Radiology最新文献

{"title":"Associations of Popliteal Artery Wall Morphology at MRI and Knee Osteoarthritis in the Osteoarthritis Initiative.","authors":"Farhad Pishgar, Peyman Mirghaderi, Ehsan Alipour, Armaghan Mahmoudian, Ali Kiadaliri, Chun Yuan, Niranjan Balu, Ali Guermazi, Majid Chalian","doi":"10.1148/radiol.253583","DOIUrl":"10.1148/radiol.253583","url":null,"abstract":"<p><p>Background Vascular factors have been associated with osteoarthritis (OA), but the association between popliteal artery wall morphology and symptomatic and radiographic knee OA outcomes over time remains unclear. Purpose To evaluate the associations between MRI-derived measures of popliteal artery wall morphology (localized wall thickening and global wall burden) and symptomatic and radiographic knee OA outcomes. Materials and Methods In this secondary analysis of the Osteoarthritis Initiative (February 2004 to October 2015), baseline knee MRI scans were analyzed to quantify maximum wall thickness and normalized wall index (NWI). Symptomatic OA was assessed using Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain scores, and radiographic OA using Kellgren-Lawrence (KL) grade. Cross-sectional and longitudinal associations were assessed using linear regression and Cox proportional hazards models, respectively, adjusted for demographic and clinical covariates. Results A total of 9333 knees from 4710 participants (mean age, 61.1 years ± 9.2 [SD]; 2753 female) were included. Each 1-mm increase in maximum wall thickness was associated with higher WOMAC pain scores (β = 2.0; <i>P</i> < .001) and KL grades (β = 0.1; <i>P</i> < .001), as well as symptomatic worsening (hazard ratio = 1.10 [95% CI: 1.02, 1.19]; <i>P</i> = .02) and incident radiographic OA (hazard ratio = 1.21 [95% CI: 1.02, 1.44]; <i>P</i> = .044). Each 10% increase in NWI was associated with higher WOMAC pain scores (β = 6.7; <i>P</i> < .001) and symptomatic worsening (hazard ratio = 1.38 [95% CI: 1.20, 1.58]; <i>P</i> < .001) but was inversely associated with KL grade (β = -0.1; <i>P</i> = .001) and incident radiographic OA (hazard ratio = 0.60 [95% CI: 0.43, 0.83]; <i>P</i> = .004). Conclusion Localized popliteal arterial wall thickening was associated with symptomatic OA worsening and incident radiographic OA, whereas diffuse wall remodeling was associated with symptomatic OA worsening and was inversely associated with radiographic outcomes. Clinical trial registration no. NCT00080171</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"320 2","pages":"e253583"},"PeriodicalIF":17.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13507909/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148670499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Growth Patterns and Risk Stratification of Incidental Small Pure Ground-Glass Nodules across Independent Cohorts.","authors":"Hao Liu, Xinyu Fu, Minhua Ye, Zimin Wang, Bingyu Hu, Sikai Wu, Cien Sun, Pengfei Sheng, Zhongxiao Chen, Hua Sun, William C Cho, Pasan Witharana, Dehua Ma, Jianfei Shen","doi":"10.1148/radiol.253139","DOIUrl":"https://doi.org/10.1148/radiol.253139","url":null,"abstract":"<p><p>Background The widespread adoption of low-dose CT has increased the detection of small pure ground-glass nodules (pGGNs). The management of this type of nodule currently remains a clinical challenge. Purpose To characterize long-term growth trajectories of incidental small pGGNs, identify risk factors for nodule growth and progression, and assess the reproducibility of these findings in an independent cohort. Materials and Methods This retrospective study included patients with incidental small pGGNs (<10 mm) detected with low-dose chest CT during routine wellness assessments at Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University (center 1) and the First Affiliated Hospital of Wenzhou Medical University (center 2) from June 2017 to June 2019. All patients underwent annual CT follow-up for 5 years. To adjust for intrapatient clustering, all nodule-level and risk factor analyses used mixed-effects models with the individual patient as a random effect. Latent class trajectory modeling was used to identify growth patterns, with independent risk factors evaluated via multivariable mixed-effects logistic regression. Results This study included 910 patients (median age, 47 years [IQR, 41-57 years]; 422 men, 488 women; 1206 pGGNs). At center 1 (<i>n</i> = 754 pGGNs), 8.6% (65 of 754) of nodules exhibited growth, and bubble lucency (hazard ratio = 2.39; <i>P</i> = .02), vessel sign (hazard ratio = 2.98; <i>P</i> = .006), and family history of lung cancer (hazard ratio = 2.83; <i>P</i> = .02) were independent risk factors associated with nodule growth. At center 2 (<i>n</i> = 452 pGGNs), vessel sign (hazard ratio = 2.98; <i>P</i> = .04) and family history (hazard ratio = 3.94; <i>P</i> = .01) were associated with nodule growth. Latent class trajectory modeling identified a distinct progressive growth pattern in patients from both centers, for which family history of lung cancer was an independent risk factor at center 1 (odds ratio = 4.23; <i>P</i> = .01) and center 2 (odds ratio = 5.01; <i>P</i> =.003), with vessel sign being another risk factor at center 2 (odds ratio = 4.38; <i>P</i> = .01). Conclusion Across two independent clinical cohorts, a distinct progressive growth pattern associated with family history of lung cancer was identified in patients with incidental pGGNs detected at low-dose chest CT. © The Author(s) 2026. Published by the Radiological Society of North America under a CC BY 4.0 license. <i>Supplemental material is available for this article.</i> See also the editorial by Lee and Um in this issue.</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"320 2","pages":"e253139"},"PeriodicalIF":17.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148813982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Strategic Framework to Address Growing Imbalances between Physician Supply and Demand for Radiology: A Review from the International Society for Strategic Studies in Radiology.","authors":"Miriam A Bredella, Kristen DeStigter, Bhavin Jankharia, Pek-Lan Khong, Federico Lubinus, Geoffrey D Rubin, Christopher P Hess","doi":"10.1148/radiol.260425","DOIUrl":"https://doi.org/10.1148/radiol.260425","url":null,"abstract":"<p><p>Radiology is undergoing a global transformation across its workforce, workplace, and workflows. Imaging demand and examination complexity continue to increase, while workforce growth has lagged. Although these challenges are global, they manifest differently across regions and practice settings. Workforce shortages, burnout, and a weakening academic mission with less emphasis on research and teaching are systemic problems. In response to growing workloads, digital technologies, including artificial intelligence, have been introduced to increase capacity. Although these tools have the potential to improve efficiency, quality, and education, their effect on radiologist productivity has been mixed. Practice models such as teleradiology and hybrid work offer more flexibility for radiologists and have improved access to imaging services, especially in rural and underserved regions. However, remote work can lead to isolation, as well as lack of collaboration and exchange of ideas, which may contribute to burnout. In addition, declining reimbursement, rising costs, and growing unreimbursed noninterpretive tasks put a strain on the radiology workforce. This review from the International Society for Strategic Studies in Radiology (IS3R) examines international experiences on the challenges affecting the global workforce capacity, imaging demand, workplace, work design, and the work environment for radiology, and offers potential solutions.</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"320 2","pages":"e260425"},"PeriodicalIF":17.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148814034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advanced MRI in Multiple Sclerosis: Clinical Translation of Imaging Biomarkers in the 2024 McDonald Criteria.","authors":"Nara M Michaelson, Sittaya Buathong, Constantina A Treaba, John D Mayfield, Aneri Bhatt, Caterina Mainero, Eric C Klawiter, Susie Y Huang","doi":"10.1148/radiol.252139","DOIUrl":"10.1148/radiol.252139","url":null,"abstract":"<p><p>MRI continues to be the foundation of multiple sclerosis (MS) diagnosis and management. The latest innovations in technology have paved the way for earlier diagnosis, more informed treatment selection, better means of evaluating the disease course, and personalized therapeutic responses. This review summarizes the most recent developments in advanced MRI for MS, highlighting biomarkers that were newly added in the 2024 revisions of the McDonald criteria, describing the ongoing importance of established imaging measures, reviewing the applications of artificial intelligence in clinical practice, and suggesting future directions. Special emphasis is placed on the official adoption of the optic nerve as a fifth site of dissemination in space, as well as on the central vein sign and paramagnetic rim lesions as key supporting evidence in MS. Additionally, ongoing limitations and challenges in integrating these markers into clinical practice across both academic and community settings are identified. The overall goal is to improve diagnosis and prognostication in MS through imaging approaches that have higher contrast resolution and are more quantitative and biologically informed.</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"320 2","pages":"e252139"},"PeriodicalIF":17.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13507914/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148707641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"What We Left in the Chart: Rebuilding Clinical Context Using Large Language Models.","authors":"Enis C Yilmaz, David R Cardoza-Ochoa","doi":"10.1148/radiol.261918","DOIUrl":"https://doi.org/10.1148/radiol.261918","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"320 2","pages":"e261918"},"PeriodicalIF":17.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148670559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How I Do It: Understanding and Leveraging Generative AI for Chest Radiology Reporting.","authors":"Eun Kyoung Hong, Seungho Lee, Mizuki Nishino","doi":"10.1148/radiol.253456","DOIUrl":"https://doi.org/10.1148/radiol.253456","url":null,"abstract":"<p><p>Generative artificial intelligence (AI) has introduced new possibilities for assisting radiologists in diagnostic interpretation and workflow, particularly through automated report generation. Chest radiography has emerged as a leading use case for generative AI due to its high clinical volume, its diagnostic complexity, and the availability of large public datasets. This article describes a practical, experience-based approach to understanding and implementing generative AI for chest radiograph reporting. It outlines key concepts and the underlying architecture of generative models, followed by a clinical framework that positions AI as a draft assistant, highlighting both its utility and limitations. Through representative examples, the article demonstrates how generative AI can accelerate workflow, promote reporting consistency, and support trainee education, while also addressing known pitfalls such as hallucinations. It discusses implementation strategies, interface considerations, and safety checkpoints that support effective human-in-the-loop review. The article concludes with future directions that emphasize the importance of context-aware modeling, explainability, and multi-institutional evaluation. The aim is to equip radiologists with essential knowledge and insights to engage safely and effectively with generative AI tools in thoracic radiology practice, using chest radiography as a paradigm for broader integration across diagnostic imaging.</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"320 2","pages":"e253456"},"PeriodicalIF":17.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148670482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Selection or Treatment? Interpreting EVT Benefit beyond 24 Hours in Basilar Artery Occlusion.","authors":"Runze Jiang, Shenglong Li","doi":"10.1148/radiol.260216","DOIUrl":"https://doi.org/10.1148/radiol.260216","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"320 2","pages":"e260216"},"PeriodicalIF":17.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148797527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Not All Small Pure Ground-Glass Nodules Behave Alike.","authors":"Kyung Soo Lee, Sang-Won Um","doi":"10.1148/radiol.262182","DOIUrl":"https://doi.org/10.1148/radiol.262182","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"320 2","pages":"e262182"},"PeriodicalIF":17.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148814050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Photon-counting CT: An Emerging Tool for Liver Fibrosis Assessment: An Early Career Perspective.","authors":"Chen Jiang Wu, Yu Shi","doi":"10.1148/radiol.261999","DOIUrl":"https://doi.org/10.1148/radiol.261999","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"320 2","pages":"e261999"},"PeriodicalIF":17.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148670437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multinational Validation of a Radiography-Based AI Tool for Appendicular Fracture Detection and Corresponding Reader Performance.","authors":"Huibert C Ruitenbeek, Mathias Willadsen Brejnebøl, Gozde Gunes, Bastiaan J van der Zwart, Marna G Bouwhuis, Jessica D Lantinga-van den Brink, Dimitar Ivanov Radev, Yahya Fekri Avval, Tazio Maleitzke, Kristine Ifigenia Bunyoz, Frederik Jager Bruun, Katharina Beller, Haoyin Cao, Luis A Becker, Maximilian Muellner, Anders Lenskjold, Katharina Ziegeler, Dominik Deppe, Daniel Bos, Mikael Ploug Boesen, Kay-Geert A Hermann, Edwin H G Oei, Jacob J Visser","doi":"10.1148/radiol.243548","DOIUrl":"10.1148/radiol.243548","url":null,"abstract":"<p><p>Background Artificial intelligence (AI) models for fracture detection have shown promise in improving diagnostic efficiency and accuracy in radiology workflows. However, despite potential differences in imaging protocols and clinical workflows in different health care systems and settings, the performance across countries of these models and their effects on reader performance remain underexplored. Purpose To evaluate the performance of a pretrained fracture detection tool across three European hospitals and assess its impact on clinician performance. Materials and Methods In this retrospective, multireader, multicase crossover study, the radiographs of patients aged 21 years or older with suspected appendicular fractures acquired from April 2018 to July 2022, across three European hospitals, were analyzed. Reference standards were established by expert local radiologists. AI tool performance was evaluated with the area under the receiver operator characteristic curve, sensitivity, and specificity. Sensitivity and specificity were statistically compared across hospitals and per-anatomy subgroup with the χ² test and across readers with generalized estimating equations. Results A total of 1500 patients (mean age, 43.3 years ± 19.5 [SD]; 820 men) were included. The hospital-level sensitivities of the AI tool ranged from 400 of 500 (80%; 95% CI: 76, 84) to 430 of 500 (86%; 95% CI: 83, 90) and specificities ranged from 465 of 500 (93%; 95% CI: 91, 95) to 480 of 500 (96%; 95% CI: 95, 98). Anatomy-based subgroup analysis revealed lower sensitivity of 105 of 140 (75%) for wrist/hand and finger examinations in one hospital versus 125 of 138 (91%) and 150 of 160 (94%) (<i>P</i> = .004), but no differences in overall sensitivity or specificity were observed across all hospitals (<i>P</i> = .23 and <i>P</i> = .10, respectively). Use of the AI tool improved overall reader sensitivity (11 percentage points [95% CI: 6.8, 14.4; <i>P</i> < .001]) while maintaining specificity (0.6 percentage points [95% CI: -0.6, 1.7; <i>P</i> = .34]). Conclusion No differences in AI performance were observed across centers, and AI assistance was associated with higher reader sensitivity. © The Author(s) 2026. Published by the Radiological Society of North America under a CC BY 4.0 license. <i>Supplemental material is available for this article</i> See also the editorial by Maas and Beekman in this issue.</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"320 2","pages":"e243548"},"PeriodicalIF":17.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148797444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}