Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes最新文献_第2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 : 2025-05-01 Epub 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":"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":"245-256"},"PeriodicalIF":2.9,"publicationDate":"2025-05-01","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

Development and Evaluation of an Automated Protocol Recommendation System for Chest CT Using Natural Language Processing With CLEVER Terminology Word Replacement. 利用自然语言处理和 CLEVER 术语单词替换技术开发和评估胸部 CT 自动协议推荐系统。

IF 2.9 3区医学

Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes Pub Date : 2025-05-01 Epub Date: 2024-09-24 DOI: 10.1177/08465371241280219

Patrik Rogalla, Jennifer Fratesi, Sonja Kandel, Demetris Patsios, Farzad Khalvati, Sean Carey

{"title":"Development and Evaluation of an Automated Protocol Recommendation System for Chest CT Using Natural Language Processing With CLEVER Terminology Word Replacement.","authors":"Patrik Rogalla, Jennifer Fratesi, Sonja Kandel, Demetris Patsios, Farzad Khalvati, Sean Carey","doi":"10.1177/08465371241280219","DOIUrl":"10.1177/08465371241280219","url":null,"abstract":"Purpose: To evaluate the clinical performance of a Protocol Recommendation System (PRS) automatic protocolling of chest CT imaging requests. Materials and Methods: 322 387 consecutive historical imaging requests for chest CT between 2017 and 2022 were extracted from a radiology information system (RIS) database containing 16 associated patient information values. Records with missing fields and protocols with <100 occurrences were removed, leaving 18 protocols for training. After freetext pre-processing and applying CLEVER terminology word replacements, the features of a bag-of-words model were used to train a multinomial logistic regression classifier. Four readers protocolled 300 clinically executed protocols (CEP) based on all clinically available information. After their selection was made, the PRS and CEP were unblinded, and the readers were asked to score their agreement (1 = severe error, 2 = moderate error, 3 = disagreement but acceptable, 4 = agreement). The ground truth was established by the readers' majority selection, a judge helped break ties. For the PRS and CEP, the accuracy and clinical acceptability (scores 3 and 4) were calculated. The readers' protocolling reliability was measured using Fleiss' Kappa. Results: Four readers agreed on 203/300 protocols, 3 on 82/300 cases, and in 15 cases, a judge was needed. PRS errors were found by the 4 readers in 1%, 2.7%, 1%, and 0.7% of the cases, respectively. The accuracy/clinical acceptability of the PRS and CEP were 84.3%/98.6% and 83.0%/99.3%, respectively. The Fleiss' Kappa for all readers and all protocols was 0.805. Conclusion: The PRS achieved similar accuracy to human performance and may help radiologists master the ever-increasing workload.","PeriodicalId":55290,"journal":{"name":"Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes","volume":" ","pages":"257-264"},"PeriodicalIF":2.9,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142309183","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

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 : 2025-05-01 Epub Date: 2024-11-13 DOI: 10.1177/08465371241297807

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

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 : 2025-05-01 Epub Date: 2024-11-14 DOI: 10.1177/08465371241298597

Ryan D Postle, Bruce B Forster

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 : 2025-05-01 Epub Date: 2024-11-30 DOI: 10.1177/08465371241305023

Francois H Cornelis, Debkumar Sarkar, Stephen B Solomon

引用次数: 0

Risk of Bias in Liver Imaging Reporting and Data System Studies Using QUADAS-2. 使用 QUADAS-2 进行肝脏成像报告和数据系统研究的偏差风险。

IF 2.9 3区医学

Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes Pub Date : 2025-05-01 Epub Date: 2024-10-16 DOI: 10.1177/08465371241280874

Haresh Naringrekar, Andreu F Costa, Eric Lam, Christian B van der Pol, Mustafa R Bashir, Jean-Paul Salameh, Matthew D F McInnes

{"title":"Risk of Bias in Liver Imaging Reporting and Data System Studies Using QUADAS-2.","authors":"Haresh Naringrekar, Andreu F Costa, Eric Lam, Christian B van der Pol, Mustafa R Bashir, Jean-Paul Salameh, Matthew D F McInnes","doi":"10.1177/08465371241280874","DOIUrl":"10.1177/08465371241280874","url":null,"abstract":"Purpose: Use a tailored version of the Quality Assessment of Diagnostic Accuracy Studies tool to evaluate risk of bias and applicability across LIRADS related publications. Method: A tailored QUADAS-2 tool was created through consensus approach to assess risk of bias and applicability across 37 LI-RADS related publications. Studies were selected from 2017 to 2022 using the assistance of experienced hospital librarians to search for studies evaluating the diagnostic accuracy of CT, MRI, or contrast-enhanced ultrasound for HCC using LI-RADS through multiple different databases. QUADAS-2 assessments were performed in duplicate and independently by 2 authors with experience using the QUADAS-2 tool. Disagreements were resolved with a third expert reviewer. Consensus QUADAS-2 assessments were tabulated for each domain. Results: Using the tailored QUADAS-2 tool, 31 of the 37 included LI-RADS studies were assessed as high risk of bias, and 9 out of 37 studies demonstrated concerns for applicability. Patient selection (21 out of 37 studies) and flow/timing (24 out of 37 studies) domains demonstrated the highest risk of bias. 6 out of 37 studies in the index domain demonstrated high risk of bias. 2 out of 37 studies showed high risk of bias in the reference standard domain. Conclusion: A significant proportion of LI-RADS research is at risk of bias with concerns for applicability. Identifying risk of bias in such research is essential to recognize limitations of a study that may affect the validity of the results. Areas for improvement in LI-RADS research include reducing selection bias, avoiding inappropriate exclusions, and decreasing verification bias.","PeriodicalId":55290,"journal":{"name":"Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes","volume":" ","pages":"273-286"},"PeriodicalIF":2.9,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142481456","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

Paranoid About Androids: A Review of Robotics in Radiology. 对机器人的偏执：放射学机器人技术回顾。

IF 2.9 3区医学

Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes Pub Date : 2025-05-01 Epub Date: 2024-10-12 DOI: 10.1177/08465371241290076

Hannah Hughes, Francois H Cornelis, Mariano Scaglione, Michael N Patlas

{"title":"Paranoid About Androids: A Review of Robotics in Radiology.","authors":"Hannah Hughes, Francois H Cornelis, Mariano Scaglione, Michael N Patlas","doi":"10.1177/08465371241290076","DOIUrl":"10.1177/08465371241290076","url":null,"abstract":"In tandem with the ever-increasing global population, the demand for diagnostic radiology service provision is on the rise and at a disproportionate rate compared to the number of radiologists available to practice. The current \"revolution in robotics\" promises to alleviate personnel shortages in many sectors of industry, including medicine. Despite negative depictions of robots in popular culture, their multiple potential benefits cannot be overlooked, in particular when it comes to health service provision. The type of robots used for interventional procedures are largely robotic-assistance devices, such as the Da Vinci surgical robot. Advances have also been made with regards to robots for image-guided percutaneous needle placement, which have demonstrated superior accuracy compared to manual methods. It is likely that artificial intelligence will come to play a key role in the field of robotics and will result in an increase in the levels of robotic autonomy attainable. However, this concept is not without ethical and legal considerations, most notably who is responsible should an error occur; the physician, the robot manufacturer, software engineers, or the robot itself? Efforts have been made to legislate in order to protect against the potentially harmful effects of unexplainable \"black-box\" decision outputs of artificial intelligence systems. In order to be accepted by patients, studies have shown that the perceived level of trustworthiness and predictability of robots is crucial. Ultimately, effective, widespread implementation of medical robotic systems will be contingent on developers remaining cognizant of factors that increase human acceptance, as well as ensuring compliance with regulations.","PeriodicalId":55290,"journal":{"name":"Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes","volume":" ","pages":"232-238"},"PeriodicalIF":2.9,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142481455","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 : 2025-05-01 Epub 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":"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":"287-301"},"PeriodicalIF":2.9,"publicationDate":"2025-05-01","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

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 : 2025-05-01 Epub 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":"313-323"},"PeriodicalIF":2.9,"publicationDate":"2025-05-01","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

Evaluation of a BERT Natural Language Processing Model for Automating CT and MRI Triage and Protocol Selection. 评估 BERT 自然语言处理模型，实现 CT 和 MRI 分诊及方案选择自动化。

IF 2.9 3区医学

Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes Pub Date : 2025-05-01 Epub Date: 2024-06-04 DOI: 10.1177/08465371241255895

Jason Yao, Abdullah Alabousi, Oleg Mironov

{"title":"Evaluation of a BERT Natural Language Processing Model for Automating CT and MRI Triage and Protocol Selection.","authors":"Jason Yao, Abdullah Alabousi, Oleg Mironov","doi":"10.1177/08465371241255895","DOIUrl":"10.1177/08465371241255895","url":null,"abstract":"Purpose: To evaluate the accuracy of a Bidirectional Encoder Representations for Transformers (BERT) Natural Language Processing (NLP) model for automating triage and protocol selection of cross-sectional image requisitions. Methods: A retrospective study was completed using 222 392 CT and MRI studies from a single Canadian university hospital database (January 2018-September 2022). Three hundred unique protocols (116 CT and 184 MRI) were included. A BERT model was trained, validated, and tested using an 80%-10%-10% stratified split. Naive Bayes (NB) and Support Vector Machine (SVM) machine learning models were used as comparators. Models were assessed using F1 score, precision, recall, and area under the receiver operating characteristic curve (AUROC). The BERT model was also assessed for multi-class protocol suggestion and subgroups based on referral location, modality, and imaging section. Results: BERT was superior to SVM for protocol selection (F1 score: BERT-0.901 vs SVM-0.881). However, was not significantly different from SVM for triage prediction (F1 score: BERT-0.844 vs SVM-0.845). Both models outperformed NB for protocol and triage. BERT had superior performance on minority classes compared to SVM and NB. For multiclass prediction, BERT accuracy was up to 0.991 for top-5 protocol suggestion, and 0.981 for top-2 triage suggestion. Emergency department patients had the highest F1 scores for both protocol (0.957) and triage (0.986), compared to inpatients and outpatients. Conclusion: The BERT NLP model demonstrated strong performance in automating the triage and protocol selection of radiology studies, showing potential to enhance radiologist workflows. These findings suggest the feasibility of using advanced NLP models to streamline radiology operations.","PeriodicalId":55290,"journal":{"name":"Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes","volume":" ","pages":"265-272"},"PeriodicalIF":2.9,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141238206","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