Search Results (2,113)

Exosomes are membranous vesicles that play a crucial role as intercellular messengers. Cells secrete exosomes, which can be found in a variety of bodily fluids such as amniotic fluid, semen, breast milk, tears, saliva, urine, blood, bile, ascites, and cerebrospinal fluid. Exosomes have a distinct bilipid protein structure and can be as small as 30–150 nm in diameter. They may transport and exchange multiple cellular messenger cargoes across cells and are used as a non-invasive biomarker for various illnesses. Due to their unique features, exosomes are recognized as the most effective biomarkers for cancer and other disease detection. We give a review of the most current applications of exosomes derived from various sources in the prognosis and diagnosis of multiple diseases. This review also briefly examines the significance of exosomes and their applications in biomedical research, including the use of aptamers and antibody–antigen functionalized biosensors. Full article

Purpose: To effectively reduce cancer burden, genetic testing programs should identify high-risk individuals prior to cancer development, when risk-reduction strategies can be implemented. We evaluated trends in BRCA1/BRCA2 testing use after implementation of a publicly funded testing program. Methods: We conducted a retrospective, near population-based study of women who underwent BRCA1/BRCA2 testing in Ontario, Canada, (2007–2016) (n = 15,986). Temporal trends were evaluated using linear and Poisson regression. Results: Although annual utilization of testing increased over time (p < 0.001), mean age at testing increased from 49.9 years (SD 13.8) in 2007 to 53.8 years (SD 13.7) in 2016 (p < 0.001). The proportion of women with a cancer history at testing also increased from 53.5% in 2007 to 66.3% in 2015 (p < 0.001); the proportion of women free from breast cancer did not change significantly (49.2% in 2007 versus 45.1% in 2015, p = 0.90). As a proportion of all tested, those with breast cancer tested within 3 months of diagnosis increased over time (0.39% of tests in 2007 versus 13.6% of tests in 2015; p < 0.001). Conclusions: While the institution of a publicly funded genetic testing program was associated with rising utilization, increasing age at testing and decreasing testing of unaffected women suggest limitations in identifying high-risk individuals eligible for risk-reduction. Full article

Background/Objectives: Breast cancer was the leading cause of malignant tumors among women in 2022. About two-thirds of breast cancer cases are hormone-receptor-positive. In these patients, aromatase inhibitors are a mainstay of treatment, but associated musculoskeletal symptoms can negatively affect patient compliance. Aromatase-inhibitor-induced carpal tunnel syndrome represents one of the main causes of aromatase inhibitor discontinuation, with a non-compliance rate of up to 67%, potentially leading to increased cancer mortality. This study investigates estrogen receptor expression in aromatase-inhibitor-induced carpal tunnel syndrome tissues, in order to better define its etiopathogenesis and derive preventive or therapeutic measures that can improve aromatase inhibitor patient compliance. To our knowledge, there is no study on this subject in the literature. Methods: Between 2023 and 2024, we recruited 14 patients at the Jewish Hospital of Rome, including seven patients with aromatase-inhibitor-induced carpal tunnel syndrome (study group) and seven with postmenopausal idiopathic carpal tunnel syndrome (control group). Each patient was evaluated based on a clinical visit, a questionnaire, instrumental exams, and serum hormone dosages and were treated with open carpal tunnel release surgery, during which transverse carpal ligament and flexor tenosynovium samples were collected. For immunohistochemical experiments, sections were treated with anti-estrogen receptor α and anti-estrogen receptor β antibodies. Results: The immunohistochemical features in the study and control groups were similar, demonstrating that tissues affected by aromatase-inhibitor-induced carpal tunnel syndrome are targets of direct estrogen action and that estrogen deprivation is correlated with disease etiogenesis. Surgery was effective in patient treatment. Conclusions: Aromatase-inhibitor-induced carpal tunnel syndrome represents a newly defined form of the disease. This syndrome represents one of the main causes of aromatase inhibitor discontinuation, due to its negative impact on the patient’s quality of life. The identification by clinicians of aromatase inhibitor use as a possible risk factor for carpal tunnel syndrome development is of essential importance, as early diagnosis and prompt management can improve patient compliance and overall breast cancer treatment outcomes. Full article

Background: Breast cancer is the most prevalent malignancy among women globally. In Romania, it is the most frequent form of cancer affecting women, with approximately 12,000 new cases diagnosed annually, and the second most common cause of cancer-related mortality, second only to lung cancer. Methods: This study looked at 79 breast cancer patients from Oltenia, concentrating on epidemiology, histology, diagnostic features, and treatments. Patients were chosen based on inclusion criteria such as histopathologically verified diagnosis, availability of clinical and treatment data, and follow-up information. The analyzed biological material consisted of tissue samples taken from the breast parenchyma and axillary lymph nodes. Even though not the primary subject of this paper, all patients underwent immunohistochemical (IHC) evaluation both preoperatively and postoperatively. Results: We found invasive ductal carcinoma to be the predominant type, while ductal carcinoma in situ (DCIS) and mixed types were rare. We performed cross-tabulations of metastasis versus nodal status and age versus therapy type; none reached significance (all p > 0.05), suggesting observed differences were likely due to chance. A chi-square test comparing surgical interventions (breast-conserving vs. mastectomy) in patients who did or did not receive chemotherapy showed, χ² = 3.17, p = 0.367, indicating that chemotherapy did not significantly influence surgical choice. Importantly, adjuvant chemotherapy and radiotherapy were used at similar rates across age groups, whereas neoadjuvant hormonal (endocrine) therapy was more common in older patients (but without statistical significance). Conclusions: Finally, we discussed the consequences of individualized care and early detection. Romania’s shockingly low screening rate, which contributes to delayed diagnosis, emphasizes the importance of improved population medical examination and tailored treatment options. Also, the country has one of the lowest rates of mammography uptake in Europe and no systematic population screening program. Full article

Breast cancer is the leading cause of cancer-related mortality among women world-wide, and early screening is critical for improving patient survival. Medical imaging plays a central role in breast cancer screening, diagnosis, and treatment monitoring. However, conventional imaging modalities—including mammography, ultrasound, and magnetic resonance imaging—face limitations such as low diagnostic specificity, relatively slow imaging speed, ionizing radiation exposure, and dependence on exogenous contrast agents. Photoacoustic imaging (PAI), a novel hybrid imaging technique that combines optical contrast with ultrasonic spatial resolution, has shown great promise in addressing these challenges. By revealing anatomical, functional, and molecular features of the breast tumor microenvironment, PAI offers high spatial resolution, rapid imaging, and minimal operator dependence. This review outlines the fundamental principles of PAI and systematically examines recent advances in its application to breast cancer screening, diagnosis, and therapeutic evaluation. Furthermore, we discuss the translational potential of PAI as an emerging breast imaging modality, complementing existing clinical techniques. Full article

Palindromic antimicrobial peptides (PAMs) constitute versatile scaffolds for the design and optimization of anticancer agents with applications in therapy, diagnosis, and/or monitoring. In the present study, fluorolabeled peptides derived from the palindromic sequence RWQWRWQWR containing fluorescent probes, such as 2-Aminobenzoyl, 5(6)-Carboxyfluorescein, and Rhodamine B, were obtained. RP-HPLC analysis revealed that the palindromic peptide conjugated to Rhodamine B (RhB-RWQWRWQWR) exhibited the presence of isomers, likely corresponding to the open-ring and spiro-lactam forms of the fluorescent probe. This equilibrium is dependent on the peptide sequence, as the RP-HPLC analysis of dimeric peptide (RhB-RRWQWR-hF-KKLG)₂K-Ahx did not reveal the presence of isomers. The antibacterial activity of the fluorescent peptides depends on the probe attached to the sequence and the bacterial strain tested. Notably, some fluorescent peptides showed activity against reference strains as well as sensitive, resistant, and multidrug-resistant clinical isolates of E. coli, S. aureus, and E. faecalis. Fluorolabeled peptides 1-Abz (MIC = 62 µM), RhB-1 (MIC = 62 µM), and Abz-1 (MIC = 31 µM) exhibited significant activity against clinical isolates of E. coli, S. aureus, and E. faecalis, respectively. The RhB-1 (IC₅₀ = 61 µM), Abz-1 (IC₅₀ = 87 µM), and RhB-2 (IC₅₀ = 35 µM) peptides exhibited a rapid, significant, and concentration-dependent cytotoxic effect on HeLa cells, accompanied by morphological changes characteristic of apoptosis. RhB-1 (IC₅₀ = 18 µM) peptide also exhibited significant cytotoxic activity against breast cancer cells MCF-7. These conjugates remain valuable for elucidating the possible mechanisms of action of these novel anticancer peptides. Rhodamine-labeled peptides displayed cytotoxicity comparable to that of their unlabeled analogues, suggesting that cellular internalization constitutes a critical early step in their mechanism of action. These findings suggest that cell death induced by both unlabeled and fluorolabeled peptides proceeds predominantly via apoptosis and is likely contingent upon peptide internalization. Functionalization at the N-terminal end of the palindromic sequence can be evaluated to develop systems for transporting non-protein molecules into cancer cells. Full article

Background: Delays in breast cancer treatment negatively affect prognosis and have increased over time. Data on waiting times in Switzerland are limited. Patients and Methods: This study analyzed cancer registry data from 2003 to 2005 (2628 patients) and 2015 to 2017 (421 patients) to evaluate waiting times for diagnosis, surgery, and radiotherapy; temporal trends; and survival in women with stage I–III invasive breast cancer treated with surgery without chemotherapy. Associations with demographic/clinical factors and overall survival (OS) were assessed using ANOVA, uni-/multivariable regression, Kaplan–Meier, and Cox regression. Results: From 2003 to 2005, mean intervals were biopsy-to-diagnosis 4.3 days, diagnosis-to-surgery 18.8 days, biopsy-to-surgery 26.8 days, and surgery-to-radiotherapy 56.7 days. Longer diagnosis-to-surgery times were associated with metropolitan areas, public hospitals, basic insurance, mastectomy, and older age (all p < 0.001). Radiotherapy delays were also longer in metropolitan areas and after mastectomy (p < 0.001). Between 2003–2005 and 2015–2017, diagnosis-to-surgery times rose in Eastern Switzerland (from 21.3 to 31.2 days), while radiotherapy timing remained stable. Five-year overall survival improved (from 76.7% to 88.4%), but was not significantly impacted by diagnosis-to-surgery intervals. Conclusions: Despite timely surgery in Switzerland (2003–2005), disparities existed, and time to surgery increased by 2015–2017. Reducing waiting times remains important despite no significant short-term OS impact. Full article

Cancer metabolic reprogramming plays a critical role in tumor progression and therapeutic resistance, underscoring the need for advanced analytical strategies. Metabolomics, leveraging mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy, offers a comprehensive and functional readout of tumor biochemistry. By enabling both targeted metabolite quantification and untargeted profiling, metabolomics captures the dynamic metabolic alterations associated with cancer. The integration of metabolomics with machine learning (ML) approaches further enhances the interpretation of these complex, high-dimensional datasets, providing powerful insights into cancer biology from biomarker discovery to therapeutic targeting. This review systematically examines the transformative role of ML in cancer metabolomics. We discuss how various ML methodologies—including supervised algorithms (e.g., Support Vector Machine, Random Forest), unsupervised techniques (e.g., Principal Component Analysis, t-SNE), and deep learning frameworks—are advancing cancer research. Specifically, we highlight three major applications of ML–metabolomics integration: (1) cancer subtyping, exemplified by the use of Similarity Network Fusion (SNF) and LASSO regression to classify triple-negative breast cancer into subtypes with distinct survival outcomes; (2) biomarker discovery, where Random Forest and Partial Least Squares Discriminant Analysis (PLS-DA) models have achieved >90% accuracy in detecting breast and colorectal cancers through biofluid metabolomics; and (3) prognostic modeling, demonstrated by the identification of race-specific metabolic signatures in breast cancer and the prediction of clinical outcomes in lung and ovarian cancers. Beyond these areas, we explore applications across prostate, thyroid, and pancreatic cancers, where ML-driven metabolomics is contributing to earlier detection, improved risk stratification, and personalized treatment planning. We also address critical challenges, including issues of data quality (e.g., batch effects, missing values), model interpretability, and barriers to clinical translation. Emerging solutions, such as explainable artificial intelligence (XAI) approaches and standardized multi-omics integration pipelines, are discussed as pathways to overcome these hurdles. By synthesizing recent advances, this review illustrates how ML-enhanced metabolomics bridges the gap between fundamental cancer metabolism research and clinical application, offering new avenues for precision oncology through improved diagnosis, prognosis, and tailored therapeutic strategies. Full article

Accurate classification of breast cancer histopathology images is critical for early diagnosis and treatment planning. Yet, conventional deep learning models face significant challenges under limited annotation scenarios due to their reliance on large-scale labeled datasets. To address this, we propose Dynamic Cross-Scale Swin Transformer (DCS-ST), a robust and efficient framework tailored for histopathology image classification with scarce annotations. Specifically, DCS-ST integrates a dynamic window predictor and a cross-scale attention module to enhance multi-scale feature representation and interaction while employing a semi-supervised learning strategy based on pseudo-labeling and denoising to exploit unlabeled data effectively. This design enables the model to adaptively attend to diverse tissue structures and pathological patterns while maintaining classification stability. Extensive experiments on three public datasets—BreakHis, Mini-DDSM, and ICIAR2018—demonstrate that DCS-ST consistently outperforms existing state-of-the-art methods across various magnifications and classification tasks, achieving superior quantitative results and reliable visual classification. Furthermore, empirical evaluations validate its strong generalization capability and practical potential for real-world weakly-supervised medical image analysis. Full article

Breast cancer continues to be one of the leading causes of women’s deaths around the world, and this has emphasized the necessity to have novel and interpretable diagnostic models. This work offers a clear learning deep learning model that integrates the mobility of MobileNet and two bio-driven optimization operators, the Firefly Algorithm (FLA) and Dingo Optimization Algorithm (DOA), in an effort to boost classification appreciation and the convergence of the model. The suggested model demonstrated excellent findings as the DOA-optimized MobileNet acquired the highest performance of 98.96 percent accuracy on the fusion test, and the FLA-optimized MobileNet scaled up to 98.06 percent and 95.44 percent accuracies on mammographic and ultrasound tests, respectively. Further to good quantitative results, Grad-CAM visualizations indeed showed clinically consistent localization of the lesions, which strengthened the interpretability and model diagnostic reliability of Grad-CAM. These results show that lightweight, compact CNNs can be used to do high-performance, multimodal breast cancer diagnosis. Full article

Background/Objectives: After diagnosis, it is common for women with breast cancer to gain weight, which is associated with worse clinical outcomes. However, traditional measures such as body weight, BMI, and waist circumference do not detect key changes in body composition, such as fat redistribution or muscle loss. The objective of this exploratory study was to assess the evolution of body composition and muscle strength after one year of treatment, and their relationship with metabolic biomarkers. Methods: Prospective observational study in newly diagnosed breast cancer patients. Body composition was assessed using bioelectrical impedance analysis (BIA) and ultrasound (US); muscle strength was measured by handgrip dynamometry. Biomarkers analyzed included glucose, insulin, Homeostatic Model Assessment of Insulin Resistance (HOMA-IR), glycosylated hemoglobin (HbA1c), total cholesterol (and its fractions), triglycerides, C-reactive protein (CRP), 6-interleukin (IL-6), vitamin D, myostatin, and fibroblast growth factor 21 (FGF-21). Results: Sixty-one women (mean age 58 years) were included. After one year, fat mass and related parameters significantly increased, while skeletal muscle mass and muscle strength decreased. Sarcopenic obesity prevalence rose from 1.16% to 4.9%. No significant changes were found in biomarkers, but positive correlations were observed between fat parameters and insulin, HOMA-IR, and triglycerides, and negative correlations with HDL-cholesterol. Conclusions: BIA and US can detect unfavorable changes in body composition that are not reflected in conventional measurements. At one year post-diagnosis, women showed increased fat accumulation, muscle loss, and reduced strength, even without significant metabolic biomarker changes. Further research is warranted to elucidate the long-term clinical implications of these findings and the external validity in larger cohorts. Full article

Purpose: This study aimed to develop a predictive nomogram integrating AI-based BI-RADS lexicons and lesion-to-nipple distance (LND) ultrasound features to differentiate mass-type ductal carcinoma in situ (DCIS) from invasive ductal carcinoma (IDC) visible on ultrasound. Methods: The final study cohort consisted of 170 women with 175 pathologically confirmed malignant breast lesions, including 26 cases of DCIS and 149 cases of IDC. LND and AI-based features from the S-Detect system (BI-RADS lexicons) were analyzed. Rare features were consolidated into broader categories to enhance model stability. Data were split into training (70%) and validation (30%) sets. Logistic regression identified key predictors for an LND nomogram. Model performance was evaluated using receiver operating characteristic (ROC) curves, 1000 bootstrap resamples, and calibration curves to assess discrimination and calibration. Results: Multivariate logistic regression identified smaller lesion size, irregular shape, LND ≤ 3 cm, and non-hypoechoic echogenicity as independent predictors of DCIS. These variables were integrated into the LND nomogram, which demonstrated strong discriminative performance (AUC = 0.851 training; AUC = 0.842 validation). Calibration was excellent, with non-significant Hosmer-Lemeshow tests (p = 0.127 training, p = 0.972 validation) and low mean absolute errors (MAE = 0.016 and 0.034, respectively), supporting the model’s accuracy and reliability. Conclusions: The AI-based comprehensive nomogram demonstrates strong reliability in distinguishing mass-type DCIS from IDC, offering a practical tool to enhance non-invasive breast cancer diagnosis and inform preoperative planning. Full article

Microcalcification clusters (MCCs) are key indicators of breast cancer, with studies showing that approximately 50% of mammograms with MCCs confirm a cancer diagnosis. Early detection is critical, as it ensures a five-year survival rate of up to 99%. However, MCC detection remains challenging due to their features, such as small size, texture, shape, and impalpability. Convolutional neural networks (CNNs) offer a solution for MCC detection. Nevertheless, CNNs are typically trained on single-resolution images, limiting their generalizability across different image resolutions. We propose a CNN trained on digital mammograms with three common resolutions: 50, 70, and 100 $\mathsf{\mu }$m. The architecture processes individual 1 cm² patches extracted from the mammograms as input samples and includes a MobileNetV2 backbone, followed by a flattening layer, a dense layer, and a sigmoid activation function. This architecture was trained to detect MCCs using patches extracted from the INbreast database, which has a resolution of 70 $\mathsf{\mu }$m, and achieved an accuracy of 99.84%. We applied transfer learning (TL) and trained on 50, 70, and 100 $\mathsf{\mu }$m resolution patches from the MEXBreast database, achieving accuracies of 98.32%, 99.27%, and 89.17%, respectively. For comparison purposes, models trained from scratch, without leveraging knowledge from the pretrained model, achieved 96.07%, 99.20%, and 83.59% accuracy for 50, 70, and 100 $\mathsf{\mu }$m, respectively. Results demonstrate that TL improves MCC detection across resolutions by reusing pretrained knowledge. Full article

Breast cancer (BC) is a neoplasm characterized by high heterogeneity and is influenced by intrinsic molecular subtypes and clinical stage, aspects that remain underexplored in the Colombian population. This study aimed to characterize metabolic alterations associated with subtypes and disease progression in a group of newly diagnosed, treatment-naive Colombian women using an untargeted metabolomics approach. To improve metabolite coverage, samples were analyzed using LC-QTOF-MS and GC-QTOF-MS, along with amino acid profiling. The Luminal B subtype exhibited elevated levels of long-chain acylcarnitines and higher free fatty acid concentrations than the other subtypes. It also presented elevated levels of carbohydrates and essential glycolytic intermediates, suggesting that this subtype may adopt a hybrid metabolic phenotype characterized by increased glycolytic flux as well as enhanced fatty acid catabolism. Tumor, Node, and Metastasis (TNM) staging analysis revealed progressive metabolic reprogramming of BC. In advanced stages, a sustained increase in phosphatidylcholines and a decrease in lysophosphatidylcholines were observed, reflecting lipid alterations associated with key roles in tumor progression. In early stages (I-II), plasma metabolites with high discriminatory power were identified, such as glutamic acid, ribose, and glycerol, which are associated with dysfunctions in energy and carbohydrate metabolism. These results highlight metabolomics as a promising tool for the early diagnosis, clinical follow-up, and molecular characterization of BC. Full article

Objective: The aim of this study was to determine whether preoperative MRI has an impact on surgical planning in breast cancer patients. Tumor extent and molecular breast cancer subtypes were evaluated. Methods: This was a single-center study including 137 female patients with a first diagnosis of invasive breast cancer. Each patient had a standard clinical preoperative workup and an additional breast MRI. The interdisciplinary tumor board made written recommendations regarding the surgical therapy of each patient with and without the knowledge of the MRI findings. Results: The addition of MRI led to changes in surgical recommendations in 32 (23%) of the 137 patients. The highest rate of change in surgical therapy recommendations was observed in patients with multifocal tumors (53%). Molecular subtype had no influence on the changes in surgical therapy recommendations (p = 0.8). Conclusions: Patients with multifocal breast tumors were more likely to have a change in surgical therapy following MRI. Full article