The Royal College of Radiologists Open最新文献_第6页

Exploring how different stakeholders view the use of artificial intelligence in MRI 探讨不同利益相关者如何看待人工智能在MRI中的应用

The Royal College of Radiologists Open Pub Date : 2025-01-01 DOI: 10.1016/j.rcro.2025.100246

Agnieszka Peplinski, David Adams, Marc Miquel, Joe Martin

引用次数: 0

Evaluating the Clinical Impact of AI-Driven Autonomous Chest Radiograph Reporting 评估人工智能驱动的自主胸片报告的临床影响

The Royal College of Radiologists Open Pub Date : 2025-01-01 DOI: 10.1016/j.rcro.2024.100172

Farah Afzal , Mubashrah Aziz , Sadia Tahir

引用次数: 0

Precision of automated cardiac chambers and great vessel volume segmentation in difficult cases using an open-source full-body segmentation model 基于开源全身分割模型的高精度自动心室和大血管容量分割

The Royal College of Radiologists Open Pub Date : 2025-01-01 DOI: 10.1016/j.rcro.2024.100167

Lisa Sommerfeld, Matthias May

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Utilisation of Brainomix in suspected stroke patients Brainomix在疑似脑卒中患者中的应用

The Royal College of Radiologists Open Pub Date : 2025-01-01 DOI: 10.1016/j.rcro.2024.100164

Afolabi Obasa, Anu Thomas

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Evaluation of an AI tool to measure mammographic density for use in a FAST MRI trial 评估用于FAST MRI试验中测量乳房x线摄影密度的人工智能工具

The Royal College of Radiologists Open Pub Date : 2025-01-01 DOI: 10.1016/j.rcro.2024.100197

Sandra Gomes , Lucy Warren , Mark Halling-Brown , Ken Young , Matthew Trumble , Katherine Klimczak , Jan Rose , Tony Timlin

引用次数: 0

Genomic and demographic landscape of non-small cell lung cancer within an ethnically-diverse population – The implications for radiation oncology and personalised medicine 不同种族人群中非小细胞肺癌的基因组和人口统计学特征——对放射肿瘤学和个体化治疗的影响

The Royal College of Radiologists Open Pub Date : 2025-01-01 DOI: 10.1016/j.rcro.2025.100341

Matthew C Knox , Nader Aryamanesh , Lee L Marshall , Winny Varikatt , Chamitha Weerasinghe , Lucinda Burke , Eric Hau , Adnan Nagrial , Simon Ashworth , Sophia C Kamran , Harriet E Gee

{"title":"Genomic and demographic landscape of non-small cell lung cancer within an ethnically-diverse population – The implications for radiation oncology and personalised medicine","authors":"Matthew C Knox , Nader Aryamanesh , Lee L Marshall , Winny Varikatt , Chamitha Weerasinghe , Lucinda Burke , Eric Hau , Adnan Nagrial , Simon Ashworth , Sophia C Kamran , Harriet E Gee","doi":"10.1016/j.rcro.2025.100341","DOIUrl":"10.1016/j.rcro.2025.100341","url":null,"abstract":"<div><h3>Introduction</h3><div>Genomics and personalised medicine are increasingly important in managing non-small cell lung cancer (NSCLC). However, clinical trials driving practice changes are frequently lacking in ethnic diversity, with limited published data for these groups. We report real-world demographic and genomic data from an ethically-diverse Australian population.</div></div><div><h3>Methods</h3><div>Retrospective review of all sequential patients with NSCLC referred to a tertiary oncology service (two centres) for radiotherapy between April 2020 to February 2022. Clinicopathological data (including histopathology/genomic results) were extracted from medical records. Genomic data was only routinely available for non-squamous pathologies. We compiled and summarised this genomic data and made various correlations to clinicopathological features in the cohort, including country of birth. The Genomics Evidence Neoplasia Information Exchange (GENIE) is a publicly accessible registry of genomic and clinical data associated with 185000 patients across all tumour types. We compared the genomic features of our cohort to this population-based registry.</div></div><div><h3>Results</h3><div>174 patients with 189 unique malignancies were identified. Nearly 60% of patients were born overseas. 113 specimens underwent next generation sequencing (NGS). 72% of tested specimens had ≥1 mutation identified with EGFR (39%), KRAS (20.4%) and TP53 (17.7%) genes being most represented. Mutation prevalence patterns were related to ethnicity, with East Asian ancestry predicting EGFR mutation (72% vs 31%; p<0.002). Smoking exposure and Australian birth (34% vs 6%; p=0.009) predicted KRAS mutation. Genomic mutations differed compared with the GENIE cohort, with our cohort having less ATM, ERBB4, KRAS, STK11 and TP53 mutations, with a numerical trend to more EGFR mutations (39% vs 26%; p=0.19), correlating to ethnic diversity with our larger Asian representation (25% vs 7%; p<0.00001).</div></div><div><h3>Conclusions</h3><div>An ethnically-diverse population with NSCLC had significant genomic differences compared to major clinical databases/tissue repositories, relative to clinicopathological features. Under-representation of ethnic minorities casts doubt on the applicability of trial results due to the clinical impact of mutations in real-world populations. Further efforts to increase ethnic breadth of trial enrolment and radiotherapy-specific gene content in panel design are essential to improving personalised radiation oncology practice.</div></div>","PeriodicalId":101248,"journal":{"name":"The Royal College of Radiologists Open","volume":"3 ","pages":"Article 100341"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143636382","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

Imaging findings of haemorrhagic hepatic cysts with enhancing mural nodules: Comparison with mucinous cystic neoplasms 出血性肝囊肿伴壁结节增强的影像学表现：与黏液性囊性肿瘤的比较

The Royal College of Radiologists Open Pub Date : 2025-01-01 DOI: 10.1016/j.rcro.2025.100349

Emma Choon Hwee Lee , Tracy Jiezhen Loh , Anh Nguyen Tuan Tran , Albert Su Chong Low , Hui Lin Wong

{"title":"Imaging findings of haemorrhagic hepatic cysts with enhancing mural nodules: Comparison with mucinous cystic neoplasms","authors":"Emma Choon Hwee Lee , Tracy Jiezhen Loh , Anh Nguyen Tuan Tran , Albert Su Chong Low , Hui Lin Wong","doi":"10.1016/j.rcro.2025.100349","DOIUrl":"10.1016/j.rcro.2025.100349","url":null,"abstract":"<div><h3>Aims</h3><div>Haemorrhagic hepatic cysts (HHCs) with enhancing mural nodules are an uncommon entity which can have overlapping imaging findings with hepatic mucinous cystic neoplasms (MCNs), leading to misdiagnosis. We aim to find differentiating imaging features between the two entities in this study.</div></div><div><h3>Materials & methods</h3><div>Patients with histologically proven HHCs and hepatic MCNs between January 2011 and January 2022 were identified from the Singapore General Hospital Department of Pathology database. Those with pre-operative computed tomography (CT) or magnetic resonance imaging (MRI) studies were included in our study.</div></div><div><h3>Results</h3><div>A total of ten patients met the inclusion criteria. Six had histologically proven HHCs and four had hepatic MCNs. Most of the patients with HHCs were female (83 %) while all patients with hepatic MCNs were female. Most of the HHCs were associated with three or more cysts (67 %), while the hepatic MCNs were either solitary or associated with fewer cysts. Most of the mural nodules of HHCs demonstrated progressive enhancement, T2-w hypointense rim with hyperintense centre. None of the hepatic MCNs contained mural nodules. HHCs were mostly unilocular without septa. Only one HHC had septa which arose from the cyst wall without indentation (17 %). All hepatic MCNs had septa that mostly arose from the cyst wall without indentation (75 %).</div></div><div><h3>Conclusion</h3><div>HHCs with enhancing mural nodules are a recognised entity with some imaging features that help to distinguish them from hepatic MCNs. HHCs are typically unilocular whilst the presence of septa and septa arising from the cyst wall without external indentation favour hepatic MCNs.</div></div>","PeriodicalId":101248,"journal":{"name":"The Royal College of Radiologists Open","volume":"3 ","pages":"Article 100349"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196301","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

Harnessing artificial intelligence for improving public health outcomes equitably – reality or rhetoric? A narrative review 利用人工智能公平地改善公共卫生结果——现实还是空谈？叙述性回顾

The Royal College of Radiologists Open Pub Date : 2025-01-01 DOI: 10.1016/j.rcro.2025.100243

Chibuchi Amadi-Livingstone , Hashum Mahmood

引用次数: 0

A Practical Consideration of the Ethical Challenges of AI in Healthcare – A Systematic Review 人工智能在医疗保健中的伦理挑战的实际考虑-系统回顾

The Royal College of Radiologists Open Pub Date : 2025-01-01 DOI: 10.1016/j.rcro.2024.100225

Angelica Akrami , Fatema Aftab

引用次数: 0

Radiological research trends in the use of convolutional neural networks for breast cancer detection, diagnosis and classification: A bibliometric analysis of the 100 most-cited articles 使用卷积神经网络进行乳腺癌检测、诊断和分类的放射学研究趋势：对100篇被引用最多的文章的文献计量学分析

The Royal College of Radiologists Open Pub Date : 2025-01-01 DOI: 10.1016/j.rcro.2024.100188

Ka Lee Li , Fatema Aftab , Martin Ga Zen Tam , Sai Ka Li

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