Search Results (1,621)

Pyruvate dehydrogenase complex deficiency (PDCD) is a heterogenous mitochondrial inborn error in carbohydrate oxidation manifesting as congenital lactic acidosis. PDCD presents diagnostic and therapeutic challenges. While no curative treatment exists for PDCD, certain therapeutic modalities may improve prognosis and ameliorate symptom severity. This article examines the effectiveness of treatments for PDCD and presents a case series of three patients with PDCD. A scoping literature review was conducted for treatments of PDCD. Patient data for case reports was extracted retrospectively from electronic medical records from a large tertiary hospital. We reviewed and summarized findings from seven preclinical studies and ten human studies, which showed that dichloroacetate and the ketogenic diet were the most frequently studied treatments. Therapeutic approaches observed select positive outcomes such as reduced lactate levels, improved neuropathological manifestations, and increased longevity. However, most interventions have yet to be rigorously investigated. Early diagnosis of PDCD is integral, as treatment methods may offer improved clinical and biochemical outcomes. Clinical trials of existing and novel treatments are necessary to improve management and further understand the prognostic potential of this metabolic disorder. Full article

Glaucoma is a multifaceted optic neuropathy, characterized by the progressive loss of retinal ganglion cells. This damage frequently continues even after intraocular pressure (IOP) has been effectively lowered. This resistance to conventional IOP-lowering therapy underscores the critical role of interacting IOP-independent mechanisms; specifically metabolic failure and systemic mitochondrial dysfunction have emerged as key parallel drivers. This review analyzes the paradigm shift from a pressure-centric model to a bioenergetic one, focusing on mitochondrial function, peripheral blood mononuclear cell (PBMC) biomarkers, and oxygen consumption dynamics. We synthesize evidence demonstrating that glaucoma patients exhibit a metabolic vulnerability, characterized by lower PBMC oxygen consumption rates and depleted systemic nicotinamide adenine dinucleotide levels relative to healthy individuals. Furthermore, compromised systemic respiratory performance correlates with more rapid worsening of visual fields and structural thinning, independent of IOP status. Moreover, we delineate the role of Complex I defects, SARM1-mediated axonal degeneration, and proteomic alterations, which indicate defective mitophagy. These findings establish systemic metabolic profiling as a valuable supplementary tool for assessing patient risk and support the clinical translation of neuroprotective therapies targeting mitochondrial bioenergetics, specifically nicotinamide, pyruvate, coenzyme Q₁₀, and metformin. Full article

Background/Objectives: Tafamidis, a transthyretin kinetic stabilizer, increases circulating transthyretin levels in treated patients. While this effect is well documented, its underlying mechanism remains incompletely understood. This study aimed to evaluate the performance of physiologically based pharmacokinetic (PBPK) model performance and to calibrate a hypothesis-consistent quantitative systems pharmacology (QSP) model of tafamidis and transthyretin dynamics to explore mechanistic hypotheses underlying the clinically observed increase in circulating transthyretin and the associated dose–response relationship. The PBPK model constitutes the primary framework, while the coupled QSP component illustrates how tafamidis exposure predictions can be used to evaluate mechanistic hypotheses of TTR turnover. Methods: A PBPK–QSP model was constructed in Simcyp (V23) using LUA-based modules. The PBPK part was parameterized from the literature and validated against data from therapeutic single-dose, therapeutic multiple-dose, and supratherapeutic dose clinical studies. The QSP part of the model describes tafamidis–TTR binding kinetics, stabilization, and clearance of bound complexes. Simulations were performed in thirty virtual healthy male subjects aged 30–40 years, incorporating physiological variability in baseline TTR concentrations. Results: Mean predicted versus observed ratios of tafamidis AUC and $C_{max}$ values were within a 1.3-fold range across validation studies. The integrated model reproduced the clinically reported 33% increase in TTR concentration through a calibrated clearance-scaling factor. It supports the hypothesis that reduced clearance of tafamidis-bound TTR may explain the observed effect without modifying TTR synthesis. Dose-sensitivity simulations indicated that patients with low baseline TTR may achieve adequate stabilization at reduced doses, while those with higher baseline TTR concentration may require higher doses. Conclusions: The developed PBPK–QSP model not only reproduces tafamidis pharmacokinetics and TTR responses but also proposes a plausible mechanistic hypothesis consistent with clearance modulation of stabilized TTR contributing to the clinical effect. Full article

Background: Artificial intelligence (AI) shows promising results in lymphoma detection, prediction, and classification. However, translating these findings into practice requires a rigorous assessment of potential biases, clinical utility, and further validation of research models. Objective: The goal of this study was to summarize existing studies on artificial intelligence models for the histopathological detection of lymphoma. Design: This study adhered to the PRISMA Extension for Scoping Reviews (PRISMA-ScR) guidelines. A systematic search was conducted across three major databases (Scopus, PubMed, Web of Science) for English-language articles and reviews published between 2016 and 2025. Seven precise search queries were applied to identify relevant publications, accounting for variations in study modality, algorithmic architectures, and disease-specific terminology. Results: The search identified 612 records, of which 36 articles met the inclusion criteria. These studies presented 36 AI models, comprising 30 diagnostic and six prognostic applications, with Convolutional Neural Networks (CNNs) being the predominant architecture. Regarding data sources, 83% (30/36) of datasets utilized Hematoxylin and Eosin (H&E)-stained images, while the remainder relied on diverse modalities, including IHC-stained slides, bone marrow smears, and other tissue preparations. Studies predominantly utilized retrospective, private cohorts with sample sizes typically ranging from 50 to 400 patients; only a minority leveraged open-access repositories (e.g., Kaggle, TCGA). The primary application was slide-level multi-class classification, distinguishing between specific lymphoma subtypes and non-neoplastic controls. Beyond diagnosis, a subset of studies explored advanced prognostic tasks, such as predicting chemotherapy response and disease progression (e.g., in CLL), as well as automated biomarker quantification (c-MYC, BCL2, PD-L1). Reported diagnostic performance was generally high, with accuracy ranging from 60% to 100% (clustering around 90%) and AUC values spanning 0.70 to 0.99 (predominantly >0.90). Conclusions: While AI models demonstrate high diagnostic accuracy, their translation into practice is limited by unstandardized protocols, morphological complexity, and the “black box” nature of algorithms. Critical issues regarding data provenance, image noise, and lack of representativeness raise risks of systematic bias, hence the need for rigorous validation in diverse clinical environments. Full article

Achieving natural esthetics has become essential for successful dental restorations and supports the use of modern non-metal materials. However, complexity in esthetic features of natural teeth, determined by both inherent color factors and hierarchical and gradient microstructures, makes recording, determination, and reproduction difficult. This often leads to misunderstanding during manufacturing and dissatisfaction with the final outcome, even when using advanced digital tools. The aim of this study was to investigate a new, easy-to-handle digital tool for determining the color of restorative materials. An industrial-level handheld color identifier, the NCS Colourpin SE, together with the corresponding NCS color system, was tested on three materials: dental resin nanocomposite, self-glazed zirconia (SGZ), and Decore zirconia pellets. The repeatability and impacts of geometrical contributions such as surface roughness and thickness on different colors were measured. The Colourpin SE offered promising repeatability. Decore zirconia showed more than 90% repeatability for most of the colors, independent of thickness. The NCS scanner showed slightly better repeatability than earlier in clinical trials with an intraoral scanner. The shades A3.5 and A3 had lower repeatability, varying from 50 to 90%. It identified effects of material thickness and surface roughness, where the thicker samples were identified with higher blackness levels, and surface roughness seemed to be coupled with a lower blackness level in color identification codes. Small but consistent differences between materials were detected, suggesting that material and manufacturing methods affect the final shade. The NCS Colourpin SE shows potential to be developed into an affordable and easy-to-handle scanner for the identification of a patient’s tooth color, enabling synchronization with digital workflows and improving the match between restoration and the patient’s natural teeth. Nevertheless, further research and development in customized applications for color identification in esthetic dentistry is still required through multidisciplinary collaboration. Full article

Background: Early childhood caries (ECC) affects a substantial proportion of young children worldwide, and timely screening is essential for early intervention and referral. While deep learning has shown promise for automated dental diagnostics, many existing approaches rely on computationally heavy models that limit deployment in community and mobile settings. This study investigates whether compact convolutional neural networks can achieve clinically meaningful performance for screening dental caries from oral photographs. Methods: We curated a dataset of 435 intraoral images from children aged 3–14 years, annotated by licensed dentists, and performed patient-level stratified splitting to prevent data leakage. Three convolutional neural networks (ResNet-18, MobileNetV3-Small, and EfficientNet-B0) were fine-tuned using ImageNet-pretrained weights and comparatively evaluated for the detection of dental caries from oral photographs. Models were trained with class-weighted cross-entropy loss and evaluated on a held-out test set using sensitivity, specificity, balanced accuracy, ROC-AUC, and PR-AUC with bootstrap 95% confidence intervals. Results: ResNet-18 achieved the highest balanced accuracy (0.929), weighted F1-score (0.954), and perfect sensitivity (1.00), while EfficientNet-B0 achieved the strongest threshold-independent discrimination with the highest ROC-AUC (0.978) and PR-AUC (0.990). MobileNetV3-Small maintained competitive performance (ROC-AUC 0.952; PR-AUC 0.976) with substantially lower computational complexity. Conclusions: In addition to performance evaluation, we incorporated an interpretability analysis using Grad-CAM to examine model decision behavior. The resulting attribution maps predominantly highlighted clinically relevant tooth regions associated with caries, providing evidence that the models rely on meaningful dental features rather than background artifacts. These results demonstrate that compact, deployment-friendly architectures can achieve clinically meaningful performance for ECC detection, supporting their suitability for scalable, real-world screening applications. Full article

Background: Acne scars remain a very common complaint in dermatology practices. Even though many treatment options are available, proper treatment remains a challenge. Complex treatment methods that are based on the synergy effect are the ones that result in better effects and patient satisfaction. Methods: Three healthy female patients with a total of 106 atrophic acne scars were recruited to the split-face study with placebo control, where a series of three microneedling procedures in monthly intervals combined with 5% retinyl palmitate-loaded oleogel was compared to the same microneedling protocol with placebo. Patients’ quality of life was measured using the Dermatology Life Quality Index (DLQI) and Skindex-29 questionnaires. Patients’ satisfaction with treatment and intensity of post-procedure symptoms were assessed as well. Results: In clinical evaluation, a modest effect was observed regarding the reduction in atrophic acne scars, whereas moderate-to-marked improvement in acne scar reduction was noted by the patients. Additionally, mild to marked improvement was noted by patients regarding skin quality, moisture level, elasticity, and skin tone. No significant side effects were noted. All the above resulted in good patient satisfaction with the treatment, and willingness to repeat the procedures again. No significant differences regarding acne scar reduction, treatment-related symptoms, and skin quality improvement were noted between active substance and placebo-treated sides of the face. Conclusions: Microneedling remains a key method in the therapeutic arsenal for acne scarring. By combining it with 5% retinyl palmitate-loaded oleogel modest effects can be noted after a series of three procedures, with good overall treatment tolerability and patients’ satisfaction. Full article

Lymph node metastasis (LNM) is one of the most powerful prognostic determinants in head and neck squamous cell carcinoma (HNSCC). The extent and pattern of nodal involvement critically influence staging accuracy, therapeutic decision-making, and patient outcomes. However, the biological and clinical implications of nodal disease remain complex and continue to evolve. We aim to synthesize current clinical and translational evidence regarding the prognostic and therapeutic impact of LNM in HNSCC and to highlight emerging trends relevant to precision staging. A narrative review was conducted through a structured literature search in PubMed and Scopus (2008–2025), with emphasis on studies published in the last five years. Meta-analyses, large cohort studies, and evidence-based guidelines addressing prognostic factors, biological mechanisms, and management strategies were critically appraised. LNM is consistently associated with reduced overall and disease-free survival across major head and neck subsites. Key independent prognostic variables include the number of metastatic nodes, extranodal extension, and involved cervical levels. Recent advances, such as refinements in the AJCC 8th edition, sentinel lymph node mapping, high-resolution imaging, and molecular profiling, have improved early detection and refined risk stratification. LNM remains central to prognostic evaluation and treatment selection in HNSCC. Integrating biological insights with molecular diagnostics and advanced imaging will be essential to achieving precision staging and individualized therapeutic strategies. Full article

Early risk stratification of COVID-19 severity in oncology patients is critical for improving clinical outcomes and optimizing hospital resource allocation. This study proposes a rule-based clinical decision support system (CDSS) designed for integration into digital triage workflows. In practical terms, the score is intended to be applied at hospital admission or triage, where demographic and comorbidity information is routinely available. The computed score can automatically flag high-risk oncology patients for intensified monitoring or early ICU evaluation, supporting rapid resource allocation while preserving clinician decision-making. Using retrospective clinical data from hospitalized oncological patients with confirmed SARS-CoV-2 infection, we developed a scoring algorithm based on four common comorbidities: age ≥ 70, obesity, diabetes mellitus, and hypertension. Each factor was assigned a weighted contribution to a cumulative score ranging from 0 to 7. Patients were classified into three risk levels (low, moderate, high), correlating with observed rates of ICU admission and mortality. The system is built for low-complexity implementation in electronic health records (EHRs) or web-based triage dashboards and includes a software logic model with pseudocode. Results indicate that the score effectively distinguishes patient risk levels with statistical significance (p < 0.01), and can function as an early triage mechanism. The proposed model does not require laboratory data or imaging, making it particularly suitable for rapid deployment in both hospital and remote settings. This work demonstrates a pragmatic, interpretable, and scalable approach to clinical decision support in pandemic contexts involving vulnerable populations such as cancer patients. Full article

Background: Fibromyalgia (FM) and eating disorders (ED) represent distinct clinical entities traditionally managed within separate medical specialties, yet emerging evidence suggests significant comorbidity and potential shared pathophysiological mechanisms. Both conditions disproportionately affect women, involve complex multifactorial etiologies and substantially impair quality of life. Despite documented clinical overlaps, the mechanistic connections linking these conditions remain poorly characterized, and integrated treatment approaches are lacking. Objective: This narrative review examines the role of oxidative stress and nuclear factor erythroid 2-related factor 2 (Nrf2) pathway dysfunction as a unifying molecular mechanism connecting fibromyalgia and eating disorders, with emphasis on implications for integrated rehabilitation strategies. Methods: We synthesized current evidence on oxidative stress pathophysiology in fibromyalgia and eating disorders, focusing on Nrf2-Keap1 pathway function, clinical comorbidity patterns and rehabilitation interventions targeting antioxidant defense mechanisms. In PubMed, representative search strings included “(fibromyalgia [MeSH] OR fibromyalgia [Title/Abstract]) AND (“eating disorders” [MeSH] OR “anorexia nervosa” [MeSH] OR “bulimia nervosa” [MeSH])” and “fibromyalgia AND (“oxidative stress” OR Nrf2 OR “redox”)”. Articles in English published through December 2025 were considered, with additional records identified by manually screening reference lists. Results: Fibromyalgia patients exhibit elevated oxidative stress markers, impaired antioxidant enzyme function and compromised Nrf2 activity correlating with disease severity, with studies reporting approximately 30–50% reductions in coenzyme Q10 levels compared with healthy controls. Similarly, eating disorders demonstrate mitochondrial dysfunction and oxidative stress dysregulation, though patterns differ across eating disorder phenotypes. Nrf2 serves as the master regulator of cellular antioxidant defense, coordinating expression of over 500 genes involved in detoxification, cytoprotection, inflammation modulation and metabolic regulation. Evidence suggests Nrf2 activity is regulated by energy balance, potentially linking nutritional status with cellular stress responses. Rehabilitation interventions, including graduated exercise and nutritional optimization with Nrf2-activating foods (cruciferous vegetables, polyphenols, omega-3 fatty acids), offer mechanism-based therapeutic approaches through hormetic Nrf2 activation and direct Keap1 modification. Conclusions: Multidisciplinary rehabilitation programs integrating physical therapy, exercise prescription and nutritional strategies targeting Nrf2 activation offer evidence-based, mechanism-driven approaches to address shared oxidative stress pathophysiology. Nrf2 pathway dysfunction represents a promising and biologically plausible molecular target that may help to unify our understanding of fibromyalgia and eating disorders pending confirmation from prospective clinical studies in comorbid populations. Future research should prioritize prospective clinical trials testing Nrf2-targeted interventions in comorbid populations and collaborative patient-centered care models. Full article

Background/Objectives: Predicting individual response to depression therapy prior to treatment initiation remains a critical clinical challenge, as the response rate to both selective serotonin reuptake inhibitors (SSRIs) and repetitive transcranial magnetic stimulation (rTMS) is approximately 50%, leaving treatment selection largely trial-based. This study presents a computer-aided decision (CAD) framework that predicts depression therapy outcomes from pre-treatment electroencephalogram (EEG) signals using advanced time-frequency representations and pretrained convolutional neural networks (CNNs). Methods: EEG signals from 30 SSRI patients and 46 rTMS patients are transformed into time-frequency images using Continuous Wavelet Transform (CWT), Variational Mode Decomposition (VMD), and their pixel-level fusion. Four pretrained CNN architectures, including ResNet-18, MobileNet-V3, EfficientNet-B0, and TinyViT-Hybrid, are fine-tuned and evaluated under both image-independent and subject-independent 6-fold cross-validation (CV). Results: Results reveal a clear therapy-specific pattern: CWT-based representations yield superior discrimination for SSRI outcome prediction, with ResNet-18 achieving 99.43% image-level accuracy, while VMD-based representations are statistically superior for rTMS outcome prediction, with ResNet-18 reaching 98.77%. Pixel-level fusion of CWT and VMD does not consistently improve performance over the best individual representation in either therapy context. Pairwise Wilcoxon signed-rank tests confirm a two-tier architectural hierarchy in which ResNet-18 and TinyViT-Hybrid significantly outperform MobileNet-V3 and EfficientNet-B0 across all conditions, while remaining statistically indistinguishable from each other. At the subject level, the framework achieves 82.50% and 83.53% accuracy for SSRI and rTMS, respectively, under strict subject-independent evaluation. Per-channel analysis reveals occipital dominance for SSRI under CWT and frontotemporal dominance for rTMS under VMD, consistent with known neurophysiological mechanisms. Conclusions: These findings demonstrate that the choice of time-frequency representation is therapy-specific and at least as important as architectural complexity, and that competitive performance can be achieved without recurrent or attention layers by combining well-designed spectral images with a simple pretrained residual network. Full article

Background/Objectives: New models of care are needed to address the barriers people who use substances (PWUS) and/or experience homelessness face when accessing primary healthcare. This study reports findings from the evaluation of a six-month pilot of a mobile health clinic (MHC) co-delivered by primary healthcare, local government, and lived-experience recovery organisations in the North East of England, UK. Methods: Pragmatic mixed-methods process evaluation with data sources including a patient survey, overt observations, qualitative interviews, and routine patient data. Qualitative data were analysed using inductive and deductive thematic analysis; quantitative data were analysed descriptively. RE-AIM framework dimensions were applied to inform interpretation. Results: N = 164 patients accessed the bus between 1 April and 31 October 2025, with survey data indicating that most patients were PWUS (n = 96, 84%), with experience of homelessness (n = 67, 61%) and/or lived in the most deprived neighbourhoods, with complex physical and mental health needs (Reach). Patients expressed satisfaction with the service, valuing the compassionate and comprehensive support provided. There was qualitative evidence of further re-engagement with statutory healthcare following attendance on the bus (Effectiveness). Local organisations were mostly keen to be involved in the pilot, with participation benefiting from existing local relationships and infrastructure (Adoption). The flexible yet consistent approach of those involved in service delivery was viewed as positive. There was some uncertainty around the functions of the bus and the role of some delivery staff (Implementation). Limited funding was perceived as a barrier to sustaining the bus, alongside lack of capacity within local organisations (Maintenance). Conclusions: The study highlighted the positive impact that an MHC can have on this marginalised population and provides further evidence for the need for clinical care that provides relational support and attends to the social determinants of health. The study indicates the potential for interdisciplinary working to improve access to healthcare for PWUS, and underlines that delivering healthcare at a neighbourhood level is reliant on strong community networks. Wider system change is still needed to further support the population. Full article

Graves’ disease (GD) is an autoimmune disorder characterized by hyperthyroidism and a hypermetabolic state involving complex endocrine, metabolic, and immune interactions. Cocaine- and amphetamine-regulated transcript (CART) is a neuropeptide involved in energy balance, neuroendocrine signaling, and neuroimmune modulation; however, its circulating levels and clinical relevance in GD remain unclear. In this single-center prospective study, serum CART levels were evaluated in 44 patients with GD and 44 age- and sex-matched healthy controls. Associations with thyroid function, autoimmune markers, metabolic parameters, and thyroid ultrasound heterogeneity were analyzed. Serum CART concentrations were measured using an enzyme-linked immunosorbent assay, and clinical, biochemical, and ultrasonographic data were recorded. Serum CART levels did not differ significantly between GD patients and healthy controls. However, within the GD group, CART levels varied significantly according to thyroid ultrasound heterogeneity, with lower levels observed in patients with severe parenchymal heterogeneity. Serum CART levels showed positive correlations with body mass index and insulin resistance indices, while inverse correlations were observed with thyrotropin receptor antibody and anti-thyroid peroxidase antibody levels. No significant associations were identified between serum CART levels and circulating thyroid hormone concentrations. These findings suggest that serum CART may reflect metabolic and autoimmune heterogeneity rather than hypothalamic–pituitary–thyroid axis activity in GD, supporting its role as a context-sensitive, hypothesis-generating biomarker. Full article

Carbapenems are essential for the treatment of severe infections caused by Gram-negative bacteria, particularly in critically ill and immunocompromised patients. However, the global rise of carbapenem-resistant Enterobacterales (CRE), Pseudomonas aeruginosa, and Acinetobacter baumannii has significantly eroded their effectiveness, and the phenomenon is now recognized as a major public health threat. Resistance is driven by the complex and evolving interplay of enzymatic and non-enzymatic mechanisms, occurring within highly successful clonal lineages and mobile genetic platforms. This review summarizes advances since 2020 in the molecular basis of carbapenem resistance, integrating enzymatic mechanisms across Ambler classes A, B, C, and D with emerging non-enzymatic contributors, including porin remodeling, efflux pump upregulation, target-site alterations, and outer-membrane adaptations. Particular attention is given to adaptive genome dynamics, such as IS26-mediated gene amplification, plasmid multimerization, and heteroresistance, that generate unstable resistance phenotypes and complicate routine susceptibility testing. Newly introduced β-lactam/β-lactamase inhibitor combinations exert distinct selective pressures: ceftazidime–avibactam favors KPC Ω-loop variants and permeability defects, often restoring carbapenem susceptibility, whereas meropenem–vaborbactam and imipenem–relebactam resistance is driven mainly by porin loss and β-lactamase gene amplification. Cefiderocol resistance is multifactorial, frequently involving impaired siderophore uptake and heteroresistance, while sulbactam–durlobactam remains active against OXA-producing A. baumannii but is compromised by metallo-β-lactamases and PBP3 alterations. Carbapenem resistance is increasingly characterized by convergent, multi-layered adaptations that undermine both established and novel therapies. While high-level randomized evidence remains limited for some resistance mechanisms, emerging mechanistic, microbiological, and clinical data support the need for mechanism-aware diagnostics, repeated susceptibility assessment during therapy, and stewardship strategies informed by resistance biology. Integrating molecular context into routine practice will be critical to preserving emerging treatment options and limiting the global impact of carbapenem resistance. 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