Search Results (1,919)

Breast cancer continues to be the most frequently diagnosed cancer among women worldwide and remains a leading cause of cancer-related mortality. Despite advances in imaging and biopsy-based approaches, current diagnostic methods are invasive, costly, and often insufficient to capture the molecular heterogeneity of tumors. Extracellular vesicles (EVs) have emerged as promising non-invasive biomarkers owing to their role in intercellular communication and their enrichment with tumor-specific cargo. This study conducted a systematic review and meta-analysis of published literature to investigate proteomic alterations in EVs derived from breast cancer samples. From an initial 1097 records screened, four eligible studies were identified, reporting 628 differentially expressed proteins, of which 38 were consistently observed across multiple datasets. Functional enrichment analyses revealed predominant localization of these proteins to vesicle-associated compartments and significant involvement in biological processes related to cell growth, immune regulation, and tumor progression. Pathway analysis further highlighted integrin-mediated interactions, platelet activation, and hemostasis pathways as key molecular mechanisms represented within breast cancer EVs. Overall, the findings reveal a distinct EV proteomic signature in breast cancer that could support early detection and patient monitoring through minimally invasive testing. Future large-scale and standardized studies are needed to validate these candidate proteins and advance EV proteomics toward clinical application in breast cancer management. Full article

Background: Lung cancer is the leading cause of cancer-related death worldwide. Screening with low-dose computed tomography (LDCT) enables early detection of low-stage non-small cell lung cancer (NSCLC), increasing the chances of curative surgery. The aim of the present study was to analyze selected surgical aspects of treatment among patients diagnosed with NSCLC through LDCT-based screening in Szczecin, the first program of this kind in Poland. Methods: A group of 52 patients who were screened and operated on was compared with patients diagnosed and operated on outside the screening program during the same time period and a group of patients diagnosed and operated on prior to the screening program being implemented. Results: The screened population demonstrated a significantly higher frequency of stage IA cancer diagnosis, smaller tumor volume, more lobectomies, and fewer pneumonectomies compared with the other two groups. In addition, the waiting time for surgery was shorter, the duration of the procedure longer, and the length of hospitalization was reduced among the screened patients. No significant differences were observed in postoperative mortality or perioperative complications. Adenocarcinoma occurred significantly more often in the screened population than in the other groups, and tumors were more frequently classified as grade G2. A significant correlation was found between the need for blood transfusion and the occurrence of perioperative complications. Conclusions: The implementation of an LDCT-based screening program for lung cancer has a significant impact on the workload and case profile of thoracic surgery departments. Several aspects of surgical treatment differ significantly between patients diagnosed through screening and patients diagnosed outside of the program. Full article

This study introduces a class-balanced Convolutional Neural Network (CNN) framework specifically designed for the binary classification of breast tumors in digital mammography. The proposed method systematically addresses the pervasive issue of class imbalance in medical imaging datasets by implementing advanced dataset balancing strategies, which resulted in a significant reduction in false negatives that is critical in early breast cancer detection. The proposed architecture is designed for high-resolution mammograms and employs regularization techniques, such as dropout and L2 weight decay, which are intended to enhance generalization and reduce the risk of overfitting. Comprehensive data augmentation and normalization further enhance the model’s robustness and adaptability to real-world clinical variability. Evaluated on the MIAS dataset, our balanced CNN achieved an accuracy of 98.84%, exhibiting both sensitivity and overall reliability. This work demonstrates that a class-balanced CNN can deliver both high diagnostic accuracy and computational efficiency, indicating potential for future use in clinical screening workflows. The system’s ability to minimize diagnostic errors and support radiologists with reliable, data-driven predictions represents an exploratory step toward improving automated breast cancer detection. Full article

Background/Objectives: Prostate cancer is the second most common cause of cancer-related mortality among the male population worldwide. It is among the leading reasons for the increasing number of years of life lost, working years of life lost, and gross domestic product (GDP) loss in Bulgaria. The primary objective of this study is to evaluate the burden of prostate cancer in Bulgaria, including calculating years of life lost (YLL), years of working life lost (YWLL), and the associated indirect costs. Methods: An observational time-series study was conducted using official national data from the National Statistical Institute (NSI), the INFOSTAT database, and the National Social Security Institute. The study covered the period 2008–2023 and included all registered male deaths attributed to malignant neoplasm of the prostate (ICD-10: C61). YLL, YWLL, and indirect costs were calculated using the human capital approach. Due to restricted access to age-specific mortality files, additional mortality records were obtained through formal data requests to NSI. Results: Prostate cancer led to 127,457 YLL and 6345 YWLL, with productivity losses reaching €88.2 million. Mortality showed an overall increasing trend up to 2020, while YWLL declined due to deaths shifting to older age groups. Conclusions: Despite the advancements in prostate cancer diagnosis and treatment, our findings demonstrate a negative trend regarding YLL, YWLL, and indirect costs associated with the disease, in contrast to other European countries. Strengthening early screening, reducing diagnostic delays, and improving national cancer registry capacity are critical to mitigating future health and economic losses. Full article

Background: Colorectal cancer (CRC) is the second leading cause of cancer-related mortality worldwide, accounting for approximately 10% of all cancer cases. Despite the proven effectiveness of conventional screening modalities such as colonoscopy and fecal immunochemical testing (FIT), their invasive nature, high cost, and limited patient compliance hinder widespread adoption. Recent advancements in artificial intelligence (AI) and bowel sound-based signal processing have enabled non-invasive approaches for gastrointestinal diagnostics. Among these, bowel sound analysis—historically considered subjective—has reemerged as a promising biomarker using digital auscultation and machine learning. Objective: This review explores the potential of AI-powered bowel sound analytics for early detection, screening, and characterization of colorectal cancer. It aims to assess current methodologies, summarize reported performance metrics, and highlight translational opportunities and challenges in clinical implementation. Methods: A narrative review was conducted across PubMed, Scopus, Embase, and Cochrane databases using the terms colorectal cancer, bowel sounds, phonoenterography, artificial intelligence, and non-invasive diagnosis. Eligible studies involving human bowel sound-based recordings, AI-based sound analysis, or machine learning applications in gastrointestinal pathology were reviewed for study design, signal acquisition methods, AI model architecture, and diagnostic accuracy. Results: Across studies using convolutional neural networks (CNNs), gradient boosting, and transformer-based models, reported diagnostic accuracies ranged from 88% to 96%. Area under the curve (AUC) values were ≥0.83, with F1 scores between 0.71 and 0.85 for bowel sound classification. In CRC-specific frameworks such as BowelRCNN, AI models successfully differentiate abnormal bowel sound intervals and spectral patterns associated with tumor-related motility disturbances and partial obstruction. Distinct bowel sound-based signatures—such as prolonged sound-to-sound intervals and high-pitched “tinkling” proximal to lesions—demonstrate the physiological basis for CRC detection through bowel sound-based biomarkers. Conclusions: AI-driven bowel sound analysis represents an emerging, exploratory research direction rather than a validated colorectal cancer screening modality. While early studies demonstrate physiological plausibility and technical feasibility, no large-scale, CRC-specific validation studies currently establish sensitivity, specificity, PPV, or NPV for cancer detection. Accordingly, bowel sound analytics should be viewed as hypothesis-generating and potentially complementary to established screening tools, rather than a near-term alternative to validated modalities such as FIT, multitarget stool DNA testing, or colonoscopy. Full article

Introduction/Object: Gastrointestinal cancers are among the most common types of neoplasms and are often associated with malnutrition, which affects physical performance, treatment tolerance and prognosis. This paper aims to synthesize, through a systematic search, the evidence on the impact of nutritional interventions on nutritional status in patients with digestive cancers prone to malnutrition. Methods: A systematic search was performed in PubMed, MDPI, Web of Science and ScienceDirect, for articles published between 2009 and 2025. Overall, 14,503 records were identified, and after screening of titles, abstracts and full-text evaluation, 80 studies (cross-sectional and cohort) were included. Data extraction was performed by a single researcher, using pre-established criteria and a standardized table, and the assessment of study quality was performed qualitatively, taking into account study design, sample size, nutritional assessment methods and clarity of reporting of results. Results: Evidence suggests that individualized and early applied nutritional interventions contribute to maintaining weight and protein status, improve tolerance to oncological treatments and may positively influence patient survival. Conclusions: Nutritional therapy plays a crucial role in preventing complications and supporting the body during oncological treatment, optimizing patients’ quality of life. This review provides a clear synthesis of the current evidence and recognizes methodological limitations related to the qualitative assessment of the included studies. Full article

Background/Objectives: This study seeks to evaluate the clinical characteristics of newly diagnosed male breast cancers within the traditionally underserved Bronx population at risk for poorer health outcomes. Methods: We retrospectively searched our database for male patients who presented for mammographic evaluation between 1 January 2016 and 1 October 2024. The primary outcomes were the prevalence of biopsy-proven male breast cancer and its association with gynecomastia and TNM stage at diagnosis. Clinical data, including TNM staging, receptor status, risk factors, and patient demographics, were recorded for patients with biopsy-proven breast cancer based on biopsy results. Two dedicated breast imagers retrospectively evaluated mammograms of these patients to determine by consensus the presence of gynecomastia. Analyses were descriptive in nature. Results: During the study period, 423 screening mammograms and 1775 diagnostic mammograms were performed on male patients. Twenty-six male patients with biopsy-proven breast cancer were identified (two were bilateral and four were multifocal). In total, 69% of our male breast cancer patients (18 out of 26) demonstrated gynecomastia, which was similar across demographic groups, ranging from 63 to 75%. Out of the three patients with Stage 4 disease, two were Black and one was White. Stage 3 or higher disease was seen in 29% of our Black patients, 12% of our White patients, and 0% of our Hispanic patients. Conclusions: Male breast cancer in this Bronx population was frequently associated with gynecomastia and showed notable demographic disparities. Black patients presented with more advanced disease than other demographic groups. These descriptive findings highlight areas of further investigation and may help inform future outreach and early detection efforts in high-risk, underserved communities. This retrospective, single-institution analysis was limited by a small sample size and did not include formal statistical testing; therefore, the findings are descriptive and warrant validation with larger cohorts. Full article

Objective: To develop and validate a genetic diagnostic model for colorectal cancer (CRC). Methods: First, differential expression genes (DEGs) between colorectal cancer and normal groups were screened using the TCGA database. Subsequently, a two-sample Mendelian randomization analysis was performed using the eQTL genomic data from the IEU OpenGWAS database and colorectal cancer outcomes from the R12 Finnish database to identify associated genes. The intersecting genes from both methods were selected for the development and validation of the CRC genetic diagnostic model using nine machine learning algorithms: Lasso Regression, XGBoost, Gradient Boosting Machine (GBM), Generalized Linear Model (GLM), Neural Network (NN), Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Random Forest (RF), and Decision Tree (DT). Results: A total of 3716 DEGs were identified from the TCGA database, while 121 genes were associated with CRC based on the eQTL Mendelian randomization analysis. The intersection of these two methods yielded 27 genes. Among the nine machine learning methods, XGBoost achieved the highest AUC value of 0.990. The top five genes predicted by the XGBoost method—RIF1, GDPD5, DBNDD1, RCCD1, and CLDN5—along with the five most significantly differentially expressed genes (ASCL2, IFITM3, IFITM1, SMPDL3A, and SUCLG2) in the GSE87211 dataset, were selected for the construction of the final colorectal cancer (CRC) genetic diagnostic model. The ROC curve analysis revealed an AUC (95% CI) of 0.9875 (0.9737–0.9875) for the training set, and 0.9601 (0.9145–0.9601) for the validation set, indicating strong predictive performance of the model. SHAP model interpretation further identified IFITM1 and DBNDD1 as the most influential genes in the XGBoost model, with both making positive contributions to the model’s predictions. Conclusions: The gene expression profile in colorectal cancer is characterized by enhanced cell proliferation, elevated metabolic activity, and immune evasion. A genetic diagnostic model constructed based on ten genes (RIF1, GDPD5, DBNDD1, RCCD1, CLDN5, ASCL2, IFITM3, IFITM1, SMPDL3A, and SUCLG2) demonstrates strong predictive performance. This model holds significant potential for the early diagnosis and intervention of colorectal cancer, contributing to the implementation of third-tier prevention strategies. Full article

Background: The persistently low 5-year survival rate for pancreatic cancer (PC) underscores the critical need for early detection. However, population-wide screening remains impractical. Artificial Intelligence (AI) models using electronic health record (EHR) data offer a promising avenue for pre-symptomatic risk stratification. Objective: To systematically review and meta-analyze the performance of AI models for PC prediction based exclusively on structured EHR data. Methods: We systematically searched PubMed, MedRxiv, BioRxiv, and Google Scholar (2010–2025). Inclusion criteria encompassed studies using EHR-derived data (excluding imaging/genomics), applying AI for PC prediction, reporting AUC, and including a non-cancer cohort. Two reviewers independently extracted data. Random-effects meta-analysis was performed for AUC, sensitivity (Se), and specificity (Sp) using R software version 4.5.1. Heterogeneity was assessed using I² statistics and publication bias was evaluated. Results: Of 946 screened records, 19 studies met the inclusion criteria. The pooled AUC across all models was 0.785 (95% CI: 0.759–0.810), indicating good overall discriminatory ability. Neural Network (NN) models demonstrated a statistically significantly higher pooled AUC (0.826) compared to Logistic Regression (LogReg, 0.799), Random Forests (RF, 0.762), and XGBoost (XGB, 0.779) (all p < 0.001). In analyses with sufficient data, models like Light Gradient Boosting (LGB) showed superior Se and Sp (99% and 98.7%, respectively) compared to NNs and LogReg, though based on limited studies. Meta-analysis of Se and Sp revealed extreme heterogeneity (I² ≥ 99.9%), and the positive predictive values (PPVs) reported across studies were consistently low (often < 1%), reflecting the challenge of screening a low-prevalence disease. Conclusions: AI models using EHR data show significant promise for early PC detection, with NNs achieving the highest pooled AUC. However, high heterogeneity and typically low PPV highlight the need for standardized methodologies and a targeted risk-stratification approach rather than general population screening. Future prospective validation and integration into clinical decision-support systems are essential. Full article

Women with intellectual disabilities (IDs) face persistent health inequities, particularly in preventive services such as breast cancer screening, where participation rates remain disproportionately low. These disparities contribute to higher mortality and poorer survivorship outcomes, often linked to later-stage diagnoses. To better understand these challenges and inform the development of inclusive screening programs, this qualitative study conducted in Austria explored barriers, facilitators, and needs related to breast cancer screening from the dual perspectives of 17 women with mild-to-moderate IDs aged 45 and older and 10 caregivers. Semi-structured focus groups and interviews were analyzed thematically within a constructivist framework, integrating perspectives from both groups. Barriers included social taboos around sexuality, psychological distress, exclusion through standardized procedures, and unclear responsibility among stakeholders. Facilitators involved person-centered communication, accessible information, emotional and practical support, and familiar healthcare environments. Women with IDs expressed a strong desire for education, autonomy, and inclusion, while caregivers played a pivotal role in enabling access. These findings demonstrate that low screening participation among women with IDs is driven by systemic and organizational barriers rather than lack of health awareness or willingness to participate. Without structurally inclusive design, organized screening programs risk perpetuating preventable inequities in early detection. Embedding accessibility, clear accountability, and person-centered communication as standard features of breast cancer screening is therefore a public health priority to reduce avoidable late-stage diagnoses and narrow survival disparities for women with IDs. Full article

Lung cancer (LC) remains the leading cause of cancer-related death worldwide, largely due to late-stage diagnosis and the limited performance of current screening strategies. In this preliminary study, headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME/GC-MS) was used to comprehensively characterize the urinary volatilome of LC patients and healthy controls (HCs), with the dual aim of defining an LC-associated volatilomic signature and identifying volatile organic metabolites (VOMs) with discriminatory potential. A total of 56 VOMs spanning multiple chemical classes were identified, revealing a distinct metabolic footprint between groups. LC patients exhibited markedly increased levels of terpenoids and aldehydes, consistent with heightened oxidative stress, including lipid peroxidation, and perturbed metabolic pathways, whereas HCs showed a predominance of sulphur-containing compounds and volatile phenols, likely reflecting active sulphur amino acid metabolism and/or microbial-derived processes. Multivariate modelling using partial least squares-discriminant analysis (PLS-DA, R² = 0.961; Q² = 0.941; p < 0.001), supported by hierarchical clustering, demonstrated robust and clearly separated group stratification. Among the detected VOMs, octanal, dehydro-p-cymene, 2,6-dimethyl-7-octen-2-ol and 3,7-dimethyl-3-octanol displayed the highest discriminative power, emerging as promising candidate urinary biomarkers of LC. These findings provide proof-of-concept that HS-SPME/GC-MS-based urinary volatilomic profiling can capture disease-specific molecular signatures and may serve as a non-invasive approach to support the early detection of LC, warranting validation in independent cohorts and integration within future multi-omics diagnostic frameworks. Full article

Background: Accurate preoperative staging is the cornerstone of therapeutic decision-making in gastric cancer (GC), yet standard modalities often fail to capture the full extent of disease, particularly in diffuse and poorly cohesive histotypes. This review aims to provide a comprehensive update on diagnostic imaging for GC, evaluating the established roles of CT, EUS, and PET/CT alongside the emerging capabilities of Magnetic Resonance Imaging (MRI) and Artificial Intelligence (AI). Methods: A structured narrative review was conducted by searching indexed biomedical databases for studies published between 2015 and 2024. A structured literature search screening process identified 410 relevant studies focusing on T, N, and M staging accuracy, quantitative imaging biomarkers, and radiomics. Results: While Multidetector CT remains the universal first-line modality, its sensitivity declines in infiltrative tumors and low-volume peritoneal carcinomatosis. EUS retains superiority for early (T1-T2) lesions but may offer limited value in advanced stages. Conversely, MRI (leveraging diffusion-weighted imaging (DWI) and multiparametric protocols) indicates superior soft-tissue contrast, potentially outperforming CT in the assessment of serosal invasion, nodal involvement, and occult peritoneal metastases. Furthermore, emerging fibroblast activation protein inhibitor (FAPI) PET tracers show promise in overcoming the limitations of FDG in mucinous and diffuse GC. Finally, radiomics and deep learning models are providing novel quantitative biomarkers for non-invasive risk stratification. Conclusions: Contemporary GC staging requires a tailored, multimodality approach. Evidence supports the increasing integration of MRI and quantitative imaging into clinical workflows to overcome the limitations of conventional techniques and support precision oncology. Full article

Melanoma is highly dangerous and can spread rapidly to other parts of the body. It has an increasing fatality rate among different types of cancer. Timely detection of skin malignancies can reduce overall mortality. Therefore, clinical screening methods require more time and accuracy for diagnosis. An automated, computer-aided system would facilitate earlier melanoma detection, thereby increasing patient survival rates. This paper identifies melanoma images using a Convolutional Neural Network. Skin images are preprocessed using Histogram Equalization and Gabor transforms. A Gabor filter-based Convolutional Neural Network (CNN) classifier trains and classifies the extracted features. We adopt Gabor filters because they are bandpass filters that transform a pixel into a multi-resolution kernel matrix, providing detailed information about the image. This study suggests a method with accuracy, sensitivity, and specificity of 98.58%, 98.66%, and 98.75%, respectively. This research supports SDGs 3 and 4 by facilitating early melanoma detection and enhancing AI-driven medical education. Full article

The early diagnosis and treatment of civilizational diseases remain a significant challenge worldwide. Although advances in medical technology have led to the introduction of more screening options over time, these measures are still insufficient to effectively reduce mortality from deadly diseases such as lung cancer (LC), cardiovascular diseases (CVD), diabetes, and chronic obstructive pulmonary disease (COPD). These conditions pose a major public health burden, underlying the urgent need for more comprehensive and efficient prevention strategies. Recently, the concept of ‘multiscreening’ has emerged as a promising approach. Multiscreening involves the simultaneous screening for multiple diseases using integrated diagnostic methods, potentially improving early detection rates and optimizing resource utilization. In 2024, Rzyman W. et al. launched the MULTIPREVENT epidemiological study, which aims to develop and validate a low-dose computed tomography (LDCT)-based screening test for civilizational diseases. This study represents a step forward in the pursuit of more effective, minimally invasive diagnostic tools that could facilitate earlier intervention and improve patient outcomes. To better understand the potential of multiscreening approaches and their clinical utility, it is essential to evaluate the existing predictive models used for identifying individuals at high risk for these diseases. This narrative review focuses primarily on lung cancer risk prediction models used in LDCT screening while situating these approaches within the broader conceptual framework of the MULTIPREVENT project, aimed at future integration of multi-disease prevention strategies. With this analysis, we aim to provide insights that will guide the development of more accurate, integrative screening tools that could reduce the global burden of these diseases. Full article

Background: This study investigates the global burden of early-onset bladder cancer (EOBC) from 1990 to 2021, highlighting regional disparities and the growing role of metabolic risk factors. Early-onset bladder cancer (EOBC), diagnosed before age 50, is an emerging global health concern. While less common than kidney cancer, EOBC contributes substantially to mortality and disability-adjusted life years (DALYs), with marked sex disparities. Its global epidemiology remains unassessed systematically. Methods: Using GBD 1990–2021 data, we analyzed EOBC incidence, prevalence, mortality, and DALYs across 204 countries in individuals aged 15–49. Trends were examined via segmented regression, EAPC, and Bayesian age-period-cohort modeling. Inequality was quantified using SII and CI. Decomposition and SDI-efficiency frontier analyses were introduced. Results: From 1990 to 2021, EOBC incidence rose 62.2%, prevalence 73.1%, deaths 15.3%, and DALYs 15.8%. Middle-SDI regions bore the highest burden. Aging drove trends in high-SDI areas and population growth in low-SDI regions. Over 25% of high-SDI countries underperformed in incidence/prevalence control. Smoking remained the leading risk factor, with rising hyperglycemia burdens in high-income areas. Males carried over twice the female burden, peaking at age 45–49. Conclusions: EOBC shows sustained global growth with middle-aged concentration and significant regional disparities. Structural inefficiencies highlight the need for enhanced screening, early warning, and tailored resource allocation. Full article