Frontiers in radiology最新文献

Background: The aim of this systematic review was to determine the patency and complications related to percutaneous metallic biliary stent placement for malignant biliary obstruction in the current literature.

Methods: This review was performed using the Preferred Reporting Items of Systematic Reviews and Meta-Analyses guidelines. EMBASE and PubMed were queried yielding 891 articles, 18 of which were included in the final analysis. The Newcastle-Ottawa Quality Assessment Scale was used to appraise article quality. Patient demographics, technical success rate, and procedure outcomes were recorded. Complications were classified as "major" if they resulted in blood transfusion or additional invasive procedures or were reported as such in the literature. Complications that did not meet these criteria were classified as "minor".

Results: A total of 1,453 patients (677 female; weighted age 66.8 years) underwent biliary stent placement. The weighted technical success rate was 97.7%. The incidence of stent occlusion was 13.5% with 6.6% of patients requiring further intervention to maintain patency. There were 277 (19.1%) complications, of which 87 were classified as major. The most common complications were pancreatitis (93, 6.4%), cholangitis (69, 4.8%), and bleeding (64, 4.4%). In cases of bleeding, 4.7% of patients needed a blood transfusion and 15.6% required a procedure to treat bleeding. There were 6 (0.4%) procedure-related deaths.

Conclusion: In conclusion, percutaneous metallic stent placement for malignant biliary obstruction has a high technical success rate and relatively low rate of occlusion. Although nearly one in five procedures resulted in a complication, most cases were minor.

Objective: This study constructs a deep learning-based combined algorithm named WaveAttention ResNet (WARN) to investigate the classification accuracy for seven common retinal diseases and the feasibility of AI-assisted diagnosis in this field.

Methods: First, a deep learning-based classification network is constructed. The network is built upon ResNet18, integrated with the Convolutional Block Attention Module (CBAM) and wavelet convolution modules, forming the WARN method for retinal disease classification. Second, the public OCTDL dataset is used to train WARN, which contains classification data for seven retinal disease types: age-related macular degeneration (AMD), diabetic macular edema (DME), epiretinal membrane (ERM), normal (NO), retinal artery occlusion (RAO), retinal vein occlusion (RVO), and vitreomacular interface disease (VID). During this process, ablation experiments and significance tests are conducted on WARN, and comprehensive analyses of various indicators for WARN, ResNet-18, ResNet-50, Swin Transformer v2, EfficientNet, and Vision Transformer (ViT) are performed in retinal disease classification tasks. Finally, data provided by Shanxi Eye Hospital are used for testing, and classification results are analyzed.

Results: WARN demonstrates excellent performance on the public OCTDL dataset. Ablation experiments and significance tests confirm the effectiveness of WARN, achieving an accuracy of 90.68%, F1-score of 91.29%, AUC of 97.50%, precision of 93.31%, and recall of 90.68% with relatively short training time. In the dataset from Shanxi Eye Hospital, WARN also performs well, with a recall of 90.85%, precision of 79.94%, and accuracy of 89.18%.

Conclusion: This study fully confirms that the constructed WARN is efficient and feasible for classifying seven common retinal diseases. It further highlights the enormous potential and broad application prospects of AI technology in the field of auxiliary medical diagnosis.

Transarterial microembolization (TAME) has gained recognition as a minimally invasive treatment for chronic musculoskeletal pain, demonstrating significant efficacy with a favorable safety profile ( 1, 2). However, complications remain underreported. This case report describes the first documented severe adverse event in a patient with a chronic venous ulcer undergoing TAME for a micro arteriovenous fistula (AVF). The patient developed significant complications, including extensive leg swelling, skin changes, and cellulitis requiring prolonged inpatient care. These findings highlight the importance of patient selection and embolic agent considerations to mitigate potential risks associated with TAME.

Intoduction: The palatal rugae have been suggested to be just as unique as the human fingerprint. Therefore, endeavors have been made to utilize this uniqueness for the identification of disaster victims. With the rise of digital 3D dental data, computational comparisons of palatal rugae have become possible. But a direct comparison of the full palatal scan by iterative closest point (ICP) has shown to be tedious and demands a knowledge of superimposition software.

Methods: Here, we propose (1) an automatic extraction of the palatal rugae ridges from the 3D scans, followed by (2) ICP of the extracted ridges.

Results: Pairwise comparisons of palates take less than a second, and in this study, it was possible to distinguish between palates from the same individual vs. palates from different individuals with a receiver operating characteristic area-under-the-curve of 0.994.

Discussion: This shows that the extraction of the palatal rugae ridges is a potential efficient addition to the toolbox of a forensic odontologist for disaster victim identification.

Background: Previous studies have reported that quantum noise inherently present in CT images hinders the generation of CT-based ventilation image (CTVI), while quantum noise reduction approaches that do not affect CTVI have not yet been reported.

Aims: The purpose of this study was to evaluate the impact of noise reduction preprocessing on the accuracy and robustness of CTVI in relation to quantum noise present in CT images.

Methods and material: To reproduce the quantum noise, Gaussian noise (SD: 30, 80, 150 HU) was added to each inhalation and exhalation CT image. CTVI_ref and CTVI_noise was generated from CT_ref and CT_noise. A median filter and the noise reduction by the CNN were also applied to the CT image, which contained the quantum noise, and CTVI_med and CTVI_cnn was created in the same manner as CTVI_ref. We evaluated whether the regions classified as high, middle, or low in CTVI_ref were accurately represented as high, middle, or low in CTVI_noise, CTVI_med and CTVI_cnn. Additionally, to evaluate the ventilation function of each voxel, we compared two-dimensional histograms of CTVI_ref, CTVI_noise, CTVI_med and CTVI_cnn.

Statistical analysis used: Cohen's kappa coefficient and Spearman's correlation were used to assess the agreement between CTVI_ref and each of the following: CTVI_noise, CTVI_med, and CTVI_cnn.

Results: CTVI_cnn significantly improved categorical consistency and voxel-level correlation of CTVI, particularly under high-noise conditions (150 HU), outperforming both CTVI_noise and CTVI_med.

Conclusions: CNN-based denoising effectively improved the accuracy and robustness of CTVI under quantum noise.

Purpose: Brain tumor segmentation with MRI is a challenging task, traditionally relying on manual delineation of regions-of-interest across multiple imaging sequences. However, this data-intensive approach is time-consuming. We aimed to optimize the process by using a deep learning (DL) based model while minimizing the number of MRI sequences required to segment gliomas.

Methods: We trained a 3D U-Net DL model using the annotated 2018 MICCAI BraTS dataset (training dataset, n = 285), focusing on sub-segmenting enhancing tumor (ET) and tumor core (TC). We compared the performances of models trained on four different combinations of MRI sequences: T1C-only, FLAIR-only, T1C + FLAIR and T1 + T2 + T1C + FLAIR to evaluate whether a smaller MRI data subset could achieve comparable performance. We evaluated the performance on the four different sequence combinations using 5-fold cross-validation on the training dataset, then on our test dataset (n = 358) consisting of samples from a separately held-out 2018 BraTS validation set (n = 66) and 2021 BraTS datasets (n = 292). Dice scores on both cross-validation and test datasets were assessed to measure model performance.

Results: Dice scores on cross-validation showed that T1C + FLAIR (ET: 0.814, TC: 0.856) matched or outperformed those of T1 + T2 + T1C + FLAIR (ET: 0.785, TC: 0.841), T1C-only (ET: 0.781, TC: 0.852) and FLAIR-only (ET: 0.008, TC: 0.619). Results on the test dataset also showed that T1C + FLAIR (ET: 0.867, TC: 0.926) matched or outperformed those of T1 + T2 + T1C + FLAIR (ET: 0.835, TC: 0.908), T1C-only (ET: 0.726, TC: 0.928), and FLAIR-only (ET: 0.056, TC: 0.543). T1C + FLAIR excelled in both ET and TC, exceeding the performance of the four-sequence dataset. T1C-only matched T1C + FLAIR in TC performance. Similarly, T1C and T1C + FLAIR also outperformed in ET delineation by sensitivity (0.829) and Hausdorff distance (5.964) on the test set. Across all configurations, specificity remained high (≥0.958). T1C performed well in TC delineation (sensitivity: 0.737), but the inclusion of all sequences led to improvement (0.754). Hausdorff distances clustered in a narrow range (17.622-33.812) for TC delineation across the configurations.

Conclusions: DL-based brain tumor segmentation can achieve high accuracy using only two MRI sequences (T1C + FLAIR). Reduction of multiple sequence dependency may enhance DL generalizability and dissemination in both clinical and research contexts. Our findings may ultimately help mitigate human labor intensity of a complex task integral to medical imaging analysis.

Objectives: To compare two established scoring schemes for the 2D-T1-weighted "black-blood" MRI sequence (T1-BB) for superficial cranial arteries (SCA) in the diagnosis of giant cell arteritis (GCA).

Methods: Ten arterial segments were evaluated in T1-BB images with two different methods: a visual semiquantitative scheme (T1-BB-VISUAL) and a composite scheme that included both the semiquantitative assessment and a quantitative wall thickness measurement (T1-BB-COMP). The expert clinical diagnosis after ≥6 months of follow-up was the diagnostic reference standard. Diagnostic accuracy and agreement on the segment and patient levels were evaluated for the two different rating schemes.

Results: Retrospectively, 151 consecutive patients with clinically suspected GCA were included. The study cohort consisted of 82 patients with and 69 without GCA. For the T1-BB-COMP and the T1-BB-VISUAL, the sensitivity was 81.7% vs. 87.8% (p = 0.025), the specificity was 91.3% vs. 88.4% (p = 0.16) and the proportion of correct diagnoses was 86.1% vs. 88.1% (p = 0.26), respectively. The overall agreement between the two methods for 1,201 rated arterial segments was very good at 91.6% with a kappa of 0.80. The agreement was higher for segments with a larger calibre than for smaller segments: common superficial temporal arteries 98.0%, occipital arteries 93.2%, frontal branches 89.8% and parietal branches 86.9%. The correlation of wall thickness measurements between readers was strong (Spearman's rho of 0.68). The time needed to apply the T1-BB-VISUAL was about half as long as for the T1-BB-COMP (4.5 vs. 8.95 minutes).

Conclusion: In suspected GCA, the additional measurement of the wall thickness of SCAs in 2D-T1-BB MRI does not lead to a better diagnostic performance compared to visual semiquantitative scoring alone. Visual scoring is preferred due to higher efficiency and reliability.

Spinal lesions encompass a diverse range of pathologies, including primary and secondary tumors, infectious processes, vascular malformations, traumatic injuries, and degenerative conditions, each with distinct imaging characteristics crucial for accurate diagnosis and management. Imaging plays vital roles in assessing lesion morphology, anatomical localization, and neurological impact, guiding clinical decision-making and therapeutic planning. This review systematically explores spinal lesions based on their anatomical compartments, highlighting key radiological features and providing a comprehensive reference for radiologists.