Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes最新文献

{"title":"CARJ Editor's Award 2024.","authors":"Birgit B Ertl-Wagner, Courtney R Green, Michael N Patlas","doi":"10.1177/08465371241276679","DOIUrl":"https://doi.org/10.1177/08465371241276679","url":null,"abstract":"","PeriodicalId":55290,"journal":{"name":"Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes","volume":" ","pages":"8465371241276679"},"PeriodicalIF":2.9,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142047555","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}

{"title":"The Virtual Reality Radiology Workstation: Current Technology and Future Applications.","authors":"Iain A D Gamba, Angus Hartery","doi":"10.1177/08465371241230278","DOIUrl":"10.1177/08465371241230278","url":null,"abstract":"<p><p>Virtual reality (VR) and augmented reality (AR) technology hold potential across many disciplines in medicine to expand the delivery of education and healthcare. VR-AR applications in radiology, in particular, have gained prominence and have demonstrated advantages in many areas within the field. Recently, VR software has emerged to redesign the physical radiology workstation (ie, reading room) to expand the possibilities of diagnostic interpretation. Given the novelty of this technology, there is limited research investigating the potential applications of a simulated radiology workstation. In this review article, we explore VR-simulated reading room technology in its current form and illustrate the practical applications this technology will bring to future radiologists and learners. We also discuss the limitations and barriers to adopting this technology that must be overcome to truly understand its potential benefits. VR reading room technology offers great potential in radiology, but further research is needed to appreciate its benefits and identify areas for improvement. The findings and insights presented in this review contribute to the ongoing discourse on future technological advancements in radiology and healthcare, offering valuable recommendations for further research and practical implementation.</p>","PeriodicalId":55290,"journal":{"name":"Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes","volume":" ","pages":"479-487"},"PeriodicalIF":2.9,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139742773","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}

{"title":"Do Habitat MRI and Fractal Analysis Help Distinguish Triple-Negative Breast Cancer From Non-Triple-Negative Breast Carcinoma.","authors":"Run Xu, Dan Yu, Peng Luo, Xuefeng Li, Lei Jiang, Shixin Chang, Guanwu Li","doi":"10.1177/08465371241231573","DOIUrl":"10.1177/08465371241231573","url":null,"abstract":"<p><p><b>Purpose:</b> To determine whether multiparametric MRI-based spatial habitats and fractal analysis can help distinguish triple-negative breast cancer (TNBC) from non-TNBC. <b>Method:</b> Multiparametric DWI and DCE-MRI at 3T were obtained from 142 biopsy- and surgery-proven breast cancer with 148 breast lesions (TNBC = 26 and non-TNBC = 122). The contrast-enhancing lesions were divided into 3 spatial habitats based on perfusion and diffusion patterns using K-means clustering. The fractal dimension (FD) of the tumour subregions was calculated. The accuracy of the habitat segmentation was measured using the Dice index. Inter- and intra-reader reliability were evaluated with the intraclass correlation coefficient (ICC). The ability to predict TNBC status was assessed using the receiver operating characteristic curve. <b>Results:</b> The Dice index for the whole tumour was 0.81 for inter-reader and 0.88 for intra-reader reliability. The inter- and intra-reader reliability were excellent for all 3 tumour habitats and fractal features (ICC > 0.9). TNBC had a lower hypervascular cellular habitat and higher FD 1 compared to non-TNBC (all <i>P</i> < .001). Multivariate analysis confirmed that hypervascular cellular habitat (OR = 0.88) and FD 1 (OR = 1.35) were independently associated with TNBC (all <i>P</i> < .001) after adjusting for rim enhancement, axillary lymph nodes status, and histological grade. The diagnostic model combining hypervascular cellular habitat and FD 1 showed excellent discriminatory ability for TNBC, with an AUC of 0.951 and an accuracy of 91.9%. <b>Conclusions:</b> The fraction of hypervascular cellular habitat and its FD may serve as useful imaging biomarkers for predicting TNBC status.</p>","PeriodicalId":55290,"journal":{"name":"Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes","volume":" ","pages":"584-592"},"PeriodicalIF":2.9,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139934333","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}

{"title":"An Introductory Guide to Artificial Intelligence in Interventional Radiology: Part 1 Foundational Knowledge.","authors":"Blair Edward Warren, Alexander Bilbily, Judy Wawira Gichoya, Aaron Conway, Ben Li, Aly Fawzy, Camilo Barragán, Arash Jaberi, Sebastian Mafeld","doi":"10.1177/08465371241236376","DOIUrl":"10.1177/08465371241236376","url":null,"abstract":"<p><p>Artificial intelligence (AI) is rapidly evolving and has transformative potential for interventional radiology (IR) clinical practice. However, formal training in AI may be limited for many clinicians and therefore presents a challenge for initial implementation and trust in AI. An understanding of the foundational concepts in AI may help familiarize the interventional radiologist with the field of AI, thus facilitating understanding and participation in the development and deployment of AI. A pragmatic classification system of AI based on the complexity of the model may guide clinicians in the assessment of AI. Finally, the current state of AI in IR and the patterns of implementation are explored (pre-procedural, intra-procedural, and post-procedural).</p>","PeriodicalId":55290,"journal":{"name":"Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes","volume":" ","pages":"558-567"},"PeriodicalIF":2.9,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140040970","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}

{"title":"Meeting the Face Behind the Medical Image: Virtual Diagnostic Radiology Consultation Clinics to Improve Patient Experience.","authors":"Rod Parsa, Yudhvir Bhatti, Navya Manoj, Jason Yao, Alex Pozdnyakov, Vineeth Bhogadi, Prasaanthan Gopee-Ramanan, Sriharsha Athreya","doi":"10.1177/08465371241226581","DOIUrl":"10.1177/08465371241226581","url":null,"abstract":"","PeriodicalId":55290,"journal":{"name":"Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes","volume":" ","pages":"674-676"},"PeriodicalIF":2.9,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139572187","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}

{"title":"Artificial Intelligence Chatbots' Understanding of the Risks and Benefits of Computed Tomography and Magnetic Resonance Imaging Scenarios.","authors":"Nikhil S Patil, Ryan S Huang, Scott Caterine, Jason Yao, Natasha Larocque, Christian B van der Pol, Euan Stubbs","doi":"10.1177/08465371231220561","DOIUrl":"10.1177/08465371231220561","url":null,"abstract":"<p><strong>Purpose: </strong>Patients may seek online information to better understand medical imaging procedures. The purpose of this study was to assess the accuracy of information provided by 2 popular artificial intelligence (AI) chatbots pertaining to common imaging scenarios' risks, benefits, and alternatives.</p><p><strong>Methods: </strong>Fourteen imaging-related scenarios pertaining to computed tomography (CT) or magnetic resonance imaging (MRI) were used. Factors including the use of intravenous contrast, the presence of renal disease, and whether the patient was pregnant were included in the analysis. For each scenario, 3 prompts for outlining the (1) risks, (2) benefits, and (3) alternative imaging choices or potential implications of not using contrast were inputted into ChatGPT and Bard. A grading rubric and a 5-point Likert scale was used by 2 independent reviewers to grade responses. Prompt variability and chatbot context dependency were also assessed.</p><p><strong>Results: </strong>ChatGPT's performance was superior to Bard's in accurately responding to prompts per Likert grading (4.36 ± 0.63 vs 3.25 ± 1.03 seconds, <i>P</i> < .0001). There was substantial agreement between independent reviewer grading for ChatGPT (κ = 0.621) and Bard (κ = 0.684). Response text length was not statistically different between ChatGPT and Bard (2087 ± 256 characters vs 2162 ± 369 characters, <i>P</i> = .24). Response time was longer for ChatGPT (34 ± 2 vs 8 ± 1 seconds, <i>P</i> < .0001).</p><p><strong>Conclusions: </strong>ChatGPT performed superior to Bard at outlining risks, benefits, and alternatives to common imaging scenarios. Generally, context dependency and prompt variability did not change chatbot response content. Due to the lack of detailed scientific reasoning and inability to provide patient-specific information, both AI chatbots have limitations as a patient information resource.</p>","PeriodicalId":55290,"journal":{"name":"Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes","volume":" ","pages":"518-524"},"PeriodicalIF":2.9,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139106944","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}

{"title":"Radiomics Studies on Ischemic Stroke and Carotid Atherosclerotic Disease: A Reporting Quality Assessment.","authors":"Ann-Marie Beaudoin, Jan Kee Ho, Adrienne Lam, Vincent Thijs","doi":"10.1177/08465371241234545","DOIUrl":"10.1177/08465371241234545","url":null,"abstract":"<p><p><b>Objective:</b> To assess the reporting quality of radiomics studies on ischemic stroke, intracranial and carotid atherosclerotic disease using the Image Biomarker Standardization Initiative (IBSI) reporting guidelines with the aim of finding avenues of improvement for future publications. <b>Method:</b> PubMed database was searched to identify relevant radiomics studies. Of 560 articles, 41 original research articles were included in this analysis. Based on IBSI radiomics reporting guidelines, checklists for CT-based and MRI-based studies were created to allow a structured and comprehensive evaluation of each study's adherence to these guidelines. <b>Results:</b> The main topics covered included radiomics studies were ischemic stroke, intracranial artery disease, and carotid atherosclerotic disease. The reporting checklist median score was 17/40 for the 20 CT-based radiomics studies and 22.5/50 for the 20 MRI-based studies. Basic items like imaging modality, region of interest, and image biomarker set utilized were included in all studies. However, details regarding image acquisition and reconstruction, post-acquisition image processing, and image biomarkers computation were inconsistently detailed across studies. <b>Conclusion:</b> The overall reporting quality of the included radiomics studies was suboptimal. These findings underscore a pressing need for improved reporting practices in radiomics research, to ensure validation and reproducibility of results. Our study provides insights into current reporting standards and highlights specific areas where adherence to IBSI guidelines could be significantly improved.</p>","PeriodicalId":55290,"journal":{"name":"Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes","volume":" ","pages":"549-557"},"PeriodicalIF":2.9,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139991975","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}

{"title":"Interventional Oncology: 2024 Update.","authors":"Ruben Geevarghese, Sylvain Bodard, Leo Razakamanantsoa, Clement Marcelin, Elena N Petre, Anthony Dohan, Adrian Kastler, Julien Frandon, Matthias Barral, Philippe Soyer, François H Cornelis","doi":"10.1177/08465371241236152","DOIUrl":"10.1177/08465371241236152","url":null,"abstract":"<p><p>Interventional Oncology (IO) stands at the forefront of transformative cancer care, leveraging advanced imaging technologies and innovative interventions. This narrative review explores recent developments within IO, highlighting its potential impact facilitated by artificial intelligence (AI), personalized medicine and imaging innovations. The integration of AI in IO holds promise for accelerating tumour detection and characterization, guiding treatment strategies and refining predictive models. Imaging modalities, including functional MRI, PET and cone beam CT are reshaping imaging and precision. Navigation, fusion imaging, augmented reality and robotics have the potential to revolutionize procedural guidance and offer unparalleled accuracy. New developments are observed in embolization and ablative therapies. The pivotal role of genomics in treatment planning, targeted therapies and biomarkers for treatment response prediction underscore the personalization of IO. Quality of life assessment, minimizing side effects and long-term survivorship care emphasize patient-centred outcomes after IO treatment. The evolving landscape of IO training programs, simulation technologies and workforce competence ensures the field's adaptability. Despite barriers to adoption, synergy between interventional radiologists' proficiency and technological advancements hold promise in cancer care.</p>","PeriodicalId":55290,"journal":{"name":"Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes","volume":" ","pages":"658-670"},"PeriodicalIF":2.9,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140040972","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}

{"title":"Deep-Learning Reconstruction of High-Resolution CT Improves Interobserver Agreement for the Evaluation of Pulmonary Fibrosis.","authors":"Akiyoshi Hamada, Koichiro Yasaka, Sosuke Hatano, Mariko Kurokawa, Shohei Inui, Takatoshi Kubo, Yusuke Watanabe, Osamu Abe","doi":"10.1177/08465371241228468","DOIUrl":"10.1177/08465371241228468","url":null,"abstract":"<p><p><b>Objective:</b> This study aimed to investigate whether deep-learning reconstruction (DLR) improves interobserver agreement in the evaluation of honeycombing for patients with interstitial lung disease (ILD) who underwent high-resolution computed tomography (CT) compared with hybrid iterative reconstruction (HIR). <b>Methods:</b> In this retrospective study, 35 consecutive patients suspected of ILD who underwent CT including the chest region were included. High-resolution CT images of the unilateral lung with DLR and HIR were reconstructed for the right and left lungs. A radiologist placed regions of interest on the lung and measured standard deviation of CT attenuation (i.e., quantitative image noise). In the qualitative image analyses, 5 blinded readers assessed the presence of honeycombing and reticulation, qualitative image noise, artifacts, and overall image quality using a 5-point scale (except for artifacts which was evaluated using a 3-point scale). <b>Results:</b> The quantitative and qualitative image noise in DLR was remarkably reduced compared to that in HIR (<i>P</i> < .001). Artifacts and overall DLR quality were significantly improved compared to those of HIR (<i>P</i> < .001 for 4 out of 5 readers). Interobserver agreement in the evaluations of honeycombing and reticulation for DLR (0.557 [0.450-0.693] and 0.525 [0.470-0.541], respectively) were higher than those for HIR (0.321 [0.211-0.520] and 0.470 [0.354-0.533], respectively). A statistically significant difference was found for honeycombing (<i>P</i> = .014). <b>Conclusions:</b> DLR improved interobserver agreement in the evaluation of honeycombing in patients with ILD on CT compared to HIR.</p>","PeriodicalId":55290,"journal":{"name":"Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes","volume":" ","pages":"542-548"},"PeriodicalIF":2.9,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139643454","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}

{"title":"Breast Cancer Screening in the Intermediate-Risk Population: Falling Through the Cracks?","authors":"Kaitlin M Zaki-Metias, Huijuan Wang, Tima F Tawil, Eda B Miles, Lisa Deptula, Pooja Agrawal, Katie M Davis, Lucy B Spalluto, Jean M Seely, Charlotte J Yong-Hing","doi":"10.1177/08465371241234544","DOIUrl":"10.1177/08465371241234544","url":null,"abstract":"<p><p>Breast cancer screening guidelines vary for women at intermediate risk (15%-20% lifetime risk) for developing breast cancer across jurisdictions. Currently available risk assessment models have differing strengths and weaknesses, creating difficulty and ambiguity in selecting the most appropriate model to utilize. Clarifying which model to utilize in individual circumstances may help determine the best screening guidelines to use for each individual.</p>","PeriodicalId":55290,"journal":{"name":"Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes","volume":" ","pages":"593-600"},"PeriodicalIF":2.9,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139991974","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}