World journal of radiology最新文献

Non-traumatic headache is a common presentation in both emergency and outpatient settings, where timely identification of raised intracranial pressure (ICP) is crucial to prevent severe neurological complications. Conventional diagnostic methods such as computed tomography and lumbar puncture have important limitations, including invasiveness, delayed availability, and limited sensitivity in certain contexts. Point-of-care ultrasound measurement of the optic nerve sheath diameter (ONSD) has emerged as a rapid, non-invasive tool for detecting elevated ICP at the bedside. The technique is based on the anatomical continuity between the intracranial subarachnoid space and the optic nerve sheath, which expands in response to increased ICP. Evidence from multiple studies and meta-analyses indicates that ONSD measurements above 5.0-5.7 mm in adults strongly correlate with elevated ICP, showing pooled sensitivities and specificities approaching 90%. This modality enables immediate triage, guides urgency of neuroimaging, reduces unnecessary radiation exposure, and can be applied in outpatient and low-resource settings. Despite these advantages, ONSD assessment is subject to operator dependency, variability in threshold values, and reduced accuracy in patients with certain ocular or systemic conditions. Advances in artificial intelligence-assisted measurement, coupled with standardized training protocols, have the potential to improve reproducibility and broaden adoption. Overall, point-of-care ultrasound-based ONSD measurement represents a valuable adjunct in the early evaluation of patients with non-traumatic headache, facilitating faster diagnosis, better resource utilization, and improved patient outcomes.

Photon-counting computed tomography (PCCT) represents a transformative advancement in neuroimaging, offering superior spatial resolution, spectral imaging capabilities, reduced radiation dose, and enhanced contrast-to-noise ratios. This review explores the technical foundations of PCCT, its advantages over conventional CT, and its growing applications in neuroimaging. PCCT has shown promise in improving neurovascular imaging, detecting small vessels, and reducing artifacts near metallic implants. It also enhances the visualization of spontaneous intracranial hypotension and cerebrospinal fluid leaks and provides superior diagnostic accuracy in acute ischemic stroke imaging. However, current limitations, including protocol complexity, high data volume, and the absence of integrated artificial intelligence noise reduction algorithms, pose challenges to widespread adoption. Future research should address these limitations and refine PCCT's applications to unlock its full clinical potential.

Spinal cord injury and non-traumatic myelopathies are major causes of lifelong disability, yet conventional magnetic resonance imaging (MRI) can underestimate microstructural damage. Diffusion tensor imaging (DTI) and tractography map white-matter integrity by measuring fractional anisotropy (FA) and mean diffusivity (MD), but their adoption in spine imaging has been limited by long scan times and complex post-processing. Supsupin et al report a two-minute cervical DTI sequence integrated into routine MRI and applied to four representative pathologies - spinal cord contusion, metastatic compression, degenerative myelopathy, and multiple sclerosis - compared with five controls. Each lesion showed distinctive tractographic and quantitative patterns: For example, reduced FA with preserved MD in contusion and combined FA decrease and MD elevation in metastatic compression. These findings highlight the potential of tractography to improve diagnosis, guide surgical planning, and monitor treatment, while maintaining clinical feasibility. Remaining challenges include limited angular resolution, motion artifacts, and the need for multicenter validation and advanced reconstruction methods. This manuscript places the study in the context of current spinal diffusion imaging and outlines future directions toward routine, precision care.

Background: Shear wave elastography (SWE) is a non-invasive ultrasound-based technique used to assess tissue stiffness, which reflects underlying pathological changes. While SWE has been widely applied for liver fibrosis evaluation, its application to other abdominal organs, such as the spleen and pancreas, is gaining interest. However, normal stiffness values and inter-system agreement remain poorly defined.

Aim: To assess the feasibility and agreement of liver, spleen, and pancreas stiffness using three SWE methods.

Methods: This single-center observational study enrolled 50 healthy adult volunteers. Liver, spleen, and pancreas stiffness were assessed using three SWE methods: Point-SWE (p-QElaXto) and 2-Dimensional-SWE (2D-QElaXto) with Esaote MyLab 9, and 2D-SWE with SuperSonic Imagine. Feasibility, inter-operator reproducibility, and concordance among systems were evaluated. Stiffness was expressed as median kPa values, and technical reliability was assessed using the interquartile range/median ratio and stability index thresholds.

Results: Liver and spleen stiffness assessment was feasible in > 98% of patients, while pancreas stiffness was measurable in 84%-88% depending on the SWE technique. Mean liver stiffness ranged between 3.9-4.7 kPa across techniques, spleen stiffness ranged from 19.4-23.0 kPa, and pancreas stiffness from 5.2-7.6 kPa. Inter-operator agreement was excellent for liver (intraclass correlation coefficient > 0.90) and good to moderate for spleen and pancreas (intraclass correlation coefficient from 0.43 to 0.90). Bland-Altman analysis confirmed good correlation but also systematic differences among devices, especially in pancreas measurements.

Conclusion: This is the first study to establish normal liver, spleen, and pancreas stiffness using MyLab 9 SWE integrated methods as compared to SuperSonic Imagine, with acceptable inter-technique agreement. Liver and spleen values matched existing guidelines; pancreas SWE showed more variability and reduced reproducibility.

Background: The topography between the common carotid artery (CA), internal CA, and external CA (ECA) with the greater horn of the hyoid bone (GHHB) is of particular importance for anatomists, radiologists and neck surgeons.

Aim: To investigate these topographical relationships emphasizing anatomical classification, sexual dimorphism, and clinical significance.

Methods: A retrospective study was performed on 224 computed tomography angiographies from a cohort comprising 161 male and 63 female patients, with a mean age of 63.2 years. Multiplanar and three-dimensional reconstructions were executed utilizing Horos software. The spatial relationships between the CA and hyoid bone were categorized based on the 12-type classification system delineated by Manta et al in 2023. The data were subsequently stratified by sex and laterality.

Results: Type 0 (no arterial contact with the GHHB) was the most common configuration (46.9%), followed by type VI (ECA lateral to GHHB, 23.9%) and type VIII (internal CA and ECA lateral to GHHB, 13.2%). Bilateral symmetry was present in 54.02% of cases, mainly in males. Statistically significant sex-based differences were found (P = 0.012), while laterality was not significant (P = 0.779).

Conclusion: Carotid-hyoid topography displays significant anatomical variation with clinically essential patterns. Non-null variants, such as types VI and VIII, may increase the risk of dynamic carotid compression, especially in younger patients with cryptogenic cerebrovascular symptoms. Recognizing these variants during preoperative imaging is crucial to minimize surgical risk and inform patient care.

In the clinical diagnosis and treatment of liver tumors, the differential diagnosis between dual-phenotype hepatocellular carcinoma and intrahepatic cholangiocarcinoma has long been a challenging problem for clinicians. These two types of tumors not only exhibit overlapping pathological features but also have significantly different treatment strategies and prognoses. Misdiagnosis can directly affect patients' quality of life. Recently, a study published by Zhang et al has brought groundbreaking insights into this dilemma. Radiomics technology has demonstrated remarkable value in the differential diagnosis of these two diseases, opening up a new path for the precise diagnosis and treatment of liver tumors.

Large language models (LLMs) have emerged as transformative tools in radiology artificial intelligence (AI), offering significant capabilities in areas such as image report generation, clinical decision support, and workflow optimization. The first part of this manuscript presents a comprehensive overview of the current state of LLM applications in radiology, including their historical evolution, technical foundations, and practical uses. Despite notable advances, inherent architectural constraints, such as token-level sequential processing, limit their ability to perform deep abstract reasoning and holistic contextual understanding, which are critical for fine-grained diagnostic interpretation. We provide a critical perspective on current LLMs and discuss key challenges, including model reliability, bias, and explainability, highlighting the pressing need for novel approaches to advance radiology AI. Large concept models (LCMs) represent a nascent and promising paradigm in radiology AI, designed to transcend the limitations of token-level processing by utilizing higher-order conceptual representations and multimodal data integration. The second part of this manuscript introduces the foundational principles and theoretical framework of LCMs, highlighting their potential to facilitate enhanced semantic reasoning, long-range context synthesis, and improved clinical decision-making. Critically, the core of this section is the proposal of a novel theoretical framework for LCMs, formalized and extended from our group's foundational concept-based models - the world's earliest articulation of this paradigm for medical AI. This conceptual shift has since been externally validated and propelled by the recent publication of the LCM architectural proposal by Meta AI, providing a large-scale engineering blueprint for the future development of this technology. We also outline future research directions and the transformative implications of this emerging AI paradigm for radiologic practice, aiming to provide a blueprint for advancing toward human-like conceptual understanding in AI. While challenges persist, we are at the very beginning of a new era, and it is not unreasonable to hope that future advancements will overcome these hurdles, pushing the boundaries of AI in Radiology, far beyond even the most state-of-the-art models of today.

Thyroid-associated ophthalmopathy (TAO), an autoimmune disorder closely associated with thyroid dysfunction, requires timely diagnosis and ongoing accurate evaluation to improve patient outcomes. With the global incidence of TAO increasing and significantly affecting the quality of life of patients, there is an urgent need for effective diagnostic tools. As a noninvasive imaging technique, ultrasound plays a pivotal role in diagnosing and managing TAO, particularly in the early detection of and monitoring of disease progression. Despite its advantages, ultrasound faces challenges such as limited resolution for deep orbital structures and a lack of standardized protocols, which can lead to diagnostic inaccuracies. This paper reviews the current status of ultrasound applications in TAO, including diagnostic utility, recent technological advances, and key challenges. It proposes strategies for future research and improvement, emphasizing analysis of ultrasound imaging data to develop biomarker stratification models. We propose an integrated multimodal framework that combines ultrasound elastography with deep learning to improve diagnostic precision.

Imaging plays a crucial role in the evaluation of hepatocellular carcinoma (HCC) treatment response. Contrast enhanced computed tomography and magnetic resonance imaging with extra-cellular or hepatobiliary contrast agents are the imaging techniques of choice. Contrast enhanced ultrasound is a promising technique. In this paper, we describe radiological techniques, imaging findings after HCC treatment, and the criteria of response evaluation. The utility of the structured report is also evaluated.

Spontaneous intracerebral hemorrhage carries high early mortality and long-term disability, with hematoma expansion (HE) being the most important modifiable determinant of poor outcome. Although the computed tomography (CT) angiography (CTA) "spot sign" is a validated predictor of HE, it is not universally available, highlighting the need for accessible imaging tools. In this invited editorial, we discuss the study by Parry et al, who developed a simplified five-point prediction score based solely on non-contrast CT findings - baseline hematoma volume ≥ 30 mL, intraventricular hemorrhage, and the island, black hole, and swirl signs. Tested prospectively in 192 patients scanned within 4 hours of onset, the score showed a stepwise rise in HE risk from 7% at a score of 0% to 100% at a score of 5. We place these findings in the context of existing CTA and non-contrast CT literature and highlight their potential to accelerate triage and treatment, particularly where CTA is unavailable. Broader multicenter validation and integration with clinical and machine-learning approaches will further define the clinical impact of this streamlined, imaging-only tool.