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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]

PURPOSE Our institution participated in the Oncology Care Model, which required us to include many of the 13 elements of the National Academy of Medicine (NAM) care plan into care pathways for our patients. We optimized our existing chemotherapy consent process to meet this need and maximized completion. METHODS Our multidisciplinary committee developed a three-phase Plan-Do-Study-Act process in our breast cancer clinic: (1) update and educate providers on our paper chemotherapy form with multiple components of the NAM care plan including prognosis and treatment effects on quality of life; (2) piloted an electronic chemotherapy consent form to decrease the administrative burden; and (3) autopopulated fields within the electronic consent. We assessed feedback after cycle 1 and created a Pareto chart. The outcome measure was percent completion of chemotherapy consent documents. RESULTS Baseline monthly random chart audit of 40 patients revealed 20% of paper chemotherapy consent forms were completed in their entirety among patients. When we re-educated clinicians about the new paper consent containing the NAM elements, compliance rose to nearly 30%. A Pareto chart confirmed that content redundancy and wordiness were leading to under-completion. After creating and piloting the electronic consent, compliance increased to 90%. Finally, autopopulation with drop-down selections increased and sustained completion to 100%. CONCLUSION Incorporating regulatory requirements into an existing workflow using Plan-Do-Study-Act methodology can reduce administrative burden on clinicians. Additional use of innovative technology can further increase clinician compliance with regulatory requirements while delivering high-value quality care to patients with cancer. [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]

Background/Objectives: The message is being used as a mode of intervention leading to preventive health behaviors and can lead to modifications in knowledge, attitudes and behaviors in a large proportion of health behaviors. The purpose of this study is to identify the effective and persuasive message types among positive and negative message types in information on specific health behaviors, to evaluate the effects by systematically classifying and analyzing related studies and to lead evidence-based practices. Method/Statistical Analysis: In this study, meta-analysis was conducted to evaluate the trends and reporting levels of the study in order to evaluate and systematically classify the effects of message types in information on health behavior. Only clinical studies with randomization comparing the effects of positive and negative message framing with respect to health behavior were selected. In addition, a case where the interventions were compared by dividing them into two groups was selected. Improvements/Applications: Among the final selected papers, 7 papers were included in the included studies through methodological quality evaluation. Comparison of the positive and negative message interventions is related to health behaviors related to breast cancer (SMD -0.04 (95% CI -1.57 to 1.50), health behaviors related to vaccination (MMR, HPV)(SMD 0.20 (95% CI -0.69 to 1.08), cancer screening, vaccination, physical activity and all health activities related to Type 2 diabetes screening (SMD -0.21 (95% CI -0.89 to 0.47). All of these were not statistically significant. In order to confirm the change in health behavior according to message framing, a study considering the same target population and outcome indicators is necessary. [ABSTRACT FROM AUTHOR]

Breast cancer (BC) is the leading cause of death among women worldwide. It affects in general women older than 40 years old. Medical images analysis is one of the most promising research areas since it provides facilities for diagnosis and decision-making of several diseases such as BC. This paper conducts a Structured Literature Review (SLR) of the use of Machine Learning (ML) and Image Processing (IP) techniques to deal with BC imaging. A set of 530 papers published between 2000 and August 2019 were selected and analyzed according to ten criteria: year and publication channel, empirical type, research type, medical task, machine learning techniques, datasets used, validation methods, performance measures and image processing techniques which include image pre-processing, segmentation, feature extraction and feature selection. Results showed that diagnosis was the most used medical task and that Deep Learning techniques (DL) were largely used to perform classification. Furthermore, we found out that classification was the most ML objective investigated followed by prediction and clustering. Most of the selected studies used Mammograms as imaging modalities rather than Ultrasound or Magnetic Resonance Imaging with the use of public or private datasets with MIAS as the most frequently investigated public dataset. As for image processing techniques, the majority of the selected studies pre-process their input images by reducing the noise and normalizing the colors, and some of them use segmentation to extract the region of interest with the thresholding method. For feature extraction, we note that researchers extracted the relevant features using classical feature extraction techniques (e.g. Texture features, Shape features, etc.) or DL techniques (e. g. VGG16, VGG19, ResNet, etc.), and finally few papers used feature selection techniques in particular the filter methods. [ABSTRACT FROM AUTHOR]

High-Intensity Focused Ultrasound (HIFU) as a promising and impactful modality for breast tumor ablation, entails the precise focalization of high-intensity ultrasonic waves onto the tumor site, culminating in the generation of extreme heat, thus ablation of malignant tissues. In this paper, a comprehensive three-dimensional (3D) Finite Element Method (FEM)-based numerical procedure is introduced, which provides exceptional capacity for simulating the intricate multiphysics phenomena associated with HIFU. Furthermore, the application of numerical procedures to an anatomically realistic breast phantom (ARBP) has not been explored before. The integrity of the present numerical procedure has been established through rigorous validation, incorporating comparative assessments with previous two-dimensional (2D) simulations and empirical data. For ARBP ablation, the administration of a 0.1 MPa pressure input pulse at a frequency of 1.5 MHz, sustained at the focal point for 10 seconds, manifests an ensuing temperature elevation to 80°C. It is noteworthy that, in contrast, the prior 2D simulation using a 2D phantom geometry reached just 72°C temperature under the identical treatment regimen, underscoring the insufficiency of 2D models, ascribed to their inherent limitations in spatially representing acoustic energy, which compromises their overall effectiveness. To underscore the versatility of this numerical platform, a simulation of a more clinically relevant HIFU therapy procedure has been conducted. This scenario involves the repositioning of the ultrasound focal point to three separate lesions, each spaced at 3 mm intervals, with ultrasound exposure durations of 6 seconds each and a 5-second interval for movement between focal points. This approach resulted in a more uniform high-temperature distribution at different areas of the tumour, leading to the ablation of almost all parts of the tumour, including its verges. In the end, the effects of different abnormal tissue shapes are investigated briefly as well. For solid mass tumors, 67.67% was successfully ablated with one lesion, while rim-enhancing tumors showed only 34.48% ablation and non-mass enhancement tumors exhibited 20.32% ablation, underscoring the need for multiple lesions and tailored treatment plans for more complex cases. [ABSTRACT FROM AUTHOR]

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]

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]

The article discusses the Pharmaceuticals Best Paper Award 2015 given to publications including synthesis and biological evaluation about a potent (R)-alpha-bis-lipoyl derivative, radiolabeled trastuzumab imaging for metastatic breast tumors and orthotopic and ways of cell-specific evaluation.

Breast cancer is one of the most common female diseases, posing a great threat to women's health, and breast ultrasound imaging is a common method for breast cancer diagnosis. In recent years, U-Net and its variants have dominated the medical image segmentation field with their excellent performance. However, the existing U-type segmentation networks have the following problems: (1) the design of the feature extractor is complicated, and the calculation difficulty is increased; (2) the skip connection operation simply combines the features of the encoder and the decoder, without considering both spatial and channel dimensions; (3) during the downsampling phase, the pooling operation results in the loss of feature information. To address the above deficiencies, this paper proposes a breast tumor segmentation network, RMAU-Net, that combines residual depthwise separable convolution and a multi-scale channel attention gate. Specifically, we designed the RDw block, which has a simple structure and a larger sensory field, to overcome the localization problem of convolutional operations. Meanwhile, the MCAG module is designed to correct the low-level features in both spatial and channel dimensions and assist the high-level features to recover the up-sampling and pinpoint non-regular breast tumor features. In addition, this paper used the Patch Merging operation instead of the pooling method to prevent the loss of breast ultrasound image information. Experiments were conducted on two breast ultrasound datasets, Dataset B and BUSI, and the results show that the method in this paper has superior segmentation performance and better generalization. [ABSTRACT FROM AUTHOR]

Breast cancer, a prevalent and life-threatening disease, necessitates early detection for the effective intervention and the improved patient health outcomes. This paper focuses on the critical problem of identifying breast cancer using a model called Attention U-Net. The model is utilized on the Breast Ultrasound Image Dataset (BUSI), comprising 780 breast images. The images are categorized into three distinct groups: 437 cases classified as benign, 210 cases classified as malignant, and 133 cases classified as normal. The proposed model leverages the attention-driven U-Net's encoder blocks to capture hierarchical features effectively. The model comprises four decoder blocks which is a pivotal component in the U-Net architecture, responsible for expanding the encoded feature representation obtained from the encoder block and for reconstructing spatial information. Four attention gates are incorporated strategically to enhance feature localization during decoding, showcasing a sophisticated design that facilitates accurate segmentation of breast tumors in ultrasound images. It displays its efficacy in accurately delineating and segregating tumor borders. The experimental findings demonstrate outstanding performance, achieving an overall accuracy of 0.98, precision of 0.97, recall of 0.90, and a dice score of 0.92. It demonstrates its effectiveness in precisely defining and separating tumor boundaries. This research aims to make automated breast cancer segmentation algorithms by emphasizing the importance of early detection in boosting diagnostic capabilities and enabling prompt and targeted medical interventions. [ABSTRACT FROM AUTHOR]

Simple Summary: Sentinel lymph node assessment is becoming a standard of care procedure in patients with surgically treatable endometrial cancer due to its cost-effectiveness and the advantages it offers in guiding post-operative management. Unlike in breast cancer, however, several key aspects regarding this technique's employment in endometrial cancer remain unclear, such as tracer injection volume and final pathology interpretation. The aim of this paper is to investigate the current literature on this technique in order to provide simple and clear insight on the matter and to facilitate the reproducibility of this technique, ultimately resulting in improving patients' oncological outcomes. As the number of patients diagnosed with endometrial cancer rises, so does the number of patients who undergo surgical treatment, consisting of radical hysterectomy, bilateral salpingo-oophorectomy, and bilateral pelvic lymphadenectomy or lymph node sampling. The latter entail intra- and post-surgical complications, such as lymphedema and increased intra-operative bleeding, which often outweigh their benefits. Sentinel Lymph Node (SLN) sampling is now common practice in surgical management of breast cancer, as it provides important information about the disease without jeopardizing surgical radicality and patient outcomes. While this technique has also been shown to be feasible in patients with endometrial cancer, there is little consensus on several aspects, such as tracer injection volume and site, pathological ultrastaging, and result interpretation. The aim of this review is to analyze the current literature on SLN assessment in order to help standardize the procedure. [ABSTRACT FROM AUTHOR]

Breast cancers can appear and progress rapidly, necessitating more frequent monitoring outside of hospital settings to significantly reduce mortality rates. Recently, there has been considerable interest in developing techniques for portable, user-friendly, and low-cost breast tumor monitoring applications, enabling frequent and cost-efficient examinations. Microwave technique-based breast cancer detection, which is based on differential dielectric properties of malignant and healthy tissues, is regarded as a promising solution for cost-effective breast tumor monitoring. This paper presents the development process of the first proof-of-concept of a breast tumor monitoring vest which is based on the microwave technique. Two unique vests are designed and evaluated on realistic 3D human tissue phantoms having different breast density types. Additionally, the measured results are verified using simulations carried out on anatomically realistic voxel models of the electromagnetic simulations. The radio channel characteristics are evaluated and analyzed between the antennas embedded in the vest in tumor cases and reference cases. Both measurements and simulation results show that the proposed vest can detect tumors even if only 1 cm in diameter. Additionally, simulation results show detectability with 0.5 cm tumors. It is observed that the detectability of breast tumors depends on the frequency, antenna selection, size of the tumors, and breast types, causing differences of 0.5–30 dB in channel responses between the tumorous and reference cases. Due to simplicity and cost-efficiency, the proposed channel analysis-based breast monitoring vests can be used for breast health checks in smaller healthcare centers and for user-friendly home monitoring which can prove beneficial in rural areas and developing countries. [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]

The incidence and histological type of spinal cancer is diverse. It is our role as physicians to explore the epidemiology of spinal cancers so that several projections can be made. Resource allocation, cost analyses, and the requirement of rehabilitation facilities all need to be considered. The objective of this paper is to provide an account of the acute spinal oncological admissions to the National Spinal Injuries Unit (NSIU) in both 2010 and 2020 with the hypothesis that upward trends will be noted. Only by exemplifying this trend, will it highlight the need to give spinal cancer the attention it deserves in the Republic of Ireland. All patients who were to undergo spinal surgery for primary or metastatic spinal cancer in the Mater Misericordiae University Hospital (MMUH) in 2010 and 2020 were included in this retrospective cohort study. A list of medical record numbers (MRNs) for all patients who underwent spinal surgery in the MMUH were included. Data pertaining to patient demographics were noted. 90 patients were included in this retrospective cohort study. 37 patients in 2010, had increased to 53 by 2020. Metastatic disease to the spine was still the most prominent reason for referral. The most common spinal region affected was the thoracic spine. Breast cancer was the most prevalent metastatic cancer to the spine in 2010. Lung cancer became the most prevalent by 2020. Posterior spinal fusion was the most frequent surgical procedure performed. The length of stay in higher care facilities decreased from 5.4 days in 2010, to 4 days in 2020. Decreased were also seen in the mean length of hospital stay, plummeting from 23.6 days in 2010, to 7.6 days in 2020. The same could not be said for the 30-day mortality rate, increasing from 5.4% in 2010, to 9.4% in 2020. The results of this study show a substantial rise in the incidence and prevalence of both primary and metastatic spinal disease here in Ireland. One can see clear improvements in operative technique, with less patients proceeding to higher levels of post-operative care, and earlier discharge times. This data can be used for future planning. The paper highlights the economic cost of spinal oncological care, but it also identifies key areas where preventative campaigns can be targeted. [ABSTRACT FROM AUTHOR]

Background: With the development of digital pathology and the renewal of deep learning algorithm, artificial intelligence (AI) is widely applied in tumor pathology. Previous researches have demonstrated that AI-based tumor pathology may help to solve the challenges faced by traditional pathology. This technology has attracted the attention of scholars in many fields and a large amount of articles have been published. This study mainly summarizes the knowledge structure of AI-based tumor pathology through bibliometric analysis, and discusses the potential research trends and foci.Methods: Publications related to AI-based tumor pathology from 1999 to 2021 were selected from Web of Science Core Collection. VOSviewer and Citespace were mainly used to perform and visualize co-authorship, co-citation, and co-occurrence analysis of countries, institutions, authors, references and keywords in this field.Results: A total of 2753 papers were included. The papers on AI-based tumor pathology research had been continuously increased since 1999. The United States made the largest contribution in this field, in terms of publications (1138, 41.34%), H-index (85) and total citations (35,539 times). We identified the most productive institution and author were Harvard Medical School and Madabhushi Anant, while Jemal Ahmedin was the most co-cited author. Scientific Reports was the most prominent journal and after analysis, Lecture Notes in Computer Science was the journal with highest total link strength. According to the result of references and keywords analysis, "breast cancer histopathology" "convolutional neural network" and "histopathological image" were identified as the major future research foci.Conclusions: AI-based tumor pathology is in the stage of vigorous development and has a bright prospect. International transboundary cooperation among countries and institutions should be strengthened in the future. It is foreseeable that more research foci will be lied in the interpretability of deep learning-based model and the development of multi-modal fusion model. [ABSTRACT FROM AUTHOR]

A letter to the editor in response to the article "Influence of mammography screening on use of mastectomies in Denmark" by K. Jørgensen and others in a 2015 issue is presented.

Introduction: Books and papers are the most relevant source of theoretical knowledge for medical education. New technologies of artificial intelligence can be designed to assist in selected educational tasks, such as reading a corpus made up of multiple documents and extracting relevant information in a quantitative way. Methods: Thirty experts were selected transparently using an online public call on the website of the sponsor organization and on its social media. Six books edited or co-edited by members of this panel containing a general knowledge of breast cancer or specific surgical knowledge have been acquired. This collection was used by a team of computer scientists to train an artificial neural network based on a technique called Word2Vec. Results: The corpus of six books contained about 2.2 billion words for 300d vectors. A few tests were performed. We evaluated cosine similarity between different words. Discussion: This work represents an initial attempt to derive formal information from textual corpus. It can be used to perform an augmented reading of the corpus of knowledge available in books and papers as part of a discipline. This can generate new hypothesis and provide an actual estimate of their association within the expert opinions. Word embedding can also be a good tool when used in accruing narrative information from clinical notes, reports, etc., and produce prediction about outcomes. More work is expected in this promising field to generate "real-world evidence." [ABSTRACT FROM AUTHOR]

This study introduces an intelligent learning model for classification tasks, termed the voting-based Double Pseudo-inverse Extreme Learning Machine (V-DPELM) model. Because the traditional method is aected by the weight of input layer and the bias of hidden layer, the number of hidden layer neurons is too large and the model performance is unstable. The V-DPELM model proposed in this paper can greatly alleviate the limitations of traditional models because of its direct determination of weight structure and voting mechanism strategy. Through extensive simulations on various real-world classification datasets, we observe a marked improvement in classification accuracy when comparing the V-DPELM algorithm to traditional V-ELM methods. Notably, when used for machine recognition classification of breast tumors, the V-DPELM method demonstrates superior classification accuracy, positioning it as a valuable tool in machine-assisted breast tumor diagnosis models. [ABSTRACT FROM AUTHOR]

Purpose. Artificial intelligence (AI) techniques are used in precision medicine to explore novel genotypes and phenotypes data. The main aims of precision medicine include early diagnosis, screening, and personalized treatment regime for a patient based on genetic-oriented features and characteristics. The main objective of this study was to review AI techniques and their effectiveness in neoplasm precision medicine. Materials and Methods. A comprehensive search was performed in Medline (through PubMed), Scopus, ISI Web of Science, IEEE Xplore, Embase, and Cochrane databases from inception to December 29, 2021, in order to identify the studies that used AI methods for cancer precision medicine and evaluate outcomes of the models. Results. Sixty-three studies were included in this systematic review. The main AI approaches in 17 papers (26.9%) were linear and nonlinear categories (random forest or decision trees), and in 21 citations, rule-based systems and deep learning models were used. Notably, 62% of the articles were done in the United States and China. R package was the most frequent software, and breast and lung cancer were the most selected neoplasms in the papers. Out of 63 papers, in 34 articles, genomic data like gene expression, somatic mutation data, phenotype data, and proteomics with drug-response which is functional data was used as input in AI methods; in 16 papers' (25.3%) drug response, functional data was utilized in personalization of treatment. The maximum values of the assessment indicators such as accuracy, sensitivity, specificity, precision, recall, and area under the curve (AUC) in included studies were 0.99, 1.00, 0.96, 0.98, 0.99, and 0.9929, respectively. Conclusion. The findings showed that in many cases, the use of artificial intelligence methods had effective application in personalized medicine. [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]

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]

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]

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]

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]

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]

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]

Pectoral nerve blocks (Pecs I and Pecs II) are superficial thoracic wall blocks which block the pectoral and intercostal nerves. They can be used as analgesia/anesthesia for breast surgery and other procedures/surgeries involving the anterior chest wall (arteriovenous graft creation surgery, minimally invasive cardiac surgery and thoracotomy, etc.). The aim of this study is to evaluate publications in the scientific field of pectoral nerve blocks. ISI Web of Knowledge-Science was used for the analysis. All scientific works published included in the Science Citation Index Expanded (SCI-E) from 1975 to January 27, 2019, were analyzed. A retrospective search was performed using key words "pectoral nerve block," "PECS I," "PECS II," "pecs block," "PECS block,""Pecs I," "Pecs II," "PECS 1," "PECS 2," and "modified pecs I block." We further analyzed these results by the "analyze" function of the software in terms of number of papers for each country, type of documentation, number of publications per year, and name of journals and authors. The number of citations to published works was also calculated by using the citation function of the same software. 72 papers were found related to pectoral nerve block. The biggest contribution was from India (24.28%), and followed by Japan (14.28%), USA (14.28%), Canada (7.14%), Egypt (7.14%), Italy (5.71%), and South Korea (5.71%). The total number of publications increased sharply in years from 2014 (n = 1) to 2018 (n = 28). We have detected that papers on the use of pecs block in breast cancer surgery, which is one of the most common surgical procedure in the world, are few in the literature. With this study, we hope to increase the awareness on this area. We believe that pecs block applications will become widespread with the increasing use of ultrasound in anesthesia and the increase in education in this field. [ABSTRACT FROM AUTHOR]

Breast cancer is the most prevalent form of cancer that can strike at any age; the higher the age, the greater the risk. The presence of malignant tissue has become more frequent in women. Although medical therapy has improved breast cancer diagnostic and treatment methods, still the death rate remains high due to failure of diagnosing breast cancer in its early stages. A classification approach for mammography images based on nonsubsampled contourlet transform (NSCT) is proposed in order to investigate it. The proposed method uses multiresolution NSCT decomposition to the region of interest (ROI) of mammography images and then uses Z-moments for extracting features from the NSCT-decomposed images. The matrix is formed by the components that are extracted from the region of interest and are then subjected to singular value decomposition (SVD) in order to remove the essential features that can generalize globally. The method employs a support vector machine (SVM) classification algorithm to categorize mammography pictures into normal, benign, and malignant and to identify and classify the breast lesions. The accuracy of the proposed model is 96.76 percent, and the training time is greatly decreased, as evident from the experiments performed. The paper also focuses on conducting the feature extraction experiments using morphological spectroscopy. The experiment combines 16 different algorithms with 4 classification methods for achieving exceptional accuracy and time efficiency outcomes as compared to other existing state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Background: Secondary lymphedema is a chronic swelling of the upper limb that may occur after treatment for breast cancer. During the acute phase, intensive treatment with a therapist is provided, while during the maintenance phase the patient needs to detect any re-swelling by self-examination. Objective: To assess the test-retest reliability and the concurrent validity of self-measurement upper limb volume among women post-breast cancer. Design: A cross-sectional study of 17 women post-breast cancer that experience a period of intensive unilateral upper limb lymphedema treatment in the past. Methods: On day 1 and day 10 at the clinic, the physiotherapist measured the volume of the upper limbs with the water displacement method (i.e. the 'gold standard' for volume measure) as well as with the more common method of plastic tape. The participants performed self-measurement twice with the paper tape under the supervision of a physiotherapist in the clinic. After a week the participants performed self-measurement at home with the paper tape. Results: The intra-class correlations measures indicated excellent values for the self-measure tape measurements on the operated side (0.97-0.99) as well as on the opposite arm (0.96-0.99). The self-measurement revealed a moderate association with the criterion measure, the water displacement ( rp 0.59-0.68, ( p < 0.05)), and strong concurrent validity with therapist tape measurements ( rp 0.88-0.95, ( p < 0.05)). Conclusions: Women post-breast cancer can self-measure upper limb volume using a paper tape which is both, reliable and valid. [ABSTRACT FROM AUTHOR]

Objective. This paper aims to systematically evaluate the intervention effect of mind-body exercise on cancer-related fatigue in breast cancer patients. Methods. Databases including PubMed, the Cochrane Library, Embase, Web of Science, CNKI, Wanfang Data, and SINOMED were retrieved to collect randomized controlled trials on the effects of mind-body exercise on relieving cancer-related fatigue in breast cancer patients. The retrieval period started from the founding date of each database to January 6, 2021. Cochrane bias risk assessment tools were used to evaluate the methodological quality assessment of the included literature, and RevMan 5.3 software was used for meta-analyses. Results. 17 pieces of researches in 16 papers were included with a total of 1133 patients. Compared with the control group, mind-body exercise can improve cancer-related fatigue in breast cancer patients. The combined effect size SMD = 0.59, 95% CI was [0.27, 0.92], p < 0.00001. Doing Tai Chi for over 40 minutes each time with an exercise cycle of ≤6 weeks can improve cancer-related fatigue in breast cancer patients more significantly. Sensitivity analysis shows that the combined effect results of the meta-analysis were relatively stable. Conclusion. Mind-body exercise can effectively improve cancer-related fatigue in breast cancer patients. [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]

Background: A 47-year-old female patient with stage four breast cancer and lymphatic involvement, as well as several secondary physical complaints and psychological stressors, presented for chiropractic care following six sessions of chemotherapy. This was her second bout with cancer, having survived Hodgkins Lymphoma. Intervention: The patient underwent three, five-day courses of concentrated chiropractic care with complementary care modalities, in addition to her oncological treatment plan. Outcomes: The patient was able to achieve full remission of her cancer as well as marked improvements in sleep, mental health, and physical adaptability. Conclusion: This case report provides an impetus for further investigations into chiropractic care and cancer, especially heavy metal toxicity, cancer, and chiropractic care. [ABSTRACT FROM AUTHOR]

Breast cancer is the most common cause of mortality due to cancer for women both in Lithuania and worldwide. The chances of survival after diagnosis differ significantly depending on the stage of disease at the time of diagnosis and other factors. One way to estimate survival is to construct a Kaplan–Meier estimate for each factor value separately. However, in cases when it is impossible to observe a large number of patients (for example, in the case of countries with lower numbers of inhabitants), dividing the data into subsets, say, by stage at diagnosis, may lead to results where some subsets contain too few data, thus causing the results of a Kaplan–Meier (or any other) method to become statistically incredible. The problem may become even more acute if researchers want to use more risk factors, such as stage at diagnosis, sex, place of living, treatment method, etc. Alternatively, Cox models can be used to analyse survival data with covariates, and they do not require the data to be divided into subsets according to chosen risks factors (hazards). We estimate the chances of survival for up to 5 years after a breast cancer diagnosis for Lithuanian females during the period of 1995–2016. Firstly, we construct Kaplan-Meier estimates for each stage separately; then, we apply a (stratified) Cox model using stage, circumstance of diagnosis, and year of diagnosis as (potential) hazards. Some directions of further research are provided in the last section of the paper. [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]

Digital breast tomosynthesis (DBT) is a 3D breast cancer screening technique that can overcome the limitations of standard 2D digital mammography. However, DBT images often suffer from artifacts stemming from acquisition conditions, a limited angular range, and low radiation doses. These artifacts have the potential to degrade the performance of automated breast tumor classification tools. Notably, most existing automated breast tumor classification methods do not consider the effect of DBT image quality when designing the classification models. In contrast, this paper introduces a novel deep learning-based framework for classifying breast tumors in DBT images. This framework combines global image quality-aware features with tumor texture descriptors. The proposed approach employs a two-branch model: in the top branch, a deep convolutional neural network (CNN) model is trained to extract robust features from the region of interest that includes the tumor. In the bottom branch, a deep learning model named TomoQA is trained to extract global image quality-aware features from input DBT images. The quality-aware features and the tumor descriptors are then combined and fed into a fully-connected layer to classify breast tumors as benign or malignant. The unique advantage of this model is the combination of DBT image quality-aware features with tumor texture descriptors, which helps accurately classify breast tumors as benign or malignant. Experimental results on a publicly available DBT image dataset demonstrate that the proposed framework achieves superior breast tumor classification results, outperforming all existing deep learning-based methods. [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]

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]

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]

Introduction: In recent years, more and more studies have proved that lipid metabolism plays an essential role in breast cancer’s proliferation and metastasisand also has a specific significance in predicting survival. Methods: This paper collected data from 725 publications related to lipid metabolism in breast neoplasm from 2012 to 2021 through the Web of Science Core Collection database. Bibliometrix, VOSviewer, and CiteSpace were used for the scientometrics analysis of countries, institutions, journals, authors, keywords, etc. Results: The number of documents published showed an increasing trend, with an average annual growth rate of 14.49%. The United States was the most productive country (n = 223, 30.76%). The journals with the largest number of publications are mostly from developed countries. Except for the retrieved topics, “lipid metabolism” (n = 272) and “breast cancer” (n = 175), the keywords that appeared most frequently were “expression” (n = 151), “fatty-acid synthase” (n = 78), “growth” (n = 72), “metabolism” (n = 67) and “cells“ (n = 66). Discussion: These findings and summaries help reveal the current research status and clarify the hot spots in this field. [ABSTRACT FROM AUTHOR]

Background: Breast cancer is one of the most prevalent malignancies diagnosed in women. Mammogram inspection in the search and delineation of breast tumors is an essential prerequisite for a reliable diagnosis. However, analyzing mammograms by radiologists is time‐consuming and prone to errors. Therefore, the development of computer‐aided diagnostic (CAD) systems to automate the mass segmentation procedure is greatly expected. Purpose: Accurate breast mass segmentation in mammograms remains challenging in CAD systems due to the low contrast, various shapes, and fuzzy boundaries of masses. In this paper, we propose a fully automatic and effective mass segmentation model based on deep learning for improving segmentation performance. Methods: We propose an effective transformer‐based encoder‐decoder model (TrEnD). Firstly, we introduce a lightweight method for adaptive patch embedding (APE) of the transformer, which utilizes superpixels to adaptively adjust the size and position of each patch. Secondly, we introduce a hierarchical transformer‐encoder and attention‐gated‐decoder structure, which is beneficial for progressively suppressing interference feature activations in irrelevant background areas. Thirdly, a dual‐branch design is employed to extract and fuse globally coarse and locally fine features in parallel, which could capture the global contextual information and ensure the relevance and integrity of local information. The model is evaluated on two public datasets CBIS‐DDSM and INbreast. To further demonstrate the robustness of TrEnD, different cropping strategies are applied to these datasets, termed tight, loose, maximal, and mix‐frame. Finally, ablation analysis is performed to assess the individual contribution of each module to the model performance. Results: The proposed segmentation model provides a high Dice coefficient and Intersection over Union (IoU) of 92.20% and 85.81% on the mix‐frame CBIS‐DDSM, while 91.83% and 85.29% for the mix‐frame INbreast, respectively. The segmentation performance outperforms the current state‐of‐the‐art approaches. By adding the APE and attention‐gated module, the Dice and IoU have improved by 6.54% and 10.07%. Conclusion: According to extensive qualitative and quantitative assessments, the proposed network is effective for automatic breast mass segmentation, and has adequate potential to offer technical assistance for subsequent clinical diagnoses. [ABSTRACT FROM AUTHOR]

The aim of the study is to assess the feasibility of Drugs Rational Usage Guideline System (DRUGS)-supported clinical pathway (CP) for breast carcinoma, cataract, inguinal hernia and 2-diabetes mellitus whether the application of such a system could improve work efficiency, medical safety, and decrease hospital cost. Four kinds of diseases which included 1773 cases (where 901 cases using paper-based clinical pathways and 872 cases using DRUGS-supported clinical pathways) were selected and their demographic and clinical data were collected. The evaluation criteria were length of stay, preoperative length of stay, hospital cost, antibiotics prescribed during hospitalization, unscheduled surgery, complications and prognosis. The median total LOS was 1 to 3 days shorter in the DRUGS-supported CP group as compared to the Paper-based CP group for all types ( p < 0.05). Totel hospital cost decreased significantly in the DRUGS-supported CP group than that in Paper-based CP group. About antibiotics prescribed during hospitalization, there were no statistically differences in the time of initial dose of antibiotic and the duration of administration except the choice of antibiotic categories. The proportion of DRUGS-supported clinical pathway conditions where a broad-spectrum antibiotic was prescribed decreased from 63.6 to 34.5 % ( p < 0.01) in the Paper-based group. While after the intervention, the differences were statistically not significant in unscheduled surgery, complications and prognosis. In this study, DRUGS-supported clinical pathway for breast carcinoma, cataract, inguinal hernia, 2-diabetes mellitus was smoothly shifted from a paper-based to an electronic system, and confer benefits at the hospital level. [ABSTRACT FROM AUTHOR]