Diagnostics最新文献

Background and Clinical Significance: Since the prevalence of fungal lung infections is increasing, certain agents, such as Cladosporium spp., have emerged as unexpected causes. Cladosporium spp. fungi are ubiquitous in environments such as soil, fruits, and wine corks; they are a part of the normal human skin flora; and they are known respiratory allergens. Case Presentation: A patient with a history of post-COVID-19 syndrome and AIDS presented with lung pathology indicative of an invasive fungal infection. The initial histopathological examination revealed numerous yeast-like cells with narrow-based budding, which led to a mistaken diagnosis of cryptococcosis. However, further detailed examination revealed sparse hyphae in the lung tissue, suggesting a more complex fungal infection. Molecular analyses and sequence BLAST alignment were performed, ultimately identifying the infectious agent as "Cladosporium species novum", a rare cause of invasive pulmonary cladosporiasis. Conclusions: Invasive pulmonary cladosporiasis is a rare condition, and the morphological features of the fungus alone were insufficient to establish a correct diagnosis. A comprehensive pathohistological and molecular approach with bioinformatics tools is essential for the correct identification of rare and potentially life-threatening fungal pathogens in immunocompromised patients.

Background/Objectives: Alzheimer's disease (AD) is a progressive, neurodegenerative disorder, which causes memory loss and loss of cognitive functioning, along with behavioral changes. Early detection is important to delay disease progression, timely intervention and to increase patients' and caregivers' quality of life (QoL). One of the major and primary challenges for preventing any disease is to identify the disease at the initial stage through a quick and reliable detection process. Different researchers across the world are still working relentlessly, coming up with significant solutions. Artificial intelligence-based solutions are putting great importance on identifying the disease efficiently, where deep learning with medical imaging is highly being utilized to develop disease detection frameworks. In this work, a novel and optimized detection framework has been proposed that comes with remarkable performance that can classify the level of Alzheimer's accurately and efficiently. Methods: A powerful vision transformer model (ViT-B16) with three efficient Convolutional Neural Network (CNN) models (VGG19, ResNet152V2, and EfficientNetV2B3) has been trained with a benchmark dataset, 'OASIS', that comes with a high volume of brain Magnetic Resonance Images (MRI). Results: A weighted average ensemble technique with a Grasshopper optimization algorithm has been designed and utilized to ensure maximum performance with high accuracy of 97.31%, precision of 97.32, recall of 97.35, and F1 score of 0.97. Conclusions: The work has been compared with other existing state-of-the-art techniques, where it comes with high efficiency, sensitivity, and reliability. The framework can be utilized in IoMT infrastructure where one can access smart and remote diagnosis services.

Introduction: The sports hernia (SH) is one of the most important causes of groin pain syndrome (GPS). However, despite its importance in GPS etiopathogenesis, SH is one of the least understood and poorly defined clinical conditions in sports medicine. The aim of this systematic review is to clearly define SH from a radiological point of view and to clarify the relationship between the radiological presentation of SH and its clinical manifestation. Methods: The PubMed/MEDLINE, Scopus, ISI, Cochrane Database of Systematic Reviews, and PEDro databases were consulted for systematic reviews on the role of SH in the onset of GPS. The inclusion and exclusion criteria were based on PICO tool. Results: After screening 560 articles, 81 studies were included and summarized in this systematic review. All studies were checked to identify any potential conflict of interest. The quality assessment of each individual study considered was performed in agreement with the Joanna Briggs Institute quantitative critical appraisal tools. Conclusions: The correct definition of SH is "weakness of the posterior wall of the inguinal canal", which, in response to a Valsalva maneuver, forms a bulging that compresses the nerves passing along the inguinal canal. Thus, from an anatomical point of view, SH represents a direct inguinal hernia "in fieri". Furthermore, an excessive dilation of the external inguinal ring represents an indirect sign of possible posterior inguinal canal wall weakness.

Background: Generalizability and domain dependency are critical challenges in developing predictive models for healthcare, particularly in medical diagnostics and radiation oncology. Predictive models designed to assess tumor recurrence rely on comprehensive and high-quality datasets, encompassing treatment planning parameters, imaging protocols, and patient-specific data. However, domain dependency, arising from variations in dose calculation algorithms, computed tomography (CT) density conversion curves, imaging modalities, and institutional protocols, can significantly undermine model reliability and clinical utility. Methods: This study evaluated dose calculation differences in the head and neck cancer treatment plans of 19 patients using two treatment planning systems, Pinnacle 9.10 and RayStation 11, with similar dose calculation algorithms. Variations in the dose grid size and CT density conversion curves were assessed for their impact on domain dependency. Results: Results showed that dose grid size differences had a more significant influence within RayStation than Pinnacle, while CT curve variations introduced potential domain discrepancies. The findings underscore the critical role of precise and standardized treatment planning in enhancing the reliability of predictive modeling for tumor recurrence assessment. Conclusions: Incorporating treatment planning parameters, such as dose distribution and target volumes, as explicit features in model training can mitigate the impact of domain dependency and enhance prediction accuracy. Solutions such as multi-institutional data harmonization and domain adaptation techniques are essential to improve model generalizability and robustness. These strategies support the better integration of predictive modeling into clinical workflows, ultimately optimizing patient outcomes and personalized treatment strategies.

Background/Objectives: Mean kurtosis (MK) values in simple diffusion kurtosis imaging (SDI)-a type of diffusion kurtosis imaging (DKI)-have been reported to be useful in the diagnosis of head and neck malignancies, for which pre-processing with smoothing filters has been reported to improve the diagnostic accuracy. Multi-parameter analysis using DKI in combination with other image types has recently been reported to improve the diagnostic performance. The purpose of this study was to evaluate the usefulness of machine learning (ML)-based multi-parameter analysis using the MK and apparent diffusion coefficient (ADC) values-which can be acquired simultaneously through SDI-for the differential diagnosis of benign and malignant head and neck tumors, which is important for determining the treatment strategy, as well as examining the usefulness of filter pre-processing. Methods: A total of 32 pathologically diagnosed head and neck tumors were included in the study, and a Gaussian filter was used for image pre-processing. MK and ADC values were extracted from pixels within the tumor area and used as explanatory variables. Five ML algorithms were used to create models for the prediction of tumor status (benign or malignant), which were evaluated through ROC analysis. Results: Bi-parameter analysis with gradient boosting achieved the best diagnostic performance, with an AUC of 0.81. Conclusions: The usefulness of bi-parameter analysis with ML methods for the differential diagnosis of benign and malignant head and neck tumors using SDI data were demonstrated.

Background: Recent studies have analyzed some cytokines in patients with papillary thyroid carcinoma (PTC), but simultaneous analysis of multiple cytokines remains rare. Nonetheless, the simultaneous assessment of multiple cytokines is increasingly recognized as crucial for understanding the cytokine characteristics and developmental mechanisms in PTC. In addition, studies applying artificial intelligence (AI) to discriminate patients with PTC based on serum multiple cytokine data have been performed rarely. Here, we measured and compared 46 cytokines in patients with PTC and healthy individuals, applying AI algorithms to classify the two groups. Methods: Blood serum was isolated from 63 patients with PTC and 63 control individuals. Forty-six cytokines were analyzed simultaneously using Luminex assay Human XL Cytokine Panel. Several laboratory findings were identified from electronic medical records. Student's t-test or the Mann-Whitney U test were performed to analyze the difference between the two groups. As AI classification algorithms to categorize patients with PTC, K-nearest neighbor function, Naïve Bayes classifier, logistic regression, support vector machine, and eXtreme Gradient Boosting (XGBoost) were employed. The SHAP analysis assessed how individual parameters influence the classification of patients with PTC. Results: Cytokine levels, including GM-CSF, IFN-γ, IL-1ra, IL-7, IL-10, IL-12p40, IL-15, CCL20/MIP-α, CCL5/RANTES, and TNF-α, were significantly higher in PTC than in controls. Conversely, CD40 Ligand, EGF, IL-1β, PDGF-AA, and TGF-α exhibited significantly lower concentrations in PTC compared to controls. Among the five classification algorithms evaluated, XGBoost demonstrated superior performance in terms of accuracy, precision, sensitivity (recall), specificity, F1-score, and ROC-AUC score. Notably, EGF and IL-10 were identified as critical cytokines that significantly contributed to the differentiation of patients with PTC. Conclusions: A total of 5 cytokines showed lower levels in the PTC group than in the control, while 10 cytokines showed higher levels. While XGBoost demonstrated the best performance in discriminating between the PTC group and the control group, EGF and IL-10 were considered to be closely associated with PTC.

Introduction: Among uterine tumors resembling ovarian sex cord tumors (UTROSCTs), it has been suggested that GREB1-rearranged cases are biologically distinct from ESR1-rearranged cases and might be considered as a separate entity. Objectives: The aim of this systematic review was to assess the difference between GREB1- and ESR1-rearranged UTROSCTs with regard to several clinico-pathological parameters. Methods: Three electronic databases were searched from their inception to February 2025 for all studies assessing the presence of GREB1 and ESR1 rearrangements in UTROSCTs. Exclusion criteria comprised overlapping patient data, case reports, and reviews. Statistical analysis was performed to compare clinicopathological variables between GREB1- and ESR1-rearranged UTROSCTs. Dichotomous variables were compared by using Fisher's exact test; continuous variables were compared by using Student's t-test. A p-value < 0.05 was considered significant. Results: Six studies with 88 molecularly classified UTROSCTs were included. A total of 36 cases were GREB1-rearranged, and 52 cases were ESR1-rearranged. GREB1-rearranged UTROSCTs showed a significantly older age (p < 0.001), larger tumor size (p = 0.002), less common submucosal/polypoid growth (p = 0.005), higher mitotic index (p = 0.010), more common LVSI (p = 0.049), and higher likelihood to undergo hysterectomy (p = 0.008) compared to ESR1-rearranged cases. No significant differences were detected with regard to margins, cytological atypia, necrosis, retiform pattern, and rhabdoid cells. No significant differences were found in the immunohistochemical expression of any of the assessed markers (wide-spectrum cytokeratins, α-inhibin, calretinin, WT1, CD10, CD56, CD99, smooth muscle actin, desmin, h-caldesmon, Melan-A/MART1, SF1, or Ki67). GREB1-rearranged UTROSCTs showed significantly lower disease-free survival compared to ESR1-rearranged UTROSTCs (p = 0.049). Conclusions: In conclusion, GREB1-rearranged UTROSCTs occur at an older age, are less likely to display a submucosal/polypoid growth, and exhibit larger size, a higher mitotic index, more common lymphovascular space invasion, and lower disease-free survival compared to ESR1-rearranged UTROSCTs. Nonetheless, the similar immunophenotype suggests that they belong to the same tumor family. Further studies are necessary to confirm this point.

Background: Recent studies indicate that the organo-axial subtype of a sigmoid volvulus is more prevalent than the conventional mesentero-axial subtype. Our study aimed to assess the clinical and radiological findings that differentiate between these two subtypes, as well as to ascertain treatment outcomes and prognostic characteristics. Methods: A retrospective review included 54 patients, during which abdominal plain radiographs and computed tomography images were analyzed by two radiologists, and data on recurrence, mortality, and treatment outcomes were documented. Results: The mesentero-axial subtype comprised 40 cases (74%). No distinct radiographic findings were observed to differentiate between the two groups. In computed tomography, the sole significant parameter for differentiation was the number of transition zones. The diameter of the segment exhibiting a volvulus was greater in instances of the mesentero-axial subtype. The endoscopic detorsion treatment proved ineffective in five patients within the mesentero-axial sigmoid volvulus cohort. Conclusions: Identifying these two types of SV on CT images is essential because of their distinct prognoses and therapeutic results.

Background/Objectives: The detection of unknown primary tumours in the palatine tonsils (PTs) on imaging relies heavily on asymmetry in size between the right and left sides, but the expected normal range in asymmetry is not well documented. This study aimed to document the expected range of asymmetry in the size of the PTs in adults without cancer. Methods: This retrospective study evaluated 250 pairs of normal PTs on MRIs of adults without head and neck cancer. The size (volume, V) of the PTs on the left and right sides were measured, and the percentage difference in volume (ΔV%) between the two sides was calculated. An additional analysis of PT volumes in 29 patients with ipsilateral early-stage palatine tonsillar cancer (PTCs) was performed. Results: In patients without PTC, the normal PTs had a mean volume of 3.0 ± 1.7 cm³, and there was a difference in size between the left and right PTs, showing a median ΔV% of 11.6% (range: 0.1-79.0%); most patients had a ΔV% of ≤40% (95%) for PTs. In patients with ipsilateral PTC, the normal PT had a smaller size compared with PTC (p < 0.01), showing a median ΔV% of 132.9% (range: 8.5-863.2%). Compared with patients without PTC, those with PTC showed a greater ΔV% (p < 0.01). An optimal ΔV% threshold of >39.6% achieved the best accuracy of 95% for identifying PTC. Conclusions: PTs are asymmetrical in size in adults without PTC. An additional analysis involving patients with PTC confirmed a threshold of ΔV% of 40% for PTs, which may be clinically valuable to help detect pathology using MRI.

The increasing prevalence of cardiovascular complications in cancer patients due to cardiotoxic treatments has necessitated advanced monitoring and predictive solutions. Cardio-oncology is an evolving interdisciplinary field that addresses these challenges by integrating artificial intelligence (AI) and smart cardiac devices. This comprehensive review explores the integration of artificial intelligence (AI) and smart cardiac devices in cardio-oncology, highlighting their role in improving cardiovascular risk assessment and the early detection and real-time monitoring of cardiotoxicity. AI-driven techniques, including machine learning (ML) and deep learning (DL), enhance risk stratification, optimize treatment decisions, and support personalized care for oncology patients at cardiovascular risk. Wearable ECG patches, biosensors, and AI-integrated implantable devices enable continuous cardiac surveillance and predictive analytics. While these advancements offer significant potential, challenges such as data standardization, regulatory approvals, and equitable access must be addressed. Further research, clinical validation, and multidisciplinary collaboration are essential to fully integrate AI-driven solutions into cardio-oncology practices and improve patient outcomes.