Search Results (4,026)

Endometrial cancer is the most common gynaecologic malignancy in high-income countries, with a rising incidence largely driven by reproductive factors, obesity, and prolonged exposure to unopposed oestrogens. Although most cases are sporadic, approximately 2–5% are associated with hereditary cancer syndromes, of which Lynch syndrome represents the most important contributor. Lynch syndrome results from germline mutations in DNA mismatch repair (MMR) genes and is associated with a substantially increased lifetime risk of endometrial cancer, reaching up to 71% in carriers of MutS homologue 6 (MSH6) mutations. Hereditary cancer predisposition typically follows an autosomal dominant inheritance pattern and may be suspected based on clinical warning signs such as early disease onset, multiple primary malignancies, a strong family history, or the presence of microsatellite instability in tumour tissue. In addition to Lynch syndrome, rarer genetic conditions—including Cowden syndrome (PTEN), Li–Fraumeni syndrome (TP53), polymerase proofreading–associated polyposis (POLE/POLD1), and hereditary breast and ovarian cancer syndromes (BRCA1/2)—also contribute to hereditary endometrial cancer risk. Recognition of these genetic backgrounds is essential for accurate diagnosis, personalised surveillance, and the implementation of targeted preventive and therapeutic strategies. Despite major advances in molecular diagnostics, hereditary endometrial cancer remains frequently underdiagnosed, leading to missed opportunities for cancer prevention among affected individuals and their families. This comprehensive review summarises current evidence on hereditary predispositions to endometrial cancer, with a particular emphasis on Lynch syndrome, and discusses underlying genetic mechanisms, inheritance patterns, diagnostic strategies, and clinical implications for screening, genetic counselling, and treatment optimisation. Full article

Recently, deep learning methods have seen major advancements and are preferred for medical image analysis. Clinically, deep learning techniques for cancer image analysis are among the main applications for early diagnosis, detection, and treatment. Consequently, segmentation of breast histology images is a key step towards diagnosing breast cancer. However, the use of deep learning methods for image analysis is constrained by challenging features in the histology images. These challenges include poor image quality, complex microscopic tissue structures, topological intricacies, and boundary/edge inhomogeneity. Furthermore, this leads to a limited number of images required for analysis. The U-Net model was introduced and gained significant traction for its ability to produce high-accuracy results with very few input images. Many modifications of the U-Net architecture exist. Therefore, this study proposes the watershed encoder–decoder neural network (WEDN) to segment cancerous lesions in supervised breast histology images. Pre-processing of supervised breast histology images via augmentation is introduced to increase the dataset size. The augmented dataset is further enhanced and segmented into the region of interest. Data enhancement methods such as thresholding, opening, dilation, and distance transform are used to highlight foreground and background pixels while removing unwanted parts from the image. Consequently, further segmentation via the connected component analysis method is used to combine image pixel components with similar intensity values and assign them their respective labeled binary masks. The watershed filling method is then applied to these labeled binary mask components to separate and identify the edges/boundaries of the regions of interest (cancerous lesions). This resultant image information is sent to the WEDN model network for feature extraction and learning via training and testing. Residual convolutional block layers of the WEDN model are the learnable layers that extract the region of interest (ROI), which is the cancerous lesion. The method was evaluated on 3000 images–watershed masks, an augmented dataset. The model was trained on 2400 training set images and tested on 600 testing set images. This proposed method produced significant results of $98.53\%$ validation accuracy, $96.98\%$ validation dice coefficient, and $97.84\%$ validation intersection over unit (IoU) metric scores. Full article

Advance care planning (ACP) is fundamentally important for patients diagnosed with glioblastoma (GBM), a highly aggressive primary brain tumor with a grim prognosis. The urgency for early ACP is profoundly amplified by the characteristic, progressive neurocognitive decline that frequently impairs critical reasoning and leads to the loss of decisional capacity. ACP is a proactive process ensuring that future medical interventions align with a patient’s deeply held values and goals. Proactive ACP discussions are associated with less aggressive end-of-life (EOL) care, improved quality of life for patients and care partners, earlier hospice enrollment, and reduced psychological distress for surrogate decision makers. Despite guidelines recommending early integration, ACP prevalence remains low due to clinician discomfort with EOL discussions, a perceived lack of adequate training, and a widespread “culture of shared avoidance”. Experts recommend initiating ACP at or shortly after diagnosis, normalizing it as standard cancer care. Using structured communication strategies, such as the REMAP tool, and empowering allied health providers to champion these conversations are key integration strategies. Ultimately, early and skillful ACP is an ethical imperative that safeguards patient autonomy and minimizes the burden on loved ones. Full article

Background: Drug-induced liver injury (DILI), a major type of adverse drug reaction, has become one of the leading causes of acute liver injury and liver failure worldwide. Its clinical significance lies not only in acute hepatocyte necrosis and functional failure but also in its role as a key initiating factor for liver cancer progression. Therefore, early diagnosis of DILI is of great importance. Methods: This study employed ultra-performance liquid chromatography-mass spectrometry (UPLC-MS/MS) to perform widely targeted metabolomics analysis on acetaminophen (APAP)-induced liver injury mice and healthy mice. Results: UPLC-QTRAP-MS/MS identified 41 differentially expressed metabolites primarily involved in glycerophospholipid metabolism, arginine and proline metabolism, primary bile acid biosynthesis, and glutathione metabolism pathways. The significant elevation of serum and hepatic alanine aminotransferase (ALT) and aspartate aminotransferase (AST) confirmed the successful establishment of the drug-induced liver injury (DILI) model. ROC curve analysis indicated 11 metabolites with AUC values exceeding 0.90 as potential biomarkers, including (R)-2-Hydroxybutyric acid, Glu-Gln, γ-Glu-Gln, 2-Methyllactic acid, L-Serine, Hyodeoxycholic acid, 3-Epideoxycholic acid, and Glycochenodeoxycholic acid 7-sulfate. Conclusions: We propose that these differential metabolites may serve as candidate biomarkers for DILI. Our findings provide a novel metabolomic signature derived directly from the injured tissue and offer a theoretical foundation for further research into early diagnosis of drug-induced liver injury. Full article

Symptomatic severe aortic stenosis has an extremely high risk of death, ranging from 60 to 90% at five years if left untreated. This has informed the recommendation for urgent intervention upon diagnosis, especially when symptoms develop. Asymptomatic severe aortic stenosis has a four-year mortality between 30 and 50% if left untreated, which is similar to some metastatic cancers. Conservative management for patients with severe asymptomatic aortic stenosis was previously advocated, likely owing to the relative invasiveness of surgical aortic valve replacement. The advent of low-risk transcatheter aortic valve implantation with good medium-term durability has prioritized the need for a paradigm shift in the treatment of asymptomatic severe aortic stenosis towards a more proactive strategy of early intervention to reduce significant adverse events. This article provides a state-of-the-art overview of the contemporary management of patients with asymptomatic severe aortic stenosis. 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

Background/Objectives: In the modern healthcare landscape, breast cancer remains one of the most prevalent malignancies and a leading cause of mortality among women worldwide. Early and accurate prediction of breast cancer plays a pivotal role in effective diagnosis, treatment planning, and improving survival outcomes. However, due to the complexity and heterogeneity of medical data, achieving high predictive accuracy remains a significant challenge. This study proposes an intelligent hybrid system that integrates traditional machine learning (ML), deep learning (DL), and ensemble learning approaches for enhanced breast cancer prediction using the Wisconsin Breast Cancer Dataset. Methods: The proposed system employs a multistage framework comprising three main phases: (1) data preprocessing and balancing, which involves normalization using the min–max technique and application of the Synthetic Minority Over-sampling Technique (SMOTE) to mitigate class imbalance; (2) model development, where multiple ML algorithms, DL architectures, and a novel ensemble model are applied to the preprocessed data; and (3) model evaluation and validation, performed under three distinct training–testing scenarios to ensure robustness and generalizability. Model performance was assessed using six statistical evaluation metrics—accuracy, precision, recall, F1-score, specificity, and AUC—alongside graphical analyses and rigorous statistical tests to evaluate predictive consistency. Results: The findings demonstrate that the proposed ensemble model significantly outperforms individual machine learning and deep learning models in terms of predictive accuracy, stability, and reliability. A comparative analysis also reveals that the ensemble system surpasses several state-of-the-art methods reported in the literature. Conclusions: The proposed intelligent hybrid system offers a promising, multidisciplinary approach for improving diagnostic decision support in breast cancer prediction. By integrating advanced data preprocessing, machine learning, and deep learning paradigms within a unified ensemble framework, this study contributes to the broader goals of precision oncology and AI-driven healthcare, aligning with global efforts to enhance early cancer detection and personalized medical care. Full article

Early and accurate diagnosis is critical for disease surveillance, therapeutic guidance, and relapse monitoring. Sensor arrays have emerged as a multi-analyte detection tool via non-specific interactions to generate unique fingerprint patterns with high levels of selectivity and discrimination. Conjugated polymers (CPs), with their tunable π-conjugated backbones, exceptional light-harvesting capability, and efficient “molecular wire effect,” provide an ideal and versatile material platform for such arrays, enabling significant optical signal amplification and high sensitivity. This review systematically outlines the rational design and functionalization strategies of CPs for constructing high-performance sensor arrays. It delves into the structure–property relationships that govern their sensing performance, covering main-chain engineering, side-chain functionalization, and microenvironmental regulation. Representative applications are discussed, including non-small cell lung cancer, breast cancer, bacterial and viral infections, Alzheimer’s disease, and diabetic nephropathy, highlighting the remarkable diagnostic capabilities achieved through tailored CP materials. Finally, future perspectives are focused on novel material designs and device integration to advance this vibrant field. Full article

Introduction: The addition of immunotherapy to neoadjuvant treatment for early triple-negative breast cancer (TNBC) has been adopted in clinical practice in France since March 2022, with little real-world data published on the topic. The aim of this study was to evaluate real-world data on treatment feasibility, efficacy, and related toxicities, with a specific focus on immune-related adverse events (irAEs). Methods: We conducted a retrospective analysis of patients who completed at least the neoadjuvant sequence of pembrolizumab combined with chemotherapy for early-stage TNBC at Montpellier Cancer Institute from April 2022 to July 2024. Adverse events were graded according to the Common Terminology Criteria for Adverse Events (CTCAE) v5.0. The pathological complete response (pCR) was defined as the absence of residual invasive disease in the breast and axillary lymph nodes (ypT0/Tis ypN0). Results: We reviewed data from 92 patient records. The median age at diagnosis was 50 years (range: 27–76). The history of autoimmune disease was noted in 3.2% of patients. Grade 3–4 irAEs were observed in 20% of patients and included hepatitis (8.6%), colitis (3.3%), skin toxicity (2.1%), myocarditis (2%), arthralgia (1%), autoimmune hemolytic anemia (1%), hypothyroidism (1%), and adrenal insufficiency (1%). No treatment-related deaths were reported. Immunotherapy was discontinued due to irAEs in 29.3% of patients in the study population. The pCR rate was 61,1%, with no significant association between the number of neoadjuvant pembrolizumab cycles and the pCR rate (p = 0.7). Patients experiencing grade 3–4 irAEs had a pCR rate of 80%, compared to 56.7% in those without such toxicities (p = 0.079). Initial positivity of antinuclear antibodies (ANA) was not associated with an increased incidence of irAEs. Conclusions: The immune-related adverse events and efficacy data observed in our cohort were broadly comparable to those reported in the KEYNOTE-522 trial, with no treatment-related deaths. Patients with grade 3–4 irAEs tended to have higher pCR rates. Full article

Background and purpose: The role of adjuvant radiation therapy (RT) in early-stage HER2-positive breast cancer treated with breast-conserving surgery (BCS) and systemic therapy remains uncertain in the era of HER2-targeted regimens. This study evaluates the survival impact of RT in patients aligned with the HERO RT de-escalation trial (NRG-BR008). Materials and methods: We queried the National Cancer Database for patients with early-stage HER2-positive invasive breast carcinoma treated with BCS and systemic therapy, stratified into HERO trial-aligned cohorts: Arm 1 (adjuvant systemic therapy) vs. Arm 2 (neoadjuvant systemic therapy, pathologic complete response). Within each cohort, patients receiving adjuvant RT were compared with those omitting RT. In the primary analysis, patients were propensity score matched (PSM) on demographics, diagnosis years, tumor characteristics, and trial stratification variables. Inverse probability of treatment weighting (IPTW) was additionally performed as a sensitivity analysis. Overall survival was evaluated using Kaplan–Meier, Cox regression, and restricted mean survival time (RMST). Results: In Arm 1 (818 patients, 94 deaths), 5-year OS was 96.9% with RT vs. 88.0% without RT, and 10-year OS was 94.3% vs. 68.5% (log-rank p < 0.001). RT omission was associated with higher mortality in the PSM Cox model (HR, 4.78; 95% CI, 2.84–8.02; p < 0.001), with an RMST advantage favoring RT of +2.86 months at 5 years and +12.55 months at 10 years (p < 0.001). In Arm 2 (176 patients, 10 deaths), 5-year OS was 97.6% with RT vs. 91.1% without RT, and OS at 107 months was 94.8% vs. 91.1% (log-rank p = 0.13). RT omission was not statistically significant in the PSM Cox model (HR, 3.40; 95% CI, 0.82–14.05; p = 0.09), though RMST favored RT (+1.83 months at 5 years, p = 0.004; +3.91 months at 107 months, p = 0.03). IPTW analyses were directionally consistent in Arm 1 (HR, 3.26; 95% CI, 2.52–4.21; p < 0.001) and inconclusive in Arm 2 (HR, 1.78; 95% CI, 0.80–3.95; p = 0.16). Conclusions: In this HERO-aligned national cohort, RT omission was associated with inferior OS in patients treated with adjuvant systemic therapy after BCS. Findings in the neoadjuvant pCR cohort were imprecise and hypothesis-generating. Given the retrospective registry design, lack of recurrence-specific endpoints, and potential residual confounding, results should not be interpreted as causal but support continued RT use outside prospective de-escalation trials. Full article

Background/Objectives: Triple-negative breast cancer (TNBC) is an aggressive subtype, with limited diagnostic options and no targeted early detection tools. Liquid biopsy represents a minimally invasive approach for detecting tumor-derived molecular alterations in body fluids. This scoping review aimed to comprehensively synthesize all liquid biopsy-derived molecular biomarkers evaluated for the diagnosis of TNBC in adults. Methods: This review followed the Arksey and O’Malley framework and PRISMA-ScR guidelines. Systematic searches of PubMed, Scopus, Embase, and Web of Science identified primary human studies evaluating circulating molecular biomarkers for TNBC diagnosis. Non-TNBC, non-human, hereditary, treatment-response, and nonmolecular studies were excluded. Data on study design, patient characteristics, biospecimen type, analytical platforms, biomarker class, and diagnostic performance were extracted and synthesized descriptively by biomolecule class. Results: Thirty-two studies met the inclusion criteria, comprising 15 protein-based, 12 RNA-based, and 6 DNA-based studies (one reporting both protein and RNA). In total, 1532 TNBC cases and 3137 participants in the comparator group were analyzed. Protein biomarkers were the most frequently studied, although only APOA4 appeared in more than one study, with conflicting results. RNA-based biomarkers identified promising candidates, particularly miR-21, but validation cohorts were scarce. DNA methylation markers showed promising diagnostic accuracy yet lacked replication. Most studies were small retrospective case–control designs with heterogeneous comparators and inconsistent diagnostic reporting. Conclusions: Evidence for liquid biopsy-derived biomarkers in TNBC remains limited, heterogeneous, and insufficiently validated. No biomarker currently shows reproducibility suitable for clinical implementation. Robust, prospective, and standardized studies are needed to advance liquid biopsy-based diagnostics in TNBC. 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

Triple-negative breast cancer (TNBC), which is characterized by a lack of the estrogen receptor, the progesterone receptor, and HER2 expression, is the most aggressive breast cancer subtype and has a poor prognosis and high recurrence rates because of frequent chemotherapy resistance. As a crucial epigenetic regulator, DNA methylation modulates gene expression through aberrant methylation patterns, contributing to tumor progression and therapeutic resistance. Early diagnosis and treatment of TNBC are vital for its prognosis. The development of DNA methylation testing technology and the application of liquid biopsy provide technological support for early diagnosis and treatment. Additionally, preclinical and early-phase clinical studies suggest that epigenetic therapies targeting DNA methylation may hold promise for TNBC treatment, pending larger clinical trials. Furthermore, research on DNA methylation-based prognostic models enables personalized precision treatment for patients, helping to reduce unnecessary therapies and improve overall survival. The emerging role of DNA methylation patterns in predicting the therapeutic response and overcoming drug resistance is highlighted. In this narrative review, we integrate current research findings and clinical perspectives. We propose that DNA methylation presents promising research prospects for the diagnosis, treatment and prognosis prediction of TNBC. Future efforts should focus on translating methylation-driven insights into clinically actionable strategies, ultimately advancing precision oncology for this challenging disease. Full article

MicroRNAs (miRNAs) are promising biomarkers for the early detection of various diseases, particularly cancer, driving active development of highly sensitive and selective detection technologies. This study aims to establish a novel miRNA detection technique that utilizes image analysis to track the Brownian motion (diffusivity) of fluorescent probe-modified miRNA particles. This method identifies the presence and concentration of miRNAs by exploiting the change in particle size upon hybridization with the target. Furthermore, the use of a probe modified with a photo-crosslinkable artificial nucleic acid (CNV-D) enables the covalent capture of the target miRNA, ensuring high selectivity in biological samples even under stringent washing conditions. By integrating Hybridization Chain Reaction (HCR), the complex size is significantly amplified, dramatically enhancing the detection sensitivity. Consequently, we successfully demonstrated the highly sensitive and specific detection of the cancer biomarker miR-21 in serum, achieving an exceptionally low limit of detection (LOD) of 1 fM. This technology holds great potential to contribute to the early diagnosis of cancer. Full article