Search Results (407)

Arrhythmogenic cardiomyopathy (ACM; MIM #107970) is a primitive heart muscle disease characterized by progressive myocardial loss and fibrosis or fibrofatty replacement, predisposing patients to ventricular arrhythmias, sudden cardiac death, and heart failure. Despite advances in imaging and genetics, early diagnosis remains challenging due to incomplete penetrance, variable phenotypic expressivity, and the fact that fatal arrhythmic events may often occur in the early stages of the disease. In this context, the identification of reliable biomarkers could enhance diagnostic accuracy, support risk stratification, and guide clinical management. This narrative review examines the current landscape of potential and emerging biomarkers in ACM, including troponins, natriuretic peptides, inflammatory proteins, microRNAs, fibrosis-related markers, and other molecules. Several of these biomarkers have demonstrated associations with disease severity, arrhythmic burden, or structural progression, although their routine clinical utility remains limited. The increasing relevance of genetic testing and non-invasive tissue characterization—particularly through cardiac imaging techniques—should also be emphasized as part of a multimodal diagnostic strategy in which biomarkers may play a complementary role. Although no single biomarker currently meets the criteria for a standalone diagnostic application, ongoing research into multi-marker panels and novel molecular targets offers promising perspectives. In conclusion, the integration of circulating biomarkers with imaging findings, genetic data, and clinical parameters may open new avenues for improving early detection and supporting personalized therapeutic strategies in patients with suspected ACM. Full article

Senescence is a dynamic, multifaceted process implicated in tissue aging, organ dysfunction, and intricately associated with numerous chronic diseases. As senescent cells accumulate, they drive inflammation, fibrosis, and metabolic disruption through the senescence-associated secretory phenotype (SASP). Despite its clinical relevance, senescence remains challenging to detect non-invasively due to its heterogeneous nature and the lack of universal biomarkers. Recent advances in the development of specific imaging probes for positron emission tomography (PET) enable in vivo visualization of senescence-associated pathways across key organs, such as the lung, heart, kidney, and metabolic processes. For instance, [¹⁸F]FPyGal, a β-galactosidase-targeted tracer, has demonstrated selective accumulation in senescent cells in both preclinical and early clinical studies, while FAP-targeted radioligands are emerging as tools for imaging fibrotic remodeling in the lung, liver, kidney, and myocardium. This review examines a new generation of PET radioligands targeting hallmark features of senescence, with the potential to track and measure the process, the ability to be translated into clinical interventions for early diagnosis, and longitudinal monitoring of senescence-driven pathologies. By integrating organ-specific imaging biomarkers with molecular insights, PET probes are poised to transform our ability to manage and treat age-related diseases through personalized approaches. Full article

Background: This narrative review summarizes recent progress in artificial intelligence (AI), especially radiomics and deep learning, for non-invasive diagnosis and molecular profiling of gliomas. Methodology: A thorough literature search was conducted on PubMed, Scopus, and Embase for studies published from January 2020 to July 2025, focusing on clinical and technical research. In key areas, these studies examine AI models’ predictive capabilities with multi-parametric Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). Results: The domains identified in the literature include the advancement of radiomic models for tumor grading and biomarker prediction, such as Isocitrate Dehydrogenase (IDH) mutation, O6-methylguanine-dna methyltransferase (MGMT) promoter methylation, and 1p/19q codeletion. The growing use of convolutional neural networks (CNNs) and generative adversarial networks (GANs) in tumor segmentation, classification, and prognosis was also a significant topic discussed in the literature. Deep learning (DL) methods are evaluated against traditional radiomics regarding feature extraction, scalability, and robustness to imaging protocol differences across institutions. Conclusions: This review analyzes emerging efforts to combine clinical, imaging, and histology data within hybrid or transformer-based AI systems to enhance diagnostic accuracy. Significant findings include the application of DL to predict cyclin-dependent kinase inhibitor 2A/B (CDKN2A/B) deletion and chemokine CCL2 expression. These highlight the expanding capabilities of imaging-based genomic inference and the importance of clinical data in multimodal fusion. Challenges such as data harmonization, model interpretability, and external validation still need to be addressed. Full article

Background: Lung cancer remains the leading cause of cancer-related mortality globally, largely due to delayed diagnosis in its early stages. While conventional diagnostic tools like low-dose CT and tissue biopsy are routinely used, they suffer from limitations including invasiveness, radiation exposure, cost, and limited sensitivity for early-stage detection. Liquid biopsy, a minimally invasive alternative that captures circulating tumor-derived biomarkers such as ctDNA, cfRNA, and exosomes from body fluids, offers promising diagnostic potential—yet its sensitivity in early disease remains suboptimal. Recent advances in Artificial Intelligence (AI) and radiomics are poised to bridge this gap. Objective: This review aims to explore how AI, in combination with radiomics, enhances the diagnostic capabilities of liquid biopsy for early detection of lung cancer and facilitates personalized monitoring strategies. Content Overview: We begin by outlining the molecular heterogeneity of lung cancer, emphasizing the need for earlier, more accurate detection strategies. The discussion then transitions into liquid biopsy and its key analytes, followed by an in-depth overview of AI techniques—including machine learning (e.g., SVMs, Random Forest) and deep learning models (e.g., CNNs, RNNs, GANs)—that enable robust pattern recognition across multi-omics datasets. The role of radiomics, which quantitatively extracts spatial and morphological features from imaging modalities such as CT and PET, is explored in conjunction with AI to provide an integrative, multimodal approach. This convergence supports the broader vision of precision medicine by integrating omics data, imaging, and electronic health records. Discussion: The synergy between AI, liquid biopsy, and radiomics signifies a shift from traditional diagnostics toward dynamic, patient-specific decision-making. Radiomics contributes spatial information, while AI improves pattern detection and predictive modeling. Despite these advancements, challenges remain—including data standardization, limited annotated datasets, the interpretability of deep learning models, and ethical considerations. A push toward rigorous validation and multimodal AI frameworks is necessary to facilitate clinical adoption. Conclusion: The integration of AI with liquid biopsy and radiomics holds transformative potential for early lung cancer detection. This non-invasive, scalable, and individualized diagnostic paradigm could significantly reduce lung cancer mortality through timely and targeted interventions. As technology and regulatory pathways mature, collaborative research is crucial to standardize methodologies and translate this innovation into routine clinical practice. Full article

Considering the growing demand for modern facility agriculture, it is essential to develop non-destructive technologies for assessing lettuce nutritional status. To overcome the limitations of traditional methods, which are destructive and time-consuming, this study proposes a multimodal non-destructive nitrogen detection method for lettuce based on multi-source imaging. The approach integrates terahertz time-domain spectroscopy (THz-TDS) and near-infrared hyperspectral imaging (NIR-HSI) to achieve rapid and non-invasive nitrogen detection. Spectral imaging data of lettuce samples under different nitrogen gradients (20–150%) were simultaneously acquired using a THz-TDS system (0.2–1.2 THz) and a NIR-HSI system (1000–1600 nm), with image segmentation applied to remove background interference. During data processing, Savitzky–Golay smoothing, MSC (for THz data), and SNV (for NIR data) were employed for combined preprocessing, and sample partitioning was performed using the SPXY algorithm. Subsequently, SCARS/iPLS/IRIV algorithms were applied for THz feature selection, while RF/SPA/ICO methods were used for NIR feature screening, followed by nitrogen content prediction modeling with LS-SVM and KELM. Furthermore, small-sample learning was utilized to fuse crop feature information from the two modalities, providing a more comprehensive and effective detection strategy. The results demonstrated that the THz-based model with SCARS-selected power spectrum features and an RBF-kernel LS-SVM achieved the best predictive performance (R² = 0.96, RMSE = 0.20), while the NIR-based model with ICO features and an RBF-kernel LS-SVM achieved the highest accuracy (R² = 0.967, RMSE = 0.193). The fusion model, combining SCARS and ICO features, exhibited the best overall performance, with training accuracy of 96.25% and prediction accuracy of 95.94%. This dual-spectral technique leverages the complementary responses of nitrogen in molecular vibrations (THz) and organic chemical bonds (NIR), significantly enhancing model performance. To the best of our knowledge, this is the first study to realize the synergistic application of THz and NIR spectroscopy in nitrogen detection of facility-grown lettuce, providing a high-precision, non-destructive solution for rapid crop nutrition diagnosis. Full article

Mid-infrared (MIR) spectroscopy enables non-invasive identification of chemical species by probing absorption spectra associated with molecular vibrational modes, where spectral filters play a central role. Conventional plasmonic metasurfaces have been explored for MIR filtering in reflection and transmission modes but typically suffer from broad spectral profiles and low efficiencies. All-dielectric metasurfaces, although characterized by low intrinsic losses, are largely limited to reflection mode operation. To overcome these limitations, we propose a hybrid metal-dielectric metasurface that combines the advantages of both platforms while simplifying fabrication compared to conventional Fabry–Pérot filters. The proposed filter consists of silicon (Si) crosses atop gold (Au) square patches and demonstrates a transmission efficiency of 87% at the operating wavelength of 4.28 µm, with a full width half maximum (FWHM) as narrow as 43 nm and a quality factor of approximately 99.5 at λ = 4.28 μm. Numerical simulations attribute this performance to hybridization of Mie lattice resonances in both the gold patches and silicon crosses. By providing narrowband, high-transmission filtering in the MIR, the hybrid metasurface offers a compact and versatile platform for selective gas detection and imaging. This work establishes hybrid metal–dielectric metasurfaces as a promising direction for next-generation MIR spectroscopy. Full article

Background: Cervical artery dissection (CAD) is a leading cause of acute ischemic stroke among young and middle-aged patients. Currently, the growing availability of high-resolution magnetic resonance imaging (MRI), particularly fat-saturated T1-weighted black-blood SPACE sequences, allows the non-invasive, rapid, and reliable diagnosis of multiple arterial dissections. Methods: We reported our experience from two tertiary stroke centers of patients diagnosed with spontaneous multiple cervical artery dissections, detected with high-resolution MRI, during a three-year period (2022–2025). Results: Among 95 consecutive patients with CAD, 11 patients (mean age: 48 ± 9 years, 6 (55%) females) were diagnosed with multiple symptomatic or asymptomatic CADs, whereas in 84 patients (mean age: 49 ± 11 years, 32 (38%) females) a single CAD was detected. In all patients, high-resolution MRI and MR-angiography were performed, whereas digital subtraction angiography (DSA) with simultaneous evaluation of renal arteries was conducted in nine patients. A history of trauma or chiropractic manipulations, intense physical exercise prior to symptom onset, recent influenza-like illness, and recent childbirth in a young female patient were reported as predisposing risk factors. Cervicocranial pain, cerebral infarctions leading to focal neurological signs, and Horner’s syndrome were among the most commonly documented symptoms. Characteristic findings in the high-resolution 3D T1 SPACE sequence were detected in all patients. Fibromuscular dysplasia and Eagle syndrome were detected in four patients and one patient, respectively. Eight patients were treated with antiplatelets, whereas three patients received anticoagulation with low-molecular-weight heparin. There was only one case of stroke recurrence during a mean follow-up period of 9 ± 4 months. Conclusions: This case series highlights the utility of specific high-resolution MRI sequences as a very promising method for detecting multiple CADs in young patients. The systematic use of these sequences could enhance the sensitivity of detecting multiple cervical CADs, affecting also the thorough investigation for underlying connective tissue vasculopathies, stratifying the risk for first-ever or recurrent ischemic stroke, and influencing acute reperfusion and secondary prevention therapeutic strategies. Full article

Background/Objectives: Squamous cell carcinoma of the nail unit (SCCNU) is a rare yet often underrecognized malignancy that can lead to delayed diagnosis and significant functional morbidity. This review aims to comprehensively summarize the current understanding of SCCNU, focusing on its clinical, dermoscopic, and molecular features, diagnostic approaches, and evolving management strategies, including the role of emerging technologies and immunotherapy. Methods: A detailed literature review was conducted using peer-reviewed publications, case series, and institutional guidelines related to SCCNU. Emphasis was placed on studies addressing clinical presentation, dermoscopic patterns, molecular pathology, histologic subtypes, imaging, biopsy techniques, staging systems, and both conventional and novel therapeutic approaches. Comparative analyses of histopathological variants and diagnostic algorithms were included. Results: SCCNU presents in patients with diverse clinical manifestations, often mimicking benign nail disorders, leading to diagnostic delays. Dermoscopy improves lesion visualization, revealing features such as vascular changes and onycholysis. Histologically, SCCNU exhibits two main subtypes: basaloid (HPV-related) and keratinizing (HPV-negative) types. Molecular analyses have identified TP53 as the most frequently mutated gene, with additional alterations in HRAS, BRAF, and TERT. Imaging modalities such as MRI and LC-OCT aid in staging and surgical planning. Management is centered on complete excision—often via Mohs micrographic surgery—while topical, intralesional, and HPV-directed therapies are under investigation. Immunohistochemical markers (p16, Ki-67, AE1/AE3) and neoadjuvant immunotherapy represent promising adjuncts. Conclusions: Early diagnosis through non-invasive imaging, improved molecular characterization, and personalized treatment strategies are essential to advancing care in SCCNU. Future directions include clinical trials evaluating immunotherapy, vaccine strategies, and precision-guided surgical approaches to preserve function and minimize recurrence. Full article

Gastroesophageal reflux disease (GERD) is associated with symptoms such as heartburn, resulting from gastric content reflux. Alginate-based raft-forming gel formulations represent a non-pharmacological strategy for GERD management by forming a floating gel barrier in the stomach. This study evaluated three commercial anti-reflux oral gel systems under simulated fed-state gastric conditions, using in vitro magnetic resonance relaxometry techniques. Magnetic resonance imaging (MRI) was performed in 0.01 M hydrochloric acid (HCl) to visualize gel raft formation, spatial structure, and spatial distribution of effective T₂ relaxation time. Nuclear magnetic resonance (NMR) relaxometry in 0.01 M deuterium chloride (DCl) measured T₁ and T₂ relaxation times of the protons that were initially included in the preparation to assess its molecular mobility within the gel matrix. Two formulations formed floating, coherent gels, whereas the remaining one exhibited only polymer swelling without flotation. In one case, relaxometry data revealed a solid-like component that can be detected, indicating enhanced mechanical stability. The performance of each formulation was influenced by interactions among alginate, bicarbonates, and calcium ions, which determined gel consistency and flotation behavior. MRI and NMR relaxometry in vitro provide valuable non-invasive insights into the structural and functional behavior of alginate-based gel formulations. This approach supports the rational design of advanced gel-based therapies for GERD by linking molecular composition with in situ performance. Full article

Brain gliomas are highly infiltrative and heterogenous tumors, whose early and accurate detection as well as therapeutic management are challenging. Artificial intelligence (AI) has the potential to redefine the landscape in neuro-oncology and can enhance glioma detection, imaging segmentation, and non-invasive molecular characterization better than conventional diagnostic modalities through deep learning-driven radiomics and radiogenomics. AI algorithms have been shown to predict genotypic and phenotypic glioma traits with remarkable accuracy and facilitate patient-tailored therapeutic decision-making. Such algorithms can be incorporated into surgical planning to optimize resection extent while preserving eloquent cortical structures through preoperative imaging fusion and intraoperative augmented reality-assisted navigation. Beyond resection, AI may assist in radiotherapy dose distribution optimization, thus ensuring maximal tumor control while minimizing surrounding tissue collateral damage. AI-guided molecular profiling and treatment response prediction models can facilitate individualized chemotherapy regimen tailoring, especially for glioblastomas with MGMT promoter methylation. Applications in immunotherapy are emerging, and research is focusing on AI to identify tumor microenvironment signatures predictive of immune checkpoint inhibition responsiveness. AI-integrated prognostic models incorporating radiomic, histopathologic, and clinical variables can additionally improve survival stratification and recurrence risk prediction remarkably, to refine follow-up strategies in high-risk patients. However, data heterogeneity, algorithmic transparency concerns, and regulatory challenges hamstring AI implementation in neuro-oncology despite its transformative potential. It is therefore imperative for clinical translation to develop interpretable AI frameworks, integrate multimodal datasets, and robustly validate externally. Future research should prioritize the creation of generalizable AI models, combine larger and more diverse datasets, and integrate multimodal imaging and molecular data to overcome these obstacles and revolutionize AI-assisted patient-specific glioma management. Full article

Cultural heritage (CH) relics are irreplaceable records of human civilization, encompassing diverse historical, technological, and artistic achievements. Extracting their structural and compositional information without affecting their physical integrity is a critical challenge. This review summarizes recent advances in non-destructive techniques (NDTs) for CH analysis and emphasizes the balance between the depth of analysis and conservation ethics. Techniques are broadly categorized into spectrum-based, X-ray-based, and digital-based methods. Spectroscopic techniques such as Fourier transform infrared (FTIR), Raman, and nuclear magnetic resonance (NMR) spectroscopy provide molecular-level insights into organic and inorganic components, often requiring minimal or no sampling. X-ray-based techniques, including conventional and spatially resolved XRD/XRF and total reflection XRF (TRXRF), provide powerful means for crystal and elemental analysis, including in situ pigment identification and trace material analysis. Digital-based methods include high-resolution imaging, three-dimensional modeling, data fusion, and AI-driven diagnosis to achieve the non-invasive visualization, monitoring, and virtual restoration of CH assets. This review highlights a methodology shift from traditional molecular-level detection to data-centric and AI-assisted diagnosis, reflecting the paradigm shift in heritage science. Full article

Background: Gliomas are among the most complex and lethal primary brain tumors, necessitating precise evaluation of both anatomical subregions and molecular alterations for effective clinical management. Methods: To find a solution to the disconnected nature of current bioimage analysis pipelines, where anatomical segmentation based on MRI and molecular biomarker prediction are done as separate tasks, we use here Molecular-Genomic and Multi-Task (MGMT-Net), a one deep learning scheme that carries out the task of the multi-modal MRI data without any conversion. MGMT-Net incorporates a novel Cross-Modality Attention Fusion (CMAF) module that dynamically integrates diverse imaging sequences and pairs them with a hybrid Transformer–Convolutional Neural Network (CNN) encoder to capture both global context and local anatomical detail. This architecture supports dual-task decoders, enabling concurrent voxel-wise tumor delineation and subject-level classification of key genomic markers, including the IDH gene mutation, the 1p/19q co-deletion, and the TERT gene promoter mutation. Results: Extensive validation on the Brain Tumor Segmentation (BraTS 2024) dataset and the combined Cancer Genome Atlas/Erasmus Glioma Database (TCGA/EGD) datasets demonstrated high segmentation accuracy and robust biomarker classification performance, with strong generalizability across external institutional cohorts. Ablation studies further confirmed the importance of each architectural component in achieving overall robustness. Conclusions: MGMT-Net presents a scalable and clinically relevant solution that bridges radiological imaging and genomic insights, potentially reducing diagnostic latency and enhancing precision in neuro-oncology decision-making. By integrating spatial and genetic analysis within a single model, this work represents a significant step toward comprehensive, AI-driven glioma assessment. Full article

Plant gums have long served as essential binding media in polychrome cultural heritage, contributing to pigment adhesion, surface cohesion, and long-term stability. This review evaluates recent advances in analytical technologies, including FTIR, Raman spectroscopy, GC-MS, LC-MS/MS, MALDI-TOF MS, hyperspectral imaging, and immunological assays, for the identification of gums such as gum arabic, peach gum, and tragacanth in diverse cultural contexts. Drawing on case studies from 19th-century watercolours, ancient Egyptian coffins, and Maya murals, the paper demonstrates how these methods enable precise chemical characterization even in complex, aged, and mineral-rich matrices. Such information directly aids conservators in selecting compatible restoration materials, tailoring treatment protocols, and assessing deterioration mechanisms. Persistent challenges remain, including gum degradation, spectral interference from pigments and restoration materials, sample heterogeneity, and limited reference libraries, particularly for non-European species. Future research directions emphasize multi-modal, non-invasive workflows that integrate hyperspectral imaging with spectroscopic and chromatographic methods, drone-assisted micro-Raman for inaccessible surfaces, machine learning-assisted spectral databases, and bio-inspired adhesives replicating historical rheology. By linking molecular identification to conservation decision-making, plant gum analysis not only deepens our understanding of historical material practices but also strengthens the scientific basis for sustainable heritage preservation strategies. Full article

Background/Objectives: Glioblastomas (GBMs) and brain metastases (BMs) are both frequent brain lesions. Distinguishing between them is crucial for suitable therapeutic and follow-up decisions, but this distinction is difficult to achieve, as it includes clinical, radiological and histopathological correlation. However, non-invasive AI examination of conventional and advanced MRI techniques can overcome this issue. Methods: We retrospectively selected 78 patients with confirmed GBM (39) and single BM (39), with conventional MRI investigations, consisting of T2W FLAIR and CE T1W acquisitions. The MRI images (DICOM) were evaluated by an AI segmentation tool, comparatively evaluating tumor heterogeneity and peripheral edema. Results: We found that GBMs are less edematous than BMs (p = 0.04) but have more internal necrosis (p = 0.002). Of the BM primary cancer molecular subtypes, NSCCL showed the highest grade of edema (p = 0.01). Compared with the ellipsoidal method of volume calculation, the AI machine obtained greater values when measuring lesions of the occipital and temporal lobes (p = 0.01). Conclusions: Although extremely useful in radiomics analysis, automated segmentation applied alone could effectively differentiate GBM and BM on a conventional MRI, calculating the ratio between their variable components (solid, necrotic and peripheral edema). Other studies applied to a broader set of participants are necessary to further evaluate the efficacy of automated segmentation. Full article

Objective: The aim of this study was to evaluate the accuracy of machine and deep learning approaches on radiomics features obtained by Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) and contrast enhanced mammography (CEM) in the characterization of breast cancer and in the prediction of the tumor molecular profile. Methods: A total of 153 patients with malignant and benign lesions were analyzed and underwent MRI examinations. Considering the histological findings as the ground truth, three different types of findings were used in the analysis: (1) benign versus malignant lesions; (2) G1 + G2 vs. G3 classification; (3) the presence of human epidermal growth factor receptor 2 (HER2+ vs. HER2−). Radiomic features (n = 851) were extracted from manually segmented regions of interest using the PyRadiomics platform, following IBSI-compliant protocols. Highly correlated features were excluded, and the remaining features were standardized using z-score normalization. A feature selection process based on Elastic Net regularization (α = 0.5) was used to reduce dimensionality. Synthetic balancing of the training data was applied using the ROSE method to address class imbalance. Model performance was evaluated using repeated 10-fold cross-validation and AUC-based metrics. Results: Among the 153 patients enrolled in the studies, 113 were malignant lesions. Among the 113 malignant lesions, 32 had high grading (G3) and 66 had the HER2+ receptor. Radiomic features derived from both CEM and DCE-MRI showed strong discriminative performance for malignancy detection, with several features achieving AUCs above 0.80. Gradient Boosting Machine (GBM) achieved the highest accuracy (0.911) and AUC (0.907) in differentiating benign from malignant lesions. For tumor grading, the neural network model attained the best accuracy (0.848), while LASSO yielded the highest sensitivity (0.667) for detecting high-grade tumors. In predicting HER2+ status, the neural network also performed best (AUC = 0.669), with a sensitivity of 0.842. Conclusions: Radiomics-based machine learning models applied to multiparametric CEM and DCE-MRI images offer promising, non-invasive tools for breast cancer characterization. The models effectively distinguished benign from malignant lesions and showed potential in predicting histological grade and HER2 status. These results demonstrate that radiomic features extracted from CEM and DCE-MRI, when analyzed through machine and deep learning models, can support accurate breast cancer characterization. Such models may assist clinicians in early diagnosis, histological grading, and biomarker assessment, potentially enhancing personalized treatment planning and non-invasive decision-making in routine practice. Full article