Search Results (26,363)

Introduction: Idiopathic pulmonary fibrosis (IPF) is associated with high morbidity and mortality and a substantial burden of comorbidities, which may influence prognosis and survival. This study aimed to evaluate the burden of comorbidity in patients with IPF receiving antifibrotic therapy using the Charlson, TORVAN, and GAP indices and to analyse their relationships and prognostic impact on survival. Methods: Retrospective observational study including patients with IPF diagnosed according to ATS/ERS/JRS/ALAT criteria. Patients receiving antifibrotic therapy between June 2010 and September 2025 were included. Baseline comorbidities were recorded, and the Charlson, TORVAN, and GAP indices were calculated. Associations between indices were assessed using chi-square tests and kappa statistics. Survival was analysed using Kaplan–Meier curves and compared with the log-rank test. Cox proportional hazards regression and model comparison metrics (Harrell’s C-index and Akaike Information Criterion) were also performed to assess the independent prognostic value of each index. Results: Seventy-two patients were included (76.7% male; mean age 73.8 ± 7.4 years). Pirfenidone was prescribed in 63.9% and nintedanib in 36.1%. The most frequent comorbidities were gastro-oesophageal reflux disease (62.5%), arterial hypertension (57.5%), pulmonary hypertension (32.9%), diabetes mellitus (24.7%), and non-metastatic solid tumours (17.6%), including lung cancer. Survival differed significantly according to GAP stage (p = 0.020) and Charlson categories (p = 0.006). The TORVAN stage was associated with the GAP stage (p < 0.001; kappa = 0.246), whereas the Charlson index showed no association with GAP or TORVAN. Conclusions: In this cohort of patients with IPF receiving antifibrotic therapy, both the GAP and Charlson indices were associated with survival. These findings suggest that combining disease-specific and comorbidity indices may provide a more comprehensive prognostic assessment, although further validation in larger cohorts is required. Full article

The advent of breast cancer molecular subtyping has transformed management, enabling treatment personalisation and de-escalation beyond traditional stage-based approaches. Established biomarkers, such as Ki-67 in luminal disease, HER2 amplification, and PD-L1 expression in triple-negative breast cancer, underpin seminal clinical trials yet remain imperfect predictors of response and long-term outcome. MicroRNAs have emerged as promising next-generation biomarkers and therapeutic tools. As master regulators of gene expression, both tumour-derived and circulating microRNAs can refine diagnosis and molecular subclassification, inform prognosis and therapeutic selection, act as treatment sensitisers, and potentially serve as direct therapeutic targets. Well-characterised miRNAs such as miR-221 have been implicated in endocrine resistance, while recent liquid-biopsy approaches have enabled the identification of circulating miR-145 and exosomal miR-155 as predictors of pathological complete response in HER2-positive disease. Their detectability in tissue, blood and other biofluids offers a minimally invasive means to dynamically monitor cancer behaviour and response, supporting more precise therapeutic decision-making. This review synthesises the current evidence for miRNA-based biomarkers across oestrogen-receptor positive, HER2-positive and triple-negative breast cancer and outlines their potential integration into biomarker-driven clinical trial designs and personalised treatment strategies. Full article

Background: Adenoid cystic carcinoma (ACC) demonstrates marked clinical heterogeneity that is inadequately explained by conventional histopathologic and staging systems alone. Recent studies have identified two molecular subtypes based on transcriptomic profiling and MYC/TP63 expression (ACC I: MYC-high/TP63-low; ACC II: MYC-low/TP63-high) with potential prognostic significance. However, the magnitude and consistency of their survival impact remain uncertain. Methods: A systematic review and meta-analysis were conducted in accordance with PRISMA guidelines. PubMed, Embase, and PubMed Central were searched through January 2026 for studies reporting overall survival in ACC stratified by MYC/TP63 molecular subtype. Hazard ratios (HRs) were pooled using random-effects models. Heterogeneity, subgroup analyses by classification method, sensitivity analyses, cumulative meta-analysis, influence diagnostics, and publication bias assessment were performed. Results: Five independent cohorts from two publications comprising 247 patients (90 ACC I, 157 ACC II) were included. ACC I was associated with significantly worse overall survival compared with ACC II, with a pooled HR of 3.88 (95% CI: 2.55–5.90; p < 0.001). No statistical heterogeneity was observed (I² = 0%). Prognostic separation was consistent across RNA sequencing and immunohistochemistry-based classification methods. Conclusions: Transcriptomic and MYC/TP63-based molecular subtyping provides strong and reproducible prognostic stratification in ACC. ACC I tumors confer an approximately four-fold higher mortality risk compared with ACC II tumors. Incorporation of molecular subtype into routine diagnostic and clinical decision-making may improve risk stratification, surveillance strategies, and future trial design in ACC. Full article

Anaplastic lymphoma kinase (ALK) rearrangement is a well-established oncogenic driver alteration in non-small cell lung cancer (NSCLC), and ALK tyrosine kinase inhibitors (TKIs), particularly lorlatinib, have significantly improved the prognosis of ALK-positive NSCLC patients. Although high programmed death-ligand 1 (PD-L1) expression (≥50%) is generally associated with favorable responses to immune checkpoint inhibitors (ICIs), PD-L1 has not been shown to reliably predict ICI benefit in ALK-rearranged disease, and optimal management after ALK TKI resistance remains challenging. Herein, we report a case of an elderly patient with ALK-rearrangement and exceptionally high PD-L1 expression (TPS ≥ 95%) NSCLC who experienced disease progression following first-line lorlatinib with genetically confirmed MET amplification. The patient subsequently received an exploratory combination of continued lorlatinib plus envafolimab and achieved partial response (PR) with manageable tolerability after 4 months, highlighting a potential sequential strategy that may warrant further investigation in select ALK-positive NSCLC patients exhibiting both bypass pathway activation and exceptionally high PD-L1 expression. Full article

Introduction: Stroke remains a leading cause of mortality and long-term disability worldwide. Accurate prognosis prediction is essential for timely intervention and personalized treatment planning. However, previous studies have often overlooked the role of patients’ medical history, age-specific risk factors, and time-dependent mortality patterns. This study aimed to develop and evaluate machine learning models for predicting mortality in stroke patients by incorporating vital signs, blood test results, demographic characteristics, and medical history, while also exploring subgroup-specific factors. Methods: We retrospectively analyzed data from 1780 stroke patients admitted to Hallym University Sacred Heart Hospital between 2018 and 2023. Input features included both original and binarized forms of vital signs and blood test values, along with age and medical history. Random Forest models were developed to predict mortality at 1, 2, and 3 years post-admission, as well as overall mortality. Model performance was assessed using AUC and 95% confidence intervals, and variable importance was evaluated using Mean Decrease Gini and SHAP values. Results: The highest predictive performance was observed in a model for patients under 60 using binarized input features, achieving an AUC of 0.995 (CI: 0.98–1). Across all models, pulse rate consistently emerged as the most important predictor. Additional key features included platelet count and diastolic blood pressure. SHAP analysis revealed that pulse rate was associated with higher mortality risk. Subgroup analyses based on age and medical history improved interpretability and predictive power. Conclusions: This study demonstrates that integrating clinical indicators with demographic and medical history variables can significantly enhance the accuracy and interpretability of mortality prediction models in stroke patients. The results underscore the importance of stratified modeling and continuous monitoring of vital signs, particularly pulse rate, to support precision stroke care. Full article

Background: High-grade soft tissue sarcomas (STSs) are heterogeneous tumours lacking robust prognostic or predictive biomarkers. Regnase-1, an immune RNase, enhances antitumour immunity by limiting immunosuppressive tumour microenvironment (TME) components (e.g., myeloid-derived suppressor cells (MDSCs)), but remains unexplored in STS. As CD68⁺ tumour-associated macrophages (TAMs) drive TME suppression and poor prognosis in non-translocation-driven STS, we evaluated Regnase-1 and CD68⁺ TAMs to assess Regnase-1 as an indicator of an immunologically activated TME. Methods: Immunohistochemistry scoring of Regnase-1 and CD68⁺ TAMs was performed in 91 patients. Overall survival (OS) was assessed by Kaplan–Meier and Cox regression, and findings were validated in an independent “The Cancer Genome Atlas” Sarcoma (TCGA-SARC) cohort (n = 212). Results: In UPS, Regnase-1-high predicted longer OS (17.0 months vs. not reached; p = 0.0247) and lower mortality (univariate hazard ratio (HR) = 0.3; p = 0.0343; multivariate HR = 0.4; p = 0.0413), but not after radiotherapy. CD68⁺ TAM-high predicted shorter OS (13.0 months vs. not reached; p = 0.0274) and higher mortality (HR = 2.0, 95% CI 1.1–3.7; p = 0.0325). Both Regnase-1 effects were reproduced in TCGA-SARC. Regnase-1-high tumours showed inflammatory/interferon enrichment, reduced TGF-β signalling, and SERPINE1 upregulation. Conclusions: Regnase-1 marked a pro-inflammatory TME and favourable outcome in UPS, but this effect may reverse upon radiotherapy. Full article

The integration of artificial intelligence (AI) into the diagnosis and prognosis of heart diseases is transforming cardiovascular and cardiac healthcare, improving predictive accuracy, and personalizing treatment plans. This review presents a novel contribution by providing a comprehensive overview of both diagnosis and prognosis in heart diseases through AI, covering ML and DL models. Following the PRISMA guidelines, a total of 84 recent research articles sourced from significant journals are reported. A bibliometric analysis using the VOSviewer tool was performed to map the impact of AI, enabling a detailed examination of academic connections and contributions. The findings reveal that DL models were employed 63% for diagnosis tasks, while ML models were utilized in 37% of the studies. Key recommendations include the incorporation of essential model evaluation metrics, as clinical validation indicators, integrating explainable artificial intelligence (XAI) to improve the transparency and interpretability of models, and adopting standardized frameworks to enable smooth clinical integration. This review highlights the potential of AI to improve cardiac and cardiovascular diagnosis and prognosis, providing an overview of its strengths, limitations, challenges and the possible application as AI-driven tools in patient monitoring and to support specialists in the decision-making process. Full article

The immunological composition of the microenvironment has shown relevance for diagnosis, prognosis, and therapy in solid tumors but remains underexplored in acute leukemias. We investigated the significance of the acute myeloid leukemia (AML) bone marrow microenvironment in predicting chemosensitivity and long-term remission in pediatric patients. We analyzed 32 non-promyelocytic pediatric AML patients at diagnosis using a NanoString PanCancer IO 360 assay, RNA sequencing, and deep-phenotype flow cytometry analyses. The findings were validated using the pediatric TARGET AML dataset. A short signature of three interferon (IFN)-related genes (GBP1, PARP12, and TRAT1) distinguished patients with chemosensitive disease and reduced minimal residual disease after induction chemotherapy. The signature stratified patients overall, and within the clinically defined “standard-risk” group, patients with high gene expression at diagnosis had significantly longer overall survival. The leukemia microenvironment associated with this signature showed enrichment of non-exhausted CD4⁺ and CD8⁺ T cytotoxic lymphocytes and expansion of CD8⁺ T effector memory cells re-expressing CD45RA (TEMRA) in patients with a favorable prognosis. Our results show the importance of the bone marrow microenvironment in pediatric AML and provide tools for a refined stratification of “standard-risk” patients, lacking adequate risk-oriented therapies. They also offer a promising guide for tackling immune pathways and exploiting immune-targeted therapies. Full article

Gastrointestinal (GI) malignancies—which comprise esophageal, gastric, colorectal, hepatobiliary, and pancreatic cancers—remain a leading global cause of oncologic morbidity and mortality. The prognosis for many patients (especially those diagnosed with advanced-stage disease) remains poor despite conventional therapies—namely, surgery, chemotherapy, and radiation. Immunotherapy, however, has emerged as a new strategy in oncology, and, in particular, the advent of cancer vaccines now provides an investigational approach to improving clinical outcomes in patients with GI malignancies. This review aims to provide a comprehensive overview of multiple vaccine-based strategies developed to better target GI cancers, spanning from early preclinical studies to the most recently completed clinical trials. We first introduce the main vaccine therapy classes and the immunologic rationale underlying each. We then summarize key findings from past and ongoing trials using a cancer-type-based approach, primarily focusing on vaccine safety and immunogenicity, and commenting on limitations in overall efficacy. Finally, we identify the challenges of applying mostly early-phase trials to clinical practice as well as future directions for integrating these vaccine-based approaches into personalized treatments for GI cancer patients. Full article

The QR distribution, recently introduced for modeling lifetime data under Type-II censoring, offers a flexible framework for survival and reliability analysis. This study provides the first comprehensive evaluation of multiple modern estimation techniques for the QR distribution under Type-II censoring. We systematically compare classical maximum likelihood estimation with stochastic gradient descent variants (Momentum and Adam), Bayesian approaches including Maximum A Posteriori estimation, Markov Chain Monte Carlo, and Variational Inference, as well as machine learning-integrated methods such as amortized neural network inference. Using both synthetic and the real Veterans’ Administration Lung Cancer dataset, we evaluate these methods in terms of parameter estimation accuracy, computational efficiency, and convergence behavior. The results demonstrate the strengths of optimization-based, Bayesian, and neural approaches, highlighting their practical utility in handling complex censored survival data. This research validates the distribution’s effectiveness in capturing survival dynamics, offering valuable insights for clinical applications and highlighting areas for methodological improvement. Full article

Background: Cytoreductive surgery (CRS) combined with intraperitoneal chemotherapy (IPC) is an established treatment for select patients with colorectal peritoneal metastases. However, the impact of evolving treatment strategies and accumulating institutional experience on oncological outcomes remains poorly understood. This study aimed to characterise temporal changes in treatment approaches over a decade at a single high-volume centre and evaluate their association with outcomes by comparing early and later patient cohorts. Methods: A total of 160 patients who underwent CRS with IPC for colorectal peritoneal metastases between 2011 and 2019 were retrospectively analysed and categorised into early (2011–2013, n = 42) and late (2014–2019, n = 118) cohorts. Overall survival (OS) and disease-free survival (DFS) were compared between the groups. Prognostic factors were assessed using Cox proportional hazard regression models. Results: The later cohort demonstrated significantly improved OS compared to the early cohort (median OS, 25.1 vs. 13.8 months; 5-year OS, 25.9% vs. 11.9%; log-rank p = 0.015). DFS showed a non-significant trend toward improvement (p = 0.176). In the multivariate analysis, cohort period (HR 0.63, p = 0.029), completeness of cytoreduction (HR 4.51, p < 0.001), tumour location, preoperative CEA level, and receipt of preoperative systemic chemotherapy were independently associated with OS. Conclusions: These findings suggest that accumulated institutional experience and changes in treatment strategies may be associated with improved OS in patients undergoing CRS with IPC for colorectal peritoneal metastases. Full article

Thyroid cancer is the most prevalent endocrine malignancy, and papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer. Although the prognosis is generally favorable, a better understanding of the molecular mechanisms involved in this pathology could lead to new treatment opportunities. Dysregulation of miRNA expression has been correlated with tumor development, and miR-1179 has been previously identified as one of the most downregulated miRNAs in PTC. This study aimed to explore the role of miR-1179 in thyroid tumorigenesis. miR-1179 was overexpressed in the TPC-1, B-CPAP, and HTori-3 thyroid cell lines to characterize its function and identify mRNA targets. The relevance of our data for human PTC was then addressed by analyzing TCGA and independent PTC. We showed that miR-1179 triggered apoptosis and inhibited cell migration. We identified ELF3 as a direct target of miR-1179 and other effectors, including NOTCH3 and CX3CL1. Finally, we revealed the existence of an inverse correlation between decreased expression of miR-1179 and increased expression of ELF3, NOTCH3, and CX3CL1 mRNA in human PTC. Our findings suggest that miR-1179 is a tumor suppressor gene and that its loss may contribute to thyroid tumor progression by promoting the expression of ELF3, NOTCH3, and CX3CL1. Full article

Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide, with prognosis critically dependent on the stage at diagnosis. Traditional tissue biopsy presents well-known limitations, including tumor heterogeneity and invasiveness. Liquid biopsy, encompassing the analysis of circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), exosomes, and other cell-free biomarkers, has emerged as a transformative approach for non-invasive tumor profiling. This comprehensive narrative review outlines the recent evidence published on the current state and future perspectives of liquid biopsy in CRC, with a focused emphasis on the role of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in data analysis and clinical translation. Methods: A narrative review of the literature was conducted by searching PubMed/MEDLINE, EMBASE, and ClinicalTrials.gov for articles published between January 2020 and January 2026, using a predefined Boolean search string combining terms related to liquid biopsy biomarkers, colorectal cancer, and artificial intelligence methodologies. Filters were applied to include only English-language human studies. Additional relevant sources were consulted to ensure comprehensive coverage of the available literature. Liquid biopsy platforms, particularly ctDNA sequencing and methylation profiling, demonstrate increasing clinical utility across the CRC care continuum from population screening to post-surgical minimal residual disease (MRD) detection and real-time therapy monitoring. AI-driven analytical frameworks, including Random Forest, Convolutional Neural Networks, LSTM models, and more recently Large Language Models (LLMs), substantially augment the sensitivity and specificity of liquid biopsy interpretation, enabling multimodal data integration. The convergence of liquid biopsy technology and AI-driven analytics represents a paradigm shift toward precision oncology in CRC. Remaining challenges include analytical standardization, model explainability, regulatory harmonization, and equitable access. Future integration of federated learning frameworks and LLM-based clinical decision support tools will be essential for responsible clinical translation. Full article

Background and Objectives: Molecular classification has emerged as a key determinant of prognosis in endometrial cancer and has recently been incorporated into the 2023 FIGO staging system. Tumors are categorized into four molecular subgroups—POLE-mutated (POLEmut), p53-abnormal (p53abn), mismatch repair-deficient (dMMR), and no specific molecular profile (NSMP)—each associated with distinct biological behavior and clinical outcomes. However, real-world data evaluating the relationship between molecular classification, FIGO stage distribution, and survival outcomes remain limited. Materials and Methods: This retrospective study included patients diagnosed with endometrial cancer between 2014 and 2022 at Dokuz Eylül University Hospital. Tumor samples were classified according to the ProMisE molecular algorithm using next-generation sequencing for POLE mutations and immunohistochemical evaluation of mismatch repair proteins and p53 expression. Clinicopathological characteristics, recurrence patterns, and survival outcomes were analyzed. Appropriate statistical analyses were performed. Results: A total of 156 patients were included (mean age 60.2 ± 10.0 years). The most common histology was endometrioid carcinoma (51.9%). Molecular subgroup distribution was NSMP (58.3%), dMMR (25%), p53abn (11.5%), and POLEmut (5.1%). Most patients presented with early-stage disease (83.4%). According to the 2023 FIGO molecular staging, 8.3% were classified as stage 2C m-p53abn and 5.8% as Stage 1Am-POLEmut. After a median follow-up of 39.5 months, the overall survival rate was 81.6%. Survival differed significantly across molecular subgroups, with the most favorable outcomes observed in the POLEmut (100%), followed by NSMP (85.2%), dMMR (78.4%), and p53abn (64.7%) (p = 0.011). Lymph node metastasis was significantly more frequent in the p53abn subgroup (p = 0.002), whereas distant metastasis rates did not differ between groups. Conclusions: Molecular classification was associated with differences in FIGO stage distribution and survival outcomes in this retrospective cohort and may provide additional prognostic information beyond traditional clinicopathological factors. The integration of molecular profiling into routine practice and staging systems may enable improved risk assessment and facilitate more personalized therapeutic strategies in endometrial cancer. Full article

The management of deep subgingival carious lesions presents significant challenges for achieving durable adhesive restorations due to limited access, moisture control, and proximity to periodontal tissues. Two main approaches are currently adopted to manage these cases: Deep Margin Elevation (DME) and Surgical Crown Lengthening (SCL). This systematic review (PROSPERO registration CRD420250654262) aimed to compare the performance and survival of restorations placed following DME versus SCL in teeth with deep subgingival margins. A comprehensive literature search was conducted in PubMed, B-ON, and the Cochrane Library for studies published between 2014 and 2025. Following PRISMA guidelines, six studies were included. Methodological quality and risk of bias were assessed using ROBINS-I, RoB 2, and the CARE guidelines. The available evidence indicates that both DME and SCL provide satisfactory periodontal stability, high restoration survival rates, and a low incidence of recurrent caries. DME emerged as a minimally invasive strategy that facilitates adhesive procedures by relocating deep margins to more accessible positions, potentially improving marginal integrity while preserving tooth structure and gingival architecture, particularly in patients with a thick gingival biotype. The choice between DME and SCL should be individualized. Further long-term clinical studies are required to clarify their impact on adhesive interface durability in subgingival environments. Full article