Frontiers in radiology最新文献

Objectives: To investigate the evaluation of the effectiveness of contrast-enhanced ultrasound (CEUS) in the diagnosis of small hepatocellular carcinoma (HCC).

Methods: A thorough search was conducted for pertinent literature using PubMed, SCOPUS, Web of Science, Science Direct, and Wiley Library. Rayyan QRCI was used throughout this extensive procedure.

Results: Our results included thirteen studies with a total of 2016 patients, and 1672 (82.9%) were males. The follow-up duration ranged from 3 months to 24 months. CEUS was useful in anticipating the early recurrence of HCC, predicting the early recurrence of solitary lesion HCC patients, and differentiating between HCC and intrahepatic cholangiocarcinoma <3 Cm, distinguishing HCC from dysplastic nodules from tiny liver nodules, CEUS in cirrhotic patients. When paired with CEUS, conventional ultrasonography can detect minor HCC and assist in patient monitoring for those who receive an early diagnosis of HCC. CEUS showed high concordance with CECT for diagnosing lesions 2.1-3.0 cm in size. Notable limitations included heterogeneity in protocols and predominance of Asian populations (12/13 studies).

Conclusion: CEUS offers significant clinical value as a noninvasive diagnostic tool, particularly for 1-3 cm lesions in cirrhotic patients and cases where CT is contraindicated, though protocol standardization and Western population validation remain needed.

Background: Intervertebral disc anomalies, such as degeneration and herniation, are common causes of spinal disorders, often leading to chronic pain and disability. Accurate diagnosis and classification of these anomalies are critical for determining appropriate treatment strategies. Traditional methods, such as manual image analysis, are prone to subjectivity and time-consuming. With the advancements in deep learning, automated and precise classification of intervertebral disc anomalies has become a promising alternative.

Objective: This study aims to propose a deep learning-based method for classifying intervertebral disc abnormalities, with the goal of improving diagnostic accuracy and clinical efficiency in spinal health management.

Methods: From August 2021 to March 2024, a dataset consisting of 574 CT images of intervertebral discs was collected and labeled into four clinically relevant categories: normal intervertebral discs, Schmorl's nodes, disc bulges, and disc protrusions. The dataset was divided into 500 images for model training, and 74 images for validation. A YOLOv8-seg network was employed for classification, with multiple preprocessing techniques applied to ensure data consistency and enhance model performance.

Results: The IDAICS demonstrated high accuracy in classifying various intervertebral disc anomalies, including disc degeneration, herniation, and bulging, with a classification accuracy of over 93.2%, with a kappa coefficient of 0.905 (P < 0.001).

Conclusion: This deep learning-based classification approach provides an efficient and reliable alternative to manual assessment, enabling automated diagnosis of intervertebral disc abnormalities. It offers significant potential to enhance clinical decision-making and improve spinal health management outcomes.

Introduction: Adrenocortical carcinoma presents significant diagnostic challenges due to its histological heterogeneity and variable clinical behavior. This study aimed to evaluate the diagnostic value of radiomic features in predicting mitotic activity (low/high-grade) and morphological variants (conventional, oncocytic, myxoid) of adrenocortical carcinoma.

Materials and methods: A retrospective analysis of 32 patients with histologically confirmed ACC (18 conventional, 9 oncocytic and 5 myxoid cases) was performed, with mitotic data available for 25 cases (13 low-grade and 12 high-grade cases). Radiomic features including Gray-Level Co-occurrence Matrix (GLCM), Run-Length (GLRLM), Size-Zone (GLSZM), Dependence (GLDM), Neighboring-Tone (NGTDM) and first order features were extracted from four-phase CT using PyRadiomics after manual 3D segmentation. Statistical analysis included Mann-Whitney U, Kruskal-Wallis tests, ROC curve (AUC, sensitivity, specificity) and PPV, NPV assessment.

Results: Our analysis demonstrated statistically significant differences between tumor grades with firstorder_Skewness (AUC = 0.924, 95% CI: 0.819-0.986; p = 0.005) showing high predictive performance in the venous phase. Radiomic features did not show statistically significant differences between morphological variants of ACC after adjustment for multiple comparisons.

Conclusion: Our results confirm the value of CT radiomics for preoperative stratification of ACC grade, but the question of differentiation of morphological variants remains unresolved and requires further validation in larger cohorts.

Introduction: Neurofibromatosis type 2 related Schwannomatosis (NF2-SWN) is a genetic disorder characterized by the growth of vestibular schwannomas (VS), which often leads to progressive hearing loss and vestibular dysfunction. Accurate volumetric assessment of VS tumors is crucial for effective monitoring and treatment planning. Since tumor growth dynamics are often subtle, the resolution of MRI scans plays a critical role in detecting small volumetric changes that inform clinical decisions. This study evaluates the impact of MRI voxel resolution on the accuracy of manual and AI-driven volumetric segmentation of VS in NF2-SWN patients.

Methods: Ten patients with NF2-SWN, totaling 17 tumors, underwent high-resolution MRI scans with varying voxel sizes on different MRI machines at Yale New Haven Hospital. Tumors were segmented using both manual and AI-based methods, and the effect of voxel size on segmentation precision was quantified through volume measurements, Dice similarity coefficients, and Hausdorff distances.

Results: Results indicate that larger voxel sizes (1.2 × 0.9 × 4.0 mm) significantly reduced segmentation accuracy when compared to smaller voxel sizes (0.5 × 0.5 × 0.8 mm). In addition, AI-based segmentation outperformed manual methods, particularly at larger voxel sizes.

Discussion: These findings highlight the importance of optimizing voxel resolution for accurate tumor monitoring and suggest that AI-driven segmentation may improve consistency and precision in NF2-SWN tumor surveillance.

Future AI systems may need to provide medical professionals with explanations of AI predictions and decisions. While current XAI methods match these requirements in principle, they are too inflexible and not sufficiently geared toward clinicians' needs to fulfill this role. This paper offers a conceptual roadmap for how XAI may be integrated into future medical practice. We identify three desiderata of increasing difficulty: First, explanations need to be provided in a context- and user-dependent manner. Second, explanations need to be created through a genuine dialogue between AI and human users. Third, AI systems need genuine social capabilities. We use an imaginary stroke treatment scenario as a foundation for our roadmap to explore how the three challenges emerge at different stages of clinical practice. We provide definitions of key concepts such as genuine dialogue and social capability, we discuss why these capabilities are desirable, and we identify major roadblocks. Our goal is to help practitioners and researchers in developing future XAI that is capable of operating as a participant in complex medical environments. We employ an interdisciplinary methodology that integrates medical XAI, medical practice, and philosophy.

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.