BJR open最新文献

Tuberculosis (TB) remains the leading cause of death from a single infectious agent globally, despite being a potentially curable disease. This disease typically affects the lungs but may involve many extrapulmonary sites, especially in patients with risk factors such as HIV infection. The clinical features of extrapulmonary TB may mimic many different disease entities, particularly at less common thoracic sites such as the heart, chest wall, and breast. Imaging has an important role in the early diagnosis of TB, helping to detect disease, guide appropriate laboratory investigation, demonstrate complications, and monitor disease progress and response to treatment. Imaging supports the clinical objective of achieving effective treatment outcome and complication prevention. This review aims to highlight the imaging spectrum of TB affecting both pulmonary and extrapulmonary sites in the thorax. We also briefly provide key background information about TB, such as epidemiology, pathogenesis, and diagnosis.

Objectives: Artificial intelligence (AI) enabled devices may be able to optimize radiologists' productivity by identifying normal and abnormal chest X-rays (CXRs) for triaging. In this service evaluation, we investigated the accuracy of one such AI device (qXR).

Methods: A randomly sampled subset of general practice and outpatient-referred frontal CXRs from a National Health Service Trust was collected retrospectively from examinations conducted during November 2022 to January 2023. Ground truth was established by consensus between 2 radiologists. The main objective was to estimate negative predictive value (NPV) of AI.

Results: A total of 522 CXRs (458 [87.74%] normal CXRs) from 522 patients (median age, 64 years [IQR, 49-77]; 305 [58.43%] female) were analysed. AI predicted 348 CXRs as normal, of which 346 were truly normal (NPV: 99.43% [95% CI, 97.94-99.93]). The sensitivity, specificity, positive predictive value, and area under the ROC curve of AI were found to be 96.88% (95% CI, 89.16-99.62), 75.55% (95% CI, 71.34-79.42), 35.63% (95% CI, 28.53-43.23), and 91.92% (95% CI, 89.38-94.45), respectively. A sensitivity analysis was conducted to estimate NPV by varying assumptions of the prevalence of normal CXRs. The NPV ranged from 88.96% to 99.54% as prevalence increased.

Conclusions: The AI device recognized normal CXRs with high NPV and has the potential to increase radiologists' productivity.

Advances in knowledge: There is a need for more evidence on the utility of AI-enabled devices in identifying normal CXRs. This work adds to such limited evidence and enables researchers to plan studies to further evaluate the impact of such devices.

Dual-energy computed tomography (DECT) is an advanced imaging technique that acquires data using two distinct X-ray energy spectra, typically at 80 and 140 kVp, to differentiate materials based on their atomic number and electron density. This capability allows for the enhanced visualisation of various pathologies, including bone marrow oedema (BMO), by providing high-resolution images with notable energy spectral separation while maintaining radiation doses comparable to conventional CT. DECT's ability to create colour-coded virtual non-calcium (VNCa) images has proven particularly valuable in detecting traumatic bone marrow lesions (BMLs) and subtle fractures, offering a reliable alternative or complement to MRI. DECT has emerged as a significant tool in the detection and characterisation of bone marrow pathologies, especially in traumatic injuries. Its ability to generate high-resolution images and distinguish between different tissue types makes it a valuable asset in clinical diagnostics. With its comparable diagnostic accuracy to MRI and the added advantage of reduced examination time and increased availability, DECT represents a promising advancement in the imaging of BMO and related conditions.

Objectives: The imaging support workforce is a key enabler in unlocking imaging capacity and capability, yet no evidence exists of the workforce size and configuration. This research provides the first comprehensive analysis of workforce data to explore the deployment of the support workforce within National Health Service (NHS) imaging services in England.

Methods: Using a census methodology, an anonymized electronic staff record (ESR) data set extracted in December 2022 was analysed to identify support workers and their employment bandings at NHS Trust, regional and national (England) level. Support workforce proportions, median values, and Spearman's rank correlations were calculated.

Results: Analysis of 137 NHS Trusts, comprising 100% of acute trusts (n = 124) and specialist trusts with imaging services (n = 13), identified that the support workforce (pay bands 2-4) constitutes 23.6% of the imaging staff base. Ranking trusts into 3 categories based on the proportion of support workers in their imaging establishment, median values ranged from 30.7% (high) to 22.2% (medium) and 10.5% (low). Two opposing deployment models of band 2 and band 3 support workers were identified.

Conclusions: Comprising almost one-quarter of the imaging establishment, models of deployment at bands 2 and 3 are highly variable. Assistant practitioners (band 4) are under-utilised, providing an opportunity to introduce innovations to address workforce demands.

Advances in knowledge: This census is the first to provide evidence of the size and structure of the support workforce, the first step in enabling effective workforce transformation. Further research is required to explain the two opposing deployment models.

Endovascular aortic aneurysm repair (EVAR) is an established approach to treating abdominal aortic aneurysms, however, challenges arise when the aneurysm involves visceral branches with insufficient normal segment of the aorta to provide aneurysm seal without excluding those vessels. To overcome this, a range of technological developments and solutions have been proposed including fenestrated, branched, physician-modified stents, and chimney techniques. Understanding the currently available evidence for each option is essential to select the most suitable procedure for each patient. Overall, the evidence for fenestrated endovascular repair is the most comprehensive of these techniques and shows an early post-operative advantage over open surgical repair (OSR) but with a catch-up mortality in the mid-term period. In this review, we will describe these endovascular options, pre- and post-procedure radiological assessment and current evidence of outcomes.

Objectives: To evaluate the incidence and spectrum of findings in patients referred for CT imaging of extremity soft tissue infection in the adult emergency department (ED) setting before and during the COVID-19 pandemic.

Methods: Two hundred thirteen CT exams in the pre-COVID cohort (February 1, 2018-January 31, 2020) and 383 CT exams in the COVID cohort (February 1, 2020-January 31, 2022) were evaluated in this multicentre, retrospective study. Demographic information and clinical histories were collected, along with regional data on COVID-19 hospitalizations and deaths.

Results: Comparable age and sex distribution was found in the pre-COVID (average age of 53.5 years; male: female ratio of 71:29) and COVID (average age of 54.6 years; male: female ratio of 69:31) cohorts. The frequency of reported clinical risk factors (diabetes mellitus, injected drug use, prior surgery, animal bite) was not significantly different between the two cohorts. Findings of simultaneous involvement of both superficial and deep soft tissue infection on CT imaging were significantly higher in the COVID cohort (53.4%) than in the pre-COVID cohort (33.7%). CT findings of phlegmon (49.1% vs 22.1%), ulcers (48.8% vs 30%), osteomyelitis (21.7% vs 13.1%), as well as localized (18.8% vs 11.7%) and extensive (3.7% vs 2.3%) soft tissue gas were significantly more common in the COVID cohort than in the pre-COVID cohort.

Conclusions: During the COVID-19 pandemic, the number of ED CT exams for the evaluation of extremity soft tissue infection increased, with this imaging also showing more advanced disease. Pandemic-related modifications to human behaviour and re-distribution of healthcare resources may underlie these observed changes.

Advances in knowledge: This multi-centre study shows an increase in extremity soft tissue infection presenting to the ED during the pandemic. This finding is important for future pandemic preparations, as it can aid in the decision-making process around resource allocation.

Augmented reality (AR) exists on a spectrum, a mixed reality hybrid of virtual projections onto real surroundings. Superimposing conventional medical imaging onto the living patient offers vast potential for radiology, potentially revolutionising practice. The digital technology and user-interfaces that allow us to appreciate this enhanced environment however are complex, expensive, and development mainly limited to major commercial technology (Tech) firms. Hence, it is the activity of these consumer-based businesses that will inevitably dictate the available technology and therefore clinical application of AR. The release of mixed reality head-mounted displays in 2024, must therefore prompt a review of the current status of AR research in radiology, the need for further study and a discussion of the complicated relationship between consumer technology, clinical utility, and the risks of monopolisation.

Objectives: Accurate beam modelling is essential for dose calculation in stereotactic radiation therapy (SRT), such as CyberKnife treatment. However, the present deep learning methods only involve patient anatomical images and delineated masks for training. These studies generally focus on traditional intensity-modulated radiation therapy (RT) plans. Nevertheless, this paper aims to develop a deep CNN-based method for CyberKnife plan dose prediction about brain cancer patients. It utilized modelled beam information, target delineation, and patient anatomical information.

Methods: This study proposes a method that adds beam information to predict the dose distribution of CyberKnife in brain cases. A retrospective dataset of 88 brain and abdominal cancer patients treated with the Ray-tracing algorithm was performed. The datasets include patients' anatomical information (planning CT), binary masks for organs at risk (OARs) and targets, and clinical plans (containing beam information). The datasets were randomly split into 68, 6, and 14 brain cases for training, validation, and testing, respectively.

Results: Our proposed method performs well in SRT dose prediction. First, for the gamma passing rates in brain cancer cases, with the 2 mm/2% criteria, we got 96.7% ± 2.9% for the body, 98.3% ± 3.0% for the planning target volume, and 100.0% ± 0.0% for the OARs with small volumes referring to the clinical plan dose. Secondly, the model predictions matched the clinical plan's dose-volume histograms reasonably well for those cases. The differences in key metrics at the target area were generally below 1.0 Gy (approximately a 3% difference relative to the prescription dose).

Conclusions: The preliminary results for selected 14 brain cancer cases suggest that accurate 3-dimensional dose prediction for brain cancer in CyberKnife can be accomplished based on accurate beam modelling for homogeneous tumour tissue. More patients and other cancer sites are needed in a further study to validate the proposed method fully.

Advances in knowledge: With accurate beam modelling, the deep learning model can quickly generate the dose distribution for CyberKnife cases. This method accelerates the RT planning process, significantly improves its operational efficiency, and optimizes it.

Large language models (LLMs) are transforming the field of natural language processing (NLP). These models offer opportunities for radiologists to make a meaningful impact in their field. NLP is a part of artificial intelligence (AI) that uses computer algorithms to study and understand text data. Recent advances in NLP include the Attention mechanism and the Transformer architecture. Transformer-based LLMs, such as GPT-4 and Gemini, are trained on massive amounts of data and generate human-like text. They are ideal for analysing large text data in academic research and clinical practice in radiology. Despite their promise, LLMs have limitations, including their dependency on the diversity and quality of their training data and the potential for false outputs. Albeit these limitations, the use of LLMs in radiology holds promise and is gaining momentum. By embracing the potential of LLMs, radiologists can gain valuable insights and improve the efficiency of their work. This can ultimately lead to improved patient care.

Metabolic imaging in clinical practice has long relied on PET with fluorodeoxyglucose (FDG), a radioactive tracer. However, this conventional method presents inherent limitations such as exposure to ionizing radiation and potential diagnostic uncertainties, particularly in organs with heightened glucose uptake like the brain. This review underscores the transformative potential of traditional deuterium MR spectroscopy (MRS) when integrated with gradient techniques, culminating in an advanced metabolic imaging modality known as deuterium MRI (DMRI). While recent advancements in hyperpolarized MRS hold promise for metabolic analysis, their widespread clinical usage is hindered by cost constraints and the availability of hyperpolarizer devices or facilities. DMRI, also denoted as deuterium metabolic imaging (DMI), represents a pioneering, single-shot, and noninvasive paradigm that fuses conventional MRS with nonradioactive deuterium-labelled substrates. Extensively tested in animal models and patient cohorts, particularly in cases of brain tumours, DMI's standout feature lies in its seamless integration into standard clinical MRI scanners, necessitating only minor adjustments such as radiofrequency coil tuning to the deuterium frequency. DMRI emerges as a versatile tool for quantifying crucial metabolites in clinical oncology, including glucose, lactate, glutamate, glutamine, and characterizing IDH mutations. Its potential applications in this domain are broad, spanning diagnostic profiling, treatment response monitoring, and the identification of novel therapeutic targets across diverse cancer subtypes.