Radiology最新文献

{"title":"Early Career Perspective: Muscle Composition Turns Routine MRI into Metabolic Insight.","authors":"Bahram Mohajer, Waqas Bari","doi":"10.1148/radiol.260736","DOIUrl":"https://doi.org/10.1148/radiol.260736","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"319 2","pages":"e260736"},"PeriodicalIF":15.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147841954","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":"When a Foregut Duplication Cyst Meets Fishtail Pancreas.","authors":"Bo Duan, Qingyu Ji","doi":"10.1148/radiol.252821","DOIUrl":"https://doi.org/10.1148/radiol.252821","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"319 2","pages":"e252821"},"PeriodicalIF":15.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147841994","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":"Multimodality Imaging for GFAP Astrocytopathy and Lung Adenocarcinoma.","authors":"Ye Dong,Wenlan Zhou","doi":"10.1148/radiol.252690","DOIUrl":"https://doi.org/10.1148/radiol.252690","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"19 1","pages":"e252690"},"PeriodicalIF":19.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147625752","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":"Deep Learning-based Bone Mineral Density Prediction Using Pediatric Chest Radiographs: A Multicenter Feasibility Study.","authors":"Jae Won Choi,Young Jin Ryu,Jung-Eun Cheon,Young Hun Choi,Jae-Yeon Hwang,Seunghyun Lee,Yeon Jin Cho,Seok Young Koh,Yun Jeong Lee,Young Ah Lee,Choong Ho Shin","doi":"10.1148/radiol.252761","DOIUrl":"https://doi.org/10.1148/radiol.252761","url":null,"abstract":"Background Measuring bone mineral density (BMD) is essential for pediatric bone health assessment. Dual-energy x-ray absorptiometry (DXA) is the reference standard but has limited accessibility. Purpose To develop and evaluate an artificial intelligence model for predicting BMD from pediatric chest radiography. Materials and Methods This retrospective study included patients aged younger than 18 years who underwent DXA and chest radiography within 3 months at two tertiary hospitals (internal test, 2014-2023; external test, 2022-2023). The internal dataset was temporally split into development (fivefold cross-validation) and test sets. The model combined chest radiographs and clinical variables (age, sex, height, and weight) to predict the lumbar spine (L1 through L4) areal BMD, with Z scores calculated from Korean pediatric reference. Performance was evaluated using the Pearson correlation coefficient (r) for regression and the area under the receiver operating characteristic curve (AUC) for detecting low BMD (Z score ≤ -2.0). Results A total of 1464 radiograph-DXA pairs (median age, 13 years [IQR, 11-16 years]; 824 boys) were included: 774 in the development set, 376 in the internal test set, and 314 in the external test set. The predicted BMD Z scores were strongly correlated with the DXA scores in both the internal (r = 0.85 [95% CI: 0.82, 0.88]; P < .001) and external (r = 0.76 [95% CI: 0.71, 0.81]; P < .001) test sets. For detecting low BMD, the internal test set had an AUC of 0.92 (95% CI: 0.89, 0.95), a sensitivity of 60% (50 of 84 scans; 95% CI: 48, 70), and a specificity of 95% (276 of 292 scans; 95% CI: 91, 97). The external test set achieved an AUC of 0.90 (95% CI: 0.87, 0.94), a sensitivity of 82% (54 of 66 scans; 95% CI: 70, 90), and a specificity of 85% (210 of 248 scans; 95% CI: 80, 89). Conclusion A chest radiograph-based artificial intelligence model accurately predicted pediatric BMD Z scores and identified low BMD. © RSNA, 2026 Supplemental material is available for this article.","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"33 1","pages":"e252761"},"PeriodicalIF":19.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147625753","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":"Subclinical Repetitive Head Trauma: A Potential New Risk Factor for Brain Aneurysms.","authors":"Pejman Jabehdar Maralani, Vivek Pai","doi":"10.1148/radiol.260599","DOIUrl":"https://doi.org/10.1148/radiol.260599","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"319 1","pages":"e260599"},"PeriodicalIF":15.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147676299","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":"Toward Broader Clinical Adoption of Photon-Counting CT: Where We Stand According to High-Impact Literature.","authors":"Pooyan Sahbaee, Chelsea Dunning, Andrew Primak, Elisabeth Shanblatt, Kristina Hallam, Chloe Choi, George S K Fung, Lauren Severance, Thomas O'Donnell, Jim O'Doherty, Juan Carlos Ramirez-Giraldo","doi":"10.1148/radiol.251496","DOIUrl":"https://doi.org/10.1148/radiol.251496","url":null,"abstract":"<p><p>The first commercially available whole-body photon-counting CT (PCCT) system, cleared by the Food and Drug Administration in 2021, has since been adopted across an expanding range of clinical practice settings. This review article focuses on the clinical applications of PCCT, drawing insights from a carefully selected subset of the most relevant studies among 458 identified peer-reviewed publications (mean journal impact factor: 8.5). It explores the growing use of PCCT across major imaging domains, including cardiac, thoracic, neurovascular, musculoskeletal, abdominal, and pediatric imaging. The article highlights how the core technical innovations of PCCT, including improved spatial and contrast resolution, spectral capabilities, reduced electronic noise, and enhanced dose efficiency, have translated into tangible clinical benefits such as superior image quality, improved diagnostic performance, and reduced radiation dose. This article presents the collective findings within the context of broader clinical adoption. It also covers protocol optimization and emerging evidence on disease management and cost-effectiveness, as well as current workflow challenges. It highlights the progress made in the clinical adoption of PCCT and the remaining evidence gaps, particularly the need for protocol standardization to enable large-scale multicenter studies. © 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></p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"319 1","pages":"e251496"},"PeriodicalIF":15.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147779507","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":"From Anatomy to Physiology: The Role of Phase-resolved Functional MRI in CTD-ILD.","authors":"Amir Ali Rahsepar, Fereidoun Abtin","doi":"10.1148/radiol.260876","DOIUrl":"https://doi.org/10.1148/radiol.260876","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"319 1","pages":"e260876"},"PeriodicalIF":15.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147779509","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":"Erratum for: AI Improves Nodule Detection on Chest Radiographs in a Health Screening Population: A Randomized Controlled Trial.","authors":"Ju Gang Nam, Eui Jin Hwang, Jayoun Kim, Nanhee Park, Eun Hee Lee, Hyun Jin Kim, Miyeon Nam, Jong Hyuk Lee, Chang Min Park, Jin Mo Goo","doi":"10.1148/radiol.269006","DOIUrl":"https://doi.org/10.1148/radiol.269006","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"319 1","pages":"e269006"},"PeriodicalIF":15.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147779522","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":"Do We Trust the Algorithm? Machine Learning for Coronary Plaque Quantification.","authors":"Michelle Claire Williams","doi":"10.1148/radiol.260858","DOIUrl":"https://doi.org/10.1148/radiol.260858","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"319 1","pages":"e260858"},"PeriodicalIF":15.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147779539","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":"MRI Diagnosis of Meniscus Tears in the Knee: An Updated Systematic Review and Meta-analysis.","authors":"Jie C Nguyen, Carlos Yaya-Quezada, Wondwossen T Lerebo, Vandan S Patel, Majid Chalian, Dyan V Flores, Tetyana Gorbachova, Kimia K Kani, Megan K Mills, Kathryn J Stevens, Jennifer S Weaver, Robert D Boutin","doi":"10.1148/radiol.252288","DOIUrl":"https://doi.org/10.1148/radiol.252288","url":null,"abstract":"<p><p>Background Updated benchmarks are needed on the diagnostic performance of MRI for detecting meniscus tears compared with arthroscopy. Purpose To conduct an updated systematic review and meta-analysis on the diagnostic performance of MRI in the detection of tears of the native menisci compared with arthroscopy and with subgroup analyses to identify factors that impact accuracy. Materials and Methods A literature search was conducted using PubMed, Scopus, and Embase databases to identify peer-reviewed publications on MRI diagnosis of meniscus tears using knee arthroscopy as the reference standard. Random-effects models, pooled weighted sensitivity and specificity, and summary receiver operating characteristic curve analyses were used to determine diagnostic performance, changes according to publication year, and differences based on study design, patient characteristics, imaging parameters, and diagnostic criteria for tears. A meta-regression model was also used. Results Seventy-five studies (published 1986-2023) from 28 countries included 8507 patients (8517 knees). Pooled weighted sensitivity was higher for medial tears (91.0% [95% CI: 89.3, 92.4]) than for lateral tears (78.5% [95% CI: 74.5, 82.0]). In contrast, specificity was higher for lateral tears (94.0% [95% CI: 92.5, 95.3]) than for medial tears (87.7% [95% CI: 85.2, 89.8]). Specificity for diagnosing lateral tears decreased over the years (<i>P</i> = .01). The highest pooled sensitivity for lateral tears was found in studies using both surfacing linear signal intensity and meniscus distortion for diagnosis (81.1%) compared with studies using only signal intensity (79.2%) or not reporting criteria (60.8%) (<i>P</i> = .02); likewise in meta-regression, using both surfacing signal intensity and meniscus distortion was a predictor of higher performance compared with not reporting criteria (adjusted odds ratio, 3.74 [95% CI: 1.37, 10.18]; <i>P</i> = .01). No differences in sensitivity or specificity (<i>P</i> value range, .29-.59) were found between studies using one or more versus two or more images for diagnosing tears in either meniscus. Conclusion The reported accuracy of knee MRI for meniscus tears was consistently high, regardless of individual study designs, with sensitivity higher for the medial meniscus and specificity higher for the lateral meniscus. © RSNA, 2026 <i>Supplemental material is available for this article.</i></p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"319 1","pages":"e252288"},"PeriodicalIF":15.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147779552","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}