The Royal College of Radiologists Open最新文献

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Personal view: Understanding and addressing gaps between randomized controlled trials and real-world evidence in oncology 个人观点:理解和解决肿瘤随机对照试验和真实世界证据之间的差距
The Royal College of Radiologists Open Pub Date : 2025-01-01 DOI: 10.1016/j.rcro.2025.100289
Anuj Kumar , Sarbani Ghosh Laskar , Katherine Wakeham , Kamal Akbarov , Jai Prakash Agarwal
{"title":"Personal view: Understanding and addressing gaps between randomized controlled trials and real-world evidence in oncology","authors":"Anuj Kumar , Sarbani Ghosh Laskar , Katherine Wakeham , Kamal Akbarov , Jai Prakash Agarwal","doi":"10.1016/j.rcro.2025.100289","DOIUrl":"10.1016/j.rcro.2025.100289","url":null,"abstract":"","PeriodicalId":101248,"journal":{"name":"The Royal College of Radiologists Open","volume":"3 ","pages":"Article 100289"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395744","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}
引用次数: 0
Primary unilateral breast angiosarcoma in adolescence 青少年原发性单侧乳腺血管肉瘤
The Royal College of Radiologists Open Pub Date : 2025-01-01 DOI: 10.1016/j.rcro.2025.100288
Nidhi Kishore Agrawal, Sapna Abdul Rahim, Alpna Jain
{"title":"Primary unilateral breast angiosarcoma in adolescence","authors":"Nidhi Kishore Agrawal,&nbsp;Sapna Abdul Rahim,&nbsp;Alpna Jain","doi":"10.1016/j.rcro.2025.100288","DOIUrl":"10.1016/j.rcro.2025.100288","url":null,"abstract":"<div><div>Primary breast angiosarcoma is a rare soft tissue malignancy of vascular origin, particularly in young patients. Here, we present the case of an 18-year-old autistic female with Addison's disease who developed a rapidly enlarging unilateral breast mass initially diagnosed as an abscess. Despite treatment, the lesion progressed to chest wall and lymph node metastases within three months. This case emphasizes the importance of including malignancy in the differential diagnosis of atypical breast presentations, even in young patients, and highlights the diagnostic and therapeutic challenges associated with this aggressive tumour.</div></div>","PeriodicalId":101248,"journal":{"name":"The Royal College of Radiologists Open","volume":"3 ","pages":"Article 100288"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395745","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}
引用次数: 0
The ability of artificial intelligence to correctly identify acute haemorrhage in A&E patients on a non-contrast CT head: a review of false positives in Brainomix in a single centre 人工智能在非对比CT头部上正确识别急诊科患者急性出血的能力:单一中心Brainomix假阳性的回顾
The Royal College of Radiologists Open Pub Date : 2025-01-01 DOI: 10.1016/j.rcro.2025.100238
Aisha Hameed, Lois Aikins, Garryck Tan
{"title":"The ability of artificial intelligence to correctly identify acute haemorrhage in A&E patients on a non-contrast CT head: a review of false positives in Brainomix in a single centre","authors":"Aisha Hameed,&nbsp;Lois Aikins,&nbsp;Garryck Tan","doi":"10.1016/j.rcro.2025.100238","DOIUrl":"10.1016/j.rcro.2025.100238","url":null,"abstract":"","PeriodicalId":101248,"journal":{"name":"The Royal College of Radiologists Open","volume":"3 ","pages":"Article 100238"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143336596","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}
引用次数: 0
Technology-enhanced learning with PGVLE: standardising neuroradiology training through the NODE initiative 通过PGVLE技术增强学习:通过NODE计划标准化神经放射学培训
The Royal College of Radiologists Open Pub Date : 2025-01-01 DOI: 10.1016/j.rcro.2025.100258
Nitin Menon , Adam Thomas , Usman Ahmed
{"title":"Technology-enhanced learning with PGVLE: standardising neuroradiology training through the NODE initiative","authors":"Nitin Menon ,&nbsp;Adam Thomas ,&nbsp;Usman Ahmed","doi":"10.1016/j.rcro.2025.100258","DOIUrl":"10.1016/j.rcro.2025.100258","url":null,"abstract":"","PeriodicalId":101248,"journal":{"name":"The Royal College of Radiologists Open","volume":"3 ","pages":"Article 100258"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143297854","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}
引用次数: 0
Large language models are well suited for data-mining free-text radiology referrals from multiple sources: Let chat-GPT do the heavy lifting for you 大型语言模型非常适合数据挖掘来自多个来源的自由文本放射学参考:让chat-GPT为您完成繁重的工作
The Royal College of Radiologists Open Pub Date : 2025-01-01 DOI: 10.1016/j.rcro.2024.100201
Ernest Montana , Constantinus F. Buckens , Geraldine Dean , Susan Cheng-Shelmerdine , Stavroula Kyriaza , Gareth Davies
{"title":"Large language models are well suited for data-mining free-text radiology referrals from multiple sources: Let chat-GPT do the heavy lifting for you","authors":"Ernest Montana ,&nbsp;Constantinus F. Buckens ,&nbsp;Geraldine Dean ,&nbsp;Susan Cheng-Shelmerdine ,&nbsp;Stavroula Kyriaza ,&nbsp;Gareth Davies","doi":"10.1016/j.rcro.2024.100201","DOIUrl":"10.1016/j.rcro.2024.100201","url":null,"abstract":"","PeriodicalId":101248,"journal":{"name":"The Royal College of Radiologists Open","volume":"3 ","pages":"Article 100201"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143179328","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}
引用次数: 0
In the tutelage of ChatGPT: Assessing AI's capability to simplify radiology physics, a bilingual approach 在ChatGPT的指导下:评估人工智能简化放射学物理的能力,一种双语方法
The Royal College of Radiologists Open Pub Date : 2025-01-01 DOI: 10.1016/j.rcro.2024.100203
Shardul Tyagi , Namrita Sachdev
{"title":"In the tutelage of ChatGPT: Assessing AI's capability to simplify radiology physics, a bilingual approach","authors":"Shardul Tyagi ,&nbsp;Namrita Sachdev","doi":"10.1016/j.rcro.2024.100203","DOIUrl":"10.1016/j.rcro.2024.100203","url":null,"abstract":"","PeriodicalId":101248,"journal":{"name":"The Royal College of Radiologists Open","volume":"3 ","pages":"Article 100203"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143180138","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}
引用次数: 0
Radiogenomic AI model predicts immune status in IDH wildtype glioblastoma: PRECISE-GBM study 放射基因组AI模型预测IDH野生型胶质母细胞瘤的免疫状态:precision - gbm研究
The Royal College of Radiologists Open Pub Date : 2025-01-01 DOI: 10.1016/j.rcro.2024.100234
Prajwal Ghimire, Marc Modat, Thomas Booth
{"title":"Radiogenomic AI model predicts immune status in IDH wildtype glioblastoma: PRECISE-GBM study","authors":"Prajwal Ghimire,&nbsp;Marc Modat,&nbsp;Thomas Booth","doi":"10.1016/j.rcro.2024.100234","DOIUrl":"10.1016/j.rcro.2024.100234","url":null,"abstract":"","PeriodicalId":101248,"journal":{"name":"The Royal College of Radiologists Open","volume":"3 ","pages":"Article 100234"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181482","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}
引用次数: 0
Analysis of modern versus conventional radiotherapy techniques for gastric mucosa-associated lymphoid tissue (MALT) lymphoma 胃粘膜相关淋巴组织(MALT)淋巴瘤现代与传统放疗技术的对比分析
The Royal College of Radiologists Open Pub Date : 2025-01-01 DOI: 10.1016/j.rcro.2025.100346
Grace Kusumawidjaja , Syazana Mohamed Rashid , Thamizhisai Swaminathan , Zubin Master , Sze Huey Tan , Kevin Lee Min Chua , Fang Yue Yong , Kheng-Wei Yeoh
{"title":"Analysis of modern versus conventional radiotherapy techniques for gastric mucosa-associated lymphoid tissue (MALT) lymphoma","authors":"Grace Kusumawidjaja ,&nbsp;Syazana Mohamed Rashid ,&nbsp;Thamizhisai Swaminathan ,&nbsp;Zubin Master ,&nbsp;Sze Huey Tan ,&nbsp;Kevin Lee Min Chua ,&nbsp;Fang Yue Yong ,&nbsp;Kheng-Wei Yeoh","doi":"10.1016/j.rcro.2025.100346","DOIUrl":"10.1016/j.rcro.2025.100346","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Localized MALT (gMALT) lymphoma patients are primarily treated with radiotherapy (RT). Considering the different resource capabilities of departments globally, the incremental benefits with modern techniques has not yet been determined. Here we report dosimetric differences of current RT techniques.</div></div><div><h3>Materials and methods</h3><div>Twelve stage IE gMALT patients treated with RT between January 2011 and December 2016 were analyzed. RT planning were recreated for conventional (parallel-opposed, 3D-conformal-RT [3D], 3D-field-in-field [3DFIF]) and modern techniques (volumetric-modulated-arc-therapy [VMAT], intensity-modulated-RT, helical tomotherapy). Prior to treatment, patients fasted for 4 h. RT prescription dose was 30Gy in 20 fractions. Planning target volume (PTV) was defined as entire stomach with 1–2 cm isometric expansion. OARs (heart, kidneys, liver and cord) constraints were determined according to QUANTEC. Dosimetric data were summarized and compared.</div></div><div><h3>Results</h3><div>Median age was 65.5y (range, 50–78). At the median follow-up of 70.5 m all patients are alive with no disease relapse post-RT nor any grade ≥3 treatment side effects. Compared to conventional RT, modern RT techniques were similar in providing excellent dose distribution and all OARs sparing. Specific to PTV coverage, VMAT was superior compared to 3DFIF (p &lt; 0.001) and 3D (p ≤ 0.008). However, PTV coverage improvement was not clinically relevant. In OAR sparing, VMAT had better heart-sparing effect than 3DFIF (p &lt; 0.01) or 3D (P &lt; 0.01). Specific to kidneys, all 3 techniques fulfilled constraints. All techniques fulfilled cord and liver constraints.</div></div><div><h3>Conclusion</h3><div>While modern RT techniques offer significant advantages, conventional techniques were sufficient to achieve good target volume coverage and reduce dose to the OARs in most patients. Some individuals, such as those with challenging anatomy, may be good candidates for modern approaches. The insights gained from this study can be used to optimize 3D or 3D field-in-field (3DFIF) plans for these patients. With recent data showing good outcomes with lower RT doses, it also would be important to investigate the utility of advanced techniques globally.</div></div>","PeriodicalId":101248,"journal":{"name":"The Royal College of Radiologists Open","volume":"3 ","pages":"Article 100346"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Deep learning for Radiotherapy Autosegmentation Workflow (DRAW): System engineering and preliminary experience with an autosegmentation solution built using open-source software 放疗自动分割工作流(DRAW)的深度学习:使用开源软件构建的自动分割解决方案的系统工程和初步经验
The Royal College of Radiologists Open Pub Date : 2025-01-01 DOI: 10.1016/j.rcro.2024.100210
Santam Chakraborty , Indranil Mallick , Sandip Dutta , Jayanta Mukhopadhyay , Surajit Kundu , Sougata Maity , Oindrila Roychowdhury , Moses Arunsingh , Tapesh Bhattacharyya , Sanjoy Chatterjee , Rimpa Basu Achari
{"title":"Deep learning for Radiotherapy Autosegmentation Workflow (DRAW): System engineering and preliminary experience with an autosegmentation solution built using open-source software","authors":"Santam Chakraborty ,&nbsp;Indranil Mallick ,&nbsp;Sandip Dutta ,&nbsp;Jayanta Mukhopadhyay ,&nbsp;Surajit Kundu ,&nbsp;Sougata Maity ,&nbsp;Oindrila Roychowdhury ,&nbsp;Moses Arunsingh ,&nbsp;Tapesh Bhattacharyya ,&nbsp;Sanjoy Chatterjee ,&nbsp;Rimpa Basu Achari","doi":"10.1016/j.rcro.2024.100210","DOIUrl":"10.1016/j.rcro.2024.100210","url":null,"abstract":"","PeriodicalId":101248,"journal":{"name":"The Royal College of Radiologists Open","volume":"3 ","pages":"Article 100210"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143175925","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}
引用次数: 0
Smart reporting: ChatGPT vs radiologists in MSK knee MRI analysis 智能报告:ChatGPT与放射科医生在MSK膝关节MRI分析中的对比
The Royal College of Radiologists Open Pub Date : 2025-01-01 DOI: 10.1016/j.rcro.2025.100247
Vikash Rustagi , Sakina Naqvi
{"title":"Smart reporting: ChatGPT vs radiologists in MSK knee MRI analysis","authors":"Vikash Rustagi ,&nbsp;Sakina Naqvi","doi":"10.1016/j.rcro.2025.100247","DOIUrl":"10.1016/j.rcro.2025.100247","url":null,"abstract":"","PeriodicalId":101248,"journal":{"name":"The Royal College of Radiologists Open","volume":"3 ","pages":"Article 100247"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143319091","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}
引用次数: 0
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