Diagnostics最新文献

Background/Objective: To evaluate the association between optical coherence tomography angiography (OCTA)-derived choriocapillaris flow (CCflow), retinal vascular fractal dimension (FD), and drusen burden in eyes with dry age-related macular degeneration (AMD). Methods: This retrospective study included 113 eyes from 73 patients with dry AMD. Eyes were classified into large and small drusen groups based on median drusen area. OCTA-derived CCflow and FD indices of the superficial and deep capillary plexuses were analyzed. Patient-level clustered analyses were performed using linear mixed-effects and generalized estimating equation models to account for inter-eye correlation. Results: Eyes with large drusen showed significantly lower CCflow compared with those with small drusen (p < 0.001), whereas FDsup did not differ between groups, and FDdeep demonstrated only a near-significant trend toward higher values. CCflow was moderately and negatively correlated with drusen area (ρ = -0.452, p < 0.001), whereas FDdeep showed no significant correlation in unadjusted analyses (ρ = 0.137, p = 0.148). In patient-level age-adjusted multivariable models accounting for inter-eye dependency, CCflow remained independently associated with drusen burden, while FDdeep demonstrated an independent association only after adjustment for age. Conclusions: Reduced CCflow is independently associated with increased drusen burden in dry AMD. FD metrics provide complementary descriptive information regarding microvascular remodeling but do not function as independent biomarkers. CCflow may serve as a robust quantitative indicator of early choroidal compromise in dry AMD.

Background: Although thrombophilia represents a major risk factor for adverse maternal outcomes, particularly in the postpartum period, methods for its systematic screening remain costly and limited. This case-control study aimed to evaluate whether routinely available hematological inflammatory indices combined with postpartum uterine ultrasonographic assessment can predict the presence of thrombophilia in peripartum women. Methods: Eighty women with previously diagnosed and treated thrombophilia undergoing cesarean section at term were prospectively enrolled and matched by age and parity with 80 control patients without thrombophilia. Hematological inflammatory markers derived from complete blood counts obtained within 24 h before delivery and the postpartum uterine ultrasonographic score were analyzed. Multivariable logistic regression was performed to identify independent predictors of thrombophilia, and odds ratios (ORs) with 95% confidence intervals (CIs) were calculated. Results: Impaired postpartum uterine involution-defined as a postpartum uterine ultrasonographic score ic-was significantly more frequent in thrombophilia cases than in controls (OR > 1, 95% CI excluding 1; p < 0.05). Thrombophilia patients exhibited significantly higher Neutrophil-to-Lymphocyte and Platelet Ratio and Cumulative Inflammatory Index values when compared with the controls, with both emerging as independent predictors in the multivariable model (OR > 1, 95% CI excluding 1; p < 0.05). The final model demonstrated good discriminative performance, with an overall classification accuracy of 88.6% and excellent specificity for excluding thrombophilia when the postpartum uterine ultrasonographic score was 0. Conclusions: The integration of postpartum uterine ultrasonographic assessment with simple hematological inflammatory indices provides a non-invasive, cost-effective approach for identifying women at increased risk of underlying thrombophilia in the immediate postpartum period. This strategy may support targeted thromboprophylaxis and rationalize the use of specialized thrombophilia testing.

Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) and Obesity Hypoventilation Syndrome (OHS) are core components of the obesity-related respiratory disease spectrum, and their comorbidity has become a major challenge in the global public health field. This review systematically summarizes the epidemiological characteristics, pathophysiological mechanisms, diagnostic criteria, diagnostic technologies and treatment strategies of OSAHS-OHS comorbidity, with a focus on the cutting-edge progress of digital therapeutics and metabolic intervention, as well as the historical evolution and current status of clinical management. We also conduct an in-depth analysis of the unresolved controversies and practical challenges in the current clinical management of this comorbidity. OSAHS-OHS comorbid patients have a significantly higher risk of cardiovascular complications than those with a single disease, and chronic intermittent hypoxia (CIH) forms a vicious cycle with obesity through multiple pathophysiological pathways. The combination of multi-dimensional assessment tools and portable monitoring devices has improved the screening efficiency of OSAHS-OHS comorbidity, and the selection of respiratory support therapies such as continuous positive airway pressure (CPAP) and non-invasive ventilation (NIV) depends on patient phenotypes. Digital therapeutics and novel metabolic intervention drugs have shown promising clinical value in the management of this comorbidity. The multidisciplinary collaboration model is the key to improving the prognosis of comorbid patients, while current clinical management is still faced with challenges such as policy lag, ethical controversies and uneven resource allocation. Future research should focus on individualized therapeutic targets, the integration of digital technologies and the optimization of health policies to achieve precise and efficient management of OSAHS-OHS comorbidity.

Background: Neuroimaging protocols for neurotologic disease are often developed without consideration of patient-specific factors such as biological differences, clinical presentation variability, and comorbidities. This lack of tailored design contributes to insufficient detection, delayed diagnosis, and inappropriate treatment. Objectives: To critically examine the literature on diagnostic limitations of neuroimaging for neurotologic lesions and identify gaps in protocol validation, accuracy, and clinical translation. Methods: A systematic review of PubMed and Google Scholar was conducted, focusing on studies published between 2015 and 2025 that evaluated diagnostic imaging outcomes in patients with neurotologic lesions. Eligible studies included prospective cohorts, retrospective analyses, and consensus statements. Outcomes of interest included the sensitivity and specificity of imaging modalities, prevalence of misdiagnosis, and the influence of biological, anatomical, and clinical variability on diagnostic performance. Results: The literature demonstrates that neurotologic disorders are frequently associated with diagnostic challenges, including atypical clinical presentations, overlapping symptoms, and stroke mimics, which complicate image interpretation. Standard magnetic resonance imaging (MRI) protocols often miss subtle or early ischemic changes, resulting in delayed intervention. Few studies stratify outcomes by patient characteristics, and most protocols were developed in generalized populations without comprehensive validation. Evidence on advanced imaging modalities (positron emission tomography (PET), single-photon emission computed tomography (SPECT), high-resolution MRI) remains limited, and large-scale prospective studies addressing diagnostic accuracy gaps are lacking. In summary, a total of 27 studies met inclusion criteria. Conclusions: Current neuroimaging methods are insufficiently validated across diverse patient populations, contributing to the underdiagnosis and mismanagement of neurotologic disease. Improved diagnostic accuracy will require large-scale, prospective research, standardized outcome reporting, and imaging protocols designed to account for patient-specific variability.

Background/Objectives: Response to neoadjuvant chemotherapy (NAC) varies substantially among breast cancer patients and is only partially explained by tumor-intrinsic factors. The gut microbiome has emerged as a potential modulator of chemotherapy efficacy, yet its role in breast cancer remains underexplored. This study aimed to characterize gut microbial composition, functional potential, and microbially derived metabolites in breast cancer patients undergoing NAC. Methods: baseline stool samples from 39 chemotherapy-naïve breast cancer patients undergoing NAC were analyzed using shotgun metagenomic sequencing and targeted metabolomics. Patients were stratified by pathological complete response (pCR, n = 17; no pCR, n = 22). Microbial taxonomic and functional profiles, short-chain fatty acids (SCFAs) and bile acids were assessed, with subgroup analysis performed in triple-negative breast cancer (TNBC). Results: Patients achieving pCR exhibited significantly higher baseline microbial richness compared to non-responders (p = 0.040). Differential abundance analysis revealed enrichment of Dialister, Kineothrix, and Jutongia in responders, whereas Rothia, Leuconostoc, Klebsiella, Jingyaoa, Cuneatibacter, Youxingia, and Bittarella were enriched in non-responders. SCFAs (acetate, propionate and butyrate) positively correlated with microbial glucose catabolic pathways, while caproate was negatively associated with multiple amino acid, lipid, vitamin, and cell wall biosynthesis pathways, including peptidoglycan maturation. Metabolomic analysis identified higher deoxycholic acid (DCA) levels in non-responders and increased C6 levels in responders, although these associations did not remain significant after multiple testing correction. Similar trends were observed in the TNBC subgroup (n = 15). Conclusions: Baseline gut microbiome diversity, taxonomic composition, and functional metabolic potential are associated with response to neoadjuvant chemotherapy in breast cancer, supporting the gut microbiome and its produced metabolites as a potential biomarker of treatment efficacy.

Background/Objectives: Accurate and reliable automated dermoscopic lesion classification remains challenging. This is due to pronounced dataset bias, limited expert-annotated data, and poor cross-dataset generalization of conventional supervised deep learning models. In clinical dermatology, these limitations restrict the deployment of data-driven diagnostic systems across diverse acquisition settings and patient populations. Methods: Motivated by these challenges, this study proposes a transformer-based, dermatology-specific foundation model. The model learns transferable visual representations from large collections of unlabeled dermoscopic images via self-supervised pretraining. It integrates large-scale dermatology-oriented self-supervised learning with a hierarchical vision transformer backbone. This enables effective capture of both fine-grained lesion textures and global morphological patterns. The evaluation is conducted across three publicly available dermoscopic datasets: ISIC 2018, HAM10000, and PH2. The study assesses in-dataset, cross-dataset, limited-label, ablation, and computational-efficiency settings. Results: The proposed approach achieves in-dataset classification accuracies of 94.87%, 97.32%, and 98.17% on ISIC 2018, HAM10000, and PH2, respectively. It outperforms strong transformer and hybrid baselines. Cross-dataset transfer experiments show consistent performance gains of 3.5-5.8% over supervised counterparts. This indicates improved robustness to domain shift. Furthermore, when fine-tuned with only 10% of the labeled training data, the model achieves performance comparable to fully supervised baselines. Conclusions: This highlights strong data efficiency. These results demonstrate that dermatology-specific foundation learning offers a principled and practical solution for robust dermoscopic lesion classification under realistic clinical constraints.

Background/Objectives: Breast cancer is the most common cancer in women. The neutrophil/lymphocyte ratio (NLR) is an emerging biomarker from peripheral blood that has been associated with breast cancer prognosis in some studies; however, some studies fail to demonstrate an association. We stratified breast cancer patients into invasive lobular carcinoma (ILC) and invasive ductal carcinoma (IDC) cohorts to evaluate if any meaningful association could be found in either cohort between NLR and mortality. Additionally, no prior studies have examined the relationship between NLR and background parenchymal enhancement (BPE) on breast MRI, an imaging feature linked to increased breast cancer risk and a potential imaging prognostic biomarker, so we examined the relationship between BPE and NLR in the two cohorts. Methods: This retrospective study included 794 breast cancer patients who had either IDC or ILC. Radiologists' MRI reports and their BI-RADS categorization of BPE (1 = minimal, 2 = mild, 3 = moderate, 4 = marked) were extracted and recorded. The NLR was calculated from blood counts obtained prior to treatment. Tumor characteristics were also recorded. Results: For patients with ILC, NLR was found to be associated with mortality. Additionally, patients with ILC and a high BPE had a significantly higher mean NLR compared to all other groups, including low BPE groups and all IDC groups. Conclusions: There is potential value in using NLR, a readily available blood biomarker, in models predicting prognosis in ILC patients.

Background/Objectives: Accurate prognostic assessment remains crucial in metastatic renal cell carcinoma (mRCC), especially as treatment options have expanded beyond vascular endothelial growth factor (VEGF)-targeted therapies to include immune checkpoint inhibitors (ICIs) and ICI-TKI combinations. The widely used IMDC classification shows important limitations in the modern therapeutic era, highlighting the need for complementary prognostic tools. In this context, the Meet-URO and CANLPH scores-incorporating clinical, inflammatory, and nutritional markers-have emerged as promising alternatives. To evaluate and compare the prognostic performance of the Meet-URO and CANLPH scoring systems in a real-world mRCC cohort predominantly treated with first-line tyrosine kinase inhibitor (TKI) monotherapy due to limited access to ICI-based combinations. Methods: This retrospective single-center study included 112 patients with mRCC. The Meet-URO score was calculated for all patients, while the CANLPH score was assessed in 56 patients with complete laboratory data. CAR, NLR, and PHR were computed using baseline pre-treatment measurements. Overall survival (OS) and progression-free survival (PFS), the latter defined exclusively for first-line therapy, were estimated using the Kaplan-Meier method. Correlations between inflammatory markers and survival outcomes were analyzed using Spearman's rho. Results: Meet-URO demonstrated clear prognostic stratification across all five categories, with the most favorable outcomes in score group 2 and progressively poorer OS and PFS in higher-risk groups. CANLPH also showed meaningful survival discrimination, with the highest inflammatory group (score 3) exhibiting markedly reduced OS and PFS. CAR was the strongest individual predictor of survival, while NLR and PHR showed weaker associations. Conclusions: Both Meet-URO and CANLPH provide strong, complementary prognostic information in mRCC, even in a cohort largely treated with TKI monotherapy. Their integration into routine risk assessment may enhance clinical decision-making, particularly in resource-limited settings.

Background: Current histopathology- and molecular-based gold standards for diagnosing adult diffuse gliomas (ADGs) have inherent limitations in reproducibility and interobserver concordance, while being time-intensive and resource-demanding. Although hyperspectral imaging (HSI)-based computer-aided pathology shows potential for automated diagnosis, it often yields suboptimal accuracy due to the lack of complementary spatial and structural tumor information. This study introduces a multimodal fusion framework integrating HSI with routinely acquired preoperative magnetic resonance imaging (MRI) to enable automated, high-precision ADG diagnosis. Methods: We developed the Hyperspectral Attention Fusion Network (HAFNet), incorporating residual learning and channel attention to jointly capture HSI patterns and MRI-derived radiomic features. The dataset comprised 1931 HSI cubes (400-1000 nm, 300 spectral bands) from histopathological patches of six major World Health Organization (WHO)-defined glioma subtypes in 30 patients, together with their routinely acquired preoperative MRI sequences. Informative wavelengths were selected using mutual information. Radiomic features were extracted with the PyRadiomics package. Model performance was assessed via stratified 5-fold cross-validation, with accuracy and area under the curve (AUC) as primary endpoints. Results: The multimodal HAFNet achieved a macro-averaged AUC of 0.9886 and a classification accuracy of 98.66%, markedly outperforming the HSI-only baseline (AUC 0.9267, accuracy 87.25%; p < 0.001), highlighting the complementary value of MRI-derived radiomic features in enhancing discrimination beyond spectral information. Conclusions: Integrating HSI biochemical and microstructural insights with MRI radiomics of morphology and context, HAFNet provides a robust, reproducible, and efficient framework for accurately predicting 2021 WHO types and grades of ADGs, demonstrating the significant added value of multimodal integration for precise glioma diagnosis.

Background/Objectives: Maxillofacial reconstruction with a vascularized free bone flap for facial contour restoration serves as a foundation for dentition rehabilitation. Although state-of-the-art studies have reported promising results with implant-supported prostheses in such cases, evidence for dental implant prognosis remains insufficient. This study aims to synthesize the mid-term clinical outcomes of implants placed in vascularized free bone flaps, taking into account the biological responses and associated complications. Methods: Studies with a minimal 3-year follow-up, no less than 10 patients, and reporting implant survival/success rate were included. Literature published from 2000 to 2025 was collected from PubMed, Embase, and Scopus. Meta-analyses were performed to pool the implant survival and success rates for the entire cohort, the biological complication rates, the odds ratio for radiotherapy, and the pooled implant failure rates associated with radiotherapy. Parameters related to biological prognosis were collected. ROBINS-E and NOS scale were used to assess the risk of bias. Results: Of the 949 records identified, 14 retrospective and 2 cohort studies were included, yielding a total of 1165 dental implants placed in free bone flaps. On the implant level, meta-analysis demonstrated a pooled implant survival rate of 97.9% (95% CI: 0.922-0.994, I² = 64.4%) and a pooled implant success rate of 88.1% (95% CI: 0.803-0.931, I² = 68.3%). The pooled biological complication rate was 8.6% (95% CI: 0.052-0.138; I² = 69.5%). Among patients who underwent radiotherapy, the pooled implant failure rate was 13.7% (95% CI: 0.087-0.210; I² = 0.0%; p = 0.4702) with an odds ratio of 3.086 (I² = 66.5%) for radiotherapy-associated implant failure. Conclusions: Implant-related outcomes in these complex cases are generally acceptable, with high survival, moderately high success rates and overall stable biological response. Additionally, radiotherapy adds to the risk of implant failure on implant level. However, the statistical heterogeneity and inconsistent definitions of biological outcomes in the literature suggest that caution is warranted when planning implant therapy in these cases. Further studies with long-term follow-up, focused on peri-implant tissue conditions and adopting more stratified study designs to minimize confounding factors, are needed.