Search Results (321)

Background/Objectives: Axial spondyloarthritis (axSpA) is a chronic immune-mediated inflammatory disorder affecting the spine and sacroiliac joints, with variable clinical expression. This study assessed serum levels of inflammatory (TNF-α, IL-17A) and metabolic (leptin, adiponectin) biomarkers and their associations with disease activity, inflammation, structural damage, and comorbidities. Methods: This prospective cross-sectional study assessed 89 axSpA patients using clinical, laboratory, and radiological evaluations. Disease activity was measured using ASDAS-CRP and BASDAI scores. Radiographic damage was quantified using the Modified Stoke Ankylosing Spondylitis Spine Score (mSASSS). Serum concentrations of TNF-α, IL-17A, leptin, and adiponectin were quantified by enzyme-linked immunosorbent assay (ELISA). Clinical and imaging correlations were analyzed. Results: Serum leptin levels correlated significantly with higher disease activity scores, inflammatory markers (CRP, ESR), radiographic progression (syndesmophyte formation, mSASSS), and arterial hypertension. Adiponectin levels were inversely associated with disease activity, structural damage, and arterial hypertension, suggesting anti-inflammatory, bone- and cardio-protective properties. TNF-α levels showed an association with inflammatory markers and were higher in patients with peripheral enthesitis. IL-17A levels were weakly correlated with disease activity and structural severity and were significantly lower in patients with a history of anterior uveitis. Conclusions: Leptin and adiponectin may serve as complementary biomarkers in axSpA, reflecting both inflammatory burden and structural damage. While TNF-α and IL-17A remain key therapeutic targets, their correlation with structural changes appears limited. Biomarker profiling could support personalized disease monitoring. Longitudinal studies are needed to validate prognostic implications. Full article

Pneumonitis is characterized as inflammation of the lung parenchyma, and a potential adverse effect of several anti-cancer therapies. Diagnosing pneumonitis can be particularly challenging in lung cancer patients due to inherent similarities in symptoms and radiological presentation associated with pneumonitis, as well as other common conditions such as infection or disease progression. Furthermore, many lung cancer patients have underlying pulmonary conditions that might render them more susceptible to severe or fatal outcomes from pneumonitis. Novel anti-cancer agents, such as antibody–drug conjugates (ADCs) and bispecific antibodies (BsAbs), are being incorporated into the treatment of lung cancer; therefore, understanding the risk and mechanisms underlying the potential development of pneumonitis with these new therapies is important to ensure continuous improvements in patient care. This narrative review provides an overview of the incidence of pneumonitis observed with novel anti-cancer agents, characterizes potential pathophysiological mechanisms underlying pneumonitis risk and emerging predictive biomarkers, highlights management strategies, and explores future directions for minimizing the risk of pneumonitis for lung cancer patients. Full article

Purpose: Predicting colorectal cancer liver metastasis (CRLM) is essential for prognostic assessment. This study aims to develop and validate an interpretable multimodal machine learning framework based on multiparametric MRI for predicting CRLM, and to enhance the clinical interpretability of the model through SHapley Additive exPlanations (SHAP) analysis and deep learning visualization. Methods: This multicenter retrospective study included 463 patients with pathologically confirmed colorectal cancer from two institutions, divided into training (n = 256), internal testing (n = 111), and external validation (n = 96) sets. Radiomics features were extracted from manually segmented regions on axial T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI). Deep learning features were obtained from a pretrained ResNet101 network using the same MRI inputs. A least absolute shrinkage and selection operator (LASSO) logistic regression classifier was developed for clinical, radiomics, deep learning, and combined models. Model performance was evaluated by AUC, sensitivity, specificity, and F1-score. SHAP was used to assess feature contributions, and Grad-CAM was applied to visualize deep feature attention. Results: The combined model integrating features across the three modalities achieved the highest performance across all datasets, with AUCs of 0.889 (training), 0.838 (internal test), and 0.822 (external validation), outperforming single-modality models. Decision curve analysis (DCA) revealed enhanced clinical net benefit from the integrated model, while calibration curves confirmed its good predictive consistency. SHAP analysis revealed that radiomic features related to T2WI texture (e.g., LargeDependenceLowGrayLevelEmphasis) and clinical biomarkers (e.g., CA19-9) were among the most predictive for CRLM. Grad-CAM visualizations confirmed that the deep learning model focused on tumor regions consistent with radiological interpretation. Conclusions: This study presents a robust and interpretable multiparametric MRI-based model for noninvasively predicting liver metastasis in colorectal cancer patients. By integrating handcrafted radiomics and deep learning features, and enhancing transparency through SHAP and Grad-CAM, the model provides both high predictive performance and clinically meaningful explanations. These findings highlight its potential value as a decision-support tool for individualized risk assessment and treatment planning in the management of colorectal cancer. Full article

Oligometastasis represents a clinically relevant state of limited metastatic disease that could be amenable to selected local therapies in carefully chosen patients. Although initial trials such as SABR-COMET demonstrated a survival benefit with aggressive local treatment, breast cancer was underrepresented. Subsequent breast cancer-specific trials, including NRG-BR002, failed to show a clear survival benefit, highlighting uncertainties and the need for further refinement in patient selection and integration with systemic approaches. The definitions of oligometastasis continue to evolve, incorporating radiological, clinical, and biological features. Advances in imaging and molecular profiling suggest that oligometastatic breast cancer might represent a distinct biological subtype, with potential biomarkers including PIK3CA mutations and YAP/TAZ expression. Organ-specific strategies using stereotactic radiotherapy, surgery, and proton therapy have shown favorable local control in certain settings, though their impact on the overall survival remains under investigation. Emerging techniques, including circulating tumor DNA (ctDNA) analysis, are being explored to improve patient selection and disease monitoring. Ongoing trials may provide further insight into the role of local therapy, particularly in hormone receptor-positive or HER2-positive subtypes. Local and systemic strategies need to be carefully coordinated to optimize the outcomes. This review summarizes the current definitions of and evidence and therapeutic considerations for oligometastatic breast cancer and outlines potential future directions. Full article

The study’s aim was to evaluate electroretinographic (ERG) alterations in Fragile X syndrome (FXS), FMR1 premutation carriers, and controls, and to explore correlations with peripheral blood FMRP expression levels and behavioral outcomes. ERG recordings were obtained using a handheld device across three stimulus protocols in 43 premutation carriers, 39 individuals with FXS, and 23 controls. Peripheral blood FMRP expression levels were quantified using TR-FRET (Time-Resolved Fluorescence Resonance Energy Transfer). Correlations were assessed with cognitive and behavioral measures including IQ (Intelligence Quotient), ABC_FX (Aberrant Behavior Checklist for Fragile X Syndrome), SNAP-IV (Swanson, Nolan, and Pelham Teacher and Parent Rating Scale), SEQ (Sensory Experiences Questionnaire), ADAMS (Anxiety, Depression, and Mood Scale), and the Vineland III Adaptive Behavior Scale standard score. Significant group differences were observed in multiple ERG parameters, particularly in 2 Hz b-wave amplitude (p = 0.0081), 2 Hz b-wave time to peak (p = 0.0164), 28.3 Hz flash combined amplitude (p = 0.0139), 3.4 Hz red/blue flash b-wave amplitude (p = 0.0026), and PhNR amplitude (p = 0.0026), indicating both outer and inner retinal dysfunction in FXS and premutation groups. Despite high test–retest reliability for ERG (ICC range = 0.71–0.92) and FMRP (ICC = 0.70), no correlation was found between ERG metrics and FMRP or behavioral measures. However, FMRP levels strongly correlated with IQ (ρ = 0.69, p < 0.0001) and inversely with behavioral impairment [ABC_FX (ρ = −0.47, p = 0.0041), SNAP-IV (ρ = −0.48, p = 0.0039), SEQ (ρ = −0.43, p = 0.0146), and the Vineland III standard score (ρ = 0.56, p = 0.0019)]. ERG reveals distinct retinal functional abnormalities in FMR1-related conditions but does not correlate with peripheral FMRP expression levels, highlighting the need for multimodal biomarkers integrating radiological, physiological, behavioral, and molecular measures. Full article

Malignant diseases represent a major global health challenge and are among the leading causes of death worldwide. Accurate early diagnosis is essential for improving outcomes and combating these conditions effectively. Currently, the diagnosis of malignancies relies heavily on radiological imaging and pathological examinations, which are often invasive and not cost-effective. As such, there is a growing need for non-invasive and accessible methods to detect cancer in its early stages. Tumor markers—biomolecules whose levels increase in malignancy and can be measured in blood or other biological tissues and fluids—offer a promising tool. However, the sensitivity and specificity of currently available tumor markers are insufficient for early detection, limiting their use primarily to disease monitoring rather than diagnosis. While ongoing research continues to identify novel tumor markers, the development of more effective early detection strategies requires more than the discovery of new biomarkers. The continuous monitoring of patients and individuals with a high tumor risk and the personalization of tumor marker interpretation are also critical. In this review, we (i) summarize the most commonly used tumor markers, (ii) examine strategies for developing novel biomarkers, particularly through omics technologies, (iii) explore the potential of continuous monitoring using wearable biosensors for early tumor detection, and (iv) discuss approaches to personalizing tumor marker interpretation to support early diagnosis and improve treatment outcomes. Full article

Introduction: Celiac disease (CD) impairs bone development in children through inflammation and nutrient malabsorption. Osteoprotegerin (OPG), a decoy receptor for RANKL, plays a role in bone remodeling and is increasingly recognized as a potential biomarker of bone metabolism and inflammation. However, its clinical significance in pediatric CD remains unclear. Aim: To evaluate the relationship between OPG levels, growth parameters, and delayed bone age in children with CD, and to assess OPG’s potential as a biomarker of bone health and disease activity. Methods: This three-year case–control study included 146 children: 25 with newly diagnosed CD (Group A), 54 with established CD on a gluten-free diet (Group B), and 67 healthy controls (Group C). Participants underwent clinical, anthropometric, and laboratory assessments at baseline and after 6 months (Groups A and B). OPG and osteocalcin were measured, and bone age was assessed radiologically. Statistical analyses included ANOVA, Spearman’s correlations, and binomial logistic regression. Results: OPG levels were highest in newly diagnosed children (Group A), showing a non-significant decrease after gluten-free diet initiation. OPG correlated negatively with age and height in CD patients and controls, and positively with hemoglobin and iron in Group B. Logistic regression revealed no significant predictive value of OPG for delayed bone age, although a trend was observed in Group B (p = 0.091). Children in long-term remission exhibited bone maturation patterns similar to healthy peers. Conclusions: OPG levels reflect disease activity and growth delay in pediatric CD but lack predictive power for delayed bone age. While OPG may serve as a secondary marker of bone turnover and inflammatory status, it is not suitable as a standalone biomarker for skeletal maturation. These findings highlight the need for integrative biomarker panels to guide bone health monitoring in children with CD. Full article

Background/Objectives: Treatment of locally advanced rectal cancer (LARC) very often requires a neoadjuvant multimodal approach. Neoadjuvant treatment (NAT) encompasses treatments like chemoradiotherapy (CRT), short-course radiotherapy (SCRT), radiotherapy (RT) or a combination of either of these two with additional induction or consolidation chemotherapy, namely total neoadjuvant treatment (TNT). In case of complete radiological and clinical response, the non-operative watch-and-wait strategy can be adopted in selected patients. This strategy is impacted by a regrowth rate of approximately 30%. Predicting biomarkers of tumor response to NAT could improve guidance of clinicians during clinical decision making, improving treatment outcomes and decreasing unnecessary treatment exposure. To this day, there is no validated biomarker to predict tumor response to any NAT strategies in clinical use. Most research focused on CRT neglects the study of other regimens. Methods: We conducted a narrative literature review which aimed at summarizing the status of biomarkers predicting tumor response to NAT other than CRT in LARC. Results: Two hundred and fourteen articles were identified. After screening, twenty-one full-text articles were included. Statistically significant markers associated with improved tumor response pre-treatment were as follows: low circulating CEA levels; BCL-2 expression; high cellular expression of Ku70, MIB-1(Ki-67) and EGFR; low cellular expression of VEGF, hPEBP4 and nuclear β-catenin; the absence of TP53, SMAD4, KRAS and LRP1B mutations; the presence of the G-allel of LCS-6; and MRI features such as the conventional biexponential fitting pseudodiffusion (Dp) mean value and standard deviation (SD), the variable projection Dp mean value and lymph node characteristics (short axis, smooth contour, homogeneity and Zhang et al. radiomic score). In the interval post-treatment and before surgery, significant markers were as follows: a reduction in the median value of circulating free DNA, higher presence of monocytic myeloid-derived suppressor cells, lower presence of CTLA4+ or PD1+ regulatory T cells and standardized index of shape changes on MRI. Conclusions: Responders to neoadjuvant SCRT and RT tended to have a tumor microenvironment with an immune–active phenotype, whereas responders to TNT tended to have a less active tumor profile. Although some biomarkers hold great promise, scarce publications, inconsistent results, low statistical power, and low reproducibility prevent them from reliably predicting tumor response following NAT. Full article

Lymphangioleiomyomatosis (LAM) is a rare progressive disease that affects women of reproductive age and is characterized by cystic lung destruction, airflow obstruction, and lymphatic dysfunction. Current diagnostic methods are costly or lack sufficient specificity, highlighting the need for novel non-invasive approaches. Exhaled breath analysis using real-time proton mass spectrometry (PTR-MS) presents a promising strategy for identifying disease-specific volatile organic compounds (VOCs). This cross-sectional study analyzed exhaled breath samples from 51 LAM patients and 51 age- and sex-matched healthy controls. PTR-time-of-flight mass spectrometry (PTR-TOF-MS) was employed to identify VOC signatures associated with LAM. Data preprocessing, feature selection, and statistical analyses were performed using machine learning models, including gradient boosting classifiers (XGBoost), to identify predictive biomarkers of LAM and its complications. We identified several VOCs as potential biomarkers of LAM, including m/z = 90.06 (lactic acid) and m/z = 113.13. VOCs predictive of disease complications included m/z = 49.00 (methanethiol), m/z = 48.04 (O-methylhydroxylamine), and m/z = 129.07, which correlated with pneumothorax, obstructive ventilation disorders, and radiological findings of lung cysts and bronchial narrowing. The classifier incorporating these biomarkers demonstrated high diagnostic accuracy (AUC = 0.922). This study provides the first evidence that exhaled breath VOC profiling can serve as a non-invasive additional tool for diagnosing LAM and predicting its complications. These findings warrant further validation in larger cohorts to refine biomarker specificity and explore their clinical applications in disease monitoring and personalized treatment strategies. Full article

Lung cancer remains the leading cause of cancer-related mortality globally, largely due to late-stage diagnoses. While low-dose computed tomography (LDCT) has improved early detection and reduced mortality in high-risk populations, traditional screening strategies often adopt a one-size-fits-all approach based primarily on age and smoking history. This can lead to limitations, such as overdiagnosis, false positives, and the underrepresentation of non-smokers, which are especially prevalent in Asian populations. Precision medicine offers a transformative solution by tailoring screening protocols to individual risk profiles through the integration of clinical, genetic, environmental, and radiological data. Emerging tools, such as risk prediction models, radiomics, artificial intelligence (AI), and liquid biopsies, enhance the accuracy of screening, allowing for the identification of high-risk individuals who may not meet conventional criteria. Polygenic risk scores (PRSs) and molecular biomarkers further refine stratification, enabling more personalized and effective screening intervals. Incorporating these innovations into clinical workflows, alongside shared decision-making (SDM) and robust data infrastructure, represents a paradigm shift in lung cancer prevention. However, implementation must also address challenges related to health equity, algorithmic bias, and system integration. As precision medicine continues to evolve, it holds the promise of optimizing early detection, minimizing harm, and extending the benefits of lung cancer screening to broader and more diverse populations. This review explores the current landscape and future directions of precision medicine in lung cancer screening, emphasizing the need for interdisciplinary collaboration and population-specific strategies to realize its full potential in reducing the global burden of lung cancer. Full article

Objectives: Endometriosis is a common gynecologic condition characterized by the presence of endometrial-like tissue outside the uterus, often leading to pelvic pain and infertility. Diagnosis is frequently delayed, with prolonged diagnostic wandering that could be improved through enhanced first-line radiologic assessment. The uterosacral ligament (USL) is the most frequent site of deep infiltrating endometriosis (DIE). The Hôtel-Dieu (HTD) MRI classification, published in 2024, offers a structured framework for evaluating USL involvement by correlating MRI findings with the diagnostic certainty of endometriosis. Key Findings: This pictorial essay provides a practical guide for applying the HTD MRI classification, presenting key imaging criteria with illustrative examples for each USL type. The classification distinguishes between “linear” and “nodular” USL lesions, with implications for diagnostic confidence. “Nodular“ types demonstrate a 100% positive predictive value (PPV), while “linear“ types may yield higher false positive rates (FPR). The HTD MRI classification may also be complemented by innovative biomarker testing, such as microRNA signatures, especially in cases with “linear“ USL involvement. Conclusions: By standardizing the assessment of USL lesions, the HTD MRI classification enhances diagnostic accuracy, improves MRI reproducibility, and supports earlier identification of endometriosis in first-line settings. Its integration into radiologic workflows can contribute to reduced diagnostic delays. Implications for practice: The HTD MRI classification is a valuable screening tool for first-line radiologists and clinicians. Incorporating it into routine pelvic MRI interpretations may streamline diagnostic pathways, promote consistency across readers, and guide additional testing strategies, such as microRNA assays, for cases where MRI alone is less definitive. Full article

Background/Objective: Neurofilament light chain (Nf-L), neurofilament heavy chain (Nf-H), and chitinase 3-like 1 (CHI3L1) are cerebrospinal fluid (CSF) biomarkers of neuroaxonal damage and inflammation in multiple sclerosis (MS). Their longitudinal response to disease-modifying therapies and association with clinical and radiological outcomes remain incompletely understood. The aim of this study is to evaluate the impact of interferon beta (IFN-β) therapy on CSF levels of Nf-L, Nf-H, and CHI3L1 in early relapsing–remitting MS (RRMS) and assess their association with long-term clinical outcomes and MRI activity. Methods: We conducted a prospective two-year observational study involving 14 treatment-naive RRMS patients who initiated IFN-β therapy. CSF levels of Nf-L, Nf-H, and CHI3L1 were measured at baseline and after two years. Clinical disability was assessed via the Expanded Disability Status Scale (EDSS) and by studying brain MRI activity. A 15-year clinical follow-up was performed for 12 patients. Results: Nf-L levels significantly decreased after two years of IFN-β treatment (p = 0.039), while CHI3L1 levels significantly increased (p = 0.001). Nf-H levels remained stable. Nf-L and CHI3L1 levels at baseline and follow-up correlated with relapse rate and long-term EDSS. Nf-H levels correlated with EDSS scores but not with relapse or MRI activity. A trend toward a positive correlation between increasing Nf-L levels and MRI activity was observed (p = 0.07). Conclusions: CSF biomarkers demonstrate differential responses to IFN-β therapy in early RRMS. Nf-L emerges as a sensitive biomarker of treatment response and disease activity, while CHI3L1 may reflect ongoing tissue remodeling and inflammation. These findings support the utility of CSF biomarker monitoring for personalized treatment strategies in MS. Full article

Objectives: The objective of this study was to identify CT-based predictors of mechanical ventilation and mortality in patients with severe and critical viral pneumonia and to examine the association between imaging severity and outcomes in ventilated patients. Methods: We analyzed pulmonary CT scans from 148 patients with severe or critical pneumonia caused by COVID-19 (n = 98) or influenza A H1N1 (n = 50). Patients were assessed based on tomographic patterns, demographics, clinical severity scores (Charlson Comorbidity Index, SOFA, and APACHE IV), and biomarkers. Survival analyses were performed using Kaplan–Meier curves and multivariable Cox regression. Results: Bilateral, peripheral, and basal lung involvement was common across both groups. Ground-glass opacities (89.62%, p ≤ 0.001) and consolidation (61.54%, p = 0.001) were more prevalent in COVID-19, whereas pleural effusion was significantly more frequent in H1N1 (76.92%, p ≤ 0.001). COVID-19 cases more often presented with bilateral (96.94%) and peripheral lesions (77.87%). H1N1 patients were more likely to develop severe ARDS and require mechanical ventilation. In COVID-19, higher APACHE IV scores and pulmonary damage severity index were independently associated with increased mortality. Conclusions: Radiologic and clinical severity profiles differ between COVID-19 and H1N1 pneumonia. CT-based assessments combined with prognostic scores may aid early risk stratification and guide treatment decisions in patients with severe viral pneumonia. Full article

Heart failure (HF) remains a major global health challenge, driven by multifactorial pathophysiological processes, such as systemic congestion, endothelial dysfunction, and inflammation. While natriuretic peptides are well-established biomarkers for diagnosing and monitoring HF, they do not fully capture the complexity of vascular involvement. CD146, also known as melanoma cell adhesion molecule (MCAM), is a transmembrane glycoprotein primarily expressed on endothelial cells and involved in cell adhesion, vascular permeability, and angiogenesis. Its soluble form (sCD146), released in response to multiple pathophysiological stimuli, including venous and arterial endothelial stretch, oxidative stress, and inflammatory cytokine activation, has emerged as a promising biomarker reflecting both hemodynamic congestion and systemic endothelial stress. This review synthesizes current knowledge on the structure, regulation, and release mechanisms of CD146 and explores its clinical utility in HF. Elevated sCD146 levels have been associated with echocardiographic and radiological indicators of congestion, as well as with adverse outcomes. While promising, its application is limited by variability, lack of standardization, and confounding elevations in non-cardiac conditions, including malignancy. Full article

Immunoglobulin G4-related disease (IgG4-RD) is a systemic fibroinflammatory condition marked by tumefactive lesions, IgG4+ plasma cell-rich infiltrates, storiform fibrosis, and obliterative phlebitis. Its multisystem involvement and overlap with malignancies, infections, and immune disorders complicate diagnosis despite recent classification advances. This study summarizes diagnostic challenges, highlights the role of histopathology as per the 2019 classification criteria established by the American College of Rheumatology and the European League Against Rheumatism (ACR/EULAR), and explores emerging tools to improve diagnostic accuracy. ACR/EULAR classification emphasizes three cardinal histopathological features (storiform fibrosis, obliterative phlebitis, or dense lymphoplasmacytic infiltrates) combined with an IgG4+/IgG+ plasma cell ratio >40% and organ-specific IgG4+ thresholds. While serum IgG4 levels are often elevated, their poor specificity necessitates confirmatory biopsy. Diagnostic limitations include sampling variability due to patchy fibrosis, interobserver discrepancies in immunohistochemical interpretation, and differentiation from mimics like lymphoma. Emerging solutions incorporate novel biomarkers (plasmablasts, anti-annexin A11) and advanced techniques (flow cytometry, digital pathology). Future research directions should focus on AI-assisted pattern recognition, multi-omics profiling, and organ-specific criteria refinement. While histopathology remains the diagnostic cornerstone, a multidisciplinary approach integrating clinical, radiological, and laboratory data is vital. Innovations in biomarkers promise improved diagnostic accuracy and personalized care, balancing novel advancements with foundational pathological evaluation. Full article