Diagnostics最新文献

Pulmonary hypertension (PH) is a complex condition in which early diagnosis remains challenging, particularly in patients with exertional symptoms and normal or borderline resting haemodynamics. Although right heart catheterisation is the diagnostic gold standard, transthoracic echocardiography is the recommended first-line non-invasive test. However, resting echocardiography provides only a static assessment and may underestimate disease severity in early or latent pulmonary vascular disease due to preserved pulmonary vascular compliance and adaptive right ventricular responses. Because pulmonary haemodynamics are intrinsically flow-dependent, pathological abnormalities may only emerge during increased cardiac output. Stress echocardiography, performed using exercise or pharmacological stress, enables dynamic evaluation of pulmonary pressure responses, cardiac output augmentation, right ventricular contractile reserve, and ventricular interaction. Increasing evidence indicates that stress echocardiography can unmask abnormal pulmonary pressure-flow relationships, impaired pulmonary vascular reserve, and reduced right ventricular-pulmonary arterial coupling that are not apparent at rest, thereby improving functional and haemodynamic characterisation in selected patients. This Diagnostic Review outlines the physiological basis for stress echocardiographic assessment of pulmonary circulation, proposes practical recommendations for patient selection and testing protocols, and provides a framework for interpretation centered on pressure-flow relationships rather than absolute pulmonary pressure thresholds. Particular attention is given to clinical scenarios with high diagnostic yield, including unexplained exertional dyspnoea, systemic sclerosis, suspected heart failure with preserved ejection fraction, at-risk relatives of patients with pulmonary arterial hypertension, selected athletes, and paediatric populations. Stress echocardiography should not be considered a standalone diagnostic test for PH but, when performed in experienced centers and integrated within structured diagnostic pathways, it represents a valuable non-invasive adjunct to guide referral for invasive haemodynamic confirmation.

Background: Noninvasive biomarkers for the detection and monitoring of glioblastoma (GBM) are needed to improve clinical outcomes for patients. The objective of this pilot study was to evaluate the expression of a panel of 48 pre-selected microRNAs (miRNAs) in plasma specimens from GBM patients versus healthy controls to identify candidate miRNA biomarkers for noninvasive diagnosis of GBM. Methods: Selection of candidate miRNA biomarkers was based on a comprehensive literature review and data mining. RNA was extracted from plasma samples obtained prior to resection from patients with GBM (n = 30) and age- and sex-matched healthy controls (n = 30), as well as from matched FFPE GBM tissue samples when available (n = 3). Expression levels of 48 miRNAs were assessed in all samples, and expression data was processed using proprietary software to generate potential biomarkers and train linear classifiers. Results: Overall miRNA expression patterns were similar between matched plasma and FFPE tumor tissues in patients with GBM. miRNA levels were examined in pairs to determine the ratio between two miRNAs, which served to normalize the data. The top five miRNA pairs for distinguishing between GBM and healthy control plasma included miR-17-5p/miR-19b-3p (AUC 0.93, 95% CI = 0.870, 0.970), miR-20a-5p/miR-19b-3p (AUC 0.93, 95% CI = 0.870, 0.970), miR-93-5p/miR-92a-3p (AUC 0.92, 95% CI = 0.875, 0.965), miR-17-5p/miR-92a-3p (AUC 0.91, 95% CI = 0.865, 0.955), and miR-93-5p/miR-19b-3p (AUC 0.90, 95% CI = 0.850, 0.950). For the development of a multi-biomarker combination classifier consisting of up to three miRNA pair biomarkers, miRNA pairs with an AUC ≥ 0.8 were selected to build equal-weight linear classifiers. All possible combinations of three high-performing miRNA pairs were tested across the 60 samples. The top classifier (miR-20a-5p/miR-451a, miR-582-5p/miR-222-3p, and miR-17-5p/miR-222-3p) achieved an AUC value of 0.992, sensitivity of 0.93, specificity of 1, and accuracy of 0.97. Conclusions: These findings support the continued development of a plasma-based miRNA molecular diagnostic approach for the detection of GBM. The strong discriminatory performance observed in this study, including high AUC values, highlights the potential of circulating miRNA signatures as a minimally invasive diagnostic tool. As a pilot analysis, this work establishes a foundation for future prospective studies in larger, independent cohorts-including relevant disease control populations-to further define clinical performance, specificity, and utility in diagnostic and monitoring settings. Collectively, these results represent an important step toward the translation of plasma-based miRNA profiling into clinical application for GBM.

Introduction: Osteoprotegerin (OPG) is recognized as an emerging biomarker for atherosclerosis. We hypothesized that atherosclerotic lesions localized across multiple vascular beds would result in greater elevations in OPG levels in the blood. Therefore, our study aimed to assess serum OPG levels and their confounding factors in patients with hemodynamically significant multivessel atherosclerosis in varying locations. Subjects and Methods: A case-control study included 222 selected outpatients aged 50 years or older (46.4% women) with atherosclerosis confirmed by imaging (Doppler ultrasound and CT angiography) treated at a single angiology clinic. Data concerning age, smoking status, comorbidity (hypertension, diabetes mellitus, history of stroke, myocardial infarction, coronary revascularization procedures), medication, lipid profile, serum creatinine, and homocysteine levels were retrieved from medical records. Additionally, serum OPG levels were measured. Patients were divided according to serum OPG levels into terciles and the number of involved vascular beds [carotid artery disease, coronary heart disease (CHD), lower-extremity peripheral artery disease (PAD), abdominal aorta aneurysm (AAA)]. Results: The distribution of carotid artery disease, CHD, PAD, and AAA did not differ across the OPG terciles. Additionally, we did not observe differences in OPG levels between specific and multiple locations of atherosclerotic lesions. Subjects with the highest OPG levels were the oldest (75.0 ± 8.4 vs. 69.8 ± 7.1 years in the lowest tercile; p < 0.001) and were characterized by the worst kidney function (eGFR 60.8 ± 16.8 vs. 74.1 ± 13.5 mL/min/1.73 m²; p < 0.001). Conclusions: The serum OPG level did not reveal the specific location of atherosclerosis. Impaired renal function appears to be the primary determinant of serum OPG levels and a key confounder, complicating the interpretation of serum OPG as a biomarker of atherosclerosis.

As we conclude this Special Issue of Diagnostics, "Advances in the Diagnosis and Management of Vasculitis," we reflect on a vibrant collection of eleven articles that span the globe from Spain and Poland to the USA, Germany, Romania, and Turkey [...].

Background: Post-sternotomy mediastinitis (PSM) remains one of the most serious complications of cardiac surgery. This study aimed to evaluate the clinical features, management strategies, and outcomes of patients with PSM, comparing surgical and conservative treatment approaches. Methods: We retrospectively reviewed all cases of PSM (March 2014-July 2024) at our tertiary referral centre. Results: A total of 81 patients were included (39 surgically treated, 42 conservatively managed). The length of hospital stay was significantly longer in the surgical group (p = 0.003), and blood transfusions were more frequent (p = 0.005). Patients in the conservative group had higher DSW-STS risk scores (p = 0.014). Positive blood cultures were significantly more common among surgically treated patients (p < 0.001). The in-hospital mortality rate was 2.5% overall, with no difference between groups. Conclusions: These results likely reflect the greater clinical severity and complexity of patients selected for surgery, rather than an adverse effect of the procedure itself. Surgical treatment of PSM is associated with longer hospitalisation and greater need for blood transfusion, reflecting the higher clinical complexity of these cases. Nevertheless, outcomes in terms of survival were comparable to conservative management, supporting an individualised, multidisciplinary approach to optimise care for patients with post-sternotomy mediastinitis.

The widespread implementation of population-based mammographic screening has markedly increased the detection of ductal carcinoma in situ (DCIS), without a proportional reduction in breast cancer-specific mortality. This divergence has intensified concerns regarding overdiagnosis and overtreatment and has prompted increasing interest in treatment de-escalation and active surveillance strategies. Breast imaging remains indispensable for DCIS detection, extent assessment, and longitudinal monitoring. However, although imaging features correlate with histopathologic risk factors at the population level, their ability to predict individual biological progression is inherently probabilistic and limited. Overinterpretation of imaging phenotypes as surrogates of invasive destiny risks inappropriate reassurance or unjustified therapeutic escalation, particularly in the context of high-sensitivity modalities that may overestimate disease extent or trigger additional interventions without proven outcome benefits. This review examines the modality-specific roles of mammography, ultrasound, breast magnetic resonance imaging (MRI), contrast-enhanced mammography (CEM), and emerging artificial intelligence (AI) approaches within contemporary DCIS management, with particular attention to their implementation in active surveillance trials such as LORIS, COMET, LORD, and LORETTA. Across modalities, imaging primarily reflects lesion morphology, spatial distribution, and vascular behaviour, and functions most reliably as a risk-filtering and safety-gating instrument aimed at excluding radiologically unsafe scenarios, including occult invasion, underestimated disease extent, or imaging evolution incompatible with continued observation. By delineating both the capabilities and the epistemological limits of imaging, this review proposes a structured clinical decision framework in which imaging supports-but does not independently determine-risk-adapted management. Disciplined integration of imaging into multidisciplinary decision-making is essential to enable safe de-escalation, prevent false reassurance, and align DCIS care with patient-centred and value-based principles.

Background/Objectives: To assess the diagnostic performance of dynamic susceptibility contrast (DSC) perfusion MRI parameters and machine learning methods for differentiating intracranial dural metastases (IDMs) from meningiomas. Methods: This retrospective diagnostic accuracy study included 56 patients (mean age: 57.6 ± 11.2 years; 20 men) with dural-based intracranial lesions (65 lesions): 18 patients with IDM (27 lesions) and 38 patients with meningiomas (38 lesions). All patients underwent DSC perfusion MRI. Relative cerebral blood volume (rCBV), relative cerebral blood flow (rCBF), diffusion metrics, and dynamic time-signal intensity curve parameters were extracted. Group comparisons were performed using nonparametric statistical tests. Machine learning models, including linear discriminant analysis (LDA), were developed using patient-level grouped nested cross-validation to avoid data leakage. Diagnostic performance was evaluated using out-of-fold receiver operating characteristic (ROC) analysis, calibration assessment, and clinically oriented thresholds prioritizing metastasis sensitivity. Results: rCBV_mean and rCBF_mean were significantly higher in meningiomas than in dural metastases (median rCBV_mean: 4.71 vs. 2.95; median rCBF_mean: 3.44 vs. 2.02; both p < 0.001). Diffusion metrics and dynamic perfusion parameters, including wash-in time, percentage signal recovery, and wash-out slope, did not differ significantly between groups (p > 0.05). Univariate ROC analysis demonstrated strong discrimination for both rCBF_mean (AUC: 0.82; 95% CI: 0.72, 0.90) and rCBV_mean (AUC: 0.82; 95% CI: 0.72, 0.91). An LDA model integrating rCBF_mean and rCBV_mean achieved an out-of-fold AUC of 0.81 (95% CI: 0.72, 0.89) and improved specificity (85%) at a fixed metastasis sensitivity of 85%. Conclusions: DSC perfusion MRI-derived rCBF and rCBV are robust biomarkers for differentiating IDMs from meningiomas. An interpretable machine learning model integrating these parameters improves diagnostic specificity while maintaining high sensitivity.

Background/Objectives: Spread through air spaces (STAS) represents an aggressive invasion pattern in lung cancer and is associated with unfavorable oncologic outcomes. As STAS is currently identifiable only on postoperative pathology, reliable preoperative, noninvasive prediction remains a clinical challenge. This study aimed to evaluate the feasibility of predicting STAS using ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT)-derived radiomic and clinicoradiomic models. Methods: In this retrospective study, patients who underwent surgical resection for lung cancer with available preoperative ¹⁸F-FDG PET/CT imaging were analyzed. Radiomic features were extracted from intratumoral and peritumoral regions. Clinical, radiomic-only, and combined clinicoradiomic models were developed using LASSO-based feature selection and multivariable logistic regression. Model performance was evaluated using nested cross-validation, receiver operating characteristic analysis, calibration assessment, and decision curve analysis. Results: Radiomic features reflecting intratumoral metabolic characteristics and peritumoral tissue heterogeneity were significantly associated with STAS. The combined clinicoradiomic model demonstrated superior discriminative performance compared with the clinical and radiomic-only models (mean AUC ≈ 0.75), along with favorable calibration (Brier score = 0.20) and improved clinical net benefit across relevant threshold probabilities. Lower eosinophil count, lower SUVmin_tumor, and lower intratumoral SUV skewness emerged as independent predictors of STAS. Conclusions: Preoperative prediction of STAS in lung cancer is feasible using PET/CT-based radiomic analysis integrating intratumoral, peritumoral, and clinical features. This noninvasive approach provides biologically relevant information beyond conventional anatomical assessment and warrants further validation in prospective, multicenter cohorts.

Objectives: This study aimed to develop and validate a hybrid deep learning model combining Convolutional Neural Networks (CNN) and Vision Transformers (ViT) to automatically classify maxillary sinus membrane morphologies on Cone-Beam Computed Tomography (CBCT) images, distinguishing between Normal, Flat, Polypoid, and Obstruction types. Methods: A dataset of 959 CBCT images was collected and categorized into four morphological classes: Normal, Flat, Polypoid and Obstruction. A custom hybrid model was developed, integrating a lightweight residual CNN for local feature extraction, learnable weighted feature fusion with a bidirectional feature pyramid network and a Transformer encoder for global context modeling. The performance of proposed model was compared against six different architectures, including ResNet50, MobileNetV3L and standard ViT models, using accuracy, precision, recall and F1-score metrics. Results: The proposed hybrid model achieved the highest overall accuracy of 98.44%, outperforming six strong CNN and ViT models including ResNet50 (97.92%) and ViT-B16 (86.46%) models. In class-wise analysis, the model demonstrated superior diagnostic capability, particularly for the "Obstruction" class, achieving 100% accuracy. High discrimination was also observed for "Flat" (98.21%) and "Polypoid" (98.04%) morphologies, confirming the model's sensitivity to shape-based features. Conclusions: The proposed hybrid CNN-ViT model successfully classifies maxillary sinus membrane morphologies with high accuracy, effectively overcoming the limitations of standard ViT models on limited datasets. Detection of membrane morphology is vital for predicting surgical risks like membrane perforation and post-operative sinusitis. This model serves as a reliable clinical decision support tool, enabling clinicians to objectively assess specific risk factors before implant surgery and sinus floor elevation.

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.