Objective: To investigate the role of eosinophil counts (EC) in microvascular invasion (MVI) for enhancing the radiomics based diagnostic model. Additionally, its correlation with early recurrence and tumor immune microenvironment was explored.
Methods: Propensity score matching was employed to evaluate on 462 cases whether EC was an independent risk factor for MVI. Subgroup analyses examined EC's effect on MVI across varying hypersplenism degrees. Univariate-multivariate logistic regression identified MVI's independent factors to develop a diagnostic model. Univariate-multivariate COX regression determined early recurrence factors. Co-detection by indexing (CODEX) constructed the immune score (IS), and Spearman correlation analyzed its association with peripheral immunity.
Results: EC was an independent risk factor for MVI (=0.038, OR=1.304 (95% CI: 1.014-1.677)), and its effect on MVI disappeared with the severity of hypersplenism. The diagnostic model with EC was significantly improved (AUC=0.787 (95% CI: 0.737-0.836) vs AUC=0.748(95% CI: 0.694-0.802, =0.005)). MVI was an independent risk factor for early recurrence (<0.001, HR = 2.254 (95% CI: 1.557-3.263)). IS was negatively correlated with lymphocyte counts (R=-0.311, =0.022), and positively correlated with EC (R=0.301, =0.027) and RS (R = 0.315, = 0.018).
Conclusion: EC was an independent risk factor for MVI and was related to the tumor immune microenvironment. EC should be included in the diagnosis of MVI to improve diagnostic efficiency.
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http://dx.doi.org/10.2147/JHC.S484027 | DOI Listing |
Brain Imaging Behav
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Rib pathology is uniquely difficult and time-consuming for radiologists to diagnose. AI can reduce radiologist workload and serve as a tool to improve accurate diagnosis. To date, no reviews have been performed synthesizing identification of rib fracture data on AI and its diagnostic performance on X-ray and CT scans of rib fractures and its comparison to physicians.
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Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland.
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View Article and Find Full Text PDFJ Imaging Inform Med
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College of Engineering, Department of Computer Engineering, Koç University, Rumelifeneri Yolu, 34450, Sarıyer, Istanbul, Turkey.
This study explores a transfer learning approach with vision transformers (ViTs) and convolutional neural networks (CNNs) for classifying retinal diseases, specifically diabetic retinopathy, glaucoma, and cataracts, from ophthalmoscopy images. Using a balanced subset of 4217 images and ophthalmology-specific pretrained ViT backbones, this method demonstrates significant improvements in classification accuracy, offering potential for broader applications in medical imaging. Glaucoma, diabetic retinopathy, and cataracts are common eye diseases that can cause vision loss if not treated.
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