Frontiers in radiology最新文献

Objective: To develop a radiomics-based predictive model for capsular invasion in thymomas by applying machine learning algorithms to non-contrast and contrast-enhanced CT imaging. This study aimed to assess the influence of intratumoural and peritumoural regions on capsular invasion prediction and to compare the performance of models derived from these regions within the same dataset, thereby identifying the optimal predictive model.

Methods: Clinical and imaging data were retrospectively collected from 151 patients with thymoma who underwent treatment at Tianjin Chest Hospital between June 2018 and January 2025. Based on pathological findings, patients were categorised into capsular invasion and non-invasion groups and subsequently randomised into a training set (n = 106) and a test set (n = 45) in a 7:3 ratio. Radiomic feature selection was performed using univariate logistic regression analysis followed by least absolute shrinkage and selection operator (LASSO) regression. Predictive models were developed employing multiple machine learning algorithms, including logistic regression. Model performance was evaluated through receiver operating characteristic (ROC) curve analysis, with sensitivity, specificity, F1 score, and decision curve analysis (DCA) used to assess diagnostic accuracy and clinical applicability. DeLong's test was applied to compare the area under the curve (AUC) values between different models. Calibration curves were generated to evaluate model calibration, and model interpretability was examined using the Shapley Additive exPlanations (SHAP) method.

Results: Comparative analysis of machine learning methods across different tumour regions revealed that the support vector machine (SVM) model, developed using radiomic features from the 4 mm peritumoural region on contrast-enhanced CT scans, demonstrated optimal predictive performance. This model achieved area under the curve (AUC) values of 0.890 [95% confidence interval (CI): 0.823-0.956] in the training cohort and 0.888 (95% CI: 0.792-0.983) in the test cohort.

Conclusion: CT-based radiomics demonstrates efficacy in predicting capsular invasion in thymomas, with the peritumoural region proving particularly significant. This methodology shows potential for supporting clinicians in preoperative treatment strategy formulation.

Background: The optimal management approach for ruptured intracranial aneurysms remains debated, with limited real-world evidence from Latin American populations. This study compared in-hospital outcomes between endovascular coiling and surgical clipping using a hierarchical win ratio (WR) analysis.

Methods: We conducted a single-center retrospective cohort study of 194 patients with ruptured intracranial aneurysms treated at a tertiary referral center (2011-2022). Patients were treated with either endovascular coiling (n = 73) or surgical clipping (n = 121). The primary outcome was the win ratio, analyzing a hierarchical composite endpoint of: (1) in-hospital mortality, (2) unfavorable functional outcome at discharge (modified Rankin Scale >2), (3) major complications, and (4) prolonged ICU stay (>10 days). Secondary analyses included multivariable logistic regression and prespecified subgroup analyses by clinical severity and aneurysm location.

Results: Baseline measured characteristics were balanced between groups. The win ratio significantly favored endovascular coiling (WR 1.75, 95% CI: 1.67-1.84, p < 0.001), indicating 75% more wins in the hierarchical outcome comparison. All individual components significantly favored coiling: mortality (WR = 1.35, p < 0.001), unfavorable functional outcome (WR = 1.53, p < 0.001), major complications (WR = 1.70, p < 0.001), and prolonged ICU stay (WR = 1.25, p < 0.001). Benefits were consistent across subgroups, including Hunt & Hess grades I-II (WR = 2.00) and III-V (WR = 1.96), and across most aneurysm locations. In contrast, multivariate logistic regression for poor outcome showed a favorable but non-significant trend for coiling (OR = 0.55, p = 0.102), while confirming Hunt & Hess ≥3 (OR = 5.54, p < 0.001) and modified Fisher ≥3 (OR = 3.85, p = 0.044) as dominant prognostic factors.

Conclusion: In this Colombian cohort, hierarchical outcome analysis suggested superior in-hospital outcomes for endovascular coiling vs. surgical clipping. However, the substantial attenuation of this association in adjusted analyses indicates that these apparent advantages may largely reflect case selection patterns rather than inherent treatment superiority, as residual confounding by aneurysm complexity cannot be excluded.

Background: The accurate separation of lung parenchyma, ground-glass opacity (GGO), and intrapulmonary vessels on high-resolution computed tomography (HRCT) in coronavirus disease 2019 (COVID-19) is challenging.

Methods: We conducted a cross-sectional study that analyzed 530 adults (20-40 years) with RT-PCR-confirmed COVID-19. For texture modeling, we sampled 597 regions of interest (ROIs) representing parenchyma, GGO, and intrapulmonary vessels. Region-of-interest-labeled HRCT patches representing parenchyma, GGO, and vessels were analyzed using first- and second-order texture features that were computed across different square window sizes (5 × 5-20 × 20 pixels). Feature selection with stepwise linear discriminant analysis yielded a three-class classifier. The primary endpoint was overall classification accuracy, with the secondary endpoints including the effect of window size and identification of the most informative features.

Results: The 20 × 20-pixel window produced the highest performance, with an overall accuracy of 88.6%. Five co-occurrence-based features (average difference, inverse difference moment, co-occurrence matrix standard deviation, sum entropy, and information correlation measure 1) were the most discriminative; the majority of the errors occurred at tissue boundaries where patches spanned mixed voxels.

Conclusion: Texture-based feature extraction achieved 88.6% ROI-level accuracy and can serve as a supplementary tool during radiological interpretation of chest CT.

Introduction: Early and accurate detection of viral diseases is vital for timely treatment and public health preparedness. However, most existing computer-based prediction systems depend on centralized data storage, which raises concerns about patient privacy, compatibility between different hospitals, and limited clarity on how predictions are made. To address these issues, this study introduces U-FDL-PPE, a new federated deep-learning framework designed to support early and reliable viral disease diagnosis while protecting patient confidentiality and offering clear and understandable prediction insights.

Methods: The framework uses a decentralized learning approach that allows hospitals to train models collaboratively without exchanging raw medical images. MobileNetV2 was used as the core model for classifying chest X-rays, and Grad-CAM was included to produce heatmaps that visually explain how the model arrived at its decisions. The system was tested using the publicly available COVID-19 Radiography Database in a simulated network of three healthcare institutions. Model performance was evaluated using standard measures such as accuracy, F1-score, AUC, and confusion matrix.

Results: Across five training rounds, U-FDL-PPE recorded 88% accuracy, an F1-score of 89.66%, and a multi-class AUC of 0.5192. The confusion matrix showed consistently correct predictions across the three diagnostic categories: COVID-19, Normal, and Viral Pneumonia. The Grad-CAM heatmaps highlighted medically relevant lung regions, confirming that the framework focused on features that clinicians would expect when diagnosing these conditions.

Discussion: The results indicate that U-FDL-PPE is a practical and scalable solution for early viral disease diagnosis, particularly in environments where patient privacy must be preserved. Its combination of decentralized training and visual explanation builds greater trust among clinicians while ensuring that sensitive medical data never leaves the originating institution. The lightweight MobileNetV2 architecture also supports faster processing, making the system suitable for hospitals and clinics with limited computing resources. Overall, U-FDL-PPE provides a privacy-conscious and transparent diagnostic framework that is well-positioned for real-world implementation across healthcare networks.

Introduction: Understanding the role of various brain regions of interest (ROIs) in various cognitive functions or tasks, across healthy or neurodegenerative conditions and multiple degrees of separation, remains a key challenge in neuroscience. Conventional network measures can only capture localized or quasi-localized features of brain ROIs. Topological data analysis (TDA), particularly persistent homology, provides a threshold-free, mathematically rigorous framework for identifying topologically salient features in complex networks. In this paper, we introduce a new metric, the Homological Vertex Importance Profile (H-VIP), designed to assess the relevance of vertices that participate in persistent topological structures (e.g., connected components, cycles or cavities) in brain networks. The H-VIP quantifies the topological features of the network at the ROI (node) level by compressing its higher-order connectivity profile using homological constructs.

Methods: Leveraging homological constructs of brain connectomes, we extend two of our previously defined network-level measures-average persistence and persistence entropy-to an ROI-level measure, i.e., the H-VIP. We then applied the H-VIP to two independent datasets: structural connectomes from the Human Connectome Project and functional connectomes from the Alzheimer's Disease Neuroimaging Initiative. Persistent homology was computed for each network, and H-VIP scores were derived to evaluate vertex-level contributions. Finally, H-VIP scores were used for the prediction of multiple cognitive measures.

Results: In both anatomical and functional brain networks, H-VIP values demonstrate predictive power for various cognitive measures. Notably, the connectivity of the frontal lobe exhibited stronger correlations with cognitive performance than the whole-brain network.

Discussion: H-VIP offers a robust and interpretable means to locate, quantify, and visualize region-specific contributions to network's topological, higher-order landscape. Its ability to detect potentially impaired connectivity at the individual level suggests possible applications in personalized medicine for neurological diseases and disorders. Beyond brain connectomics, the H-VIP can be used for other types of complex networks where topological features are of importance, such as financial, social, or ecological networks.

Introduction: Motion artifacts induced by atrial fibrillation (AF) present a substantial challenge in coronary computed tomography angiography (CCTA). Wide detectors, rapid scanning, and motion correction algorithms can effectively improve image quality in CCTA. This study aims to evaluate the impact of one-beat acquisition with a motion correction algorithm (Snapshot Freeze 1, SSF1) on the image quality of prospective CCTA in patients with AF, and its diagnostic performance using an artificial intelligence assisted diagnostic system (AI-ADS).

Materials and methods: A total of 91 consecutive patients with AF, who underwent one-beat CCTA were analyzed. Images were reconstructed with SSF1. The subjective and objective image quality of the coronary arteries were evaluated. Using the invasive coronary catheter angiography (ICA) as the reference standard, the diagnostic performance of AI-ADS and AI-ADS + radiologist for stenoses above moderate and severe degrees were calculated.

Results: Effective radiation dose was 2.43 ± 0.88 mSv. The average CT values of all major coronary arteries and branches were greater than 400 HU. All vessels were diagnosable (scores ≥ 3) with good or above ratings at 96.15% (350/364) and 96.70% (352/364). The diagnostic accuracy, sensitivity, specificity and AUC of AI-ADS vs. AI-ADS + radiologist for above moderate stenoses were: (84.62% vs. 91.21%), (89.61% vs. 98.70%), (57.14% vs. 50.00%) and (0.73 vs. 0.74) on patient level; (84.07% vs. 87.64%), (74.07% vs. 85.19%), (89.96% vs. 89.08%) and (0.82 vs. 0.87) on vessel level; (90.84% vs. 93.11%), (63.59% vs. 78.34%), (95.99% vs. 95.91%) and (0.80 vs. 0.87) on segment level. For severe stenoses, these values were: (62.64% vs. 82.42%), (58.62% vs. 91.38%), (69.70% vs. 66.67%) and (0.64 vs. 0.79) on patient level; (82.97% vs. 89.29%), (46.43% vs. 75.00%), (93.93% vs. 93.57%) and (0.70 vs. 0.84) on vessel level; (92.23% vs. 95.16%), (36.92% vs. 66.92%), (98.06% vs. 98.14%) and (0.68 vs. 0.83) on segment level.

Conclusion: One-beat CCTA with SSF1 provides high-quality coronary images for patients with AF. AI-ADS automatically distinguishes coronary images with different stenoses, but the sensitivity of AI-ADS is low, especially for severe stenoses. AI-ADS + radiologist further improves the diagnostic performance.

Background: Aortic stenosis (AS) is diagnosed by echocardiography, the current gold standard, but examinations are often performed only after symptoms emerge, highlighting the need for earlier detection. Recently, artificial intelligence (AI)-based screening using non-invasive and widely available modalities such as electrocardiography (ECG) and chest x-ray(CXR) has gained increasing attention for valvular heart disease. However, single-modality approaches have inherent limitations, and in clinical practice, multimodality assessment is common. In this study, we developed a multimodal AI model integrating ECG and CXR within a cooperative learning framework to evaluate its utility for earlier detection of AS.

Methods: We retrospectively analyzed 23,886 patient records from 7,483 patients who underwent ECG, CXR, and echocardiography. A multimodal model was developed by combining a 1D ResNet50-Transformer architecture for ECG data with an EfficientNet-based architecture for CXR. Cooperative learning was implemented using a loss function that allowed the ECG and CXR models to refine each other's predictions. We split the dataset into training, validation, and test sets, and performed 1,000 bootstrap iterations to assess model stability. AS was defined echocardiographically as peak velocity ≥2.5 m/s, mean pressure gradient ≥20 mmHg, or aortic valve area ≤1.5 cm².

Results: Among 7,483 patients, 608 (8.1%) were diagnosed with AS. The multimodal model achieved a test AUROC of 0.812 (95% CI: 0.792-0.832), outperforming the ECG model (0.775, 95% CI: 0.753-0.796) and the CXR model (0.755, 95% CI: 0.732-0.777). Visualization techniques (Grad-CAM, Transformer attention) highlighted distinct yet complementary features in AS patients.

Conclusions: The multimodal AI model via cooperative learning outperformed single-modality methods in AS detection and may aid earlier diagnosis and reduce clinical burden.

[This corrects the article DOI: 10.3389/fradi.2025.1509377.].

Mucormycosis is a serious fungal infection affecting immunocompromised individuals, caused by fungi from the Mucorales order, particularly Rhizopus species. It primarily spreads through inhalation of spores, with diabetes, cancers, organ transplants, immunosuppressive drugs, and COVID-19 being major risk factors. The infection manifests in various forms such as encephalic, cutaneous, gastrointestinal, pulmonary, and rhino cerebral, often leading to tissue necrosis and blood vessel invasion. Imaging diagnosis is aided by CT and MRI scans, while 99m Tc MDP bone scintigraphy has found to be a more accurate imaging tool to look for bone remodelling and erosive changes associated with invasive fungal sinusitis including mucormycosis. Treatment involves prompt surgical debridement and addressing the underlying immune deficiency. Here we present a series of cases where 99m Tc MDP bone scintigraphy played a key role in management of mucormycosis of the head. In conclusion, 99mTc MDP scintigraphy is a promising tool for evaluation, guiding diagnosis and management of mucormycosis.