Frontiers in radiology最新文献

Background: Tunneled cuffed catheter (TCC) remains a crucial vascular access option for patients undergoing hemodialysis, particularly in those who are not candidates for arteriovenous fistulas or grafts. However, placement carries immediate and delayed complications.

Objective: This narrative review aims to provide a comprehensive overview of the complications encountered during and after the placement of a TCC for hemodialysis, highlighting current evidence, risk factors, prevention strategies, and management approaches.

Methods: A critical selection of relevant literature was performed through PubMed and Scopus databases, focusing on articles published in the last two decades. Particular attention was given to studies reporting on mechanical, infectious, thrombotic, and late-onset complications, as well as technical factors influencing outcomes.

Results: Complications of TCCs can be classified as immediate (e.g., arterial puncture, pneumothorax, bleeding), early (e.g., catheter malposition, exit-site infections), and late (e.g., central venous stenosis, catheter-related bloodstream infections, thrombosis). Patient- and procedure-related factors increase risk. Ultrasound and fluoroscopy, strict sterility, and timely management reduce complications rates.

Conclusion: TCCs are indispensable in selected patients, but understanding their complications is key to patient safety and outcomes. Optimal outcomes depend on accurate patient selection, operator expertise, and standardized post-placement care.

Purpose: This study aimed to assess the feasibility and accuracy of fiducial marker placement using US-CT/MRI fusion imaging guidance in poorly conspicuous liver tumors prior to percutaneous thermal ablation (PTA).

Method: From January 2016 to February 2018, 30 consecutive patients with 38 liver lesions that were poorly or not visible on conventional ultrasound underwent fiducial marker placement under real-time US-CT/MRI fusion imaging before the PTA procedure. Marker position was confirmed via CT or MRI immediately after placement. The shortest distance between the marker and the edge of the target lesion, the lesion size, and the depth were measured. The fiducial marker placement was considered successful if the marker was within, in contact or ≤5 mm distance from the lesion; a distance >5 mm was considered a failure.

Results: Of the 38 lesions, 28 (74%) were undetectable using ultrasound alone, while 10 (26%) were not confidently identified. After fusion, 26 lesions (68%) showed enhanced visibility, while 12 (32%) remained undetectable. Overall, the mean distance between the fiducial marker and the lesion's edge was 4 mm (range: 0-45 mm). Successful placement was achieved in 30 lesions (79%): 27, inside or in contact, and 3, at a <5 mm distance from the target lesion. Placement was unsuccessful in eight lesions (21%). No procedure-related complications occurred.

Conclusions: The present work suggests that pre-PTA placement of a fiducial marker in poorly visible tumors using real-time US-CT/MRI fusion imaging is accurate, potentially enhancing the effectiveness of subsequent PTA.

Introduction: Vietnam still faces a high burden of infectious diseases compared with developed countries, and improving its health and sanitation environment is essential for addressing both infectious and non-communicable diseases. Chest radiography is key for early detection of cardiopulmonary diseases. Artificial Intelligence (AI) research on detecting cardiopulmonary diseases from chest radiographs has advanced; however, no AI development studies have used Vietnamese data, despite its high burden of both disease types, for early detection. Therefore, we aimed to develop an AI model to classify normal and abnormal images using a Vietnamese chest radiograph dataset.

Methods: We retrospectively analyzed 12,827 normal and 4,644 abnormal cases from two Vietnamese institutions. Features were derived from principal component analysis and extracted using Vision Transformer and EfficientnetV2. We performed binary classification of normal and abnormal images using Light Gradient Boosting Machine with 5-fold cross-validation.

Results: The model achieved an F1-score of 0.668, sensitivity of 0.596, specificity of 0.931, accuracy of 0.842, and AUC of 0.897. Subgroup evaluation revealed high accuracy in both infectious and non-communicable cases, as well as in urgent cases.

Conclusion: We developed an AI system that classifies normal and abnormal chest radiographs with high clinical accuracy using Vietnamese data.

Erectile dysfunction (ED) is a worldwide health concern and clinical condition for men, leading to high medical costs and imposing significant emotional and psychological burdens on sufferers annually. ED is associated with multiple causes, including psychological factors and organic issues such as arterial insufficiency and venous leakage. Endovascular treatments have emerged as promising options for managing ED, offering minimally invasive procedures that can improve blood flow to the penis and restore erectile function. Different endovascular interventional approaches have been implemented with varying success rates and therapeutic impacts, and efforts continue to optimize these methods (both arterial and venous) for maximum effectiveness and minimal invasiveness. This narrative review aims to provide an overview of endovascular treatments for arterial and venous types of ED, discussing their mechanisms of action, efficacy, safety, and future directions.

Fibrolipomatous hamartoma is a rare benign overgrowth of tissue consisting of intermixed adipose and fibrous connective tissue within the epineurium. However, involvement of the sciatic nerve is exceptionally rare. We present the case of a 46-year-old female who exhibited a progressively enlarging mass in her right posterior thigh, accompanied by sciatica and gluteal pain. Clinical assessment and MRI revealed a large lesion along the sciatic nerve with characteristic features of fibrolipomatous hamartoma. MRI findings demonstrated characteristic features, including isointense (to fat) on T1-weighted images and hyperintense with fat suppression on short tau inversion recovery sequences, indicating a sciatic nerve fibrolipomatous hamartoma. The diagnosis was histopathologically confirmed following surgical excision. This case highlights the critical role of identifying specific MRI features of this rare entity to avoid unnecessary invasive interventional procedures. An accurate MRI-based diagnosis can significantly impact clinical decisions and improve patient care.

Objective: In this study, the accuracy of deep learning-based models developed for synthetic CT (sCT) generation from conventional Cone Beam Computed Tomography (CBCT) images of prostate cancer patients was evaluated. The clinical applicability of these sCTs in treatment planning and their potential to support adaptive radiotherapy decision-making were also investigated.

Methods: A total of 50 CBCT-CT mappings were obtained for each of 10 retrospectively selected prostate cancer patients, including one planning CT (pCT) and five CBCT scans taken on different days during the treatment process. All images were preprocessed, anatomically matched and used as input to the U-Net and ResU-Net models trained with PyTorch after z-score normalisation. The sCT outputs obtained from model outputs were quantitatively compared with the pCT with metrics such as SSIM, PSNR, MAE, and HU difference distribution.

Results: Both models produced sCT images with higher similarity to pCT compared to CBCT images. The mean SSIM value was 0.763 ± 0.040 for CBCT-CT matches, 0.840 ± 0.026 with U-Net and 0.851 ± 0.026 with ResU-Net, with a significant increase in both models (p < 0.05). PSNR values were 21.55 ± 1.38 dB for CBCT, 24.74 ± 1.83 dB for U-Net, and 25.24 ± 1.61 dB for ResU-Net. ResU-Net provided a statistically significant higher PSNR value compared to U-Net (p < 0.05). In terms of MAE, while the mean error in CBCT-CT matches was 75.2 ± 18.7 HU, the U-Net model reduced this value to 65.3 ± 14.8 HU and ResU-Net to 61.8 ± 13.7 HU (p < 0.05).

Conclusion: Deep learning models trained with simple architectures such as U-Net and ResU-Net provide effective and feasible solutions for the generation of clinically relevant sCT from CBCT images, supporting accurate dose calculation and facilitating adaptive radiotherapy workflows in prostate cancer management.

Background: Non-invasive and comprehensive molecular characterization of glioma is crucial for personalized treatment but remains limited by invasive biopsy procedures and stringent privacy restrictions on clinical data sharing. Federated learning (FL) provides a promising solution by enabling multi-institutional collaboration without compromising patient confidentiality.

Methods: We propose a multi-task 3D deep neural network framework based on federated learning. Using multi-modal MRI images, without sharing the original data, the automatic segmentation of T2w high signal region and the prediction of four molecular markers (IDH mutation, 1p/19q co-deletion, MGMT promoter methylation, WHO grade) were completed in collaboration with multiple medical institutions. We trained the model on local patient data at independent clients and aggregated the model parameters on a central server to achieve distributed collaborative learning. The model was trained on five public datasets (n = 1,552) and evaluated on an external validation dataset (n = 466).

Results: The model showed good performance in the external test set (IDH AUC = 0.88, 1p/19q AUC = 0.84, MGMT AUC = 0.85, grading AUC = 0.94), and the median Dice of the segmentation task was 0.85.

Conclusions: Our federated multi-task deep learning model demonstrates the feasibility and effectiveness of predicting glioma molecular characteristics and grade from multi-parametric MRI, without compromising patient privacy. These findings suggest significant potential for clinical deployment, especially in scenarios where invasive tissue sampling is impractical or risky.

Breast cancer is the most common malignancy among women worldwide, and imaging remains critical for early detection, diagnosis, and treatment planning. Recent advances in artificial intelligence (AI), particularly self-supervised learning (SSL) and transformer-based architectures, have opened new opportunities for breast image analysis. SSL offers a label-efficient strategy that reduces reliance on large annotated datasets, with evidence suggesting that it can achieve strong performance. Transformer-based architectures, such as Vision Transformers, capture long-range dependencies and global contextual information, complementing the local feature sensitivity of convolutional neural networks. This study provides a comprehensive overview of recent developments in SSL and transformer models for breast lesion segmentation, detection, and classification, highlighting representative studies in each domain. It also discusses the advantages and current limitations of these approaches and outlines future research priorities, emphasizing that successful clinical translation depends on access to multi-institutional datasets to ensure generalizability, rigorous external validation to confirm real-world performance, and interpretable model designs to foster clinician trust and enable safe, effective deployment in clinical practice.

Objective: Magnetic resonance-guided focused ultrasound (MRgFUS) thalamotomy is an effective treatment for essential tremor (ET) and tremor-dominant Parkinson's disease (PD), yet a substantial proportion of patients experience tremor recurrence over time. Reliable imaging biomarkers to predict long-term outcomes are lacking. The purpose of the study was to evaluate whether radiomic features extracted from 24-h post-treatment MRI can predict clinically relevant tremor recurrence at 12 months after MRgFUS thalamotomy, using a machine learning (ML) approach.

Materials and methods: Retrospective, single-center study included 120 patients (61 ET, 59 PD) treated with unilateral MRgFUS Vim thalamotomy between February 2018 and June 2023. Tremor severity was assessed using part A of the Fahn-Tolosa-Marin Tremor Rating Scale (FTM-TRS) at baseline and 12 months. Recurrence was defined as an FTM-TRS part A score ≥ 3 at 12 months. Lesions were manually segmented on 24-h post-treatment T2-weighted MRI. Forty radiomic features (18 first-order, 22 texture GLCM from Laplacian of Gaussian-filtered images) were extracted. A linear Support Vector Classifier with leave-one-out cross-validation was used for classification. Model explainability was assessed using SHapley Additive exPlanations (SHAP).

Results: Clinically relevant tremor recurrence occurred in 23 patients (19%). For the full cohort, the ML model achieved a balanced accuracy of 0.720, weighted F1-score of 0.737, and comparable sensitivity and specificity across classes. Performance was higher in PD (BA = 0.808, F1 = 0.793) than in ET (BA = 0.580, F1 = 0.696). The most predictive features were texture-derived GLCM metrics, particularly from edge-enhanced images, with first-order features contributing complementary information. No significant correlations were found between radiomic features and procedural parameters.

Conclusion: Radiomic analysis of MRgFUS lesions on 24-h post-treatment MRI can provide early prediction of 12-month tremor recurrence, with higher predictive value in PD than in ET. Texture-based features may capture microstructural characteristics linked to treatment durability. This approach could inform post-treatment monitoring and individualized management strategies.

Background: Accurate diagnosis of anterior cruciate ligament (ACL) tears on magnetic resonance imaging (MRI) is critical for timely treatment planning. Deep learning (DL) approaches have shown promise in assisting clinicians, but many prior studies are limited by small datasets, lack of surgical confirmation, or exclusion of partial tears.

Aim: To evaluate the performance of multiple convolutional neural network (CNN) architectures, including a proposed CustomCNN, for ACL tear detection using a surgically validated dataset.

Methods: A total of 8,086 proton density-weighted sagittal knee MRI slices were obtained from patients whose ACL status (intact, partial, or complete tear) was confirmed arthroscopically. Eleven deep learning models, including CustomCNN, DenseNet121, and InceptionResNetV2, were trained and evaluated with strict patient-level separation to avoid data leakage. Model performance was assessed using accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC).

Results: The CustomCNN model achieved the highest diagnostic performance, with an accuracy of 91.5% (95% CI: 89.5-93.1), sensitivity of 92.4% (95% CI: 90.4-94.2), and an AUC of 0.913. The inclusion of both partial and complete tears enhanced clinical relevance, and patient-level splitting reduced the risk of inflated metrics from correlated slices. Compared with previous reports, the proposed approach demonstrated competitive results while addressing key methodological limitations.

Conclusion: The CustomCNN model enables rapid and reliable detection of ACL tears, including partial lesions, and may serve as a valuable decision-support tool for radiologists and orthopedic surgeons. The use of a surgically validated dataset and rigorous methodology enhances clinical credibility. Future work should expand to multicenter datasets, diverse MRI protocols, and prospective reader studies to establish generalizability and facilitate integration into real-world workflows.