cause of cancer related death in North America. Triple Negative BC (TNBC), one of the most severe subtypes of BC, is extremely aggressive and has a higher chance of occurrence in women under 50 years of age. Due to a lack of regular mammographic testing in women under 50, many individuals with TNBC are diagnosed late which can decrease their survival rate. Currently, liquid biopsy is being investigated as a potentially less-invasive alternative to traditional breast tissue biopsy, but this approach is not completely reliable. Blood contains extracellular vesicles (EVs), which carry biomolecular cargo and play a role in BC progression and metastasis. Examination of small EVs could potentially yield metabolite biomarkers for early BC diagnosis. We aim to study metabolites in small EVs to find biomarkers for BC diagnosis. In this work, an untargeted nano-LC MS/MS metabolomics approach was used to analyze metabolites from small EVs derived from metastatic MDA-MB-231 and compare it with a non-cancerous MCF10A cell line. Two metabolites, LysoPC 22:6/0:0 and N-acetyl-L-Phenylalanine, unique to sEVs of MDA-MB-231, were identified, validated, and proposed as potential BC biomarkers. Metabolites from sEVs may be used for BC diagnosis.

Breast cancer incidence and mortality, by age, stage and molecular subtypes, by race/ethnicity in Canada.

Breast cancer (BC) characteristics and outcomes in Canada related to race/ethnicity are not currently documented. Age-specific and age-standardized BC incidence and mortality rates, age distribution of cases, proportions of stage, and molecular subtypes were calculated for women aged 20+, by race/ethnicity, using 2006 and 2011 Canadian Census Health and Environment Cohort databases of linked census, cancer, and death data. In 47105 BC cases, age-specific incidence rates were higher in Filipina (rate ratio (RR) = 1.27, 95%CI, 1.11-1.46) and multiethnicity (RR = 1.57, 95% CI, 1.18-2.08) compared to White women aged 40-49; and Filipina (RR = 1.16, 95% CI, 1.02-1.31) and Arab (RR = 1.3, 95% CI, 1.02-1.65) women aged 50-59. Median age at diagnosis was 63 among White women and 52-60 among other race/ethnicity groups, with 22.4%-41.1% of cases (P < .001) diagnosed before age 50 compared to 16.6% among White women. BC was diagnosed at stage I less frequently among Filipina (38.6%), Black (39.2%), South Asian (40.6%), and First Nations (40.7%) compared to White (46.5%) and Chinese (49.6%) (P < .05) women. Black women had higher proportions of BC diagnoses at stages III and IV combined (26.3%) than White women (17.0%, P = .001). The proportion of triple-negative BC among Black women (20.5%) was higher than among White (9.5%, P < .001). Compared to White, age-specific BC mortality rates were higher among Black women aged 40-49 (RR = 1.4, 1.06-1.85) as well as First Nations (RR = 1.21, 1.01-1.45) and Métis (RR = 1.48, 1.15-1.91) women aged 60-69. Compared to White women, other Canadian women had an earlier peak age of BC diagnosis and higher proportions of cases diagnosed under age 50. Although many race/ethnicity groups had lower BC incidence and mortality than White, the higher age-specific BC mortality among Black 40-49 and First Nations and Métis women 60-69 merits further investigation.

Integrative hybrid deep learning for enhanced breast cancer diagnosis: leveraging the Wisconsin Breast Cancer Database and the CBIS-DDSM dataset

The objective of this investigation was to improve the diagnosis of breast cancer by combining two significant datasets: the Wisconsin Breast Cancer Database and the DDSM Curated Breast Imaging Subset (CBIS-DDSM). The Wisconsin Breast Cancer Database provides a detailed examination of the characteristics of cell nuclei, including radius, texture, and concavity, for 569 patients, of which 212 had malignant tumors. In addition, the CBIS-DDSM dataset—a revised variant of the Digital Database for Screening Mammography (DDSM)—offers a standardized collection of 2,620 scanned film mammography studies, including cases that are normal, benign, or malignant and that include verified pathology data. To identify complex patterns and trait diagnoses of breast cancer, this investigation used a hybrid deep learning methodology that combines Convolutional Neural Networks (CNNs) with the stochastic gradients method. The Wisconsin Breast Cancer Database is used for CNN training, while the CBIS-DDSM dataset is used for fine-tuning to maximize adaptability across a variety of mammography investigations. Data integration, feature extraction, model development, and thorough performance evaluation are the main objectives. The diagnostic effectiveness of the algorithm was evaluated by the area under the Receiver Operating Characteristic Curve (AUC-ROC), sensitivity, specificity, and accuracy. The generalizability of the model will be validated by independent validation on additional datasets. This research provides an accurate, comprehensible, and therapeutically applicable breast cancer detection method that will advance the field. These predicted results might greatly increase early diagnosis, which could promote improvements in breast cancer research and eventually lead to improved patient outcomes.

Can Radiologists Replace Digital 2D Mammography with Synthetic 2D Mammography in Breast Cancer Screening and Diagnosis, or Are Both Still Needed?

Background/Objectives: Digital mammography (DM) has long been the standard for breast cancer screening, while digital breast tomosynthesis (DBT) offers an advanced 3D imaging modality capable of generating 2D Synthetic Mammography (SM) images. Despite SM’s potential to reduce radiation exposure, many clinics favor DM, with DBT and SM, due to its perceived diagnostic reliability. This study investigates whether radiologists can replace DM with SM in breast cancer screening and diagnosis or if both modalities are necessary. Methods: We retrospectively analyzed DM and SM images from 375 women aged 40–65 who underwent DM with DBT at King Khaled University Hospital from 2020–2022. Three radiologists evaluated the images using ACR BI-RADS, assessing diagnostic accuracy via the area under the receiver operating characteristic (ROC) curve (AUC). The agreement in cancer conspicuity, breast density, size, and calcifications were measured using weighted kappa (κ). Results: Among 57 confirmed cancer cases and 290 cancer-free cases, DM demonstrated higher sensitivity (82.5% vs. 78.9%) and diagnostic accuracy (AUC 0.800 vs. 0.783, p &lt; 0.05) compared to SM. However, SM detected more suspicious calcifications in cancer cases (75.6% vs. 51.2%, p &lt; 0.05). Agreement was fair for conspicuity (κ = 0.288) and calcifications (κ = 0.409), moderate for density (κ = 0.591), and poor for size (κ = 0.254). Conclusions: while SM demonstrates enhanced effectiveness in detecting microcalcifications, DM still proves superior in overall diagnostic accuracy and image clarity. Therefore, although SM offers certain advantages, it remains slightly inferior to DM and cannot yet replace DM in breast cancer screening.

Patient experiences of cancer genetic testing by non-genetics providers in the surgical setting.

As indications for hereditary cancer genetic testing (GT) for patients with breast cancer (BC) expand, breast surgery teams offer GT to newly diagnosed patients to inform surgical plans. There is, however, limited data on the experiences of patients undergoing cancer GT by non-genetic providers. This study used in-depth interviews with 21 women recently diagnosed with BC at a large academic health system to capture their experiences. Post-positivist codebook thematic analysis was used to identify major themes from the interviews. Participants reported an overall positive experience of this GT process, stating that they prefer GT at an existing appointment shortly after their diagnosis, even though they described the conversation as brief. Many participants indicated thinking about or desiring GT before the offer was made. Interestingly, most participants did not see surgical decision-making as the main reason for GT and were instead motivated by concern for relatives and to have complete information. Interview data indicated areas for improvement in patient-provider communication, and most participants agreed that additional reference information on GT in the form of written or video materials would be helpful. Offering GT at an initial breast surgery appointment is acceptable and desired by patients with a new BC diagnosis and should be considered as a way to increase access to GT for these patients. However, additional information for patients is needed to close gaps in communication and provide a trustworthy reference following a busy medical appointment.

Recent advances in microfluidic chip technologies for applications as preclinical testing devices for the diagnosis and treatment of triple-negative breast cancers

Recent advances in microfluidic chip technologies for applications as preclinical testing devices for the diagnosis and treatment of triple-negative breast cancers

The Effect of Coronavirus Pandemic on the Treatment of Gynecological and Breast Cancers

Introduction: During the COVID-19 pandemic, various groups, including women with cancer, were affected as a high-risk group in terms of disease diagnosis and treatment. Despite the importance of therapeutic changes in cancers, such as breast and gynecological cancer, there is no comprehensive study in this regard that also refers to the coronavirus. The need for a deep understanding of the impact of coronavirus on the treatment plan of this group of patients caused the researcher to design and implement this study that aims to investigate the treatment challenge among cancer patients during the COVID-19 pandemic. Materials and methods: To answer the study question, a comprehensive search was carried out in databases, such as PubMed, Web of Science Core Collection, and Scopus, using keywords, including cancer, gynecologic cancer, breast cancer, treatment, delay, and modification. Full-texted, English language and original articles were included in this study. Results: In total, 27 articles were selected for the study. The findings of this study revealed that COVID-19 greatly affects the treatment of gynecology and breast cancer. These patients experience delay or modification of cancer treatment. Increased time between diagnosis and treatment, delay, change or cancellation of surgery and change in treatment plan are the most important changes in cancer treatment during the COVID-19 pandemic. Conclusion: The COVID-19 pandemic has had a significant impact on various aspects of gynecology and breast cancer care worldwide. In the current pandemic, there has been a significant delay in the diagnosis and treatment of gynecological and breast cancer, which, due to its higher morbidity and mortality, has made the condition more difficult for cancer patients and treatment teams.

Integrating VGG 19 U‐Net for Breast Thermogram Segmentation and Hybrid Enhancement With Optimized Classifier Selection: A Novel Approach to Breast Cancer Diagnosis

ABSTRACTEarly diagnosis of breast cancer is essential for improving patient survival rates and reducing treatment costs. Despite breast thermogram images having high quality, doctors in developing countries often struggle with early diagnosis due to difficulties in interpreting subtle details. Implementing a Computer‐Aided Diagnosis (CAD) system can assist doctors in accurately analyzing these details. This article presents an innovative approach to breast cancer diagnosis using thermal images. The proposed method enhances the quality and clarity of relevant features while preserving sharp and curved edges through U‐Net‐based segmentation for automatic selection of the ROI, advanced hybrid image enhancement techniques, and a machine learning classifier. Subjective analysis compares the processed images with five conventional enhancement techniques, demonstrating the efficiency of the proposed method. The quantitative analysis further validates the effectiveness of the proposed method against five conventional methods using four quality measures. The proposed method achieves superior performance with PSNR of 15.27 for normal and 14.31 for malignant images, AMBE of 6.594 for normal and 7.46 for malignant images, SSIM of 0.829 for normal and 0.80 for malignant images, and DSSIM of 0.084 for normal and 0.14 for malignant images. The classification phase evaluates four classifiers using 13 features from three categories. The Random Forest (RF) classifier with Discrete Wavelet Transform (DWT) based features initially outperformed other classifier features but had limited performance, with accuracy, sensitivity and specificity of 81.8%, 88.8%, and 91%, respectively. To improve this, three categories of features were normalized and converted into two principal components using Principal Component Analysis (PCA) to train the RF classifier, which then showed superior performance with 97.7% accuracy, 96.5% sensitivity, and 98.2% specificity. The dataset utilized in this article is obtained from the Indira Gandhi Centre for Atomic Research (IGCAR), Kalpakkam, India. The entire proposed model is implemented in a Jupyter notebook.

A Water-Soluble Aggregation-Induced Emission Luminogen for NIR-I/NIR-II Fluorescence Imaging of Breast Cancer Bone Metastases

Advanced breast cancer is prone to bone metastasis, which is the most common bone metastatic tumor. Current clinical methods for diagnosing breast cancer bone metastases rely on serological markers, computed tomography and magnetic resonance imaging. However, these technologies cannot meet patients’ needs due to the delayed screening, complex procedures and expensive equipment. Optical imaging currently exhibits inexhaustible and vigorous vitality in the field of diagnosis thanks to its advantages of simplicity, good controllability and high resolution. Nevertheless, the development of prominent chromophores for the diagnosis of breast cancer bone metastases is an appealing yet significantly challenging task. In this contribution, we rationally designed and synthesized three water-soluble aggregation-induced emission (AIE) luminogens, named PEGTPA-BTD, PEGTPA-NTD, and PEGTPA-NSD, by introducing different moieties as electron acceptors and PEGylated triphenylamine derivatives as electron donors and hydrophilic moieties. In vitro experiments showed that PEGTPA-NSD has a longer absorption and emission wavelength, where the emission wavelength can extend into NIR-II region. Besides, PEGTPA-NSD could self-assemble into stable nanoparticles in aqueous solution. Cell experiments showed that PEGTPA-NSD had no obvious dark toxicity to tumor cells or normal cells, and were easily taken in by tumor cells for cell imaging. What’s more, PEGTPA-NSD NPs possessed excellent fluorescence imaging performance and biocompatibility in vivo for breast cancer bone metastases in NIR-I and NIR-II region, respectively. In summary, PEGTPA-NSD is the first reported aggregation-induced emission luminogens (AIEgens) that can self-assemble to nanoparticles in aqueous solution for NIR-I/NIR-II fluorescence imaging of breast cancer bone metastases. These findings would provide new strategies for optical diagnostic imaging of breast cancer bone metastases to better advance clinical technology development.

Patient-reported observations on medical procedure timeliness (PROMPT) in breast cancer: a qualitative study.

Timeliness of care is an important healthcare outcome measure. The objective of this study was to explore patient perspectives on the timeliness of breast cancer diagnosis and treatment at accredited breast cancer centers. In this qualitative study, 1hour virtual interviews were conducted with participants 18-75years old who were diagnosed and treated for stage 0-III breast cancer at a National Accreditation Program for Breast Centers facility from 2018 to 2022. Thematic analysis was used to identify key themes of participant experiences. Twenty-eight participants were interviewed. Two thematic domains were identified: etiologies of expedited or delayed care and the impact of delayed or expedited care on patients. Within these domains, multiple themes emerged. For etiologies of expedited or delayed care, participants discussed (1) the effect of scheduling appointments, (2) the COVID-19 pandemic, (3) dissatisfaction with the timeline for various parts of the diagnostic workup, and (4) delays related to patient factors, including socioeconomic status. For the impact of expedited or delayed care, patients discussed (1) the emotional and mental impact of waiting, (2) the importance of communication and clear expectations, and (3) the impact of electronic health portals. Patients desired each care interval (e.g., the time from mammogram to breast biopsy) to be approximately 7days, with longer intervals sometimes preferred prior to surgery. These patient interviews identify areas of delay and provide patient-centered, actionable items to improve the timeliness of breast cancer care.

Unsupervised Histopathological Sub-Image Analysis for Breast Cancer Diagnosis Using Variational Autoencoders, Clustering, and Supervised Learning

This paper presents an integrated approach to breast cancer diagnosis that combines unsupervised and supervised learning techniques. The method involves using a pre-trained VGG19 model to process sub-images from the BreaKHis dataset, divided into nine parts for comprehensive analysis. This will be followed by a complete description of the architecture and workings of the variational Autoencoder (VAE) used for unsupervised Learning. The encoder network maps the input features to lower dimensions, capturing the most essential information. VAE learns a compressed representation of sub-images, facilitating a more profound understanding of underlying patterns and structures. For this reason, we then employ k-means clustering on the encoded representation to find naturally occurring clusters in our data set comprising a histopathological image. Every single sub-image is later fed into the VGG19-SVM model for classification purposes. During magnification at 100x, this model has attained a fantastic accuracy rate of 98.56%. Combining unsupervised analysis with VAE/k-means clustering and supervised classification with VGG19/SVM can integrate information from both methods, thereby improving the accuracy and robustness of such a task as sub-image classification in breast cancer histopathology.

A prospective study of vitamin D, proinflammatory cytokines, and risk of fragility fractures in women on aromatase inhibitors for breast cancer.

Vitamin D is critical to bone health by regulating intestinal absorption of calcium, whereas proinflammatory cytokines, including IL-1, IL-6, IL-12, and TNF-α, are known to increase bone resorption. We hypothesized that vitamin D and these cytokines at the time of breast cancer diagnosis were predictive for fragility fractures in women receiving aromatase inhibitors (AIs). In a prospective cohort of 1,709 breast cancer patients treated with AIs, we measured the levels of 25-hydroxyvitamin D (25OHD), IL-1β, IL-6, IL-12, and TNF-α from baseline blood samples. The associations of these biomarkers were analyzed with bone turnover markers (BALP and TRACP), bone regulatory markers (OPG and RANKL), bone mineral density (BMD) close to cancer diagnosis, and risk of fragility fractures during a median of 7.5 years of follow up. Compared to patients with vitamin D deficiency, patients with sufficient levels had higher bone turnover, lower BMD, and higher fracture risk; the latter became non-significant after controlling for covariates including BMD and no longer existed when patients taking vitamin D supplement or bisphosphonates or with history of fracture or osteoporosis were excluded. There was a non-significant trend of higher levels of IL-1β and TNF-α associated with higher risk of fracture (highest vs. lowest tertile, IL-1β: adjusted HR=1.37, 95% CI=0.94-1.99; TNF-α: adjusted HR=1.38, 95% CI=0.96-1.98). Our results do not support proinflammatory cytokines or vitamin D levels as predictors for risk of fragility fractures in women receiving AIs for breast cancer.

Women With Breast Cancer in Malaysia: Social Work Perspectives on the Challenges and Social Support

It has been reported that breast cancer is the most common type of cancer affecting women in Malaysia. A diagnosis of breast cancer will usually bring upon a set of issues and challenges affecting the well-being of these women. The aim of this article is to provide a literature review regarding the biopsychosocial issues, challenges, social support and adaptation process experienced by women with breast cancer, with an application to the Malaysian setting focusing on the Malaysian sociocultural backgrounds. The discussion of this article focuses on the importance of social support in adaptation process such as from immediate family members, support group and the medical team following the challenges faced by the patients such as ones stemming from their cultural background, their biological issues and their refusal to seek treatments. The overall review of the literature is aligned with the social work values in addressing human wellbeing, including the recognition of the psychosocial and sociocultural backgrounds as powerful components of health in practice and policies. Finally, this article will provide insights for the helping profession in the country such as medical social workers in providing social work assessments and interventions suitable for Asian women with breast cancer.