BJR open最新文献

Post-mortem imaging, in particular CT (PMCT), is increasingly used for death investigation in England and Wales, yet unlike "live" clinical imaging, this data falls outside traditional health-record legislation, creating uncertainty around data ownership, access rights, and disclosure obligations. This review examines the current data governance landscape surrounding post-mortem imaging data, identifying critical gaps requiring national guidance. We explore fundamental questions of data control between coroners and commercial service providers, noting how the absence of standardized frameworks has resulted in substantial regional variation in practice. Key challenges include inconsistent approaches to data storage, whether on clinical or dedicated PACS systems, varying data-retention periods, and disparate policies for third-party access by researchers, legal teams, and bereaved families. The evolving role of radiologists as expert witnesses in coronial and criminal proceedings presents additional complexities, particularly regarding who is best placed to explain imaging findings in court. We propose recommendations including national standards for data governance, standardized contractual frameworks clarifying data-controller relationships, protocols for secure storage and access controls, and defined competencies for radiologists presenting evidence in legal settings. Establishing robust governance foundations for post-mortem imaging data is essential to ensure this technology serves the public interest effectively, while maintaining legal defensibility and ethical integrity.

Objectives: The task of issuing reports on whether nasogastric (NG) tubes are safe for enteral nutrition on chest X-ray (CXR) often falls to radiology residents. The aims of this study are to evaluate whether radiology residents are formally trained and their performance in interpreting NG tube position on CXR.

Methods: Radiology residents were invited to participate in an online study evaluating NG tube position on CXR. The CXR images comprised 20 NG tubes, 14 of which were correctly sited, while 4 were in the distal oesophagus and 2 in the lung.

Results: Twenty-eight (of 185, 15%) radiology residents responded-despite incentives to participate and directed by Training Program Directors/Heads of School. Of those, only 10 (35.7%) correctly identified all NG tube positions on CXR. The most common error was reporting a correctly sited NG tube as mal-positioned for enteral nutrition. Global error rate was 8.9%. Radiology residents who correctly interpreted all 20 NG tube CXRs were significantly more confident in their abilities on a 5-point Likert scale than those who got at least 1 NG tube CXR wrong [4.4 (0.52) versus 3.8 (0.79), P = .02].

Conclusions: This study suggests that radiology residents may not be adequately trained to interpret the position of NG tubes on CXRs. Early and compulsory training in this important skill should be instituted urgently.

Advances in knowledge: There is a critical gap in radiology training. Radiology residents may not be adequately prepared to safely interpret NG tube position on chest X-rays. New DHSC memorandum of understanding mandates competency-based education across all training programs.

Background: Microvascular invasion (MVI) is considered an independent risk factor for early recurrence after curative resection of hepatocellular carcinoma (HCC). The ability to preoperatively predict MVI could lead to personalized treatment options in high-risk patients.

Aims: To identify radiomic features from CE-CT that correlate with MVI in patients with HCC and evaluate the robustness and reproducibility of radiomic assessment by manual segmentation between readers with different experience.

Methods: Clinical, CT imaging, and histological parameters were recorded. Sixty-two HCC lesions were manually contoured by three radiologists. Radiomic features were extracted. Features best correlating with angioinvasion were selected and assessed in univariate and multivariate models by means of 100 trials of 5-fold stratified cross-validation in terms of AUC, sensitivity, and specificity. The model identified on contours from the most experienced operator was then tested on contours from the other operators to assess inter-reader reproducibility.

Results: Feature selection identified LI-RADS category and four arterial-phase radiomic texture features, with GLCM-ClusterShade and its high-frequency wavelet variant showing the highest predictive value for MVI. A bivariate logistic regression model combining these two features achieved an AUC of 79%, with 78% sensitivity and 64% specificity. The robustness of manual segmentation was strongly dependent on reader experience, and inter-operator reproducibility was suboptimal when the model was applied to contours from less experienced readers.

Conclusion: Radiomics analysis may be able to predict MVI in patients with HCC. However, segmentation methods remain a practical challenge affecting reproducibility in radiomic studies.

Advances in knowledge: This study, in agreement with the literature, identifies a radiomic model based on two textural features that could correlate with MVI in HCC. Furthermore, it aims to investigate some of the limitations in the application of radiomics in clinical practice, which still restrict it to a research setting, identifying an important limitation in manual segmentation methods. This aspect has not yet been sufficiently investigated in the literature.

Objectives: This study presents a comprehensive comparison of minimally-invasive extracranial neck imaging modalities-Colour Doppler ultrasound (CDUS), CT angiography (CTA), and MR angiography (MRA)-in acute ischaemic stroke (AIS) patients. The aim was to evaluate vessel stenosis, its related parameters, and assess the role of early CTA/MRA in AIS.

Methods: Categorical and continuous data were compared with Chi-square and independent Sample t-test, respectively. Spearman rank correlation matrix was performed for non-linear CDUS variables. The agreement between various imaging modalities was calculated with kappa (k) coefficient.

Results: AIS was most common in males, aged 61-70 years, associated with hypertension and smoking (P-value < .05). Seventy-four plaques were identified in 50 patients, with good agreement between the 3 imaging (k > 0.6). CDUS was limited in evaluating Vertebral Arteries and plaque characterization. CTA/MRA showed higher sensitivity for defining stenosis and plaques, with good-excellent agreement between them (k > 0.6). CTA and MRA identified 40 and 43 vulnerable plaques, respectively.

Conclusions: Colour Doppler ultrasound is subjective, comprehensive assessment of anatomic and hemodynamic parameters but lacks sensitivity in identifying vulnerable plaques. CTA/MRA have better sensitivity with good soft tissue differentiation especially in lesser stenosed vessels.

Advances in knowledge: Our results support preferred use of MRA/CTA as first-line modalities in time-sensitive scenarios like acute stroke and need to move beyond CDUS-based assessment. These show promise in detecting vulnerable plaque and predicting AIS risk/recurrence; in patient triage, and to guide early intensive treatment. Longitudinal studies are required to assess risk reduction by early advanced imaging.

Alpha radiation has emerged as a promising modality in cancer treatment due to its unique physical and biological properties. Among these, diffusing alpha-emitters radiation therapy (DaRT) delivers alpha radiation directly into solid tumours using inserted seeds. This review synthesizes both the biological mechanisms and therapeutic implications of alpha irradiation, with a focus on DaRT. We explore how alpha particles induce complex DNA damage, modulate the tumour microenvironment, and interact with immune therapies. Emphasis is placed on preclinical and early clinical findings that suggest DaRT's potential to improve outcomes, especially in difficult-to-treat malignancies. The high linear energy transfer (LET) radiation induces complex DNA damage in tumour cells, leading to increased cell death compared to conventional radiotherapy. Alpha particles have a short range in tissue, allowing for highly localized treatment with minimal damage to surrounding healthy tissue. Recent studies have demonstrated that alpha radiation can stimulate antitumor immune responses, potentially enhancing treatment efficacy. Clinical trials utilizing alpha-emitting radioisotopes have shown encouraging results in various cancer types, particularly for metastatic disease.

Musculoskeletal (MSK) imaging was among the first radiology subspecialties to adopt artificial intelligence (AI), with applications now spanning the entire MSK workflow, from image acquisition to reporting. Deep learning-based reconstruction protocols can accelerate MRI by reducing scan times and artefacts, improving accessibility in high-volume and resource-limited settings. Furthermore, AI interpretation tools have demonstrated strong performance in fracture detection, assessment of meniscal and ligament tears, bone tumour characterization and automated quantification of measurements, supporting greater diagnostic consistency across radiologists with varying experience levels. Large language models (LLMs) extend AI's impact beyond image analysis by simplifying reports for patients, automating classification systems, and streamlining clinical communication. Despite these advances, important challenges remain. Integration of AI into already established clinical workflows can be complex, and requires robust technical solutions, regulatory compliance, and strategies to maintain radiologist oversight. Questions of liability, cost-effectiveness, and the role of AI in medical education further underscore the need for careful implementation. AI is poised to fundamentally reshape MSK radiology by enhancing efficiency, improving diagnostic accuracy, and enabling more patient-centred communication. To fully realize this potential, adoption must balance innovation with safety, equity, and sustainability, ensuring AI remains a trusted assistive tool that strengthens rather than replaces radiologist expertise.

MSK radiologists play a critical role in emergency and trauma settings, where rapid and accurate imaging interpretation is essential for timely diagnosis and treatment. The increasing complexity of trauma cases has driven the adoption of advanced imaging modalities beyond conventional radiographs and computed tomography (CT). Dual-energy CT (DECT) and magnetic resonance imaging (MRI) have revolutionized MSK imaging, offering superior tissue characterization and improved detection of occult fractures, bone marrow edema (BME), infections, and soft tissue injuries. Emerging technologies, such as portable MRI and photon-counting CT (PCCT), further enhance diagnostic capabilities by enabling bedside imaging, reducing radiation exposure, and providing ultra-high-resolution images. MSK radiologists are integral to immediate diagnosis, triaging, differentiating acute from chronic injuries, guiding surgical interventions, and performing image-guided procedures. DECT in particular has proven invaluable in detecting BME, reducing metal artifacts, and improving soft tissue contrast, while MRI remains the gold standard for evaluating soft tissue injuries and occult fractures. Portable MRI offers a radiation-free alternative for point-of-care imaging, especially in spinal cord and soft tissue injuries. PCCT, with its superior spatial resolution and material decomposition capabilities, holds promise for advanced fracture detection and reduced radiation doses. Additionally, 3D printing has emerged as a transformative tool for preoperative planning, surgical simulation, and personalized implant design. Despite challenges such as cost, accessibility, and technical limitations, these advancements are reshaping trauma imaging. As technology evolves, MSK radiologists will continue to integrate these innovations to optimize patient care in emergency and trauma settings, ensuring faster, more accurate diagnoses.

This review explores the dual meaning of the prefix "eco"-ecology and economics-and the transformative idea of synthesizing the two into a single "eco" framework. This framework gives rise to EcoRad, which blends economic and ecologic principles to optimize radiology practice. EcoRad strives to achieve the triple bottom line by approaching economic challenges from a planetary health perspective and by using economic approaches to enhance planetary health. In effect, this expands the traditional focus on financial performance to also include social and environmental impact. With EcoRad as a guide, radiology departments are called upon to consider 5 actions that can help overcome barriers to sustainable radiology: adopt sustainable procurement and maintenance, integrate green information technology (IT) and operational efficiencies, advocate for payment models that reward green radiology, champion green budgeting, and involve patients, industry, third-party payors, and policymakers in sustainability.

Artificial intelligence (AI) is transforming radiology, with nearly 80% of approved AI as medical devices (AIaMDs) being imaging-related. As AI adoption accelerates, radiology training programs must evolve to equip future radiologists with the skills to critically evaluate, implement, and integrate AI into clinical practice. However, despite AI's growing role, its inclusion in medical curricula remains inconsistent, and assessment of AI competency is lacking. This review explores the current state of AI in UK medical training curricula with a more in-depth focus on radiology. We discuss the potential impact of AI on competency evaluations, including the Fellowship of the Royal College of Radiologists (FRCR) examinations, Annual Review of Competence Progression (ARCP), and on-call assessments. Additionally, we examine how AI-driven educational resources, such as AI-assisted training platforms, could enhance radiology education. To future-proof radiology training and careers, we propose strategies to evaluate AI literacy including nationalized structured AI teaching, and AI-focused assessments. Addressing these challenges will be crucial in ensuring that radiologists remain at the forefront of digital healthcare transformation while maintaining their core diagnostic expertise.

Low-field MRI (LF-MRI) is in the spotlight as multidisciplinary experts consider it to be one solution to expanding MRI access worldwide. The clinical scenarios and case-mix in which LF-MRI could play an especially important role in the patient diagnostic algorithm are different in High and Low- and Middle-Income Countries (LMIC). The aim of this article is to suggest a robust structure within which to envision clinical use and advancement of LF-MRI technology in LMICs. This article presents three discrete clinical scenarios-a tertiary care facility with an LF-MRI only, a tertiary care facility with an LF-MRI and an HF-MRI and a peripheral healthcare facility with an LF-MRI only-derived from a combination of the authors' observed practice and hypothetical models in an LMIC and 31 consecutive case reviews within a 32-month timeframe of our experience with the 0.064 T Hyperfine Swoop in Malawi. The authors recognize the important of a holistic approach to the ongoing multifaceted efforts at LMIC-appropriate advancement of LF-MRI technology. This ranges from continued innovation relating to deep learning methods for improved diagnostic accuracy and workflow efficiency, empowerment towards building LF-MRIs in-situ in the LMIC and multidisciplinary capacity building initiatives in LMICs.