BJR open最新文献

{"title":"Patient, tumour, and dosimetric factors influencing survival in non-small cell lung cancer patients treated with stereotactic ablative body radiotherapy.","authors":"Minal Padden-Modi, Yevhen Spivak, Ian Gleeson, Andrew Robinson, Kamalram Thippu Jayaprakash","doi":"10.1093/bjro/tzae028","DOIUrl":"10.1093/bjro/tzae028","url":null,"abstract":"<p><strong>Objectives: </strong>We aimed to analyse clinical outcomes of peripheral, early-stage non-small cell lung cancer (NSCLC) patients treated with stereotactic ablative body radiotherapy (SABR), and evaluate potential patient, tumour, and dosimetric variables influencing survival.</p><p><strong>Methods: </strong>Data were collected retrospectively from patients treated between September 2012 and December 2016 and followed up until January 2021. Patient demographics, tumour characteristics, SABR dosimetric parameters, and survival data were collected from electronic patient medical records. Descriptive statistics were performed, and SPSS software was used for survival analysis.</p><p><strong>Results: </strong>Eighty-nine patients were included of whom 49.5% were male and 50.5% female. Median age was 74 years. 98.8% of patients had T1-2 tumours and 89.9% underwent 55 Gy in 5 fractions. Median overall survival time was 58.7 months. On uni- and multi-variate analysis, neither patient nor tumour variables showed association with overall survival. However, planning target volume (PTV) and minimum dose to PTV correlated with overall survival. There was a signal for association between mean lung dose and overall survival on multivariate analysis.</p><p><strong>Conclusions: </strong>Our long-term results show SABR is an effective treatment for peripheral, early-stage NSCLC with excellent overall survival, comparable to other series. Our study found only the PTV and minimum dose to PTV had an impact on overall survival, which demonstrates the importance of generating optimal SABR plans.</p><p><strong>Advances in knowledge: </strong>Our work identified lung SABR dosimetric parameters that correlate with survival, which illustrates the importance of producing optimal lung SABR plans.</p>","PeriodicalId":72419,"journal":{"name":"BJR open","volume":"6 1","pages":"tzae028"},"PeriodicalIF":0.0,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11441645/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142333656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Imaging of thoracic tuberculosis: pulmonary and extrapulmonary.","authors":"Nuttaya Pattamapaspong, Thanat Kanthawang, Wilfred C G Peh, Nadia Hammami, Mouna Chelli Bouaziz, Mohamed Fethi Ladeb","doi":"10.1093/bjro/tzae031","DOIUrl":"10.1093/bjro/tzae031","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":72419,"journal":{"name":"BJR open","volume":"6 1","pages":"tzae031"},"PeriodicalIF":0.0,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11449295/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142373672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accuracy of an artificial intelligence-enabled diagnostic assistance device in recognizing normal chest radiographs: a service evaluation.","authors":"Amrita Kumar, Puja Patel, Dennis Robert, Shamie Kumar, Aneesh Khetani, Bhargava Reddy, Anumeha Srivastava","doi":"10.1093/bjro/tzae029","DOIUrl":"10.1093/bjro/tzae029","url":null,"abstract":"<p><strong>Objectives: </strong>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).</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>The AI device recognized normal CXRs with high NPV and has the potential to increase radiologists' productivity.</p><p><strong>Advances in knowledge: </strong>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.</p>","PeriodicalId":72419,"journal":{"name":"BJR open","volume":"6 1","pages":"tzae029"},"PeriodicalIF":0.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11441651/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142333655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual-energy CT: Impact of detecting bone marrow oedema in occult trauma in the Emergency.","authors":"Muhammad Israr Ahmad, Lulu Liu, Adnan Sheikh, Savvas Nicolaou","doi":"10.1093/bjro/tzae025","DOIUrl":"https://doi.org/10.1093/bjro/tzae025","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":72419,"journal":{"name":"BJR open","volume":"6 1","pages":"tzae025"},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11427222/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142336716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Establishing the size and configuration of the imaging support workforce: a census of national workforce data in England.","authors":"Julie Nightingale, Sarah Etty, Beverley Snaith, Trudy Sevens, Rob Appleyard, Shona Kelly","doi":"10.1093/bjro/tzae026","DOIUrl":"https://doi.org/10.1093/bjro/tzae026","url":null,"abstract":"<p><strong>Objectives: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>Analysis of 137 NHS Trusts, comprising 100% of acute trusts (<i>n</i> = 124) and specialist trusts with imaging services (<i>n</i> = 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.</p><p><strong>Conclusions: </strong>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.</p><p><strong>Advances in knowledge: </strong>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.</p>","PeriodicalId":72419,"journal":{"name":"BJR open","volume":"6 1","pages":"tzae026"},"PeriodicalIF":0.0,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11405087/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142302328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Complex abdominal aortic aneurysms: a review of radiological and clinical assessment, endovascular interventions, and current evidence of management outcomes.","authors":"Girija Agarwal, Mohamad Hamady","doi":"10.1093/bjro/tzae024","DOIUrl":"https://doi.org/10.1093/bjro/tzae024","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":72419,"journal":{"name":"BJR open","volume":"6 1","pages":"tzae024"},"PeriodicalIF":0.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11392563/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142302326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Emergency department referrals for CT imaging of extremity soft tissue infection: before and during the COVID-19 pandemic.","authors":"Andrew Nanapragasam, Lawrence M White","doi":"10.1093/bjro/tzae016","DOIUrl":"https://doi.org/10.1093/bjro/tzae016","url":null,"abstract":"<p><strong>Objectives: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p><p><strong>Advances in knowledge: </strong>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.</p>","PeriodicalId":72419,"journal":{"name":"BJR open","volume":"6 1","pages":"tzae016"},"PeriodicalIF":0.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11399226/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142302327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Augmented reality and radiology: visual enhancement or monopolized mirage.","authors":"Matthew Christie","doi":"10.1093/bjro/tzae021","DOIUrl":"https://doi.org/10.1093/bjro/tzae021","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":72419,"journal":{"name":"BJR open","volume":"6 1","pages":"tzae021"},"PeriodicalIF":0.0,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11399227/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142302325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Three-dimensional dose prediction based on deep convolutional neural networks for brain cancer in CyberKnife: accurate beam modelling of homogeneous tissue.","authors":"Yuchao Miao, Ruigang Ge, Chuanbin Xie, Xiangkun Dai, Yaoying Liu, Baolin Qu, Xiaobo Li, Gaolong Zhang, Shouping Xu","doi":"10.1093/bjro/tzae023","DOIUrl":"10.1093/bjro/tzae023","url":null,"abstract":"<p><strong>Objectives: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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).</p><p><strong>Conclusions: </strong>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.</p><p><strong>Advances in knowledge: </strong>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.</p>","PeriodicalId":72419,"journal":{"name":"BJR open","volume":"6 1","pages":"tzae023"},"PeriodicalIF":0.0,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11364489/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142115563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing radiology practice and research: harnessing the potential of large language models amidst imperfections.","authors":"Eyal Klang, Lee Alper, Vera Sorin, Yiftach Barash, Girish N Nadkarni, Eyal Zimlichman","doi":"10.1093/bjro/tzae022","DOIUrl":"10.1093/bjro/tzae022","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":72419,"journal":{"name":"BJR open","volume":"6 1","pages":"tzae022"},"PeriodicalIF":0.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11349187/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142082738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}