Search Results (3,375)

Background: Head and neck squamous cell carcinoma (HNSCC) remains the seventh most common cancer worldwide, characterized by late-stage diagnosis and poor 5-year survival rates. Oral squamous cell carcinoma (OSCC) is the most prevalent subtype. The identification of robust diagnostic, prognostic, and predictive markers is essential for personalized treatment monitoring. Methods: Following PRISMA and PICO standards, we conducted a comprehensive review of studies published over the past 10 years across PubMed/MEDLINE, Scopus, and Web of Science. The selection process was facilitated by AI-powered tools (Rayyan QCRI), and study quality was assessed using NOS or QUIPS. Results: 34 articles (including meta-analyses and original trials) were identified. Established clinical markers, such as p16-positivity (HR ≈ 0.55) and PD-L1 (CPS), remain significant. However, the molecular landscape is expanding to include high-risk lncRNA signatures (HR ≈ 2.50), immune checkpoints such as TIGIT (HR ≈ 1.85), and genomic alterations, including IL-10 promoter polymorphisms. We highlight that epigenetic silencing of p16 affects only about 25% of patients, while metabolic regulators (e.g., GLUT-1) and protein markers (e.g., MASPIN) offer critical predictive value for therapy response. Conclusions: The diagnostic and predictive paradigm is shifting toward a multi-omic approach that integrates DNA, RNA, proteins, and metabolic indicators. Future clinical use will rely on AI-driven multimarker panels and non-invasive liquid biopsies to enable real-time monitoring and de-escalation of treatment strategies. Full article

Transcranial magnetic stimulation (TMS) is a non-invasive neuromodulation technique that utilizes magnetic fields to induce cortical electric currents, enabling both the measurement and modulation of neuronal activity. Initially developed as a diagnostic tool, TMS now serves dual roles in clinical neurology, offering insight into neurophysiological dysfunctions and the therapeutic modulation of abnormal cortical excitability. This review examines key TMS outcome measures, including motor thresholds (MT), input–output (I/O) curves, cortical silent periods (CSP), and paired-pulse paradigms such as short-interval intracortical inhibition (SICI), short-interval intracortical facilitation (SICF), intracortical facilitation (ICF), long interval cortical inhibition (LICI), interhemispheric inhibition (IHI), and short-latency afferent inhibition (SAI). These biomarkers reflect underlying neurotransmitter systems and can aid in differentiating neurological conditions. Diagnostic applications of TMS are explored in Parkinson’s disease (PD), dystonia, essential tremor (ET), Alzheimer’s disease (AD), and mild cognitive impairment (MCI). Each condition displays characteristic neurophysiological profiles, highlighting the potential for TMS-derived biomarkers in early or differential diagnosis. Therapeutically, repetitive TMS (rTMS) has shown promise in modulating cortical circuits and improving motor and cognitive symptoms. High- and low-frequency stimulation protocols have demonstrated efficacy in PD, dystonia, ET, AD, and MCI, targeting the specific cortical regions implicated in each disorder. Moreover, the successful application of TMS in differentiating and treating AD and MCI underscores its clinical utility and translational potential across all neurodegenerative conditions. As research advances, increased attention and investment in TMS could facilitate similar diagnostic and therapeutic breakthroughs for other neurological disorders that currently lack robust tools for early detection and effective intervention. Moreover, this review also aims to underscore the importance of maintaining standardized TMS protocols. By highlighting inconsistencies and variability in outcomes across studies, we emphasize that careful methodological design is critical for ensuring the reproducibility, comparability, and reliable interpretation of TMS findings. In summary, this review emphasizes the value of TMS as a distinctive, non-invasive approach to probing brain function and highlights its considerable promise as both a diagnostic and therapeutic modality in neurology—roles that are often considered separately. Full article

Pregnancies complicated by diabetes, including pregestational and gestational diabetes mellitus, are associated with increased maternal and fetal morbidity. Early identification of at-risk pregnancies is crucial for timely intervention and improved outcomes. Emerging evidence highlights the interplay of genetic predisposition, epigenetic modifications, and non-invasive biomarkers in the early detection of diabetic pregnancies. Genetic factors influencing insulin signaling, glucose metabolism, and pancreatic β-cell function may contribute to susceptibility to gestational hyperglycemia. Concurrently, epigenetic alterations, such as DNA methylation and histone modifications in maternal and placental tissues, have been linked to dysregulated metabolic pathways and adverse pregnancy outcomes. Non-invasive biomarkers, including circulating cell-free DNA and microRNAs in maternal blood, show promise for early diagnosis by offering a safer and more practical alternative to invasive testing. Integrating genetic, epigenetic, and molecular marker data could enhance risk stratification and enable personalized monitoring and management strategies. This review synthesizes current knowledge on the molecular underpinnings of diabetic pregnancies, evaluates the potential of emerging biomarkers for early diagnosis, and discusses the challenges and future perspectives for translating these findings into clinical practice. Understanding these mechanisms may pave the way for precision medicine approaches, ultimately improving maternal and neonatal outcomes in pregnancies affected by diabetes. Full article

Background: Clear cell renal cell carcinoma (ccRCC) is the most prevalent kidney cancer subtype and, in most cases, it is incidentally diagnosed, as early-stage disease is often asymptomatic. Therefore, the identification of stable, noninvasive biomarkers is a major unmet clinical need. Urinary microRNAs (miRNAs) have emerged as promising candidates since they are extraordinarily stable in urine and show a close relationship with tumour biology. Methods: In this study, urinary expression levels of five miRNAs (miR-15a, miR-15b, miR-16, miR-210, and miR-let-7b) were analysed in RCC patients before surgery, 5 days after, and one month after surgery, and compared to healthy controls. Results: Non-parametric analyses revealed significant postoperative decreases for miR-15a (p = 0.002), miR-16 (p = 0.025), miR-210 (p = 0.030), and in the overall miRNA Sum (p = 0.002), suggesting that these miRNAs are directly linked to tumour presence. In the comparison between preoperative and one-month postoperative samples, miR-let-7b (p = 0.049) and the global miRNA Sum (p = 0.037) remained significantly reduced after intervention, indicating a partial normalisation of urinary miRNA profiles. Correlation analyses demonstrated positive associations between specific miRNAs and clinical parameters such as age, ischemia time, and surgical time, reinforcing their potential relevance to tumour biology and treatment response. Conclusions: These findings support urinary miRNAs as promising, minimally invasive biomarkers for ccRCC diagnosis and postoperative monitoring. Full article

Objective: Alzheimer’s disease (AD) and other forms of dementia are a heterogeneous group of neurodegenerative diseases characterized by progressive cognitive decline. Differential diagnosis between AD and other dementias is crucial for choosing the optimal treatment strategy. Currently, cerebrospinal fluid (CSF) analysis remains the most accurate diagnostic method, but its invasiveness limits its use. In this regard, the search for reliable biomarkers in the blood is an urgent task. Methods: The study included 31 dementia patients (23 women and 8 men) diagnosed via interdisciplinary consultations and neuropsychological testing (MMSE ≤ 24). CSF and blood plasma samples were collected and analyzed using Luminex technology. Biomarker concentrations were measured, and statistical analyses (ANOVA, Kruskal–Wallis, and Pearson correlation) were performed to compare groups and assess correlations. Results: Levels of Aβ40 and Aβ42 in CSF were significantly lower in patients with AD compared with non-AD dementia (p = 0.02 and p < 0.001, respectively). The Aβ42/40 ratio in CSF was higher in patients with non-AD dementia (p = 0.048). The concentration of Aβ42 in blood plasma was increased in patients with AD (p = 0.001). Positive correlations were found between Aβ42 in CSF and TDP-43 in plasma in non-AD dementia (r = 0.97, p < 0.001), as well as between neurogranin and TDP-43 in plasma in AD (r = 0.845, p < 0.001). Conclusions: The study demonstrates the potential of blood biomarkers, in particular Aβ42, for the differential diagnosis of AD and other forms of dementia. The discovered correlations between CSF and plasma biomarkers deepen the understanding of neurodegenerative processes and contribute to the development of noninvasive diagnostic methods. Full article

Micro-RNAs (miRNAs) are short, non-coding RNA molecules regulating genes’ expression. Studies published over last years demonstrated that they play an important role in allergic diseases by regulating humoral and cellular immunity, cytokine secretion and epithelium function. Some of them seem potential non-invasive biomarkers facilitating diagnosis of the most common allergic diseases, such as allergic rhinitis (miR-21, miR-126, miR-142-3p, miR-181a, miR-221), asthma (miR-16, miR-21, miR-126, miR-146a, miR-148a, miR-221, miR-223) and atopic dermatitis (miR-24, miR-124, miR-155, miR-191, miR-223, miR-483-5p), or objectively assessing severity of inflammation and endotype of the disease. In spite of the large body of literature available, its scientific value is limited due to the small numbers of study participants, heterogeneity of populations enrolled, and diverse methodology. Some studies have revealed significant changes in miRNAs’ profile in the course of allergen immunotherapy. Tolerance induction is associated with processes controlled by miRNAs: enhanced activity of Treg cells and increased production of tolerogenic IL-10 and TGF-β. Thus, miRNAs may be candidates as biomarkers of successful immunotherapy. Finally, they are also possible therapeutic agents or targets of therapies based on antagomirs blocking their activity. However, so far no studies are available that demonstrate efficacy in overcoming delivery barriers, tissue targeting or drugs’ safety. As a consequence, despite promising results of in vitro and animal model studies, translation into human therapeutic agents is uncertain. Full article

Prostate cancer remains the most frequently diagnosed malignancy in men and a leading cause of cancer-related mortality. Multiparametric MRI (mpMRI) has become the gold standard for non-invasive diagnosis, staging, and follow-up. Yet, its widespread adoption is hampered by long acquisition times, inter-reader variability, and interpretative complexity. Though most papers focus on specific applications without offering a cohesive therapeutic perspective, artificial intelligence (AI) has recently attracted attention as a potential solution to these shortcomings. For instance, deep learning models can help optimize imaging protocols for biparametric and multiparametric MRI, and AI-based reconstruction techniques have shown promise for reducing acquisition times without sacrificing diagnostic performance. Several systems have produced outcomes in the diagnostic phase that are comparable to those of skilled radiologists, as demonstrated in multicenter settings such as PI-CAI. Radiomics and radiogenomics provide more detailed insights into the biology of the disease by extracting quantitative features associated with tumor aggressiveness, extracapsular expansion, and treatment response, in addition to detection. Despite these developments, methodological variability, a lack of multicenter validation, proprietary algorithms, and unresolved standardization and governance difficulties continue to restrict clinical translation. Our work emphasizes the maturity of existing technologies, ongoing gaps, and the progressive integration necessary for successful clinical adoption by presenting AI applications aligned with the patient pathway. In this context, this review aims to outline how AI can support the entire patient journey—from acquisition and protocol selection to detection, quantitative analysis, treatment assessment, and follow-up—while maintaining a clinically centered perspective that emphasizes practical relevance over theoretical discussion, potentially enabling more reliable, effective, and customized patient care in the field of prostate cancer. Full article

Endometrial lesions are common in gynecology, exhibiting considerable clinical heterogeneity across different subtypes. Although ultrasound imaging is the preferred diagnostic modality due to its noninvasive, accessible, and cost-effective nature, its diagnostic performance remains highly operator-dependent, leading to subjectivity and inconsistent results. To address these limitations, this study proposes a hierarchical feature selection and optimization framework for endometrial lesions, aiming to enhance the objectivity and robustness of ultrasound-based diagnosis. Firstly, Kernel Principal Component Analysis (KPCA) is employed for nonlinear dimensionality reduction, retaining the top 1000 principal components. Secondly, an ensemble of three filter-based methods—information gain, chi-square test, and symmetrical uncertainty—is integrated to rank and fuse features, followed by thresholding with Maximum Scatter Difference Linear Discriminant Analysis (MSDLDA) for preliminary feature selection. Finally, the Whale Migration Algorithm (WMA) is applied to population-based feature optimization and classifier training under the constraints of a Support Vector Machine (SVM) and a macro-averaged F1 score. Experimental results demonstrate that the proposed closed-loop pipeline of “kernel reduction—filter fusion—threshold pruning—intelligent optimization—robust classification” effectively balances nonlinear structure preservation, feature redundancy control, and model generalization, providing an interpretable, reproducible, and efficient solution for intelligent diagnosis in small- to medium-scale medical imaging datasets. Full article

Background: Accurate assessment of prolonged disorders of consciousness (pDOC) is a critical clinical challenge. Misdiagnosis in pDOC can occur in up to 40% of cases, highlighting the need for more objective and reproducible biomarkers to support neurophysiological scales, thereby improving diagnosis and guiding therapeutic and prognostic decisions. Electroencephalography (EEG) microstate analysis is a promising, non-invasive method for tracking large-scale brain dynamics, but research in pDOC has predominantly relied on a canonical 4-class model. This methodological constraint may limit the ability to capture the full complexity of neural alterations present in these patients. Objective: This pilot study aimed to offer an objective method for assessing consciousness, complementing and enhancing the existing approaches established in the literature. The classical 4-class and an extended 7-class microstate model were compared to determine which more accurately characterizes the complexity of resting-state brain dynamics across different levels of consciousness in pDOC patients and healthy controls (HCs). Methods: Retrospective resting-state EEG (rsEEG) data from a cohort of pDOC patients and HC subjects were analyzed. Microstate analysis was performed using both 4-class and 7-class templates. The models were evaluated and compared based on three criteria: spatial correspondence with canonical maps (shared variance), the number of significant intra-group correlations between temporal features (Spearman test), and their ability to discriminate between the pDOC and HC groups (Wilcoxon test). Results: The 7-class microstate model provided a more accurate description of brain activity for most participants, with a greater number of microstate classes exceeding the 50% shared variance threshold compared to the 4-class model. In the pDOC group, both the 4-class and 7-class models showed a mean shared variance <50% in class D, which is associated with executive functioning across both templates. For the HC group, a prevalence of classes B and D emerged in both models, indicating higher engagement of executive functions. Furthermore, the 7-class model allowed for a group-specific analysis, which demonstrated that microstates A and F were consistently shared among 86% of pDOC patients. This suggests the potential preservation of specific intrinsic brain networks, particularly the sensory and default networks, even in the presence of severely impaired consciousness. Moreover, the 7-class model yielded a higher number of significant correlations within both groups and identified a broader set of temporal features that were significantly different between pDOC patients and HCs. These results highlight the enhanced sensitivity of the 7-class model in distinguishing subtle brain dynamics and improving the diagnostic capability for pDOC. Conclusions: The 7-class microstate model provides a more fine-grained and sensitive characterization of brain activity in both pDOC patients and healthy individuals. It demonstrated better performance in capturing individual brain dynamics, identifying shared network patterns, and discriminating between clinical populations. These findings suggest that the extended 7-class model holds greater potential for clinical utility and could lead to the development of more robust biomarkers for assessing consciousness. Full article

Amyloidosis is a group of disorders caused by extracellular deposition of insoluble fibrillar proteins, leading to progressive organ dysfunction. Cardiac amyloidosis is clinically significant, as myocardial infiltration results in restrictive cardiomyopathy, arrhythmias, and heart failure. The main subtypes are light-chain (AL) and transthyretin (ATTR) amyloidosis, while AA and isolated atrial amyloidosis (IAA) are less common. Accurate subtype identification is crucial for management and prognosis. Diagnosis requires a multimodal approach combining imaging and tissue-based techniques. Echocardiography is usually first-line, showing increased wall thickness, biatrial enlargement, and apical sparing. Cardiac magnetic resonance (CMR) provides superior tissue characterization through late gadolinium enhancement and elevated extracellular volume. Nuclear scintigraphy with 99mTc-labeled tracers enables non-invasive ATTR detection, while amyloid-specific PET tracers show potential for early diagnosis. Histochemical confirmation remains essential. Congo Red staining with apple-green birefringence under polarized light is the diagnostic gold standard, supported by Thioflavin T, PAS, and Alcian Blue stains. Immunohistochemistry and mass spectrometry aid amyloid typing, while electron microscopy provides ultrastructural confirmation. Integrating imaging, histochemical, immunohistochemical, and proteomic techniques enhances early recognition and precise classification, improving therapeutic strategies and patient outcomes. Full article

Background/Objectives: Artificial intelligence (AI) is an emerging technology for diagnosing liver fibrosis in Metabolic-Dysfunction-Associated Steatotic Liver Disease (MASLD), but a comprehensive synthesis of its performance is lacking. This systematic review (SR) aimed to evaluate the current evidence of AI models for diagnosing or staging liver fibrosis in patients with MASLD compared to conventional diagnostic tools. Methods: A comprehensive search was conducted in PubMed, Scopus, Web of Science, ScienceDirect, Embase, LILACS, IEEE Series, and Association for Computing Machinery (ACM). Primary studies applying AI to diagnose fibrosis in adults with MASLD were included. Risk of bias was assessed using the QUADAS-2 tool, and methodological reporting was evaluated according to the MINimum Information for Medical AI Reporting (MINIMAR) guideline. A narrative synthesis was performed, grouping studies by data type (clinical/laboratory vs. imaging) and summarizing diagnostic performance and clinical application. A frequency-based analysis was applied to identify the most recurrent predictive features, and an analysis of the AI architecture and application was reported. The review was registered in PROSPERO (CRD420251035919). Results: Twenty-one studies were included, encompassing 19,221 patients and 5237 images. Across studies, AI models consistently outperformed non-invasive scores such as Fibrosis-4 Index (FIB-4) and NAFLD Fibrosis Score (NFS). The most frequent predictive variables were identified. Despite an overall low risk of bias, methodological transparency and external validation were limited. Conclusions: AI is feasible for the non-invasive diagnosis of liver fibrosis in MASLD, demonstrating superior accuracy to standard clinical scores. Broader clinical application is limited by the lack of external validation and high heterogeneity among the studies. Prospective validation in diverse, multicenter cohorts is essential before AI can be integrated into routine clinical practice. Full article

Objectives: This study aimed to evaluate the diagnostic value of presepsin in differentiating benign and malignant causes of cervical lymphadenopathy and to compare its performance with conventional inflammatory markers, including C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), and neutrophil-to-lymphocyte ratio (NLR). Methods: A total of 76 individuals were enrolled, including 52 patients who underwent excisional biopsy for cervical lymphadenopathy and 24 healthy controls. Serum presepsin, CRP, ESR, and complete blood count parameters were measured preoperatively. Patients were classified according to histopathological diagnosis as reactive, granulomatous, or malignant lymphadenopathy. Correlation and receiver operating characteristic (ROC) analyses were performed to assess the diagnostic performance of biomarkers. Results: Median presepsin, CRP, ESR, NLR, and monocyte-to-lymphocyte ratio (MLR) levels were significantly higher in the patient group compared with controls (all p < 0.001). Presepsin levels correlated positively with CRP (r = 0.42), ESR (r = 0.38), and NLR (r = 0.36). Although subgroup analysis revealed no statistically significant differences in presepsin levels among reactive, granulomatous, and malignant cases (p = 0.50), ROC analysis demonstrated the highest diagnostic accuracy for presepsin (AUC = 0.85), followed by CRP (AUC = 0.78), ESR (AUC = 0.74), and NLR (AUC = 0.72). A presepsin threshold of >210 pg/mL predicted malignancy with 82.4% sensitivity and 78.6% specificity. Conclusions: Presepsin provides an objective and noninvasive tool that complements traditional inflammatory markers in the diagnostic evaluation of cervical lymphadenopathy. Its superior diagnostic performance for malignancy prediction suggests potential utility in guiding biopsy decisions and avoiding unnecessary surgical procedures in benign cases. Full article

Background/Objectives: Radiomics, the high-throughput extraction of quantitative features from medical imaging, offers a promising method for identifying laryngeal cancer imaging biomarkers. We aim to systematically review the literature on radiomics in laryngeal squamous cell carcinoma, assessing applications in tumour staging, prognosis, recurrence prediction, and treatment response evaluation. PROSPERO ID: CRD420251117983. Methods: MEDLINE and EMBASE databases were searched in May 2025. Inclusion criteria: studies published between 1 January 2010 and 31 January 2024, extracted radiomic features from CT, PET/CT, or MRI, and analysed outcomes related to diagnosis, staging, survival, recurrence, or treatment response in laryngeal cancer. Exclusion criteria: case reports, abstracts, editorials, reviews, or conference proceedings, exclusive focus on preclinical or animal models, lack of a clear radiomics methodology, or did not include imaging-based feature extraction. Results were synthesised narratively by modelling objective, alongside formal assessment of methodological quality using the Radiomics Quality Score (RQS). Results: Twenty studies met the inclusion criteria, with most using CT-based radiomics. Seven incorporated PET/CT. Radiomic models demonstrated moderate-to-high accuracy across tasks including T-staging, thyroid cartilage invasion, survival prediction, and local failure. Key predictive features included first-order entropy, skewness, and texture metrics such as size zone non-uniformity and GLCM correlation. Methodological variability, limited external validation, and small samples were frequent limitations. Conclusions: Radiomics holds strong promise as a non-invasive biomarker for laryngeal cancer. However, methodological heterogeneity identified through formal quality assessment indicates that improved standardisation, reproducibility, and multicentre validation are required before widespread clinical implementation. Full article

Background and Clinical Significance: Cutaneous Rosai-Dorfman disease (CRDD) is a rare, benign histiocytic proliferative disorder, accounting for approximately 3% of all Rosai-Dorfman disease (RDD) cases. Currently, the diagnosis of CRDD relies on invasive pathological examination due to the absence of reliable non-invasive alternatives. This case series evaluates the potential utility of high-frequency ultrasound (HFUS) as an adjunctive diagnostic tool for CRDD. Case Presentation: We present three CRDD cases, correlating HFUS features with histopathology. All cases showed hypoechoic lesions with varying infiltration depths and morphologies, though no specific diagnostic features were identified. HFUS clearly delineated involvement of the dermal and subcutaneous layers, assessed morphological characteristics like contour regularity and border definition, and evaluated vascularity. This information is crucial for clinical decision-making. HFUS also demonstrated value in therapeutic follow-up. In Case 1, it objectively showed a reduction in lesion size and decreased internal vascularity, providing clear evidence of treatment response. Conclusions: Although HFUS cannot independently diagnose CRDD and histopathology remains the gold standard, it serves as a valuable complementary tool. HFUS allows evaluation of deeper tissue structures, infiltration depth, and vascularity. As a non-invasive modality, it is useful for treatment monitoring, therapy guidance, and prognosis assessment. Integrating HFUS into the CRDD workflow enables more comprehensive and precise management. Full article

Background/Objectives: Periodontal inflammation is a key feature of periodontal diseases, but traditional diagnostic methods are limited by invasiveness and radiation exposure. This systematic review aims to evaluate the potential of thermography and infrared spectroscopy for the in vivo detection of periodontal inflammation and to assess their reliability for clinical use. Methods: In accordance with PRISMA guidelines, an electronic search of the MEDLINE (PubMed) database was conducted to identify relevant studies published between 2000 and October 2025 that investigated these imaging modalities in periodontal inflammation diagnosis. Results: The search identified 310 records; after exclusions, 13 studies were included, comprising 7 thermography studies and 6 infrared spectroscopy studies, for a total of 712 patients. The included studies demonstrated the feasibility of thermography and infrared spectroscopy for detecting inflammatory changes in periodontal tissues in vivo. These non-invasive imaging techniques may help overcome the limitations of conventional clinical and radiographic diagnostic methods, particularly invasiveness and exposure to ionizing radiation. Conclusions: This field remains underexplored, and further studies are required to validate diagnostic performance, standardize methodologies, and determine their clinical applicability in routine periodontal practice. Full article