Diagnostics最新文献

Background: Merkel cell carcinoma (MCC) is a rare and aggressive cutaneous neuroendocrine malignancy. The prognostic impact of sun exposure at the primary tumor site in localized and locally advanced MCC remains incompletely defined. We aimed to compare clinicopathologic characteristics and survival outcomes between sun-exposed and non-sun-exposed MCC in a large, multi-center Israeli cohort. Methods: We retrospectively identified 249 patients diagnosed with localized or locally advanced MCC between January 1985 and December 2020. Of these, 225 patients met eligibility criteria and were included in the analysis: 142 with sun-exposed primary tumors (cohort A) and 83 with non-sun-exposed tumors (cohort B). Baseline characteristics included age, sex, tumor size, lymph node (LN) involvement at diagnosis, disease-free survival (DFS), and overall survival (OS). Results: Median age at diagnosis was similar between cohorts (~73 years), with a male predominance in both groups. LN involvement was significantly more frequent in non-sun-exposed tumors compared with sun-exposed tumors (57.0% vs. 30.0%, p < 0.001), while tumor size distribution did not differ significantly. Median DFS was numerically longer in sun-exposed patients (58.0 vs. 47.8 months, p ≈ 0.18), whereas median OS favored non-sun-exposed patients (89.7 vs. 79.7 months, p ≈ 0.21), though neither difference reached statistical significance overall. Females demonstrated longer DFS and OS than males across both cohorts. Among LN-negative patients, non-sun-exposed tumors were associated with significantly improved OS (105.9 vs. 91.4 months, p ≈ 0.03), particularly in males. Primary tumor size further stratified outcomes: non-sun-exposed patients had significantly superior OS for tumors <2 cm and both improved DFS and OS for tumors ≥2 cm. Conclusions: In this large real-world MCC cohort, sun exposure status was associated with distinct patterns of nodal involvement and survival in clinically relevant subgroups. Non-sun-exposed MCC demonstrated favorable survival outcomes, particularly in LN-negative disease and across tumor size categories, suggesting underlying biological differences that merit further investigation.

Background: The Ki-67 labeling index (LI) is a widely used marker of tumour proliferation, yet its manual assessment is time-consuming and subject to substantial inter-observer variability. Automated methods may improve reproducibility, but their clinical relevance depends on achieving performance comparable to expert pathologists. Method: We evaluated an artificial intelligence (AI)-based, cell-level system for automated Ki-67 LI assessment that detects and classifies individual tumour cell nuclei as Ki-67-positive or -negative. After nuclear detection using a pre-existing cell detection model, a lightweight convolutional neural network classifier operating on nucleus-centred patches was trained, and then applied to cases independently assessed by three pathologists. Agreement between AI-derived and human Ki-67 LI values was compared directly with inter-pathologist agreement across a range of proliferation levels. Results: The AI-based cell classification achieved 98% AUC on a test set consisting of 71K positive and 170K negative image patches centred on nuclei. On the automated Ki-67 LI assessment, the AI system demonstrated concordance with expert pathologists comparable to human inter-observer variability. Conclusions: These results support the potential of cell-level automated Ki-67 assessment as a reproducible decision-support tool for routine histopathological practice.

Background/Objectives: Type 2 diabetes mellitus (T2DM) and atherogenic dyslipidemia have been implicated in colorectal cancer (CRC) development, but their prognostic relevance after cancer diagnosis remains unclear. This study aimed to evaluate the association between T2DM, lipid-derived atherogenic indices, and survival outcomes in patients with CRC. Methods: We conducted a retrospective cohort study including 240 CRC patients, of whom 60 had coexisting T2DM. Overall survival (OS) and disease-free survival (DFS) were analyzed using the Kaplan-Meier (KM) method and log-rank tests. In the absence of recurrence-specific data, DFS was defined as time to death or last follow-up. Lipid-related indices, including the atherogenic index of plasma (AIP), atherogenic coefficient (AC), remnant cholesterol (RC), non-high-density lipoprotein cholesterol (non-HDL-C), triglyceride-glucose (TyG) index, and triglyceride-to-HDL cholesterol ratio (TG/HDL-C), were evaluated by tertiles in KM analyses. Multivariable Cox proportional hazards models were constructed to assess the independent prognostic value of AIP, AC, and RC (entered separately as a continuous variable standardized to 1 standard deviation), adjusted for age, sex, adjuvant chemotherapy, radiotherapy, and T2DM status. Sensitivity analyses were performed in stage III-IV patients. Results: During follow-up, 28 deaths occurred. OS did not differ significantly between CRC patients and those with CRC coexisting with T2DM (log-rank p-values = 0.220). DFS analyses showed no significant differences across tertiles of any lipid-related index (all log-rank p-values > 0.05), with overlapping survival curves and no consistent dose-response patterns. In adjusted Cox models, AIP (hazard ratio [HR] per 1 SD = 0.71, 95% CI 0.48-1.06), AC (HR = 0.72, 95% CI 0.44-1.20), and RC (HR = 0.66, 95% CI 0.39-1.12) were not independently associated with DFS. Results were consistent in advanced-stage disease (stage III-IV). Conclusions: In this cohort of patients with CRC, neither T2DM nor lipid-derived indices reflecting atherogenic dyslipidemia and insulin resistance were independently associated with OS or DFS. These findings help refine the clinical interpretation of lipid-derived biomarkers in CRC, suggesting limited prognostic utility beyond established oncologic factors.

Background/Objectives: Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) is a global health crisis, but current diagnostics are limited. Liver biopsy is invasive, magnetic resonance imaging-proton density fat fraction (MRI-PDFF) is expensive, and quantitative ultrasound methods are low-accuracy, especially in patients with a high body mass index (BMI). This study introduces a novel thermo-acoustic (TA) method that generates ultrasound signals based on tissue electrical conductivity, where lean tissue (high in water and electrolytes) absorbs more radio-frequency (RF) energy than fatty tissue, providing a direct molecular contrast for fat. Methods: A prospective, cross-sectional feasibility study compared a new thermo-acoustic fat fraction (TAFF) score with the reference standard MRI-PDFF in 40 subjects with suspected fatty liver disease. Bland-Altman analysis, Deming regression, and Binary classification performance were tested. To establish system stability, a dedicated Repeatability and Reproducibility (R&R) study (N = 14) evaluated inter-operator and intra-operator consistency using an Intraclass Correlation Coefficient (ICC) derived from a two-way random-effects ANOVA model. Results: TAFF estimates demonstrated a substantial correlation (r = 0.89) with MRI-PDFF and an average absolute error of 3.04% fat fraction. Classification performance was high, with an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.92 at the 12% fat fraction threshold and 0.99 at the 20% fat fraction threshold. The R&R study confirmed robust stability (intraclass correlation = 0.89) and a negligible mean inter-operator difference of 0.36%. Estimation errors showed no statistically significant correlation with BMI or other body habitus measurements. Conclusions: These findings support thermoacoustics' potential as an accurate, non-invasive, point-of-care solution that can serve as a new imaging biomarker. By providing predictive values closely aligned with MRI-PDFF across the full MASLD spectrum, TAFF can complement currently available ultrasound methods to address the cost and access constraints of MRI for the assessment, diagnosis, and monitoring of MASLD.

Background: The relationship between testosterone and coronary artery disease (CAD) remains a subject of debate. Most studies suggest an inverse association-lower testosterone, higher risk. However, data from Central European populations undergoing coronary angiography are limited. Objectives: To investigate the association between serum testosterone levels and angiographically confirmed coronary artery stenosis in a Slovak population. Methods: This cross-sectional study included 129 consecutive stable patients (84 men, 45 women; mean age 64.3 ± 9.7 years) undergoing elective coronary angiography for suspected stable coronary artery disease. Significant coronary stenosis was defined as ≥50% luminal narrowing in any major epicardial vessel. Serum testosterone, lipid profile, and traditional risk factors were assessed. Univariate and multivariate logistic regression models were constructed to evaluate independent associations of coronary stenosis. Results: Coronary stenosis ≥ 50% was present in 74 patients (57.4%). Notably, patients with stenosis had significantly higher testosterone levels (6.62 ± 2.79 vs. 4.85 ± 3.50 ng/mL, p = 0.002). In univariate analysis, testosterone showed a significant association (OR 1.197 per ng/mL, OR 1.784 per SD, p = 0.003). In multivariate analysis adjusted for age, sex, diabetes mellitus, and LDL (low-density lipoprotein) cholesterol, testosterone remained independently associated (adjusted OR 2.043 per SD, 95% CI 1.221-3.420, p = 0.007), as did diabetes mellitus (OR 2.60, p = 0.032). Conclusions: Elevated serum testosterone is paradoxically associated with increased prevalence of coronary stenosis in our cohort. These findings from stable, chronic CAD patients may work fundamentally differently from what is observed in acute coronary syndromes, where stress-induced testosterone suppression may confound observed associations.

Background: Ultrasound imaging is an ideal tool for regular carotid plaque screening to identify individuals at high risk of stroke for clinical intervention. However, no existing study leverages multi-modal multi-view ultrasound imaging for AI-enabled auto-classification of carotid plaque vulnerability. This study aims to develop and validate an effective AI model for carotid plaque vulnerability classification through the applications of dual-modal (B-Mode and contrast-enhanced mode) dual-view (longitudinal and cross-sectional) settings to maximize the utility and potential of ultrasound imaging. Methods: Hybrid deep-learning (DL) and machine-learning (ML) methods were employed to balance between model discriminability and interpretability. B-Mode ultrasound (BMUS) and contrast-enhanced ultrasound (CEUS) images from 241 patients were retrospectively analyzed using the proposed hybrid-DL-ML variants. Results: Our findings suggest the hybrid VGG-RF model developed from a dual-modal dual-view setting outperforms those developed from other settings for identifying vulnerable carotid plaques. The VGG-RF model emerged as the best-performing model, achieving an optimal performance with an AUC of 0.908, precision of 0.765, recall of 0.929, specificity of 0.886, and F1 score of 0.839. The inherent interpretability of the VGG-RF model divulged that long-axis views of BMUS and CEUS images were the major contributing features for discriminating vulnerable carotid plaques against their counterparts. Conclusions: The present study underscored the effectiveness of AI models developed from dual-modal dual-view settings of ultrasound images. Notably, the hybrid VGG-RF model was benchmarked as the best-performing model among other studied hybrid DL-ML variants. Further studies on a larger cohort in a prospective setting are warranted to validate the findings of the current study.

Background/Objectives: The sphenoid sinus (SS) exhibits marked morphological variability, influencing the relationship of critical neurovascular skull base structures. This study aimed to characterize sphenoid sinus pneumatization (SSP) patterns and assess their impact on the course of the internal carotid artery (ICA), optic nerve (ON), Vidian nerve (VN), and maxillary nerve (MN) within a Greek adult population. Methods: A retrospective analysis of 253 adult skull base computed tomography (CT) scans was performed. The degree and direction of SSP were classified according to established radiological criteria. Anterior, lateral, and posterior extensions were evaluated. The course of adjacent neurovascular structures was categorized as typical, protruding, or dehiscent. Associations between pneumatization types and neurovascular variants were analyzed. Results: The sellar complete type was the predominant SS pattern (63.2%), followed by sellar incomplete (27.7%) and presellar (8.7%) types; agenesis was rare (0.4%). Posterior (63.6%) and lateral (46.6%) extensions were most common. Lateral and posterior pneumatization significantly correlated with protrusion and/or dehiscence of adjacent neurovascular structures, particularly the ICA, ON, and VN. LW extension was strongly associated with ON protrusion (96%), while PP and full-lateral extensions correlated with VN protrusion (56.1% and 79.9%, respectively). No significant sex- or side-related differences were identified. Conclusions: SSP demonstrates extensive morphological variability that significantly affects the anatomical course and osseous coverage of neighboring neurovascular structures. Comprehensive preoperative CT evaluation of SS anatomy is essential for planning endoscopic transsphenoidal and extended skull base procedures to minimize the risk of neurovascular injury.

Background/Objectives: Brain tumors are among the most severe neurological disorders, and their variability in size, morphology, and anatomical location complicates early and accurate diagnosis. Although magnetic resonance imaging (MRI) is the most reliable non-invasive modality for tumor detection, manual interpretation remains time-consuming, subjective, and susceptible to human error. This study aims to develop an optimization-driven hybrid machine learning framework for accurate and computationally efficient automatic brain tumor classification. Methods: The dataset includes 834 MRI images (583-training, 123-validation, 128-independent test). Because YOLOv11 detects tumor and non-tumor regions separately, the sample size doubled during region-based analysis, and all subsequent stages were conducted at the regions of interest (ROI) level. On the independent test set, YOLOv11 achieved 98.87% mAP@50, 98.54% precision, and 98.21% recall. The proposed framework combines automated tumor localization with image standardization using Gaussian noise reduction and bilinear interpolation. From the processed MR images, 39 entropy-based features were extracted. To enhance diagnostic performance and eliminate redundant information, the superb fairy-wren optimization algorithm (SFOA) was applied for feature selection and compared with particle swarm optimization (PSO), Harris hawk optimization (HHO), and puma optimization (PO). Final classification was primarily performed using k-nearest neighbors (kNN), while support vector machines (SVM) were used for comparative evaluation. Results: SFOA reduced the feature dimensionality from 39 to 5 features while achieving 99.20% classification accuracy on the independent test set. In comparison, PSO selected 10 features, HHO selected 6 features and PO selected 10 features, all achieving 98.45% accuracy. The best performance obtained with SVM was 98.45% accuracy (HHO-SVM), which remained lower than the 99.20% achieved by the proposed SFOA-kNN model. Conclusions: The results indicate that combining entropy-based feature extraction with SFOA-driven feature selection and kNN classification significantly enhances diagnostic accuracy while reducing computational complexity, highlighting the strong potential of the proposed framework for integration into computer-aided diagnosis systems to support clinical decision-making.

Background/Objective: Percutaneous coronary intervention (PCI) has a pivotal role in the treatment of coronary artery disease (CAD). Although PCI is generally guided only angiographically, advancements in intravascular imaging, particularly in optical coherence tomography (OCT), may offer significant advantages. OCT provides high-resolution cross-sectional images that allow for a more detailed assessment of lesion characteristics and procedural outcomes, which are not fully available with angiography. These findings are associated with or predictive of major adverse cardiovascular events (MACE), encouraging the use of OCT in PCI procedures. This study sought to characterize the role of post-PCI OCT imaging in PCI optimization in patients with CAD. Methods: This retrospective study includes patients who underwent OCT-guided PCI. A total of 64 patients with various types of CAD were included. The primary endpoint was the identification of suboptimal stent implantation as evaluated with OCT after stent implantation, and the secondary endpoint was the assessment of the possibility to achieve optimal stent implantation after further OCT-guided optimization based on standard definitions of optimal PCI. Results: In total, 73 vessels were studied, 42.46% (31) had a stent expansion index (SEI) of < 80%, 31.51% (23) had an SEI between 80-90%, and 26.03% (19) had an SEI of more than 90%. Minimum stent area (MSA) of more than 4.5 mm² was found in 82.19% (60) of vessels, while 17.80% (13) had an MSA below the cut-off value. Suboptimal stent implantation was identified in 35.61% (26) of vessels, including underexpansion 9.58% (7), malapposition 15.06% (11), stent edge dissection 6.85% (5), plaque burden or lipid-rich pool in the stent edges 2.73% (2), and tissue protrusion 1.36% (1). Post-PCI OCT optimization resulted in significant improvements, with only 6.84% (5) of the vessels still not achieving all OCT criteria for optimal stent implantation. Conclusions: In patients with CAD, post-PCI OCT evaluation provided useful information, otherwise unavailable by angiography alone. We identified that 35.61% (26) of the targeted vessels, were suboptimally stented. OCT imaging was able to provide procedural and strategic guidance for optimization until the appropriate results, based on our criteria, were achieved in most of the lesions.

Background/Objectives: This study aims to evaluate mandibular condylar asymmetry in individuals with different types of skeletal malocclusions using a three-dimensional imaging technique, and to determine the relationship between these anomalies and condylar asymmetry. Methods: The study included 100 individuals who visited the Department of Orthodontics Faculty of Dentistry between 2015 and 2020 and underwent Cone-Beam Computed Tomography (CBCT) imaging for various reasons. The evaluation of condylar asymmetry was performed using the Habets method, and measurements were carried out with the NemoCeph V.2017 software. Participants were categorized into skeletal Class I (2-4°), Class II (>4°), and Class III based on their ANB angles. For statistical analysis, frequency distribution, the Kruskal-Wallis H test, and Spearman's correlation coefficient were used. Results: No statistically significant relationship was found between gender and skeletal classifications (p > 0.05). In terms of age, the mean age of individuals in the Class III group was significantly lower than that of those in the Class II group (p < 0.05). A weak positive correlation was observed between condylar and ramal indices in the overall sample (p = 0.029); however, this correlation was found to be moderate and statistically significant only within the Class III group (p = 0.002). Conclusions: The presence of a significant relationship between condylar and ramal asymmetries in individuals with Class III malocclusion indicates an increased risk of developing facial asymmetry if left untreated. These findings underscore the importance of skeletal malocclusions in influencing condylar morphology.