Search Results (2,832)

Background and Objectives: Peripheral neuropathy (PNP) is a frequent and debilitating complication among patients with diabetes mellitus (DM) and other metabolic conditions, substantially affecting morbidity, functional status, and quality of life. Identifying predictors of PNP is essential for optimizing early diagnostic strategies and improving long-term management outcomes. The aim of this study was to determine the predictive factors of PNP in a cohort of patients with DM. Materials and Methods: A cross-sectional study including 117 patients diagnosed with DM assessed for PNP was conducted. All patients were evaluated clinically and biologically. PNP was clinically assessed using the Toronto Clinical Scoring System (TCSS) score and sudomotor function by Sudoscan. Results: The patients included were mostly males with type 2 DM and metabolic syndrome phenotypes. Moreover, the patients with PNP were much older than those without PNP (65 [57–69] vs. 59.50 [46–68] years, p = 0.008), with a longer duration of DM (10 [6–15.50] vs. 5.5 (2–14] years, p = 0.019), and associated autonomic diabetic neuropathy (χ² = 24.382, p < 0.001). Furthermore, TCSS and Sudoscan were correlated with a history of PNP, especially Sudoscan, which showed a very good discriminative ability for diabetic neuropathy diagnosis (AUC = 0.816). In a multivariable logistic regression including age, DM duration, and HbA1c, age was independently associated with PNP, with each additional year increasing the odds of neuropathy by approximately 6% (OR = 1.06, 95% CI 0.02–1.09, p = 0.002). When age was excluded, DM duration showed a borderline association with PNP (OR = 1.055, CI95% 0.997–1.117), suggesting potential overlap between these variables. Adding sudomotor assessment to the initial model improved the model performance (AUC 0.70–0.72). Conclusions: Age emerged as the main independent predictor of diabetic neuropathy, highlighting the role of cumulative metabolic exposure in the development of neural damage. Moreover, sudomotor assessment may have a complementary role in PNP assessment. Full article

With the increasing integration of computer technologies into education, accurately modeling students’ knowledge mastery has become a central problem in intelligent education systems. However, existing cognitive diagnosis models often suffer from sparsity in the knowledge–item association matrix (Q-matrix) and limited model capacity, which restrict their ability to capture complex student–item interaction patterns. Collaborative filtering–based approaches further exhibit insufficient capability in modeling fine-grained cognitive relationships, leading to reduced diagnostic accuracy. To address these limitations, this study proposes a cognitive diagnosis model enhanced by an augmented knowledge association matrix, termed CAG-NCD. The proposed model refines the Q-matrix to improve the expressiveness of item–knowledge correspondences and employs nonlinear interaction functions to capture relational features in students’ response processes. Specifically, convolutional neural networks are used to extract local semantic patterns from student–item interactions, while graph convolutional networks model the global structural dependencies among knowledge points. By jointly integrating semantic and structural information, the model effectively captures complex dependency relationships. Experimental results show that CAG-NCD achieves performance improvements of 3.7% on the FrcSub dataset and 4.5% on the Math dataset, significantly reducing prediction errors and enhancing the interpretability and accuracy of cognitive diagnosis across multiple datasets. Full article

Background: Oral squamous cell carcinoma (OSCC) remains a major global health burden due to delayed diagnosis. Although salivary biomarkers have been explored in previous meta-analyses, these studies were limited to specific biomarker types. Methods: This study followed PRISMA guidelines and was registered in PROSPERO (CRD 420261296936). PubMed, Scopus, MEDLINE, and CINAHL were searched for diagnostic accuracy studies of salivary biomarkers for OSCC. Studies providing sufficient data to construct 2 × 2 tables were included. Pooled sensitivity, specificity, DOR, and HSROC curves were estimated using a bivariate random-effects model, and study quality was assessed using QUADAS-2. Results: Eighteen studies comprising 1647 participants yielded 45 diagnostic datasets across multiple biomarker classes. The pooled sensitivity and specificity were 0.64 (95% CI: 0.59–0.69) and 0.71 (95% CI: 0.66–0.76), respectively. The pooled DOR was 4.53 (95% CI: 3.18–6.47), indicating moderate discriminatory ability, with an AUC of 0.75 (95% CI: 0.71–0.79). Fagan’s nomogram analysis demonstrated that these biomarkers are not suitable for screening the general population and should be reserved for enriched populations (pre-test probability > 10%). Conclusions: Salivary biomarkers demonstrate moderate but highly heterogeneous diagnostic accuracy. Clinical utility is context-dependent and limited to enriched populations with a baseline probability of OSCC >10%. In screening the general population (prevalence < 0.01%), these tests offer no significant clinical utility. They should be considered complementary triage tools rather than definitive diagnostic modalities. Full article

To address the problem of fault diagnosis for rotating machinery under complex operating conditions in real industrial systems, most existing domain generalization methods fail to sufficiently consider inter-class feature structures when learning domain-invariant representations. This limitation often leads to degraded diagnostic performance in cross-domain scenarios, particularly under class imbalance or significant operating condition variations. Moreover, existing feature extraction networks specifically designed for rotating machinery are often inadequate for fault diagnosis tasks under variable operating conditions. To overcome these challenges, this paper proposes a domain-invariant fault feature representation learning framework for multi-source domain generalization. Specifically, we design a mechanism-aware multi-branch feature extraction network inspired by excitation–modulation mechanisms of fault generation, which captures fault-sensitive characteristics from both time-domain and frequency-domain perspectives. In addition, a class-conditional feature alignment strategy based on ICM (Independent Causal Mechanism) mixing is introduced to enhance cross-domain consistency. Through feature structure regularization, discriminative information across categories is effectively preserved under domain shifts. Extensive experimental results demonstrate that the proposed method significantly improves diagnostic performance and generalization ability on the CWRU bearing dataset as well as the HUST bearing and gearbox datasets. Notably, when the number of source domains increases, the proposed framework exhibits superior training efficiency. Full article

Background: Frontal lobe involvement represents an important but heterogeneously expressed feature across neurodegenerative and vascular cognitive disorders. While frontal atrophy has been described in Alzheimer’s disease (AD), detailed volumetric assessment of frontal subregions across Alzheimer’s disease, amnestic mild cognitive impairment (aMCI), and vascular dementia (VaD) remains insufficiently characterized. The aim of this study was to evaluate frontal lobe and frontal subregional volumetric alterations across these diagnostic groups using automated MRI-based volumetry. Methods: This cross-sectional study included 120 participants divided into four groups: AD, VaD, aMCI, and cognitively healthy controls (n = 30 per group). All participants underwent standardized neuropsychological assessment and 3T brain MRI. Automated volumetric analysis of the frontal lobe and its subregions was performed using the Vol2Brain pipeline. Group differences in total intracranial volume–adjusted frontal volumes were assessed using analysis of covariance, controlling for age and sex, followed by Bonferroni-corrected post hoc comparisons. False discovery rate (FDR) correction was applied across subregional comparisons. Results: A significant main effect of diagnostic group was observed for total frontal lobe volume, with lower adjusted volumes in patients with AD compared with aMCI and cognitively healthy controls. After correction for multiple comparisons, only total frontal lobe volume remained statistically significant. At the nominal level, group differences were observed in several frontal subregions, predominantly involving prefrontal and orbitofrontal areas. However, these findings did not survive FDR correction and should be interpreted as exploratory. No consistent frontal volumetric pattern was observed in VaD. Receiver operating characteristic analysis demonstrated moderate discriminatory ability of total frontal lobe volume for distinguishing AD from cognitively healthy controls. Conclusions: Automated MRI-based volumetry revealed global frontal lobe reduction in Alzheimer’s disease, whereas subregional findings were exploratory after correction for multiple testing. Frontal volumetric measures did not demonstrate a characteristic pattern in VaD. Global frontal volume may provide complementary structural information within clinically define cognitive disorders. Full article

Background/Objective: Major depressive disorder (MDD) is a common mental illness that lacks objective diagnostic biomarkers and has a complicated pathogenesis. Peripheral blood-based gene expression profiling provides a potential, non-invasive method to identify the molecular markers associated with risk stratification and depression severity.This study aimed to investigate the gene expression of five candidate genes—CD19, MTPAP, PER3, GAR1, and SLC25A26—in the peripheral blood of drug-naïve patients with MDD and to evaluate their potential as diagnostic biomarkers. Methods: Peripheral blood samples were collected from 100 newly diagnosed, drug-naïve MDD patients and 100 age- and sex-matched healthy controls. The total RNA was extracted and reverse-transcribed for quantitative real-time PCR analysis. Fold change and ∆∆Ct values were calculated for each gene, followed by statistical analysis using t-tests and ANOVA. Receiver operating characteristic (ROC) curves were used to evaluate diagnostic performance. Gene co-expression and pathway enrichment analyses were conducted to explore functional relevance. Results: Significant upregulation was observed for CD19 (fold change = 6.15, p = 6.17 × 10⁻¹⁰), MTPAP (fold change = 3.99, p = 1.74 × 10⁻⁸), and PER3 (fold change = 2.42, p = 5.33 × 10⁻⁶) in MDD. GAR1 showed modest upregulation (fold change = 1.26, p = 0.47), while SLC25A26 (fold change = 1.24, p = 0.47) was not significantly altered. Combined ROC analysis yielded an AUC of 0.885, indicating a strong discriminative ability. Gene expression correlated with depression severity (HAM-D and PHQ-9). Conclusions: This study identifies CD19, MTPAP, and PER3 as promising peripheral blood biomarkers for MDD, with potential implications for early diagnosis, severity assessment, and personalized treatment strategies. Full article

Background: Malignant pleural effusion (MPE) represents a frequent and clinically challenging manifestation of advanced malignancy, particularly in metastatic non-small cell lung cancer (NSCLC). Its management requires integration of diagnostic imaging, symptom-directed therapeutic strategies, and, increasingly, molecular profiling technologies. Recent advancements in this field based on liquid medium (so-called liquid biopsy) have achieved a significant increase in sensitivity, enhancing our ability to investigate biofluids and suggesting their potential integration into standard diagnostic practices, far beyond the canonical plasma biopsies. Fluid obtained from MPE after cytological sample centrifugation is rich in cell-free DNA and less susceptible to nucleic acid degradation during processing, improving overall diagnostic accuracy. Methods: This narrative review summarizes current evidence on the clinical management of malignant pleural effusions in patients with metastatic NSCLC, integrating imaging, procedural management, and molecular profiling from a multidisciplinary tumor board perspective. The primary objective was to synthesize contemporary knowledge with particular attention to the feasibility, reliability, and reproducibility of pleural fluid-based molecular testing. Results: MPE poses diagnostic and therapeutic challenges for all members of the multidisciplinary tumor board, traditionally associated with an adverse prognosis. However, recent advances in cytopathology, histopathology, and liquid-based techniques demonstrate that MPE could be an important source of prognostic or predictive information. At the same time, optimal patient management requires careful integration of imaging findings and procedural strategies (such as pleurodesis or indwelling pleural catheters) with individualized systemic therapy selection. Cell-free DNA in pleural effusions is a promising field of exploration and study, potentially suitable for future guideline implementation, after validation in adequately powered studies, contributing to improving patient management, particularly useful in fragile subsets. Conclusions: The management of MPE in advanced NSCLC is evolving toward a multidisciplinary, precision-oriented model that integrates clinical evaluation, imaging, procedural interventions, and molecular testing. Liquid biopsy technology has gained enough analytical robustness and clinical feasibility to be a useful tool in routine analysis. Biofluid-based molecular testing may have outstanding potential, contributing to improving patient management, avoiding repetitive procedures, and optimizing the overall efficiency and cost-effectiveness of diagnostic practices. Moreover, collaborative projects among different specialties help in consolidating trust in the tumor board decision-making process. Full article

This paper presents an integrated multi-modal framework for detecting hyperthyroidism-associated abnormalities, namely exophthalmos and thyroid-related neck swelling, through the joint analysis of frontal facial and neck images using a deep learning-based approach. The objective of this research is to develop an integrated AI framework that improves hyperthyroid-related abnormality detection by simultaneously analyzing facial images of both the eye and neck based on pattern clinical knowledge. The multi-modal framework mimics a biological visual mechanism by using a dual-pathway architecture that concurrently processes foveal-like details of the eyes and neck. It integrates these high-resolution visual embeddings with quantitative morphological measurements to simulate a clinician’s ability to fuse observation with physical assessment. The proposed system employs a multi-faceted decision-making process derived from three distinct data components: two from frontal face analysis and one from neck region analysis. Specifically, eye regions extracted from facial images are preprocessed using the YOLOv11s model. The proposed system leverages a dual-pathway processing architecture to extract comprehensive diagnostic features. For the eye dataset, the framework utilizes a face mesh-based eye landmark (FMEL) to extract both eye regions and perform eyes unfold processing. These regions are subsequently analyzed by the proposed sclera map unwrapping engine (SMUE) to derive quantitative sclera metrics from both the left and right eyes. To optimize classification, a dual-branch architecture is employed by integrating CNN visual embeddings with SMUE-derived statistical features through a feature fusion layer. Simultaneously, the neck processing path executes the neck region of interest (ROI) prediction {upper, lower} to segment critical regions for goiter assessment via the proposed neck $\mu -\sigma$ ensemble thresholding (NSET) algorithm. The experimental results demonstrate that the proposed algorithm for eye analysis achieved a mean average precision (mAP50) of 96.4%, with a specific mAP50 of 98.6% for the hyperthyroid class. Regarding quantitative scleral measurement, the SMUE process revealed distinct morphological differences, with the experimental data group exhibiting consistently higher pixel distances across the reference points compared with the normal group. Furthermore, the proposed NSET algorithm yielded the highest performance for swollen neck classification with an mAP50 of 92.0%, significantly outperforming the baseline deep learning models while maintaining lower computational complexity. Full article

Sand production in an offshore hydrocarbon wells poses significant operational and integrity challenges, particularly in deviated wells, where complex flow geometries intensify particle transport and erosion risks. The traditional sand-monitoring method utilizes stationary acoustic sensors attached to the production flowline at the surface. However, these sensors provide limited spatial coverage and intermittent measurements, restricting their ability to detect early sanding onset or precisely localize sanding intervals. By combining with vertical seismic profiling (VSP), Distributed Acoustic Sensing (DAS) delivers continuous, high-density data along the entire length of the wellbore and is increasingly recognized as a powerful diagnostic tool for real-time downhole monitoring. This study presents a field application of DAS-VSP for detecting and characterizing sand transport in a deviated offshore production well equipped with 350 distributed fiber-optic channels spanning 0–1983 m true vertical depth (TVD) at 8 m spacing. A multistage workflow was developed, including SEGY ingestion and shot merging, channel and time window selection, trace normalization, and low-pass filtering below 20 Hz. Multi-domain signal analysis, such as RMS energy, spike-based time-domain attributes, FFT, PSD spectral characterization, and time–frequency decomposition, were used to isolate the characteristic im-pulsive low-frequency (<20 Hz) signatures associated with sand impact. An adaptive thresholding and event-clustering scheme was then applied to discriminate sanding bursts from background noise and integrate their acoustic energy over depth. The processed DAS section revealed distinct, depth-localized sand ingress zones within the production interval (1136–1909 m TVD). The derived sand log provided a quantitative measure of sand intensity variations along the deviated wellbore, with normalized RMS amplitudes ranging from 0.039 to 1.000 a.u., a mean value of 0.235 a.u., and 137 analyzed channels within the production interval. These results indicate that sand production is highly clustered within discrete depth intervals, offering new insights into sand–fluid interactions during steady-state flow. Overall, the findings confirm that DAS-VSP enables continuous real-time monitoring of the sanding behavior with a far greater depth resolution than conventional tools. This approach supports proactive sand management strategies, enhances well-integrity decision-making, and underscores the potential of DAS to evolve into a standard surveillance technology for hydrocarbon production wells. Full article

Background/Objectives: Accurate preoperative discrimination between women with ovarian pathology and healthy controls, as well as between benign and malignant ovarian tumors, remains challenging. This study aimed to evaluate the usefulness of osteopontin and galectin-7 on the diagnosis of ovarian tumors. Methods: This prospective single-center case–control study was conducted at the Third Department of Obstetrics & Gynecology, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece, between 2018 and 2024. Preoperative serum levels of osteopontin, galectin-7, and established tumor markers (CA-125, CA19-9, CA15-3, CEA, AFP) were analyzed. Biomarker distributions were compared using non-parametric tests. Associations with clinical variables were explored using correlation analyses. Logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess diagnostic performance. Results: The study population included 116 women: 52 healthy controls, 45 patients with benign ovarian tumors, and 19 patients with malignant ovarian tumors. Serum osteopontin and galectin-7 levels did not differ significantly between control and study group (p = 0.562 and p = 0.138, respectively), nor between benign and malignant tumors (p = 0.784 and p = 0.140, respectively). Osteopontin showed no discriminatory ability (AUC = 0.47), while galectin-7 demonstrated weak discrimination (AUC = 0.63). A combined model yielded modest improvement (AUC = 0.69), remaining below clinically meaningful thresholds. CA-125 was the only biomarker significantly associated with malignancy (OR = 1.03, p = 0.038). Galectin-7 levels were higher in premenopausal women and inversely correlated with age, suggesting demographic rather than malignant influence. Conclusions: Despite strong biological relevance, circulating osteopontin and galectin-7 did not provide meaningful diagnostic discrimination between women with ovarian pathology and healthy controls or between benign and malignant ovarian tumors. CA-125 remained the most informative serum marker in this setting. Future efforts should focus on multi-marker strategies integrated with imaging and clinical assessment. Full article

As key power components in aviation machinery, airborne hydraulic systems exhibit significant coupling, nonlinearity, and strong noise interference, which pose enormous challenges for their mechanical fault diagnosis—an essential link in ensuring aviation mechanical system reliability. To address this issue, a policy gradient-based optimization method is proposed to autonomously tune network parameters, aiming to enhance the accuracy and robustness of mechanical fault diagnosis. Initially, a KAN (Kolmogorov–Arnold Network) convolution submodel is adopted to strengthen the extraction of weak mechanical fault features from complex hydraulic signals. Subsequently, the policy gradient methodology is employed to iteratively refine the overall network configuration, enabling adaptive optimization of fault diagnosis-related parameters. Extensive experiments on standard hydraulic system datasets demonstrate that the proposed approach outperforms other mainstream intelligent mechanical fault diagnosis methods in terms of diagnostic accuracy, anti-interference ability, and generalization performance. Full article

Background/Objectives: The early detection of oral squamous cell carcinoma (OSCC), especially when in the presence of oral potentially malignant disorders (OPMDs), may be challenging and would assist in improving poor OSCC survival rates reported in the literature. We conducted a systematic review to evaluate the utility of adjunctive aids that could assist during clinical examination of the oral cavity to identify suspicious mucosal lesions. Methods: Three databases (CENTRAL, PubMed/MEDLINE, Embase) were screened, limiting results from 2015 to November 2025. Inclusion criteria were: articles written in English; investigating the diagnostic accuracy of diagnostic visual aids compared to conventional oral examination under white light in the assessment of oral mucosal lesions. Extracted data were analysed narratively. Studies not reporting diagnostic accuracy using biopsy results as the gold standard were excluded. Results: The search produced 137 articles; after removing duplicates, 105 were screened through inclusion/exclusion criteria, leading to 17 papers included in the review. Eight articles investigated diagnostic accuracy of narrow band imaging (NBI), seven visually enhanced lesion scopes (VELscopes), one Glasses for Oral Cancer Curing Light Exposed Screening (GOCCLES), one ViziLite chemiluminescence system, and two toluidine blue (TB). Conclusions: High study heterogeneity and lack of randomized clinical trials limit the conclusions of this review. In this context, among the investigated visual aids for expert use, NBI (sensitivity 85–100%, specificity 75–98%) emerges as the most promising tool (VELscope sensitivity 76–87.1%, specificity 21.4–90%; GOCCLES 66%, 48%; ViziLite 77.3%, 27.8%, TB 56.8–91%, 65.3–68%), due to its ability to highlight sub epithelial vascular abnormalities, considered as early indicators of dysplastic or neoplastic progression even. None of the investigated visual aids seem suited for screening purposes/use by the general dentist. Full article

Accurate detection of blood cells in microscopic images plays a crucial role in automated hematological analysis and clinical diagnosis. Herein, we proposed an improved YOLOv8n-based model for efficient and precise detection of red blood cells (RBCs), white blood cells (WBCs), and platelets in the BCCD dataset. The baseline YOLOv8n framework was enhanced by integrating GhostConv and C3Ghost modules to reduce model complexity while maintaining high detection performance. A series of ablation experiments were conducted to evaluate the individual and combined effects of these modules on model accuracy and computational efficiency. Experimental results demonstrated that the baseline model achieved an mAP@0.5 of 0.9043 with 3.01 M parameters. After incorporating GhostConv, the model maintained comparable accuracy (mAP@0.5 = 0.9040) with a reduction in parameters to 2.73 M. The C3Ghost integration further decreased parameters to 1.99 M with an mAP@0.5 of 0.8973. The combined model achieved an optimal balance between accuracy (mAP@0.5 = 0.9001) and compactness (1.71 M parameters). Results indicate that the improved YOLOv8n can effectively enhance detection efficiency without sacrificing precision. The proposed lightweight detection framework provides a promising solution for real-time blood cell analysis. Its high accuracy, reduced computational load, and strong generalization ability make it suitable for integration into automated laboratory systems, facilitating rapid and intelligent medical diagnostics in hematology and related biomedical applications. Full article

Background: Preeclampsia is a major cause of maternal and perinatal morbidity, characterized by placental dysfunction and angiogenic imbalance. The soluble fms-like tyrosine kinase-1-to-placental growth factor (sFlt-1/PlGF) ratio has emerged as a promising biomarker for preeclampsia; however, its prognostic value for maternal and neonatal outcomes remains incompletely defined. Methods: This retrospective cohort study included 320 pregnant women, of whom 68 were diagnosed with preeclampsia, and 252 served as non-preeclamptic controls. Maternal serum sFlt-1 and PlGF levels were measured after 20 weeks of gestation at the time of clinical evaluation for suspected hypertensive disorders of pregnancy. Group comparisons, effect size analysis, receiver operating characteristic (ROC) curve analysis, and multivariable regression models were used to assess diagnostic performance and associations with maternal and neonatal outcomes. Results: The sFlt-1/PlGF ratio was significantly higher in women with preeclampsia compared with non-preeclamptic pregnancies (58.5 ± 17.3 vs. 34.6 ± 19.0; p < 0.001; Cohen’s d = 1.31). ROC analysis demonstrated good discriminative ability for preeclampsia (AUC = 0.81, 95% CI: 0.75–0.87), with a high negative predictive value. Increasing sFlt-1/PlGF values were independently associated with earlier gestational age at delivery, lower birth weight, reduced Apgar (Appearance, Pulse, Grimace, Activity, and Respiration) score, and a higher likelihood of neonatal intensive care unit admission. Conclusions: The sFlt-1/PlGF ratio is a robust biomarker for preeclampsia, providing both diagnostic discrimination and prognostic information regarding maternal and neonatal outcomes. Its integration into clinical practice may support clinical risk awareness when interpreted in the context of standard clinical evaluation and support informed decision-making in pregnancies with suspected or confirmed preeclampsia. Full article

Background/Objectives: Calprotectin (CLP), a calcium-binding protein complex released predominantly from neutrophils and monocytes, plays a key role in the inflammatory response. Increased levels of CLP have been reported in various inflammatory and malignant conditions. This study aimed to evaluate serum CLP concentrations and their associations with hematological and biochemical parameters in patients with lung cancer. Methods: This prospective observational study included newly diagnosed lung cancer patients and a healthy control group. Demographic data, routine laboratory parameters, CLP levels, and inflammatory indices including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune–inflammation index (SII), systemic inflammation response index (SIRI), and pan-immune–inflammation value (PIV) were recorded. Comparisons were made between groups and across tumor molecular profile, cancer stages, and metastasis status. Correlation and ROC analyses were performed. Results: Serum CLP levels were significantly higher in the lung cancer group compared with healthy controls (p < 0.001). Among molecular subgroups, patients with positive molecular testing had significantly elevated CLP levels compared with negative and untested groups (p = 0.025). CLP did not differ significantly across cancer stages or metastasis status (p > 0.05). CLP showed a positive correlation with the SIRI (r = 0.323; p = 0.004) and PIV (r = 0.395; p < 0.001). ROC analysis revealed that CLP demonstrated good diagnostic performance for lung cancer, with an AUC of 0.930 (95% CI: 0.849–0.976), sensitivity of 79.5%, and specificity of 92.3%. Among inflammatory indices, PIV (AUC = 0.863) and SIRI (AUC = 0.810) also showed high diagnostic accuracy. Conclusions: CLP levels are significantly elevated in lung cancer and show strong discriminative ability, outperforming commonly used inflammatory indices. Although CLP is not specific to lung cancer, it may serve as a supportive, noninvasive biomarker reflecting inflammatory burden when interpreted alongside clinical evaluation, imaging findings, and other laboratory parameters. Full article