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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]

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]

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: 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]

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]

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.

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]

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]

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]

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]

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]

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]

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]

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]

Rationale and Objectives: The aims of this study were to label the versatile amino acid l-lysine with 99mTc using 2,3-dimercapto-succinic acid (DMSA) as a chelator, and to assess its tumor imaging feasibility under in vivo and in vitro conditions, and finally to determine the subcellular biodistribution of this radiopharmaceutical. Materials and Methods: DMSA-l-lysine was chemically synthesized and labeled with sodium pertechnetate. Nuclear magnetic resonance (NMR) and mass spectral analysis of DMSA-l-lysine were conducted. Radiochemical purity was determined by thin-layer chromatography (TLC) and paper chromatography. Cellular uptake, competition and subcellular localization studies were performed in rat breast cancer cells (13762). In vivo studies of planar imaging and biodistribution studies were performed on female Fischer 344 rats. Medical Internal Radiation Dose (MIRD) dosimetry estimates were calculated. Results: Radiochemical purity (determined by radio-TLC and high-performance liquid chromatography) of these compounds was >95%. 99mTc-DMSA-l-lysine showed good uptake in in vitro cell culture assays and uptake was reduced in competition studies. 99mTc-DMSA-l-lysine accumulates in the nucleus as much as in the cytoplasm and it was also shown that accumulation of the 99mTc-DMSA-l-lysine in the nucleus increases as a function of a time. There was an increase in tumor-to-blood and tumor-to-muscle count density ratios. Tumor/background ratios were 5.75 at 1 hour and 6.87 at 2 hours. In vivo tissue distribution studies revealed that radiation dosimetry of blood-forming organs were within radiation dose limits. Conclusion: DMSA-l-lysine kits can be labeled with 99mTc easily and efficiently, with high radiochemical purity and cost-effectiveness. In vitro cellular uptake and scintigraphic imaging studies demonstrated the pharmacokinetic distribution and feasibility of using 99mTc-DMSA-l-lysine for tumor imaging. [Copyright &y& Elsevier]

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]

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]

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]

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]

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]

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]

Impedance measuring acquisition systems focused on breast tumor detection, as well as image processing techniques for 3D imaging, are reviewed in this paper in order to define potential opportunity areas for future research. The description of reported works using electrical impedance tomography (EIT)-based techniques and methodologies for 3D bioimpedance imaging of breast tissues with tumors is presented. The review is based on searching and analyzing related works reported in the most important research databases and is structured according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) parameters and statements. Nineteen papers reporting breast tumor detection and location using EIT were systematically selected and analyzed in this review. Clinical trials in the experimental stage did not produce results in most of analyzed proposals (about 80%), wherein statistical criteria comparison was not possible, such as specificity, sensitivity and predictive values. A 3D representation of bioimpedance is a potential tool for medical applications in malignant breast tumors detection being capable to estimate an ap-proximate the tumor volume and geometric location, in contrast with a tumor area computing capacity, but not the tumor extension depth, in a 2D representation. [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]

Time reversal (TR) relies on the reciprocity nature of the wave equation. Waves diverging away from a source can be time reversed and back-propagated to focus back at the source. The transmitting and receiving antenna arrays utilized for TR are collectively termed as the TR mirror (TRM). TR imaging in the microwave regime has shown its promise in nondestructive inspection and target localization. This paper deals with the design of a microwave TRM for the detection of breast tumors. The first part of this paper discusses TR simulations based on a signal processing algorithm, to obtain the tumor location from the total fields. The processed time-reversed fields can be back-propagated in a numerical model to focus the wave at the tumor locations. Simulation results aid in investigating the feasibility and the robustness of the method. The second part of this paper deals with experimental studies for the breast tumor detection, using a pulsed time-domain system and the back-propagation model. The successful detection of single and multiple tumors embedded in a liquid breast tissue-based phantom is shown using the microwave system. [ABSTRACT FROM AUTHOR]

Objective. Quality of life (QoL) is a major concern in breast cancer (BC) patients. Despite efforts, no study has comprehensively addressed determinants of QoL in patients with BC. This study aimed to synthesize evidence on QoL correlations using the meta-analytic structural equation modeling (MASEM) approach. Methods. Our search in PubMed, Web of Science, Scopus, and Cochrane databases resulted in 5,238 initial relevant papers, 73 of which were eligible for final analysis with a total of 44,121 patients. We used a two-stage procedure of correlation-based MASEM to examine the relationship between QoL and body mass index (BMI), physical activity (PA), sleep, depression, fatigue, and stress. Results. Final MASEM model suggested that PA (path coefficient = 0.33, 95% CI = −0.0444; 0.6334), fatigue (path coefficient = −0.23, 95% CI = −0.6825; 0.0361), and stress (path coefficient = −0.22, 95% CI = −0.5143; 0.6875) were the most important factors related to QoL in patients with breast cancer. Final model identified variables responsible for 68% of the variation in QoL in BC. Conclusion. QoL is an important outcome in the treatment course of BC. Large-scale and meta-analysis studies could help patients to have a life with improved quality. [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]

Contemporary searches for new risk factors frequently involve genome-wide explorations of very large numbers of candidate risk variants. Given that diseases can often be classified into subtypes that possess evidence of etiologic heterogeneity, the question arises as to whether or not a search for new risk factors would be improved by looking separately within subtypes. Etiologic risk heterogeneity inevitably increases the signal in at least one of the subtypes, but this advantage may be offset by smaller sample sizes and the increased chances of false discovery. In this article, the authors show that only a relatively modest degree of etiologic heterogeneity is necessary for the subtyping strategies to have improved statistical power. In practice, effective exploitation of etiologic heterogeneity requires strong evidence that the subtypes selected are likely to exhibit substantial heterogeneity. Further, defining the subtypes that demonstrate the most heterogeneous profiles is important for optimizing the search for new risk factors. The concepts are illustrated by using data from a breast cancer study in which results are available separately for estrogen receptor-positive (ER+) and -negative (ER−) tumors. [ABSTRACT FROM AUTHOR]

Background: Primary neuroendocrine neoplasm of the breast (BNEN) is an uncommon breast neoplasm, and in most cases, it presents as hormone receptors positive and HER-2 negative. Moreover, in neuroendocrine neoplasms (NENs), the signet ring feature is a rare morphological subtype, and only a few cases have been reported. Here, we report the case of a primary breast neuroendocrine neoplasm with an unusual signet ring cell appearance in this paper. The documentation of this case, combined with a review of the literature, may add to existing knowledge about the outcome and management of this rare tumor. Methods: In the present review, we describe a unique case of HER-2-positive primary BNEN with a signet ring feature that has not been reported in English. Additionally, we performed a literature search of the PubMed and Web of Science databases and calculated statistics for clinical data and follow-up. Results: Our literature search, excluding non-English literature, identified 15 articles with data from 24 cases, including ours. The mean age was 51.25 years (range, 30-79 years), and there were 13 male patients (54%) and 11 female patients (46%). Of the 24 cases, some cases (11/24) were associated with lymph node metastases, a few cases (6/24) had distant metastasis, and the vast majority of cases (23/24) occurred in the digestive system. Primary hepatic signet ring cell neuroendocrine tumor showed slow progression and good prognosis. Lymph node involvement was identified in one of eight (12.5%) documented cases, and one of eight (12.5%) reported cases presented with distant metastatic disease. However, the prognosis of neuroendocrine tumors with signet ring cells in the pancreas and stomach was poor. Lymph node involvement was identified in 9 of 15 (60%) documented cases, and 5 of 15 (33.3%) reported cases presented with distant metastatic disease. Conclusion: NENs with a signet ring feature is uncommon, and this is the first case report of its occurrence in the breast. Current knowledge is limited to anecdotal experience based on case reports and small case series. We provide a literature review to summarize knowledge about this rare entity. [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]

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: 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]

Automated Breast Ultrasound is a highly advanced breast tumor detection modality that produces hundreds of 2D slices in each scan. However, this large number of slices poses a significant burden for physicians to review. This paper proposes a novel 2.5D tumor detection model, named "ABUSDet," to assist physicians in automatically reviewing ABUS images and predicting the locations of breast tumors in images. At the core of this approach, a sequence of data blocks partitioned from a pre-processed 3D volume are fed to a 2.5D tumor detection model, which outputs a sequence of 2D tumor candidates. An aggregation module then clusters the 2D tumor candidates to produce the ultimate 3D coordinates of the tumors. To further improve the accuracy of the model, a novel mechanism for training deep learning models, called "Deliberate Training," is proposed. The proposed model is trained and tested on a dataset of 87 patients with 235 ABUS volumes. It achieves sensitivities of 77.94%, 75.49%, and 65.19% at FPs/volume of 3, 2, and 1, respectively. Compared with the 2D and 3D object detection models, the proposed ABUSDet model achieves the highest sensitivity with relatively low false-positive rates. [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]

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]

Background: Breast cancer is the most common type of cancer, accounting for 15% of all new cases. Hormone therapy (HT) is extremely effective in reducing breast cancer recurrence. However, adherence to HT medication is often poor due to negative side effects such as fatigue and sleep disturbances. Physical activity has been identified as a possible intervention to improve quality of life and reduce side effects of HT. Objective: The objective of this systematic review was to summarise evidence of exercise interventions for women being treated with HT for breast cancer. Method: Electronic searches were conducted from inception to March 2022 using Medline, SPORTdiscus, Embase, Scopus, PsycINFO and Web of Science databases. Searches included a combination of terms related to breast cancer, exercise, sleep disturbances, fatigue and HT. Results: Ten eligible papers were identified, and their quality was assessed. Type, frequency, duration and intensity of exercise interventions varied. Exercise types included aerobics, strength/resistance training, walking and yoga. Heterogeneity of data made it difficult to draw conclusions. However, aerobic exercise interventions provide clear improvements in fatigue and sleep disturbances. Conclusions: This review identified a lack of consistency in exercise recommendations for women being treated for breast cancer. It identifies that aerobic exercise can successfully improve fatigue and sleep disturbances, consistent with existing literature. Exercise has a range of benefits for this population, including improving psychological well‐being and quality of life. Clinicians should strongly advocate for engagement in exercise to promote overall physical and psychological well‐being in women being treated for breast cancer. [ABSTRACT FROM AUTHOR]