Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes最新文献_第5页

Canadian Association of Radiologists Pediatric Imaging Referral Guideline. 加拿大放射医师协会儿童影像转诊指南。

IF 2.9 3区医学

Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes Pub Date : 2024-12-06 DOI: 10.1177/08465371241296820

Candyce Hamel, Barb Avard, Roxanne Chow, Dafydd Davies, Andrew Dixon, Gilgamesh Eamer, Juliette Garel, Chelsey Grimbly, Lucy Jamieson, Tom Kovesi, Jonathan MacLean, Vivek Mehta, Peter Metcalfe, Alan Michaud, Elka Miller, Kathy O'Brien, Anthony Otley, Daniela Pohl, Nina Stein, Nishard Abdeen

{"title":"Canadian Association of Radiologists Pediatric Imaging Referral Guideline.","authors":"Candyce Hamel, Barb Avard, Roxanne Chow, Dafydd Davies, Andrew Dixon, Gilgamesh Eamer, Juliette Garel, Chelsey Grimbly, Lucy Jamieson, Tom Kovesi, Jonathan MacLean, Vivek Mehta, Peter Metcalfe, Alan Michaud, Elka Miller, Kathy O'Brien, Anthony Otley, Daniela Pohl, Nina Stein, Nishard Abdeen","doi":"10.1177/08465371241296820","DOIUrl":"https://doi.org/10.1177/08465371241296820","url":null,"abstract":"The Canadian Association of Radiologists (CAR) Pediatric Expert Panel is made up of pediatric physicians from the disciplines of radiology, emergency medicine, endocrinology, gastroenterology, general surgery, neurology, neurosurgery, respirology, orthopaedic surgery, otolaryngology, urology, a patient advisor, and an epidemiologist/guideline methodologist. After developing a list of 50 clinical/diagnostic scenarios, a rapid scoping review was undertaken to identify systematically produced referral guidelines that provide recommendations for one or more of these clinical/diagnostic scenarios. Recommendations from 32 guidelines and contextualization criteria in the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) for guidelines framework were used to develop 133 recommendation statements across the 50 scenarios. This guideline presents the methods of development and the referral recommendations for head, neck, spine, hip, chest, abdomen, genitourinary, and non-accidental trauma clinical scenarios.","PeriodicalId":55290,"journal":{"name":"Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes","volume":" ","pages":"8465371241296820"},"PeriodicalIF":2.9,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142787946","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

Review of Imaging Peritoneal Disease and Treatment. 腹膜疾病影像学及治疗综述。

IF 2.9 3区医学

Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes Pub Date : 2024-12-06 DOI: 10.1177/08465371241296778

Colin McQuade, Mary Renton, Ashvina Chouhan, Roisin MacDermott, Ciara O'Brien

{"title":"Review of Imaging Peritoneal Disease and Treatment.","authors":"Colin McQuade, Mary Renton, Ashvina Chouhan, Roisin MacDermott, Ciara O'Brien","doi":"10.1177/08465371241296778","DOIUrl":"https://doi.org/10.1177/08465371241296778","url":null,"abstract":"Peritoneal disease can be classified as either benign or malignant in nature. Malignant peritoneal disease can be further considered as either primary or secondary in origin. Primary peritoneal malignancy includes peritoneal mesothelioma, serous carcinoma, and desmoplastic small round cell tumour. Peritoneal carcinomatosis is the most commonly encountered secondary malignant peritoneal disease, typically of ovarian, gastric, colorectal, pancreatic, small bowel neuroendocrine, or breast origin. Others include peritoneal lymphomatosis and sarcomatosis. Benign peritoneal pathology may mimic malignant disease. Differentiating benign from malignant peritoneal pathology can be challenging, but is critical to guide appropriate care and avoid unnecessary intervention. Cytoreductive surgery (CRS) and hyperthermic intraperitoneal chemotherapy (HIPEC) offers potentially curative treatment for patients with peritoneal carcinomatosis, pseudomyxoma peritonei, and peritoneal mesothelioma. For such patients, the radiologist provides crucial pre-operative information highlighting sites of disease involvement, particularly for sites which are challenging to assess at laparotomy or laparoscopy, including the hepatic dome, subdiaphragmatic space and mesenteric root. The radiologist is also essential to identify potential contraindications to surgery, as well as interpreting normal post-operative appearances, complications and assessing for disease recurrence.","PeriodicalId":55290,"journal":{"name":"Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes","volume":" ","pages":"8465371241296778"},"PeriodicalIF":2.9,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142787947","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

The Transformative Power of Digital Breast Tomosynthesis and Artificial Intelligence in Breast Cancer Diagnosis. 数字乳房断层合成和人工智能在乳腺癌诊断中的变革力量。

IF 2.9 3区医学

Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes Pub Date : 2024-12-03 DOI: 10.1177/08465371241301957

Vivianne Freitas, Sandeep Ghai, Frederick Au, Derek Muradali, Supriya Kulkarni

{"title":"The Transformative Power of Digital Breast Tomosynthesis and Artificial Intelligence in Breast Cancer Diagnosis.","authors":"Vivianne Freitas, Sandeep Ghai, Frederick Au, Derek Muradali, Supriya Kulkarni","doi":"10.1177/08465371241301957","DOIUrl":"https://doi.org/10.1177/08465371241301957","url":null,"abstract":"The integration of Digital Breast Tomosynthesis (DBT) and Artificial Intelligence (AI) represents a significant advance in breast cancer screening. This combination aims to address several challenges inherent in traditional screening while promising an improvement in healthcare delivery across multiple dimensions. For patients, this technological synergy has the potential to lower the number of unnecessary recalls and associated procedures such as biopsies, thereby reducing patient anxiety and improving overall experience without compromising diagnostic accuracy. For radiologists, the use of combined AI and DBT could significantly decrease workload and reduce fatigue by effectively highlighting breast imaging abnormalities, which is especially beneficial in high-volume clinical settings. Health systems stand to gain from streamlined workflows and the facilitated deployment of DBT, which is particularly valuable in areas with a scarcity of specialized breast radiologists. However, despite these potential benefits, substantial challenges remain. Bridging the gap between the development of complex AI algorithms and implementation into clinical practice requires ongoing research and development. This is essential to optimize the reliability of these systems and ensure they are accessible to healthcare providers and patients, who are the ultimate beneficiaries of this technological advancement. This article reviews the benefits of combined AI-DBT imaging, particularly the ability of AI to enhance the benefits of DBT and reduce its existing limitations.","PeriodicalId":55290,"journal":{"name":"Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes","volume":" ","pages":"8465371241301957"},"PeriodicalIF":2.9,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142774893","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

Modelling Impact of Process Improvement on Provincial and National CT and MRI Radiology Capacity. 流程改进对省级和国家级CT和MRI放射能力的建模影响。

IF 2.9 3区医学

Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes Pub Date : 2024-12-01 DOI: 10.1177/08465371241302748

James V Rawson, Ellen Odai Alie, Carole Dennie, Courtney R Green, Nick Neuheimer

引用次数: 0

Building a Culture of Resilience in Interventional Radiology Through Strategic Equipment Management. 通过战略设备管理在介入放射学中建立弹性文化。

IF 2.9 3区医学

Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes Pub Date : 2024-11-30 DOI: 10.1177/08465371241305023

Francois H Cornelis, Debkumar Sarkar, Stephen B Solomon

引用次数: 0

Revitalizing Radiology Electives With Interactive Learning and Practical Exposure. 通过互动学习和实际接触振兴放射学选修课。

IF 2.9 3区医学

Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes Pub Date : 2024-11-27 DOI: 10.1177/08465371241302048

Aleena Malik, Andrea S Doria, Linda Probyn, Michael N Patlas

引用次数: 0

Improving Deep Learning Models for Pediatric Low-Grade Glioma Tumours Molecular Subtype Identification Using MRI-based 3D Probability Distributions of Tumour Location. 利用基于核磁共振成像的肿瘤位置三维概率分布，改进用于小儿低级别胶质瘤肿瘤分子亚型识别的深度学习模型。

IF 2.9 3区医学

Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes Pub Date : 2024-11-15 DOI: 10.1177/08465371241296834

Khashayar Namdar, Matthias W Wagner, Kareem Kudus, Cynthia Hawkins, Uri Tabori, Birgit B Ertl-Wagner, Farzad Khalvati

{"title":"Improving Deep Learning Models for Pediatric Low-Grade Glioma Tumours Molecular Subtype Identification Using MRI-based 3D Probability Distributions of Tumour Location.","authors":"Khashayar Namdar, Matthias W Wagner, Kareem Kudus, Cynthia Hawkins, Uri Tabori, Birgit B Ertl-Wagner, Farzad Khalvati","doi":"10.1177/08465371241296834","DOIUrl":"10.1177/08465371241296834","url":null,"abstract":"Purpose: Pediatric low-grade gliomas (pLGG) are the most common brain tumour in children, and the molecular diagnosis of pLGG enables targeted treatment. We use MRI-based Convolutional Neural Networks (CNNs) for molecular subtype identification of pLGG and augment the models using tumour location probability maps. Materials and Methods: MRI FLAIR sequences of 214 patients (110 male, mean age of 8.54 years, 143 BRAF fused and 71 BRAF V600E mutated pLGG tumours) from January 2000 to December 2018 were included in this retrospective REB-approved study. Tumour segmentations (volumes of interest-VOIs) were provided by a pediatric neuroradiology fellow and verified by a pediatric neuroradiologist. Patients were randomly split into development and test sets with an 80/20 ratio. The 3D binary VOI masks for each class in the development set were combined to derive the probability density functions of tumour location. Three pipelines for molecular diagnosis of pLGG were developed: location-based, CNN-based, and hybrid. The experiment was repeated 100 times each with different model initializations and data splits, and the Areas Under the Receiver Operating Characteristic Curve (AUROC) was calculated, and Student's t-test was conducted. Results: The location-based classifier achieved an AUROC of 77.9, 95% confidence interval (CI) (76.8, 79.0). CNN-based classifiers achieved an AUROC of 86.1, 95% CI (85.0, 87.3), while the tumour-location-guided CNNs outperformed the other classifiers with an average AUROC of 88.64, 95% CI (87.6, 89.7), which was statistically significant (P-value .0018). Conclusion: Incorporating tumour location probability maps into CNN models led to significant improvements for molecular subtype identification of pLGG.","PeriodicalId":55290,"journal":{"name":"Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes","volume":" ","pages":"8465371241296834"},"PeriodicalIF":2.9,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142633266","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

Robotics in Interventional Radiology: Is the Force With Us? 介入放射学中的机器人技术：力量与我们同在吗？

IF 2.9 3区医学

Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes Pub Date : 2024-11-14 DOI: 10.1177/08465371241299645

Laurent Milot, Philippe Soyer

引用次数: 0

Patient Perspectives of Artificial Intelligence in Medical Imaging. 医学影像中人工智能的患者视角。

IF 2.9 3区医学

Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes Pub Date : 2024-11-14 DOI: 10.1177/08465371241298597

Ryan D Postle, Bruce B Forster

引用次数: 0

Planning a Successful Mid-Career Transition in Radiology: Integrating Leadership, Growth, and Personal Fulfilment. 规划放射科成功的职业生涯中期过渡：将领导力、成长和个人成就感融为一体。

IF 2.9 3区医学

Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes Pub Date : 2024-11-13 DOI: 10.1177/08465371241297807

Sonali Sharma, Cynthia Walsh, Michael N Patlas, Charlotte J Yong-Hing

引用次数: 0