Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes最新文献_第7页

The Status of Canadian Radiology Mentorship Programs, Where We Stand and Where to Improve. 加拿大放射学导师计划的现状、我们所处的位置以及需要改进的地方》（The Status of Canadian Radiology Mentorship Programs, Where We Stand and Where to Improve）。

IF 2.9 3区医学

Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes Pub Date : 2025-02-01 Epub Date: 2024-09-01 DOI: 10.1177/08465371241275204

Fatemeh Khounsarian, Daniel Marinescu, Kiana Lebel, Sonali Sharma, Jeffrey Hu, Charlotte J Yong-Hing

{"title":"The Status of Canadian Radiology Mentorship Programs, Where We Stand and Where to Improve.","authors":"Fatemeh Khounsarian, Daniel Marinescu, Kiana Lebel, Sonali Sharma, Jeffrey Hu, Charlotte J Yong-Hing","doi":"10.1177/08465371241275204","DOIUrl":"10.1177/08465371241275204","url":null,"abstract":"Background: The importance of mentorship in medicine is well-established. Access to mentors is pivotal in enhancing career opportunities and networking, increasing research productivity, and overall wellness and resilience at all career stages. Our study aims to assess the current status of radiology mentorship programs for Canadian medical students and radiology residents. Methods: We distributed an anonymous survey to Canadian radiology program directors in December 2022. The questions pertained to the existing mentorship programs' specific goals, structure, and success. Our null hypothesis was that medical students and residents have similar mentorship opportunities. Results: We have received 12 responses (a response rate of 12/16 = 75%), 9 of which had formal mentorship programs and 3 (25%) did not. Comparing the mentorship program for medical students and residents yielded a P-value = .11 > .05. This result does not reject our null hypothesis, indicating there is no significant difference between these 2 groups. Using qualitative analysis, we categorized the responses into 4 main themes: mentorship programs' goals, structures, evaluation methods, and their results. Conclusion: Although our result did not reach statistical significance (P-value = .11 > .05), the observed trend shows that one third of Canadian medical schools do not offer a radiology mentorship program for medical students, highlighting a potentially significant opportunity for improvement. Qualitative analysis shows that despite various methods for assigning mentees to mentors, developing formalized yet flexible mentorship models that allow students and residents to self-select their mentors might be more beneficial than randomly assigning mentors to them.","PeriodicalId":55290,"journal":{"name":"Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes","volume":" ","pages":"55-60"},"PeriodicalIF":2.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142114979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Feasibility of Artificial Intelligence Powered Adverse Event Analysis: Using a Large Language Model to Analyze Microwave Ablation Malfunction Data. 人工智能驱动的不良事件分析的可行性：使用大型语言模型分析微波消融故障数据。

IF 2.9 3区医学

Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes Pub Date : 2025-02-01 Epub Date: 2024-08-21 DOI: 10.1177/08465371241269436

Blair E Warren, Fahd Alkhalifah, Aida Ahrari, Adam Min, Aly Fawzy, Ganesan Annamalai, Arash Jaberi, Robert Beecroft, John R Kachura, Sebastian C Mafeld

{"title":"Feasibility of Artificial Intelligence Powered Adverse Event Analysis: Using a Large Language Model to Analyze Microwave Ablation Malfunction Data.","authors":"Blair E Warren, Fahd Alkhalifah, Aida Ahrari, Adam Min, Aly Fawzy, Ganesan Annamalai, Arash Jaberi, Robert Beecroft, John R Kachura, Sebastian C Mafeld","doi":"10.1177/08465371241269436","DOIUrl":"10.1177/08465371241269436","url":null,"abstract":"Objectives: Determine if a large language model (LLM, GPT-4) can label and consolidate and analyze interventional radiology (IR) microwave ablation device safety event data into meaningful summaries similar to humans. Methods: Microwave ablation safety data from January 1, 2011 to October 31, 2023 were collected and type of failure was categorized by human readers. Using GPT-4 and iterative prompt development, the data were classified. Iterative summarization of the reports was performed using GPT-4 to generate a final summary of the large text corpus. Results: Training (n = 25), validation (n = 639), and test (n = 79) data were split to reflect real-world deployment of an LLM for this task. GPT-4 demonstrated high accuracy in the multiclass classification problem of microwave ablation device data (accuracy [95% CI]: training data 96.0% [79.7, 99.9], validation 86.4% [83.5, 89.0], test 87.3% [78.0, 93.8]). The text content was distilled through GPT-4 and iterative summarization prompts. A final summary was created which reflected the clinically relevant insights from the microwave ablation data relative to human interpretation but had inaccurate event class counts. Conclusion: The LLM emulated the human analysis, suggesting feasibility of using LLMs to process large volumes of IR safety data as a tool for clinicians. It accurately labelled microwave ablation device event data by type of malfunction through few-shot learning. Content distillation was used to analyze a large text corpus (>650 reports) and generate an insightful summary which was like the human interpretation.","PeriodicalId":55290,"journal":{"name":"Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes","volume":" ","pages":"171-179"},"PeriodicalIF":2.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142019638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Developmental Curves of the Paediatric Brain Using FLAIR MRI Texture Biomarkers. 利用 FLAIR MRI 纹理生物标志物绘制儿科大脑发育曲线

IF 2.9 3区医学

Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes Pub Date : 2025-02-01 Epub Date: 2024-07-25 DOI: 10.1177/08465371241262175

Karissa Chan, Dania Rabba, Logi Vidarsson, Matthias W Wagner, Birgit B Ertl-Wagner, April Khademi

{"title":"Developmental Curves of the Paediatric Brain Using FLAIR MRI Texture Biomarkers.","authors":"Karissa Chan, Dania Rabba, Logi Vidarsson, Matthias W Wagner, Birgit B Ertl-Wagner, April Khademi","doi":"10.1177/08465371241262175","DOIUrl":"10.1177/08465371241262175","url":null,"abstract":"Purpose: Analysis of FLAIR MRI sequences is gaining momentum in brain maturation studies, and this study aimed to establish normative developmental curves for FLAIR texture biomarkers in the paediatric brain. Methods: A retrospective, single-centre dataset of 465/512 healthy paediatric FLAIR volumes was used, with one pathological volume for proof-of-concept. Participants were included if the MRI was unremarkable as determined by a neuroradiologist. An automated intensity normalization algorithm was used to standardize FLAIR signal intensity across MRI scanners and individuals. FLAIR texture biomarkers were extracted from grey matter (GM), white matter (WM), deep GM, and cortical GM regions. Sex-specific percentile curves were reported and modelled for each tissue type. Correlations between texture and established biomarkers including intensity volume were examined. Biomarkers from the pathological volume were extracted to demonstrate clinical utility of normative curves. Results: This study analyzed 465 FLAIR sequences in children and adolescents (mean age 10.65 ± 4.22 years, range 2-19 years, 220 males, 245 females). In the WM, texture increased to a maximum at around 8 to 10 years, with different trends between females and males in adolescence. In the GM, texture increased over the age range while demonstrating a local maximum at 8 to 10 years. Texture had an inverse relationship with intensity in the WM across all ages. WM and edema in a pathological brain exhibited abnormal texture values outside of the normative growth curves. Conclusion: Normative curves for texture biomarkers in FLAIR sequences may be used to assess brain maturation and microstructural changes over the paediatric age range.","PeriodicalId":55290,"journal":{"name":"Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes","volume":" ","pages":"145-152"},"PeriodicalIF":2.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141762797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Canadian Association of Radiologists Statement on Environmental Sustainability in Medical Imaging. 加拿大放射医师协会关于医学影像环境可持续性的声明。

IF 2.9 3区医学

Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes Pub Date : 2025-02-01 Epub Date: 2024-07-30 DOI: 10.1177/08465371241260013

Kate Hanneman, Andrew Szava-Kovats, Brent Burbridge, David Leswick, Brandon Nadeau, Omar Islam, Emil J Y Lee, Alison Harris, Candyce Hamel, Maura J Brown

{"title":"Canadian Association of Radiologists Statement on Environmental Sustainability in Medical Imaging.","authors":"Kate Hanneman, Andrew Szava-Kovats, Brent Burbridge, David Leswick, Brandon Nadeau, Omar Islam, Emil J Y Lee, Alison Harris, Candyce Hamel, Maura J Brown","doi":"10.1177/08465371241260013","DOIUrl":"10.1177/08465371241260013","url":null,"abstract":"Immediate and strategic action is needed to improve environmental sustainability and reduce the detrimental effects of climate change. Climate change is already adversely affecting the health of Canadians related to worsening air pollution and wildfire smoke, increasing frequency and intensity of extreme weather events, and expansion of vector-borne and infectious illnesses. On one hand, radiology contributes to the climate crisis by generating greenhouse gas emissions and waste during the production, manufacture, transportation, and use of medical imaging equipment and supplies. On the other hand, radiology departments are also susceptible to equipment and infrastructure damage from flooding, extreme temperatures, and power failures, as well as workforce shortages due to injury and illness, potentially disrupting radiology services and increasing costs. The Canadian Association of Radiologists' (CAR) advocacy for environmentally sustainable radiology in Canada encompasses both minimizing the detrimental effects that delivery of radiology services has on the environment and optimizing the resilience of radiology departments to increasing health needs and changing patterns of disease on imaging related to climate change. This statement provides specific recommendations and pathways to help guide radiologists, medical imaging leadership teams, industry partners, governments, and other key stakeholders to transition to environmentally sustainable, net-zero, and climate-resilient radiology organizations. Specific consideration is given to unique aspects of medical imaging in Canada. Finally, environmentally sustainable radiology programs, policies, and achievements in Canada are highlighted.","PeriodicalId":55290,"journal":{"name":"Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes","volume":" ","pages":"44-54"},"PeriodicalIF":2.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141857175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CARJ Editor's Award 2024. CARJ编辑奖2024。

IF 2.9 3区医学

Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes Pub Date : 2025-02-01 Epub Date: 2024-08-24 DOI: 10.1177/08465371241276679

Birgit B Ertl-Wagner, Courtney R Green, Michael N Patlas

引用次数: 0

Impact of Wait Time From Preoperative CT to Pancreatectomy on Overall Survival for Patients With Pancreatic Carcinoma. 从术前 CT 到胰腺切除术的等待时间对胰腺癌患者总生存期的影响

IF 2.9 3区医学

Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes Pub Date : 2025-02-01 Epub Date: 2024-09-06 DOI: 10.1177/08465371241275150

Niharika Shahi, Amer Alaref, Joshua O Cerasuolo, Noori Akhtar-Danesh, Joseph M Caswell, Pablo E Serrano, Brandon M Meyers, David W Savage, Jennifer Nelli, Michael N Patlas, Dylan Siltamaki, Abdullah Alabousi, Rabail Siddiqui, Christian B van der Pol

引用次数: 0

CARJ Outstanding Reviewers Awards for 2024.

IF 2.9 3区医学

Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes Pub Date : 2025-02-01 Epub Date: 2024-09-25 DOI: 10.1177/08465371241288415

Michael N Patlas

引用次数: 0

The Efficacy of a Named Entity Recognition AI Model for Identifying Incidental Pulmonary Nodules in CT Reports. 命名实体识别人工智能模型识别 CT 报告中偶然出现的肺结节的功效。

IF 2.9 3区医学

Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes Pub Date : 2025-02-01 Epub Date: 2024-07-27 DOI: 10.1177/08465371241266785

Alireza Mojibian, Jeff Jaskolka, Geoffrey Ching, Brian Lee, Renelle Myers, Chloe Devine, Savvas Nicolaou, William Parker

{"title":"The Efficacy of a Named Entity Recognition AI Model for Identifying Incidental Pulmonary Nodules in CT Reports.","authors":"Alireza Mojibian, Jeff Jaskolka, Geoffrey Ching, Brian Lee, Renelle Myers, Chloe Devine, Savvas Nicolaou, William Parker","doi":"10.1177/08465371241266785","DOIUrl":"10.1177/08465371241266785","url":null,"abstract":"Purpose: This study evaluates the efficacy of a commercial medical Named Entity Recognition (NER) model combined with a post-processing protocol in identifying incidental pulmonary nodules from CT reports. Methods: We analyzed 9165 anonymized CT reports and classified them into 3 categories: no nodules, nodules present, and nodules >6 mm. For each report, a generic medical NER model annotated entities and their relations, which were then filtered through inclusion/exclusion criteria selected to identify pulmonary nodules. Ground truth was established by manual review. To better understand the relationship between model performance and nodule prevalence, a subset of the data was programmatically balanced to equalize the number of reports in each class category. Results: In the unbalanced subset of the data, the model achieved a sensitivity of 97%, specificity of 99%, and accuracy of 99% in detecting pulmonary nodules mentioned in the reports. For nodules >6 mm, sensitivity was 95%, specificity was 100%, and accuracy was 100%. In the balanced subset of the data, sensitivity was 99%, specificity 96%, and accuracy 97% for nodule detection; for larger nodules, sensitivity was 94%, specificity 99%, and accuracy 98%. Conclusions: The NER model demonstrated high sensitivity and specificity in detecting pulmonary nodules reported in CT scans, including those >6 mm which are potentially clinically significant. The results were consistent across both unbalanced and balanced datasets indicating that the model performance is independent of nodule prevalence. Implementing this technology in hospital systems could automate the identification of at-risk patients, ensuring timely follow-up and potentially reducing missed or late-stage cancer diagnoses.","PeriodicalId":55290,"journal":{"name":"Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes","volume":" ","pages":"68-75"},"PeriodicalIF":2.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141789878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Value-Based Radiology in Canada: Reducing Low-Value Care and Improving System Efficiency. 加拿大基于价值的放射学：减少低价值医疗，提高系统效率。

IF 2.9 3区医学

Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes Pub Date : 2025-02-01 Epub Date: 2024-09-01 DOI: 10.1177/08465371241277110

Tyler D Yan, Sabeena Jalal, Alison Harris

{"title":"Value-Based Radiology in Canada: Reducing Low-Value Care and Improving System Efficiency.","authors":"Tyler D Yan, Sabeena Jalal, Alison Harris","doi":"10.1177/08465371241277110","DOIUrl":"10.1177/08465371241277110","url":null,"abstract":"Radiology departments are increasingly tasked with managing growing demands on services including long waitlists for scanning and interventional procedures, human health resource shortages, equipment needs, and challenges incorporating advanced imaging solutions. The burden of system inefficiencies and the overuse of \"low-value\" imaging causes downstream impact on patients at the individual level, the economy and healthcare system at the societal level, and planetary health at an overarching level. Low value imaging includes those performed for an inappropriate clinical indication, with little to no value to the management of the patient, and resulting in healthcare resource waste; it is estimated that up to a quarter of advanced imaging studies in Canada meet this criterion. Strategies to reduce low-value imaging include the development and use of referral guidelines, use of appropriateness criteria, optimization of existing protocols, and integration of clinical decision support tools into the ordering provider's workflow. Additional means of optimizing system efficiency such as centralized intake models, improved access to electronic medical records and outside imaging, enhanced communication with patients and referrers, and the utilization of artificial intelligence will further increase the value of radiology provided to patients and care providers.","PeriodicalId":55290,"journal":{"name":"Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes","volume":" ","pages":"61-67"},"PeriodicalIF":2.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142114980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Note of Thanks to 2024 CARJ Reviewers. 感谢2024年CARJ审稿人。

IF 2.9 3区医学

Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes Pub Date : 2025-02-01 Epub Date: 2024-09-27 DOI: 10.1177/08465371241288414

Ania Z Kielar, Michael N Patlas

引用次数: 0