BJR open最新文献

{"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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}

{"title":"Deuterium MR spectroscopy: potential applications in oncology research.","authors":"Almir Galvão Vieira Bitencourt, Arka Bhowmik, Eduardo Flavio De Lacerda Marcal Filho, Roberto Lo Gullo, Yousef Mazaheri, Panagiotis Kapetas, Sarah Eskreis-Winkler, Robert Young, Katja Pinker, Sunitha B Thakur","doi":"10.1093/bjro/tzae019","DOIUrl":"10.1093/bjro/tzae019","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":72419,"journal":{"name":"BJR open","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11333568/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142010041","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":"Improvement in paediatric CT use and justification: a single-centre experience.","authors":"Mariliis Tiidermann, Triin Pihlakas, Juhan Saaring, Janelle Märs, Jaanika Aasmäe, Kristiina Langemets, Mare Lintrop, Pille Kool, Pilvi Ilves","doi":"10.1093/bjro/tzae020","DOIUrl":"10.1093/bjro/tzae020","url":null,"abstract":"<p><strong>Objectives: </strong>To analyse changes in the use of paediatric (≤16 years) CT over the past decade and to evaluate the appropriateness of CT examinations at a tertiary teaching hospital.</p><p><strong>Methods: </strong>Data from 290 paediatric CTs were prospectively collected in 2022 and compared with data from 2017 (358 cases) and 2012 (538 cases). The justification of CTs was evaluated with regard to medical imaging referral guidelines and appropriateness rates were calculated.</p><p><strong>Results: </strong>Paediatric CTs decreased 39.4% over the 10 years, contrasting with a 27.6% increase in overall CTs. Paediatric CTs as the share of overall CTs dropped from 2.5% in 2012 to 1.1% in 2022 (<i>P</i> < .0001), with a concurrent rise in paediatric MRIs (<i>P</i> < .0001). Notable reductions in CT use occurred for head trauma (<i>P</i> = .0003), chronic headache (<i>P</i> < .0001), epilepsy (<i>P</i> = .037), hydrocephalus (<i>P</i> = .0078), chest tumour (<i>P</i> = .0005), and whole-body tumour (<i>P</i> = .0041). The overall appropriateness of CTs improved from 73.1% in 2017 to 79.0% in 2022 (<i>P</i> = .0049). In 15.4% of the cases, no radiological examination was deemed necessary, and in 8.7% of the cases, another modality was more appropriate. Appropriateness rates were the highest for the head and neck angiography (100%) and the chest (96%) and the lowest for the neck (66%) and the head (67%).</p><p><strong>Conclusions: </strong>Justification of CT scans can be improved by regular educational interventions, increasing MRI accessibility, and evaluating the appropriateness of the requested CT before the examination. Interventions for a more effective implementation of referral guidelines are needed.</p><p><strong>Advances in knowledge: </strong>The focus for improvement should be CTs for head and cervical spine trauma, accounting for the majority of inappropriate requests in the paediatric population.</p>","PeriodicalId":72419,"journal":{"name":"BJR open","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11322280/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141984033","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":"Unlocking the potential of photon counting detector CT for paediatric imaging: a pictorial essay.","authors":"Ieva Aliukonyte, Daan Caudri, Ronald Booij, Marcel van Straten, Marcel L Dijkshoorn, Ricardo P J Budde, Edwin H G Oei, Luca Saba, Harm A W M Tiddens, Pierluigi Ciet","doi":"10.1093/bjro/tzae015","DOIUrl":"10.1093/bjro/tzae015","url":null,"abstract":"<p><p>Recent advancements in CT technology have introduced a revolutionary innovation to practice known as the Photon-Counting detector (PCD) CT imaging. The pivotal hardware enhancement of the PCD-CT scanner lies in its detectors, which consist of smaller pixels than standard detectors and allow direct conversion of individual X-rays to electrical signals. As a result, CT images are reconstructed at higher spatial resolution (as low as 0.2 mm) and reduced overall noise, at no expense of an increased radiation dose. These features are crucial for paediatric imaging, especially for infants and young children, where anatomical structures are notably smaller than in adults and in whom keeping dose as low as possible is especially relevant. Since January 2022, our hospital has had the opportunity to work with PCD-CT technology for paediatric imaging. This pictorial review will showcase clinical examples of PCD-CT imaging in children. The aim of this pictorial review is to outline the potential paediatric applications of PCD-CT across different anatomical regions, as well as to discuss the benefits in utilizing PCD-CT in comparison to conventional standard energy integrating detector CT.</p>","PeriodicalId":72419,"journal":{"name":"BJR open","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11254292/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141636037","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":"Assessment of heart-substructures auto-contouring accuracy for application in heart-sparing radiotherapy for lung cancer.","authors":"Tom Marchant, Gareth Price, Alan McWilliam, Edward Henderson, Dónal McSweeney, Marcel van Herk, Kathryn Banfill, Matthias Schmitt, Jennifer King, Claire Barker, Corinne Faivre-Finn","doi":"10.1093/bjro/tzae006","DOIUrl":"10.1093/bjro/tzae006","url":null,"abstract":"<p><strong>Objectives: </strong>We validated an auto-contouring algorithm for heart substructures in lung cancer patients, aiming to establish its accuracy and reliability for radiotherapy (RT) planning. We focus on contouring an amalgamated set of subregions in the base of the heart considered to be a new organ at risk, the cardiac avoidance area (CAA), to enable maximum dose limit implementation in lung RT planning.</p><p><strong>Methods: </strong>The study validates a deep-learning model specifically adapted for auto-contouring the CAA (which includes the right atrium, aortic valve root, and proximal segments of the left and right coronary arteries). Geometric, dosimetric, quantitative, and qualitative validation measures are reported. Comparison with manual contours, including assessment of interobserver variability, and robustness testing over 198 cases are also conducted.</p><p><strong>Results: </strong>Geometric validation shows that auto-contouring performance lies within the expected range of manual observer variability despite being slightly poorer than the average of manual observers (mean surface distance for CAA of 1.6 vs 1.2 mm, dice similarity coefficient of 0.86 vs 0.88). Dosimetric validation demonstrates consistency between plans optimized using auto-contours and manual contours. Robustness testing confirms acceptable contours in all cases, with 80% rated as \"Good\" and the remaining 20% as \"Useful.\"</p><p><strong>Conclusions: </strong>The auto-contouring algorithm for heart substructures in lung cancer patients demonstrates acceptable and comparable performance to human observers.</p><p><strong>Advances in knowledge: </strong>Accurate and reliable auto-contouring results for the CAA facilitate the implementation of a maximum dose limit to this region in lung RT planning, which has now been introduced in the routine setting at our institution.</p>","PeriodicalId":72419,"journal":{"name":"BJR open","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11087931/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140913561","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":"Cementoplasty to cryoablation: review and current status.","authors":"Jin Rong Tan, Yet Yen Yan, Adnan Sheikh, Hugue Ouellette, Paul Mallinson, Peter L Munk","doi":"10.1093/bjro/tzae007","DOIUrl":"10.1093/bjro/tzae007","url":null,"abstract":"<p><p>Recent advances in percutaneous image-guided techniques have empowered interventional radiologists with diverse treatment options for the management of musculoskeletal lesions. Of note, there is growing utility for cementoplasty procedures, with indications ranging from stabilization of bone metastases to treatment of painful vertebral compression fractures. Likewise, cryoablation has emerged as a viable adjunct in the treatment of both primary and secondary bone and soft tissue neoplasms. These treatment options have been progressively incorporated into the multidisciplinary approach to holistic care of patients, alongside conventional radiotherapy, systemic therapy, surgery, and analgesia. This review article serves to outline the indications, technical considerations, latest developments, and evidence for the burgeoning role of cementoplasty and cryoablation in the musculoskeletal system, with an emphasis on pain palliation and tumour control.</p>","PeriodicalId":72419,"journal":{"name":"BJR open","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10965423/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140308127","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}