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Simple Summary: This position paper aims to address specific clinical questions regarding the use of cyclin-dependent kinase 4/6 inhibitors in elderly patients with early or advanced breast cancer. Its objectives are to delineate the current state of knowledge regarding the efficacy of these treatments in the elderly population and their tolerance profile, including the impact on quality of life, with a particular focus on the frailest subgroups, and to attempt to define the optimal treatment strategy for elderly and fragile patients (dosage and therapeutic sequence). Background: Breast cancer (BC) incidence increases with age, particularly in HR-positive/HER2-negative subtypes. Cyclin-dependent kinase 4 and 6 inhibitors (CDK 4/6is) alongside endocrine therapy (ET) have emerged as promising treatments for HR-positive/HER2-negative advanced and early BC. However, their efficacy, safety, and impact on quality of life (QoL) in older and frail patients remain underexplored. Methods: This position paper assesses the existing literature from 2015 to 2024, focusing on CDK4/6is use in patients aged 65 years and older with HR-positive/HER2-negative BC. Results: Our analysis methodically addresses critical questions regarding the utilization of CDK4/6is in the elderly BC patient population, organizing findings from the metastatic and adjuvant settings. In the metastatic setting, CDK4/6is significantly improve progression-free survival (PFS), paralleling benefits observed in younger patients, and suggest potential overall survival (OS) benefits, warranting further investigation. Despite an increased incidence of grade ≥ 3 adverse events (AEs), such as neutropenia and asthenia, CDK4/6is present a markedly lower toxicity profile compared to traditional chemotherapy, with manageable side effects. QoL analysis indicates that integrating CDK4/6is into treatment regimens does not significantly impact elderly BC patients' daily life and symptom management. Special attention is given to frail subgroups, and personalized approaches are recommended to balance efficacy and adverse effects, such as starting with ET alone and introducing CDK4/6is upon progression in patients with a low disease burden. Transitioning to the adjuvant setting, early results, particularly with abemaciclib, indicate positive effects on disease-free survival (DFS), emphasizing the need for continued analysis to validate these findings and assess long-term implications. However, data on older patients are insufficient to conclude whether they truly benefit from this treatment. Conclusion: Overall, CDK4/6is present a favorable benefit-risk profile in older BC patients, at least in advanced BC; however, further research is warranted to optimize treatment strategies and improve outcomes in this population [ABSTRACT FROM AUTHOR]

Mass production of vermicompost using suitable species of earthworms and selecting target organic waste materials has appeared to be a great development in the realm of biotechnological research for the sustainable eco-management. Although, for the bioconversion of organic wastes to vermicompost, suitable earthworm species play major roles, a hoard of bacterial assemblages by virtue of production of different enzymes facilitate the process of vermicomposting. The present study has documented the roles of vermicompost associated bacteria in combating, preventing, and controlling of cancer so as to open a new vista not only in the field of vermitechnology but also on biomedical research. Earthworms’ associated bacterial metabolic products having their unique physicochemical excellence have gained importance due to their roles as a facilitator of apoptosis (programed cell death in a MCF-7 cell line). The antioxidant and anticancer activities of ethyl acetate extracts’ of vermicompost associated bacterium Bacillus anthracis were undertaken by antioxidant assay which revealed maximum DPPH radical scavenging effect (75.79 ± 5.41%) of the extracts’ at 9 00 μg ml-1. Furthermore, the crude extracts obtained from the same bacteria were found to decrease the activity of SOD (superoxide dismutase) with the increase in doses. MTT assay showed potent cytotoxic activity against human breast adenocarcinoma cells (MCF-7) with the IC50 value of 46.64 ± 0.79 μg ml-1. It was further confirmed through Hoechst 33258 staining of nuclear fragmentation assay and DNA fragmentation analysis. Western blotting test has confirmed a downregulation of Akt upon application of crude extracts. Increase of SOD activity along with decrease of Akt level reflects that the mode of action is entirely PI-3K dependent. This study tends to indicate that B. anthracis isolated from vermicompost could be potentially explored for the development of new therapeutic agents, especially against cancer. [ABSTRACT FROM AUTHOR]

Simple Summary: HER2-positive breast cancer occurs in 15–30% of cases and has a poor prognosis. Digital image analysis of HER2 is promising, but its implementation in real clinical practice remains unclear. This systematic review evaluates the effectiveness of digital image analysis algorithms for HER2 in breast cancer and their performance, with a focus on testing them in real-world clinical settings. The authors aim to assess the applicability of these algorithms in practical clinical scenarios. By analyzing 25 papers from the period 2013–2024 and emphasizing mostly deep learning approaches, the review underscores the importance of standardized evaluation criteria, study designs tailored for clinical applications, and clinical validation. While direct evidence of clinical application was not found, the findings aim to guide future research and the implementation of digital image analysis in breast cancer diagnosis within clinical settings, potentially impacting the research community by advancing algorithmic applications in real clinical practice. This systematic review aims to address the research gap in the performance of computational algorithms for the digital image analysis of HER2 images in clinical settings. While numerous studies have explored various aspects of these algorithms, there is a lack of comprehensive evaluation regarding their effectiveness in real-world clinical applications. We conducted a search of the Web of Science and PubMed databases for studies published from 31 December 2013 to 30 June 2024, focusing on performance effectiveness and components such as dataset size, diversity and source, ground truth, annotation, and validation methods. The study was registered with PROSPERO (CRD42024525404). Key questions guiding this review include the following: How effective are current computational algorithms at detecting HER2 status in digital images? What are the common validation methods and dataset characteristics used in these studies? Is there standardization of algorithm evaluations of clinical applications that can improve the clinical utility and reliability of computational tools for HER2 detection in digital image analysis? We identified 6833 publications, with 25 meeting the inclusion criteria. The accuracy rate with clinical datasets varied from 84.19% to 97.9%. The highest accuracy was achieved on the publicly available Warwick dataset at 98.8% in synthesized datasets. Only 12% of studies used separate datasets for external validation; 64% of studies used a combination of accuracy, precision, recall, and F1 as a set of performance measures. Despite the high accuracy rates reported in these studies, there is a notable absence of direct evidence supporting their clinical application. To facilitate the integration of these technologies into clinical practice, there is an urgent need to address real-world challenges and overreliance on internal validation. Standardizing study designs on real clinical datasets can enhance the reliability and clinical applicability of computational algorithms in improving the detection of HER2 cancer. [ABSTRACT FROM AUTHOR]

Background The value equation of value-based healthcare (VBHC) as a single figure remains ambiguous, closer to a theoretical framework than a useful tool for decision making. The challenge lies in the way patient-centred outcomes (PCOs) might be combined to produce a single value of the numerator. This paper aims to estimate the weights of PCOs to provide a single figure in the numerator, which ultimately will allow a VBHC figure to be reached. Methods A cohort of patients diagnosed with breast cancer (n  = 690) with a 6-month follow-up recruited in 2019–20 across six European hospitals was used. Patient-reported outcomes (PROs), clinical-related outcomes (CROs), and clinical and socio-demographic variables were collected. The numerator was defined as a composite indicator of the PCOs (CI-PCO), and regression analysis was applied to estimate their weights and consequently arrive at a single figure. Results Pain showed as the highest weight followed by physical functioning , emotional functioning , and ability to work , and then by a symptom, either arm or breast. PCOs weights were robust to sensitivity analysis. The CI-PCO value was found to be more informative than the health-related quality of life (HRQoL) value. Conclusions To the best of our knowledge, this is the first research to combine the PCOs proposed by ICHOM to provide a single figure in the numerator of the value equation. This figure shows a step forward in VBHC to reach a holistic benchmarking across healthcare centres and a value-based payment. This research might also be applied in other medical conditions as a methodological pathway. [ABSTRACT FROM AUTHOR]

Breast cancer diagnosis from histopathology images is often time consuming and prone to human error, impacting treatment and prognosis. Deep learning diagnostic methods offer the potential for improved accuracy and efficiency in breast cancer detection and classification. However, they struggle with limited data and subtle variations within and between cancer types. Attention mechanisms provide feature refinement capabilities that have shown promise in overcoming such challenges. To this end, this paper proposes the Efficient Channel Spatial Attention Network (ECSAnet), an architecture built on EfficientNetV2 and augmented with a convolutional block attention module (CBAM) and additional fully connected layers. ECSAnet was fine-tuned using the BreakHis dataset, employing Reinhard stain normalization and image augmentation techniques to minimize overfitting and enhance generalizability. In testing, ECSAnet outperformed AlexNet, DenseNet121, EfficientNetV2-S, InceptionNetV3, ResNet50, and VGG16 in most settings, achieving accuracies of 94.2% at 40×, 92.96% at 100×, 88.41% at 200×, and 89.42% at 400× magnifications. The results highlight the effectiveness of CBAM in improving classification accuracy and the importance of stain normalization for generalizability. [ABSTRACT FROM AUTHOR]

Compared with intravenous (i.v.) chemotherapy, oral administration is convenient, requires fewer healthcare resources, is generally preferred by patients, and may be appropriate in older people with breast, colorectal and lung cancers. The effects of organ dysfunction on drug metabolism and drug interactions in patients with multiple comorbidities must be considered but are not specific to oral chemotherapy. Single-agent oral chemotherapy with capecitabine or vinorelbine is active in older patients with advanced or metastatic breast cancer. Choice of treatment is based mainly on different safety profiles. In the adjuvant treatment of colorectal cancer (CRC), single-agent oral capecitabine is an effective alternative to i.v. fluorouracil (5-FU) regimens. In metastatic CRC, oral, single-agent capecitabine has recently shown encouraging median overall survival in combination with bevacizumab. In non-small cell lung cancer, fit older patients, like their younger counterparts, benefit from platinum-based doublets, with carboplatin preferred to cisplatin. Single agent vinorelbine is an option for those less suited to combination chemotherapy, and oral may be an alternative to i.v. administration. For elderly cancer patients in general, metronomic chemotherapy combines good tolerability with acceptable activity. [ABSTRACT FROM AUTHOR]

This 2012 Consensus paper reviews the literature on side effects of x-ray exposure in the pediatric population as it relates to scoliosis evaluation and treatment. Alternative methods of spinal assessment and imaging are reviewed, and strategies for reducing the number of radiographs are developed. Using the Delphi technique, SOSORT members developed consensus statements that describe how often radiographs should be taken in each of the pediatric and adolescent sub-populations. [ABSTRACT FROM AUTHOR]

Simple Summary: Breast cancer is a significant cause of female cancer-related deaths in the US. Checking how severe the cancer is helps in planning treatment. Modern AI methods are good at grading cancer, but they are not used much in hospitals yet. We developed and utilized ensemble deep learning algorithms for addressing the tasks of classifying (1) breast cancer subtype and (2) breast cancer invasiveness from whole slide image (WSI) histopathology slides. The ensemble models used were based on convolutional neural networks (CNNs) known for extracting distinctive features crucial for accurate classification. In this paper, we provide a comprehensive analysis of these models and the used methodology for breast cancer diagnosis tasks. Cancer diagnosis and classification are pivotal for effective patient management and treatment planning. In this study, a comprehensive approach is presented utilizing ensemble deep learning techniques to analyze breast cancer histopathology images. Our datasets were based on two widely employed datasets from different centers for two different tasks: BACH and BreakHis. Within the BACH dataset, a proposed ensemble strategy was employed, incorporating VGG16 and ResNet50 architectures to achieve precise classification of breast cancer histopathology images. Introducing a novel image patching technique to preprocess a high-resolution image facilitated a focused analysis of localized regions of interest. The annotated BACH dataset encompassed 400 WSIs across four distinct classes: Normal, Benign, In Situ Carcinoma, and Invasive Carcinoma. In addition, the proposed ensemble was used on the BreakHis dataset, utilizing VGG16, ResNet34, and ResNet50 models to classify microscopic images into eight distinct categories (four benign and four malignant). For both datasets, a five-fold cross-validation approach was employed for rigorous training and testing. Preliminary experimental results indicated a patch classification accuracy of 95.31% (for the BACH dataset) and WSI image classification accuracy of 98.43% (BreakHis). This research significantly contributes to ongoing endeavors in harnessing artificial intelligence to advance breast cancer diagnosis, potentially fostering improved patient outcomes and alleviating healthcare burdens. [ABSTRACT FROM AUTHOR]

Triple negative breast cancer (TNBC) is an aggressive subtype of breast cancer. Conventional treatment options for TNBC often have limited efficacy and significant side effects. In recent years, traditional Chinese medicine (TCM) has shown promising results in the treatment of TNBC. TCMs include herb combinations that have synergistic effects to regulate homeostasis in the body, reduce tumor resistance, and improve patient quality of life. At present, three main TCM methods are used to treat TNBC in the clinic: strengthening the body's resistance, dispelling phlegm, and removing cancer toxins. This paper reviews the theories and mechanisms of each in TNBC treatment. The method of strengthening the body's resistance emphasizes enhancing the body's original Qi to fight against pathogenic factors; the method of dispelling phlegm seeks to eliminate phlegm stagnation and alleviate the burden on affected organs; the method of removing cancer toxins focuses on detoxification and detumescence to remove the toxic elements associated with TNBC. Although these methods treat TNBC from different etiologies, they have achieved good therapeutic effects and represent an important academic approach: That is, to cure the disease with a comprehensive view of the body and restore the balance of Yin and Yang. This knowledge lays a foundation for the future development and reasonable application of TCM in the clinic. [ABSTRACT FROM AUTHOR]

Radiologists and clinicians must automatically examine breast and tumor locations and sizes accurately. In recent years, several neural network‐based feature fusion versions have been created to improve medical image segmentation. Multi‐modal image fusion photos may efficiently identify tumors. This work uses image fusion to identify computed tomography and magnetic resonance imaging alterations. A Gauss‐log ratio operator is recommended for difference image production. The Gauss‐log ratio and log ratio difference image complement the objective of improving the difference map through image fusion. The feature change matrix extracts edge, texture, and intensity from each picture pixel. The final change detection map classifies feature vectors as "changed" or "unchanged" which has been mapped for high‐resolution or low‐resolution pixels. This paper proposes a multi‐feature blocks (MFB) based neural network for multi‐feature fusion. This neural network modeling approach globalizes pixel spatial relationships. MFB‐based feature fusion also aims to capture channel interactions between feature maps. The proposed technique outperforms state‐of‐the‐art approaches which have been discussed in detail in experimental results section. [ABSTRACT FROM AUTHOR]

Breast tumor segmentation of ultrasound images provides valuable tumor information for early detection and diagnosis. However, speckle noise and blurred boundaries in breast ultrasound images present challenges for tumor segmentation, especially for malignant tumors with irregular shapes. Recent vision transformers have shown promising performance in handling the variation through global context modeling. Nevertheless, they are often dominated by features of large patterns and lack the ability to recognize negative information in ultrasound images, which leads to the loss of breast tumor details (e.g., boundaries and small objects). In this paper, we propose a novel noise suppression and boundary refinement network, NSBR-Net, to simultaneously alleviate speckle noise interference and blurred boundary problems of breast tumor segmentation. Specifically, we propose two innovative designs, namely, the Noise Suppression Module (NSM) and the Boundary Refinement Module (BRM). The NSM filters noise information from the coarse-grained feature maps, while the BRM progressively refines the boundaries of significant lesion objects. Our method demonstrates superior accuracy over state-of-the-art deep learning models, achieving significant improvements of 3.67% on Dataset B and 2.30% on the BUSI dataset in mDice for testing malignant tumors. [ABSTRACT FROM AUTHOR]

Cancer diseases constitute a major health problem which leads to the death of millions of people annually. They are unique among other diseases because cancer cells can perfectly adapt to the environment that they create themselves. This environment is usually highly hostile and for normal cells it would be hugely difficult to survive, however neoplastic cells not only can survive but also manage to proliferate. One of the reasons is that they can alter immunological pathways which allow them to be flexible and change their phenotype to the one needed in specific conditions. The aim of this paper is to describe some of these immunological pathways that play significant roles in gynecologic neoplasms as well as review recent research in this field. It is of high importance to possess extensive knowledge about these processes, as greater understanding leads to creating more specialized therapies which may prove highly effective in the future. [ABSTRACT FROM AUTHOR]

Background: Nursing interventions in the post‐operative time period including psychological and emotional support, adverse event education, and instructions for follow‐up care contribute patient satisfaction, safety, and quality of life. However, the time spent in the post‐anesthesia care unit (PACU) and hospital continues to shorten around the world to reduce health care spending and improve patient outcomes. Nurses conducting research during the important post‐operative recovery period need to utilize unique techniques and emerging technologies to contact, recruit and collect data outside of the hospital setting including the Research Electronic Data Capture (REDCap) platform. Aims: This paper describes the feasibility and acceptability, facilitators and barriers of the software application, REDCap, to complete a repeated‐measures, descriptive correlational study in patients undergoing outpatient breast cancer surgeries. Methods & Materials: The recruitment, data collection and storage were completed utilizing the secure REDCap Platform. The Institutional Research Board (IRB)‐approved study was a repeated‐measures, descriptive, correlational study with data collection at three time points. The data points aligned with important transitions and routine visits to improve data collection feasibility and increase relevance to clinical practice. Results: The sample consisted of women diagnosed with breast cancer undergoing breast conserving surgery between August 15 and October 15, 2020. There were 123 potential participants, of which 76 started the surveys and 75 participated (61%) responded and participated in the study on Post‐operative Day 1. Fifty‐nine participants (78%) completed the surveys on post‐operative Day 14. Discussion: As the frequency of outpatient treatment increases, nurses conducting post‐operative research will need to collect the data outside of the hospital setting. Conclusion: Email provides a method of studying new phenomena by recruiting participants, providing information about the study, and collecting results in a non‐traditional setting. REDCap provides a method to facilitate nursing research through a securely encrypted integrated process. [ABSTRACT FROM AUTHOR]

Simple Summary: Metastatic breast cancer is an aggressive disease that early diagnostic attempts is of an utmost importance. A machine learning model that utilizes NCA and MRMR in this work is attempting to isolate pertinent dysregulated miRNA's for the different four cancer stages. This work compares the current clinical diagnostic approaches with the proposed ML model results. Determining the tumor origin in humans is vital in clinical applications of molecular diagnostics. Metastatic cancer is usually a very aggressive disease with limited diagnostic procedures, despite the fact that many protocols have been evaluated for their effectiveness in prognostication. Research has shown that dysregulation in miRNAs (a class of non-coding, regulatory RNAs) is remarkably involved in oncogenic conditions. This research paper aims to develop a machine learning model that processes an array of miRNAs in 1097 metastatic tissue samples from patients who suffered from various stages of breast cancer. The suggested machine learning model is fed with miRNA quantitative read count data taken from The Cancer Genome Atlas Data Repository. Two main feature-selection techniques have been used, mainly Neighborhood Component Analysis and Minimum Redundancy Maximum Relevance, to identify the most discriminant and relevant miRNAs for their up-regulated and down-regulated states. These miRNAs are then validated as biological identifiers for each of the four cancer stages in breast tumors. Both machine learning algorithms yield performance scores that are significantly higher than the traditional fold-change approach, particularly in earlier stages of cancer, with Neighborhood Component Analysis and Minimum Redundancy Maximum Relevance achieving accuracy scores of up to 0.983 and 0.931, respectively, compared to 0.920 for the FC method. This study underscores the potential of advanced feature-selection methods in enhancing the accuracy of cancer stage identification, paving the way for improved diagnostic and therapeutic strategies in oncology. [ABSTRACT FROM AUTHOR]

This paper proposes an approach to enhance the differentiation task between benign and malignant Breast Tumors (BT) using histopathology images from the BreakHis dataset. The main stages involve preprocessing, which encompasses image resizing, data partitioning (training and testing sets), followed by data augmentation techniques. Both feature extraction and classification tasks are employed by a Custom CNN. The experimental results show that the proposed approach using the Custom CNN model exhibits better performance with an accuracy of 84% than applying the same approach using other pretrained models, including MobileNetV3, EfficientNetB0, Vgg16, and ResNet50V2, that present relatively lower accuracies, ranging from 74 to 82%; these four models are used as both feature extractors and classifiers. To increase the accuracy and other performance metrics, Grey Wolf Optimization (GWO), and Modified Gorilla Troops Optimization (MGTO) metaheuristic optimizers are applied to each model separately for hyperparameter tuning. In this case, the experimental results show that the Custom CNN model, refined with MGTO optimization, reaches an exceptional accuracy of 93.13% in just 10 iterations, outperforming the other state-of-the-art methods, and the other four used pretrained models based on the BreakHis dataset. [ABSTRACT FROM AUTHOR]

Simple Summary: The current evidence regarding Indigenous* women and breast cancer in Australia shows lower prevalence but higher mortality rates. There are a range of reasons for this, including co-morbidities, lack of access to health services and low health information fluency. Perhaps most importantly, breast cancer health policy and service delivery practice do not meet the needs of Indigenous women in Australia, according to Indigenous women. Talking and listening to Indigenous women about breast cancer highlight that the solutions to improve breast cancer outcomes are available and that they are not complex. Indigenous women must be involved in the improvement of policy and practice in order for these outcomes to improve. *Terminology: We respectfully refer to Aboriginal and Torres Strait Islander people as "Indigenous". In Australia, the incidence rate of breast cancer is lower in Indigenous* women than non-Indigenous women; however, the mortality rate is higher, with Indigenous women 1.2 times more likely to die from the disease. This paper provides practical and achievable solutions to improve health outcomes for Indigenous women with breast cancer in Australia. This research employed the Context–Mechanism–Outcome (CMO) framework to reveal potential mechanisms and contextual factors that influence breast cancer outcomes for Indigenous women, stratified into multiple levels, namely, micro (interpersonal), meso (systemic) and macro (policy) levels. The CMO framework allowed us to interpret evidence regarding Indigenous women and breast cancer and provides nine practical ways to improve health outcomes and survival rates. [ABSTRACT FROM AUTHOR]

Simple Summary: Cancer Metastasis has been mostly misunderstood and underestimated by the general public regarding cancer deaths. The aims and objectives of the present report was to more fully describe the scientific activities and components that are involved in the malignant cell's fulfillment of the metastatic process and to impress the readership that metastasis is the major cause on all cancer deaths. The paper proceeds to enumerate and describe certain factors that contribute to cancer cell proliferation and subsequent metastasis such as; (a) calcium levels, (b) multiple cell membrane channels, and (c) the chemokine/receptor system. These latter components could serve to provide ideal molecular targets for possible future peptide therapeutic applications in treating cancer patients. Metastasis (Met) largely contributes to the major cause of cancer deaths throughout the world, rather than the growth of the tumor mass itself. The present report brings together several of the pertinent contributors to cancer growth and metastatic processes from an activity standpoint. Such biological activities include the following: (1) cell adherence and detachment; (2) cell-to-cell contact; (3) contact inhibition; (4) the cell interfacing with the extracellular matrix (ECM); (5) tumor cell-to-stroma communication networks; (6) chemotaxis; and (7) cell membrane potential. Moreover, additional biochemical factors that contribute to cancer growth and metastasis have been shown to comprise the following: (a) calcium levels in the extracellular matrix and in intracellular compartments; (b) cation voltage and ATP-regulated potassium channels; (c) selective and non-selective cation channels; and (d) chemokines (cytokines) and their receptors, such as CXCL12 (SDF-1) and its receptor/binding partner, CXCR4. These latter molecular components represent a promising group of an interacting and synchronized set of candidates ideal for peptide therapeutic targeting for cancer growth and metastasis. Such peptides can be obtained from naturally occurring proteins such as alpha-fetoprotein (AFP), an onco-fetal protein and clinical biomarker. [ABSTRACT FROM AUTHOR]

We present a case study on the design and implementation of a value-based bundled package of care for patients with early-stage breast cancer treated in the private health sector in Australia. Value-based healthcare is an essential change to how we deliver healthcare, shifting the focus from paying for individual services provided to a focus on the health outcomes gained over a full cycle of care. The Australian health system has unintentionally created barriers to value-based cancer care through fragmented care pathways and complex funding arrangements where patients can unexpectedly encounter high out-of-pocket costs. A team of clinicians, service providers, health systems and funding experts, private health insurers and consumers have collaborated to design and pilot a complete bundled package of care for breast cancer patients which aims to address these challenges. With 40 patients recruited to date, early evaluation results show positive patient experience of 'joined-up' care and financial transparency. This case study provides a high-level overview of the approach taken to design and implement the Breast Cancer Bundle and the lessons learned for its expansion in both public and private settings. What is known about this topic? Enabling value-based healthcare is essential to improve healthcare, focusing on outcomes gained over a full cycle of care. Patients diagnosed with cancer frequently report care to be disjointed and the cause of financial stress, thus can particularly benefit from value-based care models. What does this paper add? This case study describes the design and implementation of a bundled package of care for patients with early-stage breast cancer treated in the private health sector in Australia. What are the implications for practitioners? Lessons learned through this process provide considerations for expansion of this model of care. This article belongs to the Special Issue: Value-based Healthcare. [ABSTRACT FROM AUTHOR]

Personal health‐related resources are essential for women with breast cancer, as these help them to maintain their own health and well‐being during different phases of their illness. The purpose of this integrative review was to identify, describe and synthetise what personal health‐related resources have already been identified for this patient group. We carried out an integrative review to find papers that focused on personal health‐related resources for women with breast cancer aged 18–64 years. The search covered 1 January 2005 to 31 May 2021 and was carried out using the CINAHL, PubMed, PsycINFO, Web of Science and Cochrane Library databases. It was limited to peer‐reviewed scientific papers with abstracts published in English and 23 papers met the inclusion criteria. The data were analysed using content analysis. Personal health‐related resources for women with breast cancer consisted of three different, but inherently interconnected, categories of personal strengths, person‐centred cancer care and social support from and involvement in their cultural community. Personal health‐related resources for women with breast cancer were multifaceted. Women need nursing support to identify and use these resources and future studies are needed to strengthen how they are measured. [ABSTRACT FROM AUTHOR]

Background. During the last decades the diagnosis, treatment, and prognosis of breast cancer have changed and improved in Denmark. The first mammography screening programme started in 1991. However, for many years only about 20% of Danish women aged 50-69 were offered screening. The national roll-out of screening took place in 2008-2010. Material and methods. Based on published Danish data, this overview describes the status of diagnosis and treatment, and the screening programme. For further evaluating the potential overdiagnosis and overtreatment, additional Danish Breast Cancer Cooperative Group (DBCG) data are included. Results and conclusion. Using incidence-based mortality method, reduction in breast cancer mortality was estimated to be 25% in the target group of women after 10 years of screening in Copenhagen; an outcome comparable to that of randomised controlled trials. A recent Danish study has indicated overdiagnosis to be around 4%. Others have estimated overdiagnosis to be 33%. National DBCG data showed that the rude breast cancer incidence increased during the period 1990-2011 from 126 to 206 per 100 000. The incidence was almost constant for women younger than 50 years. In regions not offering screening, the incidence increased with 3% per year for women aged 50-69 years with similar trends for small and large tumours. After introduction of screening the increase in the age group 50-69 years was confined to small tumours ≤ 20 mm, and most pronounced for node negative patients. From the 1990s, the use of breast conserving surgery has increased from around 25% to 69% in 2010. Screening has not increased the number of mastectomies. Breast cancer treatment in Denmark is evidence based and in agreement with international recommendations. After the introduction of mammography screening the absolute number of patients with a more advanced stage at diagnosis and the absolute number of patients undergoing mastectomy have decreased. [ABSTRACT FROM AUTHOR]

Aim: This study aimed to explore the utility of latent profile analysis of illness perception, in comparison to treating illness perception as several dimensions, to predict breast cancer‐related lymphedema risk management behaviours among Chinese breast cancer patients. Methods: This is a 3‐month longitudinal study. From August 2019 to January 2021, patients who recently underwent breast cancer surgery including axillary lymphadenectomy were recruited. Illness perception and risk management behaviours were measured by breast cancer‐related lymphedema specific questionnaires before discharge following surgery (n = 268) and at 3 months postsurgery (n = 213), respectively. Results: Treating illness perception as several dimensions, 'illness coherence' and 'timeline (cyclical)' dimensions were found to be significantly associated with breast cancer‐related lymphedema risk management behaviours. Using the latent profile analysis, two illness perception profiles were identified and significant differences were revealed in breast cancer‐related lymphedema risk management behaviours between them. Overall, illness perception profiles explained smaller amounts of variability in breast cancer‐related lymphedema risk management behaviours than illness perception dimensions. Conclusion: Future studies could combine these two different perspectives of illness perception regarding breast cancer‐related lymphedema into the design of interventions to improve breast cancer‐related lymphedema risk management behaviours. Summary statement: What is already known about this topic? Risk management behaviours are essential to reduce the risk and severity of breast cancer‐related lymphedema (BCRL).Illness perception is a modifiable predictor of BCRL risk management behaviours.Traditional correlation or regression analysis treat illness perceptions as several isolated elements and make it difficult to synthesize our understanding of it. What this paper adds Treating illness perceptions as several isolated elements, illness coherence and timeline (cyclical) were main components predicting BCRL risk management behaviours.Treating illness perception as a whole, latent profile analysis assigned breast cancer patients into two distinct profiles with similar illness perception.Illness perception dimensions and illness perception profiles showed certain effects on predicting BCRL risk management behaviours. The implications of this paper This study adds our knowledge of illness perception regarding BCRL and its effect on predicting BCRL risk management behaviours.The two analysis approaches provided different perspectives on the design and implementation of future intervention. [ABSTRACT FROM AUTHOR]

Aims: The purpose of this study was to examine the efficacy of cryotherapy with frozen gloves for the prevention of the chemotherapy‐induced peripheral neuropathy of the hand. Background: Most breast cancer patients receive chemotherapy; consequently, patients frequently experience adverse effects of chemotherapy‐induced peripheral neuropathy. Methods: A quasi‐experimental and prospective self‐controlled study was conducted. Breast cancer patients wore frozen glove on the dominant hand for 90 min during their weekly treatment with paclitaxel (80 mg/m2). Treatment of the dominant hand, the intervention group, was continued for 12 weeks. The non‐dominant hand was considered the control group. Results: A total of 22 patients participated in this study, and only one patient did not reach the cumulative dose (960 mg/m2). Findings show that the incidences of sensory and motor symptoms of chemotherapy‐induced peripheral neuropathies at the following times (Time 1 to Time 4) were significantly lower in the intervention group than in the control group. However, although the incidences of motor symptoms were lower in the experimental group than in the control group, a significant difference was shown only at Time 4. Additionally, both groups of patients reported that their incidence of sensory symptoms were higher than those of motor symptoms. Conclusion: Cryotherapy with frozen gloves is useful in reducing both the sensory and motor symptoms of the chemotherapy‐induced peripheral neuropathy of the hands. Summary statement: What is already known about this topic? Most breast cancer patients receive chemotherapy; consequently, patients frequently experience adverse effects of the chemotherapy‐induced peripheral neuropathy.Previous studies have indicated that cryotherapy (frozen gloves or shocks) may effectively reduce chemotherapy‐induced peripheral neuropathy associated with the use of paclitaxel. However, there is limited evidence for the effective treatment of chemotherapy‐induced peripheral neuropathy. What this paper adds? Paclitaxel‐induced peripheral neuropathy is frequently observed in patients with breast cancer and cryotherapy may effectively reduce this.The study results show that cryotherapy with frozen gloves can effectively alleviate the sensory and motor symptoms of the hands in breast cancer patients during chemotherapy.Moreover, we also found that breast cancer patients reporting sensory symptoms had higher motor symptoms during treatment with paclitaxel. The implication of this paper: Cryotherapy is a simple, safe, and effective strategy to prevent chemotherapy‐induced peripheral neuropathy in patients with breast cancer who receive paclitaxel treatment.Moreover, a reduction in group effects may increase the willingness of patients to receive chemotherapy. [ABSTRACT FROM AUTHOR]

Simple Summary: Recent advancements in AI have revolutionized cancer research, especially in the analysis of histopathological imaging data with minimal human involvement. Early detection of lymph node metastasis in breast cancer is vital for treatment outcomes. This paper introduces a novel approach that combines representation learning and deep learning (DL) to detect small tumors (STs) without neglecting larger ones. The proposed method uses representation learning to identify STs in histopathology images, followed by DL algorithms for breast cancer detection. Extensive evaluation shows remarkable accuracy in detecting STs without compromising larger-lesion detection. This approach enables early detection, timely intervention, and potentially improved treatment outcomes. The integration of representation learning and DL offers a promising solution for ST detection in breast cancer. By reducing human involvement and leveraging AI capabilities, the proposed method achieves impressive accuracy in identifying STs. Further research and validation could enhance diagnostic capabilities and personalized treatment strategies, ultimately benefiting breast cancer patients. The early diagnosis of lymph node metastasis in breast cancer is essential for enhancing treatment outcomes and overall prognosis. Unfortunately, pathologists often fail to identify small or subtle metastatic deposits, leading them to rely on cytokeratin stains for improved detection, although this approach is not without its flaws. To address the need for early detection, multiple-instance learning (MIL) has emerged as the preferred deep learning method for automatic tumor detection on whole slide images (WSIs). However, existing methods often fail to identify some small lesions due to insufficient attention to small regions. Attention-based multiple-instance learning (ABMIL)-based methods can be particularly problematic because they may focus too much on normal regions, leaving insufficient attention for small-tumor lesions. In this paper, we propose a new ABMIL-based model called normal representative keyset ABMIL (NRK-ABMIL), which addresseses this issue by adjusting the attention mechanism to give more attention to lesions. To accomplish this, the NRK-ABMIL creates an optimal keyset of normal patch embeddings called the normal representative keyset (NRK). The NRK roughly represents the underlying distribution of all normal patch embeddings and is used to modify the attention mechanism of the ABMIL. We evaluated NRK-ABMIL on the publicly available Camelyon16 and Camelyon17 datasets and found that it outperformed existing state-of-the-art methods in accurately identifying small tumor lesions that may spread over a few patches. Additionally, the NRK-ABMIL also performed exceptionally well in identifying medium/large tumor lesions. [ABSTRACT FROM AUTHOR]

Objectives: The benefit of mammography screening in reducing population mortality from breast cancer is well established. In this paper, we estimate the effect of repeated participation at scheduled screens on case survival. Methods: We analysed incidence and survival data on 37,079 women from nine Swedish counties who had at least one to five invitation(s) to screening prior to diagnosis, and were diagnosed with breast cancer between 1992 and 2016. Of these, 4564 subsequently died of breast cancer. We estimated the association of survival with participation in up to the most recent five screens before diagnosis. We used proportional hazards regression to estimate the effect on survival of the number of scheduled screens in which subjects participated prior to the diagnosis of breast cancer. Results: There was successively better survival with an increasing number of screens in which the subject participated. For a woman with five previous screening invitations who participated in all five, the hazard ratio was 0.28 (95% confidence interval (CI) 0.25–0.33, p < 0.0001) compared to a woman attending none (86.9% vs 68.9% 20-year survival). Following a conservative adjustment for potential self-selection factors, the hazard ratio was 0.34 (95% CI 0.26–0.43, p < 0.0001), an approximate three-fold reduction in the hazard of dying from breast cancer. Conclusion: For those women who develop breast cancer, regular prior participation in mammography screening confers significantly better survival. [ABSTRACT FROM AUTHOR]

Background Value-based healthcare (VBHC) is a conceptual framework to improve the value of healthcare by health, care-process and economic outcomes. Benchmarking should provide useful information to identify best practices and therefore a good instrument to improve quality across healthcare organizations. This paper aims to provide a proof-of-concept of the feasibility of an international VBHC benchmarking in breast cancer, with the ultimate aim of being used to share best practices with a data-driven approach among healthcare organizations from different health systems. Methods In the VOICE community—a European healthcare centre cluster intending to address VBHC from theory to practice—information on patient-reported, clinical-related, care-process-related and economic-related outcomes were collected. Patient archetypes were identified using clustering techniques and an indicator set following a modified Delphi was defined. Benchmarking was performed using regression models controlling for patient archetypes and socio-demographic characteristics. Results Six hundred and ninety patients from six healthcare centres were included. A set of 50 health, care-process and economic indicators was distilled for benchmarking. Statistically significant differences across sites have been found in most health outcomes, half of the care-process indicators, and all economic indicators, allowing for identifying the best and worst performers. Conclusions To the best of our knowledge, this is the first international experience providing evidence to be used with VBHC benchmarking intention. Differences in indicators across healthcare centres should be used to identify best practices and improve healthcare quality following further research. Applied methods might help to move forward with VBHC benchmarking in other medical conditions. [ABSTRACT FROM AUTHOR]

Cancer has evolved into a substantial public health concern as the second-leading cause of mortality globally. Radiotherapy and chemotherapy have been the two most widely used cancer therapies in recent years; however, both have drawbacks. Therefore, the focus has shifted to the creation of herbal medicines, the extraction of active ingredients, replacement therapy, and the adverse effects of these medications. Ginsenoside Rh2, which is extracted from ginseng, has been identified in many cancer cells. The immune system of the body is strengthened by ginsenoside Rh2, which can also cause the proliferation, death, and differentiation of tumor cells through various pathways. For instance, it inhibits the expression of the NF- κ B signaling pathway and induces cell apoptosis, affects the expression levels of mitochondrial apoptosis proteins Bcl-2 and Bax, and cooperates with the PD-1 blockade to reactivate T cells to promote an antitumor immune response. Furthermore, ginsenosides Rh2 has the effect of reversing the toxic effect of chemotherapy drugs on normal cells, reducing myocardial damage, and relieving bone marrow function suppression. For clinical applications, it is mainly used as an adjuvant drug for preoperative neoadjuvant chemotherapy, postoperative adjuvant chemotherapy, and rescue treatment of advanced cancer. This paper summarizes the pharmacological action and mechanism of ginsenosides Rh2 in all kinds of cancer and looks forward to its future development and application. [ABSTRACT FROM AUTHOR]

Background: Detecting breast cancer in its early stages remains a significant challenge in the present context and is a leading cause of death among women, primarily due to delayed identification. This paper presents a practical and accurate approach based on deep learning to identify breast cancer in cytology images. Method: The analytical approach leverages knowledge from a related problem through a technique known as transfer learning. Convolutional neural networks (CNNs) are employed due to their remarkable performance on large datasets. Image classification architectures such as Google network (GoogleNet), Visual geographical group network (VGGNet), residual network (ResNet), and dense convolution network (DenseNet) are utilized in this approach. By applying transfer learning, the images are classified into two categories: those containing cancer cells and those without them. The performance of the proposed ensemble method is evaluated using a breast cytology image dataset. Results: The results of our proposed ensemble framework outperform conventional CNN models in terms of precision, recall, and F1 measures, achieving an impressive 86% prediction accuracy. Visual representations of validation graphs for each classifier demonstrate that the ensemble framework surpasses the performance of pre-trained CNN architectures. Conclusion: Combining the outcomes of conventional CNN architectures into an ensemble framework enhances early breast cancer detection, leading to a reduction in mortality through timely medical interventions. [ABSTRACT FROM AUTHOR]

Simple Summary: Self-management interventions can improve clinical and psychosocial outcomes for cancer survivors. However, we do not know which intervention characteristics (i.e., how they are delivered) and components (i.e., what they deliver) are beneficial. This can influence the implementation of such interventions into routine cancer care. We aimed to identify existing self-management interventions for adult cancer survivors, describe their characteristics and components, and investigate associations with quality of life. We identified 32 interventions. Studies had varying quality. A total of 22 studies reported significant improvements in quality of life, associated most often with combined individual and group delivery. Self-management interventions showed promise for improving the quality of life in cancer survivors; however, caution is required because the intervention characteristics and self-management components delivered varied considerably. Still, we highlight what may be worth adapting from existing interventions. Overall, these findings provide the foundations to help inform the development and implementation of self-management interventions for cancer survivors. Self-management can improve clinical and psychosocial outcomes in cancer survivors. Which intervention characteristics and components are beneficial is unclear, hindering implementation into practice. We systematically searched six databases from inception to 17 November 2021 for studies evaluating self-management interventions for adult cancer survivors post-treatment. Independent reviewers screened for eligibility. Data extraction included population and study characteristics, intervention characteristics (TIDieR) and components (PRISMS), (associations with) quality of life (QoL), self-efficacy, and economic outcomes. Study quality was appraised, and narrative synthesis was conducted. We identified 53 papers reporting 32 interventions. Studies had varying quality. They were most often randomised controlled trials (n = 20), targeted at survivors of breast (n = 10), prostate (n = 7), or mixed cancers (n = 11). Intervention characteristics (e.g., provider, location) varied considerably. On average, five (range 1–10) self-management components were delivered, mostly "Information about condition and its management" (n = 26). Twenty-two studies reported significant QoL improvements (6 also reported significant self-efficacy improvements); these were associated most consistently with combined individual and group delivery. Economic evaluations were limited and inconclusive. Self-management interventions showed promise for improving QoL, but study quality was variable, with substantial heterogeneity in intervention characteristics and components. By identifying what to adapt from existing interventions, these findings can inform development and implementation of self-management interventions in cancer. [ABSTRACT FROM AUTHOR]

Aim: Cognitive concerns are one of the most frequently reported symptoms by breast cancer survivors. This study aimed to evaluate perceived cognitive functioning in Portuguese women with breast cancer treated with chemotherapy. Methods: A cross‐sectional study enrolling 146 women (73 with breast cancer and 73 healthy) was conducted from August to October 2017, invited to participate through online dissemination. Participants completed self‐reported questionnaires to collect sociodemographic and clinical data and assess perceived cognitive functioning and psychological adjustment variables (anxiety and depression). Results: Compared to healthy women, women with breast cancer showed significantly lower scores on the Functional Assessment of Cancer Therapy‐Cognitive Function (FACT‐Cog) subscales and higher levels of depression. Both groups showed significant negative correlations between perceived cognitive functioning and anxiety and depression. Health status and depression seem to better explain perceived cognitive functioning, with health status adding significantly more explained variance beyond sociodemographic and psychological adjustment variables. Conclusion: The current findings provide evidence for the existence of more cognitive complaints among Portuguese women with breast cancer, compared to healthy individuals. Anxiety, depression, age and education also explain perceived cognitive functioning. Considering that health status and psychological adjustment seem to significantly explain perceived cognitive functioning, special attention should be given by health‐care professionals, including nurses, to designing clinical interventions for breast cancer patients to help manage cognitive impairment. Summary statement: What is already known about this topic? Cancer and associated treatments have diverse short‐ and long‐term side effects. Deficits in cognitive functions are one of the most frequently reported for breast cancer specifically due to chemotherapy.Subjective assessment of cognitive function is often neglected, although it might be clinically very useful and an important indicator of the impact of cognitive impairment on daily functioning.There is currently no Portuguese data on perceived cognitive impairment in cancer. What this paper adds? This Portuguese study shows that, compared to a matched healthy sample, breast cancer patients have significantly more cognitive complaints, as assessed by the FACT‐Cog scale.Furthermore, our findings showed that higher levels of anxiety and depression are associated with worse perceived cognitive functioning.Cognitive complaints in breast cancer patients are predicted by a complexity of factors, such as psychological adjustment, age, and education. The implications of this paper: Health‐care professionals, including nurses, should recognize cognitive complaints as legitimate in breast cancer patients.Findings may deepen nurses' knowledge about cognitive concerns in breast cancer patients, in order to improve the quality of care provided to this population.The results of the study highlight the importance of tackling this problem with specifically designed clinical interventions that target both the cognitive deficits and the psychological adjustment of patients, especially depressive symptomatology. [ABSTRACT FROM AUTHOR]

Background: Breast cancer is arguably one of the leading causes of death among women around the world. The automation of the early detection process and classification of breast masses has been a prominent focus for researchers in the past decade. The utilization of ultrasound imaging is prevalent in the diagnostic evaluation of breast cancer, with its predictive accuracy being dependent on the expertise of the specialist. Therefore, there is an urgent need to create fast and reliable ultrasound image detection algorithms to address this issue. Methods: This paper aims to compare the efficiency of six state-of-the-art, fine-tuned deep learning models that can classify breast tissue from ultrasound images into three classes: benign, malignant, and normal, using transfer learning. Additionally, the architecture of a custom model is introduced and trained from the ground up on a public dataset containing 780 images, which was further augmented to 3900 and 7800 images, respectively. What is more, the custom model is further validated on another private dataset containing 163 ultrasound images divided into two classes: benign and malignant. The pre-trained architectures used in this work are ResNet-50, Inception-V3, Inception-ResNet-V2, MobileNet-V2, VGG-16, and DenseNet-121. The performance evaluation metrics that are used in this study are as follows: Precision, Recall, F1-Score and Specificity. Results: The experimental results show that the models trained on the augmented dataset with 7800 images obtained the best performance on the test set, having 94.95 ± 0.64%, 97.69 ± 0.52%, 97.69 ± 0.13%, 97.77 ± 0.29%, 95.07 ± 0.41%, 98.11 ± 0.10%, and 96.75 ± 0.26% accuracy for the ResNet-50, MobileNet-V2, InceptionResNet-V2, VGG-16, Inception-V3, DenseNet-121, and our model, respectively. Conclusion: Our proposed model obtains competitive results, outperforming some state-of-the-art models in terms of accuracy and training time. [ABSTRACT FROM AUTHOR]

Simple Summary: Drugs used to treat cancer are most often very toxic. Taking their high doses may adversely affect the patient's health, as a result of which the therapy, instead of helping, harms. An example of such a drug is 6-mercaptopurine, described in this paper. Due to these negative properties, it is necessary to create such systems that would release the drug in a controlled manner only in the vicinity of diseased tissues. In this work, a drug carrier was prepared that releases the drug only in the acidic environment of cancer, due to which healthy tissues will not be exposed to it. Systems that release the drug only in an acidic environment have not been described before and the application potential of such a carrier is very large. Hydroxy double salts are layered materials that are considered to be biocompatible. For this reason, research has been initiated on the possibility of their use in drug delivery. Despite their use for several types of drugs, their potential for controlled release of mercaptopurine (MERC) has not been studied. In this work, the synthesized hydroxy double salt (HDS) material was used as a carrier for this drug for the first time. The effectiveness of HDS synthesis has been proven by such techniques as X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR) and scanning electron microscopy (SEM). Based on the FT-IR and energy-dispersive X-ray spectroscopy (EDS) results, the effectiveness of drug sorption was proven. The exact amount of drug retained was determined by the UV-Vis technique. The obtained results indicate that the drug is evenly distributed on the surface of the carrier, which is important during the controlled delivery of drugs. In the most important stage of the research, the effectiveness of drug release in response to changes in the pH of the environment was proven. The drug is not released into an environment that mimics healthy human tissues. It is released only after contact with the acidic environment that usually surrounds cancer cells. The low cellular toxicity of HDS and significant cytotoxic effect of HDS-MERC were confirmed by in vitro studies on MCF-7 human breast and DU145 prostate cancer cell lines and non-cancerous keratinocytes HaCaT. Interestingly, coupling with the HDS carrier increased the cytotoxic effect of MERC towards DU145 cells. Such an "intelligent" drug carrier for mercaptopurine has not been previously described in the literature. The obtained results indicate its great potential. [ABSTRACT FROM AUTHOR]

Aims: To explore predictors of lymphedema self‐management behaviours among Chinese breast cancer survivors based on the Integrated Theory of Health Behaviour Change, and to clarify the interrelationship among these variables. Design: Further analysis of a multicentre cross‐sectional and survey‐based study. Methods: A total of 586 participants with breast cancer were recruited from December 2021 to April 2022 in different cities in China. We used self‐reported questionnaires to collect data. Descriptive analysis, bivariate analysis and structural equation model were performed. Results: The Integrated Theory of Health Behaviour Change is suitable for predicting lymphedema self‐management behaviours. The final structural model showed good model fit. Social support, self‐efficacy and lymphedema knowledge positively affected lymphedema self‐management behaviours, directly and indirectly. Self‐regulation acted as a crucial mediator between these variables and self‐management. The direct path between social support and self‐regulation was not significant. Lymphedema knowledge and social support also influenced self‐management via illness perception, self‐efficacy and self‐regulation, sequentially. These variables explained 55.9% of the variance in lymphedema self‐management behaviours. Conclusions: The modified model based on the Integrated Theory of Health Behaviour Change fitted well in predicting lymphedema self‐management behaviours among breast cancer patients. Lymphedema knowledge, illness perception, self‐efficacy, social support and self‐regulation directly and indirectly influenced lymphedema self‐management behaviours. Impact: This study provides a theoretical basis for the assessment and interventions of lymphedema self‐management behaviours in breast cancer patients. Lymphedema self‐management behaviours should be assessed regularly and comprehensively, taking these predictors into consideration to identify potential barriers. Further research is needed to explore effective interventions integrating these significant predictors. Reporting Method: This study was reported following Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist for cross‐sectional studies. Patient or Public Contribution: No patient or public contributed to the design or conduct of the study, analysis or interpretation of the data, or in the preparation of the manuscript. What Does This Paper Contribute to the Wider Global Clinical Community?: This study focused on identifying and predicting mechanism of self‐management based on a theory of behaviour change. The results can be applied among patients with other chronic diseases or high‐risk populations, and inspire the assessment and interventions facilitating self‐management behaviours. Study Registration: This study was registered as an observational study at Chinese Clinical Trial Registry: http://www.chictr.org.cn (ChiCTR2200057084). Implications for the Profession and/or Patient Care: For breast cancer patients with poor lymphedema self‐management behaviour, attention should be raised among nurses and involved healthcare staffs that lymphedema self‐management is multi‐faced. Strategies targeted at improving social support, self‐regulation, knowledge, self‐efficacy and illness perception should be also addressed in lymphedema self‐management programs, to facilitate more effective improvement of lymphedema self‐management behaviours. [ABSTRACT FROM AUTHOR]

Aims: To perform temporal validation of a risk prediction model for breast cancer‐related lymphoedema in the European population. Design: Temporal validation of a previously developed prediction model using a new retrospective cohort of women who had undergone axillary lymph node dissection between June 2018 and June 2020. Methods: We reviewed clinical records to identify women who did and did not develop lymphoedema within 2 years of surgery and to gather data regarding the variables included in the prediction model. The model was calibrated by calculating Spearman's correlation between observed and expected cases. Its accuracy in discriminating between patients who did versus did not develop lymphoedema was assessed by calculating the area under the receiver operating characteristic curve (AUC). Results: The validation cohort comprised 154 women, 41 of whom developed lymphoedema within 2 years of surgery. The value of Spearman's coefficient indicated a strong correlation between observed and expected cases. Sensitivity of the model was higher than in the derivation cohort, as was the value of the AUC. Conclusion: The model shows a good capacity to discriminate women at risk of lymphoedema and may therefore help in developing improved care pathways for individual patients. Implications for the Profession and/or Patient Care: Identifying risk factors for lymphoedema secondary to breast cancer treatment is vital given its impact on women's physical and emotional well‐being. Impact: What problem did the study address? Risk of BCRL. What were the main findings? The prediction model has a good capacity to discriminate women at risk of lymphoedema. Where and on whom will the research have an impact? In clinical practice with women at risk of BCRL. Reporting Method: STROBE checklist. What Does this Paper Contribute to the Wider Global Clinical Community?: It presents a validated risk prediction model for BCRL. No Patient or Public Contribution: There was no patient or public contribution in the conduct of this study. [ABSTRACT FROM AUTHOR]

Many current medical image segmentation methods utilize convolutional neural networks (CNNs), with some extended U-Net-based networks relying on deep feature representations to achieve satisfactory results. However, due to the limited receptive fields of convolutional architectures, they are unable to explicitly model the varying range dependencies present in medical images. Recently, advancements in large kernel convolution have allowed for the extraction of a wider range of low frequency information, making this task more achievable. In this paper, we propose TBUnet for solving the problem of difficult to accurately segment lesions with heterogeneous structures and fuzzy borders, such as melanoma, colon polyps and breast cancer. The TBUnet is a pure convolutional network with three branches for extracting high frequency information, low frequency information, and boundary information, respectively. It is capable of extracting features in various areas. To fuse the feature maps from the three branches, TBUnet presents the FL (fusion layer) module, which is based on threshold and logical operation. We design the FE (feature enhancement) module on the skip-connection to emphasize the fine-grained features. In addition, our method varies the number of input channels in different branches at each stage of the network, so that the relationship between low and high frequency features can be learned. TBUnet yields 91.08 DSC on ISIC-2018 for melanoma segmentation, and achieves better performance than state-of-the-art medical image segmentation methods. Furthermore, experimental results with 82.48 DSC and 89.04 DSC obtained on the BUSI dataset and the Kvasir-SEG dataset show that TBUnet outperforms the advanced segmentation methods. Experiments demonstrate that TBUnet has excellent segmentation performance and generalisation capability. [ABSTRACT FROM AUTHOR]

Simple Summary: Risk stratification for early breast cancer (BC) is extremely relevant for tailoring clinical decisions but challenging due to the absence of comprehensive guidelines. Traditional criteria like tumor size, lymph node involvement, histological type, grade, lymphovascular invasion, and immune cell infiltration serve as significant prognostic indicators. Alongside hormone receptor, HER2, and BRCA1/2 testing, molecular subtyping through gene expression profiling offers valuable insights for personalized clinical decisions. "Omics" technologies, applicable to tissue and liquid biopsy samples, have expanded risk evaluation capabilities, with limitations due to the lack of prospective data in early BC. This research overview paper highlights the need for standardized methodologies and integrated pathological models across multiple analytical dimensions for earlu BC risk stratification. The aim is to provide a practical guide for histopathologists and molecular pathologists involved in early BC profiling. Early breast cancer (BC) is the definition applied to breast-confined tumors with or without limited involvement of locoregional lymph nodes. While risk stratification is essential for guiding clinical decisions, it can be a complex endeavor in these patients due to the absence of comprehensive guidelines. Histopathological analysis and biomarker assessment play a pivotal role in defining patient outcomes. Traditional histological criteria such as tumor size, lymph node involvement, histological type and grade, lymphovascular invasion, and immune cell infiltration are significant prognostic indicators. In addition to the hormone receptor, HER2, and—in specific scenarios—BRCA1/2 testing, molecular subtyping through gene expression profiling provides valuable insights to tailor clinical decision-making. The emergence of "omics" technologies, applicable to both tissue and liquid biopsy samples, has broadened our arsenal for evaluating the risk of early BC. However, a pressing need remains for standardized methodologies and integrated pathological models that encompass multiple analytical dimensions. In this study, we provide a detailed examination of the existing strategies for early BC risk stratification, intending to serve as a practical guide for histopathologists and molecular pathologists. [ABSTRACT FROM AUTHOR]

Simple Summary: The association between HPV and cancer has been the focus of research, but there has been a lack of a comprehensive high-level evidence studies to systematically examine the relationship between them. Using Mendelian randomization, this paper provided an extensive analysis of the causal effect of HPV in cancer development. Our study conclusively identified HPV16 as a risk factor implicated in the development of bladder cancer, colorectal cancer, and breast cancer, while HPV18 was identified as a risk factor for prostate cancer, ovarian cancer, lung cancer and breast cancer. The results of Mendelian randomization also showed that HPV16 may be a protective factor for prostate cancer, anal cancer, lung cancer and oropharyngeal cancer, while HPV18 may be a protective factor for vaginal cancer. Introduction: More and more studies have focused on the associations between human papillomavirus (HPV) infection and pan-cancers. However, current evidence is largely based on retrospective studies, which are susceptible to confounding factors and do not enable the establishment of causal relationships. Methods: A bidirectional two-sample Mendelian randomization (MR) design was employed to thoroughly evaluate the causal relationships between HPV and 12 site-specific cancers except cervical cancer. Single nucleoside polymers (SNPs) with strong evidence from genome-wide association studies (GWAS) were selected from HPV exposure datasets and used as instrumental variables (IVs) in this study. For the MR analysis results, MR-Egger's intercept P test, MR-PRESSO global test, Cochran's Q test and a leave-one-out test were applied for sensitivity analysis. Using HPVTIMER, we also performed immune infiltration analyses in head and neck squamous cell carcinoma (HNSCC), oropharyngeal squamous cell carcinoma (OPSCC) and vulval squamous cell carcinoma (VSCC) to evaluate the tumor-immune microenvironment. Results: Based on the evidence of MR analysis, our study conclusively identified HPV16 as a risk factor implicated in the development of bladder cancer, colorectal cancer, and breast cancer, while HPV18 was identified as a risk factor for prostate cancer, ovarian cancer, lung cancer and breast cancer. The MR results also showed that HPV16 may be a protective factor for prostate cancer, anal cancer, lung cancer and oropharyngeal cancer, while HPV18 may be a protective factor for vaginal cancer. Conclusion: An HPV infection may modulate the immune microenvironment and therefore has a potential inhibitory effect on the development of certain cancers. These conclusions provided new insights into the potential mechanisms of carcinogenesis and needed further research for validation. [ABSTRACT FROM AUTHOR]

Breast cancer rates among Asian American women are increasing. Despite this, there are limited studies on help-seeking among this population. Through a qualitative exploration, this paper examines the help-seeking experiences of Asian American breast cancer survivors. Asian American women (n = 52) with early-stage breast cancer were interviewed. Findings illustrate a continuum of experiences including: keeping diagnosis personal and not asking for help, keeping diagnosis personal but asking for limited help, and sharing diagnosis and seeking support. Results indicate that seeking support is a complex process for Asian Americans with breast cancer, with implications for survivorship and quality of life. [ABSTRACT FROM AUTHOR]

Objective. Neuromuscular blocking agents (NMBAs) are part of the three elements of general anaesthesia (sedation, analgesia, and muscle relaxation), which can relax muscles and facilitate intubation and surgery. It has been reported that cancer cells are prone to invasion or metastasis during surgery, but various anaesthetics are currently used in cancer resection, particularly NMBA, and the effects on cancer cell behavior are poorly understood. Guidelines for the correct application of NMBA in cancer surgery have not been reported; therefore, the aim of this paper is to explore the relationship between NMBA and cancer. Methods. Two investigators independently searched PubMed, Embase, the Cochrane Library, Web of Science, and CBM for articles of NMBA and cancer. Results. The available evidence suggests that cisatracurium may be more appropriate for use in anaesthesia for cancer surgery, while rocuronium deserves further attention, particularly for breast and gastric cancer surgery, and vecuronium is suitable for breast cancer and non-small-cell lung cancer, while it is used with caution in gastric cancer. Also, the relationship between NMBA (mivacurium, succinylcholine, gantacurium, and decamethonium bromide) and cancer is unclear and deserves further study. Conclusion. The effect of different NMBAs on cancer cells varies, and the effect of some NMBAs on cancer cells is unclear, and most of the current findings are only from in vitro studies, which need to be validated by further clinical studies in the future to better guide the clinical application of NMBAs. [ABSTRACT FROM AUTHOR]

This paper presents a novel ultra-wideband microwave antenna system to detect breast cancer and estimate tumor diameter. The system operates within the frequency range of 1 to 12 GHz and comprises a microstrip-fed monopole antenna that encircles the breast to identify the presence of tumors. The study demonstrates that a tumor within the breast can be detected by observing changes in the distribution of current density within the breast tissue, particularly in regions containing tumors of varying sizes. The research findings reveal that the system can identify breast tumors with the highest recorded current density of 188 A/m2 in cases with a tumor diameter of 30 mm, while the lowest recorded current density is 140 A/m2 for tumors with a diameter of 5 mm. Furthermore, the highest Specific Absorption Rate (SAR) value measured at the surface of the breast model is 0.2 W/kg. To determine the diameter of the tumors, the system collects and analyzes backscattered waves from a breast model. The investigation covers tumors with diameters ranging from 1 mm to 35 mm, and the received signals are recorded. In contrast to prior research, this study introduces an empirical model with a remarkable accuracy rate of 92.28% for characterizing the diameter of breast tumors based on the measurement analysis. [ABSTRACT FROM AUTHOR]

Simple Summary: Antibody–drug conjugates (ADCs) are a growing class of therapies that aim to delivery therapy more efficiently, with fewer side effects, than conventional chemotherapy. ADCs are composed of an antibody linked to a chemotherapy payload, allowing targeted delivery of the chemotherapy. In the last decade, several antibody–drug conjugates have improved treatment options in breast cancer. However, patients usually progress on these agents, and more research is needed into why this resistance occurs. Given the complex structure of antibody-drug conjugates, resistance may be related to changes in antigen expression, ADC processing, and the chemotherapy payload. This paper reviews the literature on the mechanisms of resistance to antibody–drug conjugates including pre-clinical and clinical studies in breast cancer and other malignancies. This review includes information on ADCs that have been approved for use in breast cancer and ADCs in development that seek to overcome the proposed mechanisms of resistance to improve treatment options for patients. Antibody–drug conjugates (ADCs), with antibodies targeted against specific antigens linked to cytotoxic payloads, offer the opportunity for a more specific delivery of chemotherapy and other bioactive payloads to minimize side effects. First approved in the setting of HER2+ breast cancer, more recent ADCs have been developed for triple-negative breast cancer (TNBC) and, most recently, hormone receptor-positive (HR+) breast cancer. While antibody–drug conjugates have compared favorably against traditional chemotherapy in some settings, patients eventually progress on these therapies and require a change in treatment. Mechanisms to explain the resistance to ADCs are highly sought after, in hopes of developing next-line treatment options and expanding the therapeutic windows of existing therapies. These resistance mechanisms are categorized as follows: change in antigen expression, change in ADC processing and resistance, and efflux of the ADC payload. This paper reviews the recently published literature on these mechanisms as well as potential options to overcome these barriers. [ABSTRACT FROM AUTHOR]

Simple Summary: In radiotherapy treatment, organs at risk and target volumes are contoured by the clinicians to prepare a dosimetry plan. In retrospective data, these structures are not often standardised to universal names across the patients plans, which is required to enable data mining and analysis. In this paper, a new method was proposed and evaluated to automatically standardise radiotherapy structures names using machine learning algorithms. The proposed approach was deployed over a dataset with 1613 patients collected from Liverpool & Macarthur Cancer Therapy Centres, New South Wales, Australia. It was concluded that machine learning techniques can standardise the dosimetry plan structures, taking into consideration the integration of multiple modalities representing each structure during the training process. In progressing the use of big data in health systems, standardised nomenclature is required to enable data pooling and analyses. In many radiotherapy planning systems and their data archives, target volumes (TV) and organ-at-risk (OAR) structure nomenclature has not been standardised. Machine learning (ML) has been utilised to standardise volumes nomenclature in retrospective datasets. However, only subsets of the structures have been targeted. Within this paper, we proposed a new approach for standardising all the structures nomenclature by using multi-modal artificial neural networks. A cohort consisting of 1613 breast cancer patients treated with radiotherapy was identified from Liverpool & Macarthur Cancer Therapy Centres, NSW, Australia. Four types of volume characteristics were generated to represent each target and OAR volume: textual features, geometric features, dosimetry features, and imaging data. Five datasets were created from the original cohort, the first four represented different subsets of volumes and the last one represented the whole list of volumes. For each dataset, 15 sets of combinations of features were generated to investigate the effect of using different characteristics on the standardisation performance. The best model reported 99.416% classification accuracy over the hold-out sample when used to standardise all the nomenclatures in a breast cancer radiotherapy plan into 21 classes. Our results showed that ML based automation methods can be used for standardising naming conventions in a radiotherapy plan taking into consideration the inclusion of multiple modalities to better represent each volume. [ABSTRACT FROM AUTHOR]

The tubule index is a vital prognostic measure in breast cancer tumor grading and is visually evaluated by pathologists. In this paper, a computer-aided patch-based deep learning tubule segmentation framework, named Tubule-U-Net, is developed and proposed to segment tubules in Whole Slide Images (WSI) of breast cancer. Moreover, this paper presents a new tubule segmentation dataset consisting of 30820 polygonal annotated tubules in 8225 patches. The Tubule-U-Net framework first uses a patch enhancement technique such as reflection or mirror padding and then employs an asymmetric encoder-decoder semantic segmentation model. The encoder is developed in the model by various deep learning architectures such as EfficientNetB3, ResNet34, and DenseNet161, whereas the decoder is similar to U-Net. Thus, three different models are obtained, which are EfficientNetB3-U-Net, ResNet34-U-Net, and DenseNet161-U-Net. The proposed framework with three different models, U-Net, U-Net++, and Trans-U-Net segmentation methods are trained on the created dataset and tested on five different WSIs. The experimental results demonstrate that the proposed framework with the EfficientNetB3 model trained on patches obtained using the reflection padding and tested on patches with overlapping provides the best segmentation results on the test data and achieves 95.33%, 93.74%, and 90.02%, dice, recall, and specificity scores, respectively. [ABSTRACT FROM AUTHOR]

Simple Summary: The literature aims to explore anti-androgenic particulars against luminal androgen receptor-specific triple-negative breast cancer, thus providing a concise report on the clinical profile of anti-androgens. This review summarizes the types of triple-negative cancer cells, along with the brief involvement of androgen receptors in the luminal type. The paper intricately describes the evidence obtained from the clinical trials and confers the conclusions, in an attempt to help clinicians set forth the optimal strategies in mitigating LAR-TNBC and creating a steadfast foundation for patient care. Triple-negative tumors are progressively delineating their existence over the extended spectrum of breast cancers, marked by intricate molecular heterogeneity, a low overall survival rate, and an unexplored therapeutic approach. Although the basal subtype transcends the group and contributes approximately 80% to triple-negative breast cancer (TNBC) cases, the exceptionally appearing mesenchymal and luminal androgen receptor (LAR) subtypes portray an unfathomable clinical course. LAR with a distinct generic profile frequently metastasizes to regional lymph nodes and bones. This subtype is minimally affected by chemotherapy and shows the lowest pathologic complete response. The androgen receptor is the only sex steroid receptor that plays a cardinal role in the progression of breast cancers and is typically overexpressed in LAR. The partial AR antagonist bicalutamide and the next-generation AR inhibitor enzalutamide are being assessed in standard protocols for the mitigation of TNBC. There arises an inevitable need to probe into the strategies that could neutralize these androgen receptors and alleviate the trajectory of concerning cancer. This paper thus focuses on reviewing literature that provides insights into the anti-androgenic elements against LAR typical TNBC that could pave the way for clinical advancements in this dynamic sphere of oncology. [ABSTRACT FROM AUTHOR]

EUSOBI and 30 national breast radiology bodies support mammography for population-based screening, demonstrated to reduce breast cancer (BC) mortality and treatment impact. According to the International Agency for Research on Cancer, the reduction in mortality is 40 % for women aged 50-69 years taking up the invitation while the probability of false-positive needle biopsy is <1 % per round and overdiagnosis is only 1-10 % for a 20-year screening. Mortality reduction was also observed for the age groups 40-49 years and 70-74 years, although with "limited evidence". Thus, we firstly recommend biennial screening mammography for average-risk women aged 50-69 years; extension up to 73 or 75 years, biennially, is a second priority, from 40-45 to 49 years, annually, a third priority. Screening with thermography or other optical tools as alternatives to mammography is discouraged. Preference should be given to population screening programmes on a territorial basis, with double reading. Adoption of digital mammography (not film-screen or phosphor-plate computer radiography) is a priority, which also improves sensitivity in dense breasts. Radiologists qualified as screening readers should be involved in programmes. Digital breast tomosynthesis is also set to become "routine mammography" in the screening setting in the next future. Dedicated pathways for high-risk women offering breast MRI according to national or international guidelines and recommendations are encouraged.Key Points: • EUSOBI and 30 national breast radiology bodies support screening mammography. • A first priority is double-reading biennial mammography for women aged 50-69 years. • Extension to 73-75 and from 40-45 to 49 years is also encouraged. • Digital mammography (not film-screen or computer radiography) should be used. • DBT is set to become "routine mammography" in the screening setting in the next future. [ABSTRACT FROM AUTHOR]

This work presents a flat‐gain antipodal Vivaldi antenna (AVA) for ultra‐wideband (UWB) microwave imaging applications. The proposed antenna demonstrates measured and simulated impedance bandwidth from 2.3 GHz to 20 GHz with a flat gain over the band, with minimum gain variation of 2.9 dBi. The antenna is tested initially for its practical utility by measuring its specific absorption rate (SAR) value. The SAR value is observed in the simulator by modeling a realistic homogeneous as well as a heterogeneous breast phantom. The SAR value obtained complies with both American and European standards when averaged over 1 g as well as 10 g of tissue. The designed antenna is further utilized to detect multiple tumors in a realistic homogeneous and heterogeneous breast phantoms developed in the laboratory environment. The widely popular delay and sum (DAS) algorithm is utilized to reconstruct the tumor images. The imaging is done outside the anechoic chamber with an in‐house monostatic microwave imaging setup just to make it more convincing with real‐world dynamic scenario. The homogeneous phantom with four embedded tumors each of radius 4 mm and the heterogeneous phantom with two tumors, one of radius 3.5 mm and another of radius 5 mm are imaged in this work. The imaging results demonstrate that tumors of different sizes can be detected accurately in the case of both homogeneous and heterogeneous breast phantoms. AVA with an oil paper layer for bandwidth enhancement as well for achieving flat‐gain response is first of its kind to be reported in this work Also, the proposed work utilizes different phantoms for imaging several tumors of differing sizes, which has not before been described. [ABSTRACT FROM AUTHOR]

Spiking neural network (SNN) has attracted extensive attention in the field of machine learning because of its biological interpretability and low power consumption. However, the accuracy of pattern recognition cannot completely surpass deep neural networks (DNNs). The main reason is that the inherent nondifferentiability of spiking neurons makes SNN unable to be trained directly by the gradient descent algorithm, and there is also no unified training algorithm for SNN. Inspired by the biological vision system, this paper proposes a parallel convolution SNN structure combined with an adaptive lateral inhibition mechanism. And, a way of dynamically evolving the time constant with the training of SNN is proposed to ensure the diversity of neurons. This paper verifies the effectiveness of the proposed methods on static datasets and neuromorphic datasets and extends it to the recognition of breast tumors. Experimental results show that the SNN has obvious advantages in dynamical datasets. For breast tumors, it is also an edge-based task, because the edge of a medical image contains the most important information in the image. This kind of information can provide great help for the noninvasive and accurate diagnosis of diseases. The Experimental results show that the proposed method is very close to the recognition results of DNNs on static datasets, and its performance on neuromorphic datasets exceeds that of DNNs. [ABSTRACT FROM AUTHOR]

Background and Objective. Breast cancer is a common malignant tumor that seriously threatens the health of women in my country and even around the world. The proliferation marker Ki-67 has been utilized to distinguish luminal B from luminal A tumors and is a reliable indicator of more aggressive breast cancer growth. If a reliable prediction method for breast cancer patients to avoid invasive damage can be found to predict Ki-67 before pathological examination, it will be very beneficial for doctors to formulate later treatment plans and provide more useful treatment options. Methodology. This paper proposes a tumor segmentation and prediction framework based on the combination of improved attention U-Net and SVM. The framework first improves on attention U-Net by introducing coefficients for learning multidimensional attention. Make the attention mechanism more aware of the main situation in the segmentation process. At the same time, the segmented breast MRI results and corresponding labels were input into the SVM classifier to accurately predict the expression of Ki-67. Results. The DSC, PPV, and sensitivity of our combined model are 0.94, 0.93, and 0.94, respectively, with better segmentation performance. And we compare with the segmentation frameworks of other papers and find that our combined model can make accurate segmentation of breast tumors. Conclusion. Our method can adapt to the variability of breast tumors and segment breast tumors accurately and efficiently. In the future, it can be widely used in clinical practice, so as to help the clinic better formulate a reasonable diagnosis and treatment plan for breast cancer patients. [ABSTRACT FROM AUTHOR]

Patient factors affect the risk of radiotherapy toxicity, but many are poorly defined. Studies have shown that race affects cancer incidence, survival, drug response, molecular pathways and epigenetics. Effects on radiosensitivity and radiotherapy toxicity are not well studied. The aim of the present study was to identify the effects of race and ethnicity on the risk of radiotherapy toxicity. A systematic review was carried out of PubMed, Ovid Medline and Ovid Embase with no year limit. PRISMA 2020 guidelines were followed. Two independent assessors reviewed papers. Of 607 papers screened, 46 fulfilled the inclusion criteria. Papers were published between 1996 and 2021 and involved 30–28,354 individuals (median 433). Most involved patients with prostate (33%), breast (26%) and lung (9%) cancer. Both early and late toxicities were studied. Some studies reported a higher risk of toxicity in White men with prostate cancer compared with other races and ethnicities. For breast cancer patients, some reported an increased risk of toxicity in White women compared with other race and ethnic groups. In general, it was difficult to draw conclusions due to insufficient reporting and analysis of race and ethnicity in published literature. Reporting of race and ethnicity in radiotherapy studies must be harmonised and improved and frameworks are needed to improve the quality of reporting. Further research is needed to understand how ancestral heritage might affect radiosensitivity and risk of radiotherapy toxicity. • Ancestral heritage is likely to affect risk of some radiation toxicities. • Insufficient evidence from reviewing effects of race and ethnicity on toxicity. • Reporting race and ethnicity in radiation studies must be harmonised and improved. • Socioeconomic and comorbidity effects must be addressed. • Radiogenomic research needs diversifying beyond euro-centricity. [ABSTRACT FROM AUTHOR]