Journal of the American College of Radiology最新文献

{"title":"Positive Screens Are More Likely in a National Lung Cancer Screening Registry Than the National Lung Screening Trial","authors":"Tina D. Tailor MD ,&nbsp;Roee Gutman PhD ,&nbsp;Na An ,&nbsp;Richard M. Hoffman MD ,&nbsp;Caroline Chiles MD ,&nbsp;Ruth C. Carlos MD, MS ,&nbsp;JoRean D. Sicks MS ,&nbsp;Ilana F. Gareen PhD, MPH","doi":"10.1016/j.jacr.2025.02.012","DOIUrl":"10.1016/j.jacr.2025.02.012","url":null,"abstract":"<div><h3>Purpose</h3><div>Although lung cancer screening (LCS) with low-dose chest CT (LDCT) is recommended for high-risk populations, little is known about how clinical screening compares with research trials. We compared Lung CT Screening Reporting and Data System (Lung-RADS) scores between a nationally screened population from the ACR’s LCS Registry (LCSR) and the National Lung Screening Trial (NLST).</div></div><div><h3>Methods</h3><div>This retrospective study included baseline LDCT examinations from the LCSR and NLST. Patient characteristics (age, gender, smoking status, pack-years, and body mass index) were obtained. NLST LDCT results were recoded to Lung-RADS version 1.1. A multivariable multinomial logistic model was used to examine variations in Lung-RADS scores by screening group (LCSR versus NLST) and patient characteristics.</div></div><div><h3>Results</h3><div>In all, 686,011 and 26,432 participants from the LCSR and NLST, respectively, were included. Compared with the NLST, the LCSR population was older (mean age [SD]: 64.0 [5.4] versus 61.4 [5.0] years); <em>P</em> &lt; .001) and included more female patients (47.9% versus 40.9%; <em>P</em> &lt; .001), and its patients were more likely to be currently smoking (61.5% versus 48.1%; <em>P</em> &lt; .001). After adjusting for age, gender, smoking history, and body mass index, the LCSR population was more significantly likely to have higher Lung-RADS scores than the NLST (adjusted odds ratio and 95% confidence interval &gt; 1 for Lung-RADS scores 2, 3, 4A, 4B, 4X relative to Lung-RADS 1).</div></div><div><h3>Conclusions</h3><div>Lung-RADS scores in clinical LCS are higher than in the NLST, even after adjusting for known confounders such as age and smoking. This would imply higher rates of follow-up testing after LCS and potentially higher cancer rates in the clinically screened population than the NLST.</div></div>","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"22 6","pages":"Pages 644-652"},"PeriodicalIF":4.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143538120","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":"JACR Leadership","authors":"Ryan K. Lee MD, MBA ,&nbsp;Michael Enea DO","doi":"10.1016/j.jacr.2025.04.004","DOIUrl":"10.1016/j.jacr.2025.04.004","url":null,"abstract":"","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"22 6","pages":"Pages 707-708"},"PeriodicalIF":4.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144189415","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":"Table of Content","authors":"","doi":"10.1016/S1546-1440(25)00241-8","DOIUrl":"10.1016/S1546-1440(25)00241-8","url":null,"abstract":"","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"22 6","pages":"Pages A1-A4"},"PeriodicalIF":4.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144189929","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":"Trends in Corporate Acquisitions of Radiology Practices and Imaging Centers Over 11 Years","authors":"Jefferson Chen MD ,&nbsp;Rahul Hegde MBBS, MD ,&nbsp;Christina LeBedis MD, MS","doi":"10.1016/j.jacr.2025.01.014","DOIUrl":"10.1016/j.jacr.2025.01.014","url":null,"abstract":"","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"22 6","pages":"Pages 662-664"},"PeriodicalIF":4.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143076734","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":"Rethinking Radiology Research Residencies","authors":"Andrew F. Voter MD, PhD","doi":"10.1016/j.jacr.2025.02.045","DOIUrl":"10.1016/j.jacr.2025.02.045","url":null,"abstract":"","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"22 6","pages":"Page 620"},"PeriodicalIF":4.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143627128","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":"Patient-Friendly Summary of the ACR Appropriateness Criteria®: Movement Disorders and Neurodegenerative Diseases","authors":"Christian P. Haskett PhD, AEMT ,&nbsp;Vincent M. Timpone MD","doi":"10.1016/j.jacr.2025.02.003","DOIUrl":"10.1016/j.jacr.2025.02.003","url":null,"abstract":"","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"22 6","pages":"Page 711"},"PeriodicalIF":4.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143384338","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":"Comparing Artificial Intelligence and Traditional Regression Models in Lung Cancer Risk Prediction Using A Systematic Review and Meta-Analysis","authors":"Sierra Leonard BSc ,&nbsp;Meet A. Patel BSc ,&nbsp;Zili Zhou MPH ,&nbsp;Ha Le MSc ,&nbsp;Prosanta Mondal PhD ,&nbsp;Scott J. Adams MD, PhD","doi":"10.1016/j.jacr.2025.02.042","DOIUrl":"10.1016/j.jacr.2025.02.042","url":null,"abstract":"<div><h3>Purpose</h3><div>Accurately identifying individuals who are at high risk of lung cancer is critical to optimize lung cancer screening with low-dose CT (LDCT). We sought to compare the performance of traditional regression models and artificial intelligence (AI)-based models in predicting future lung cancer risk.</div></div><div><h3>Methods</h3><div>A systematic review and meta-analysis were conducted with reporting according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We searched MEDLINE, Embase, Scopus, and the Cumulative Index to Nursing and Allied Health Literature databases for studies reporting the performance of AI or traditional regression models for predicting lung cancer risk. Two researchers screened articles, and a third researcher resolved conflicts. Model characteristics and predictive performance metrics were extracted. The quality of studies was assessed using the Prediction model Risk of Bias Assessment Tool. A meta-analysis assessed the discrimination performance of models, based on area under the receiver operating characteristic curve (AUC).</div></div><div><h3>Results</h3><div>One hundred forty studies met inclusion criteria and included 185 traditional and 64 AI-based models. Of these, 16 AI models and 65 traditional models have been externally validated. The pooled AUC of external validations of AI models was 0.82 (95% confidence interval [CI], 0.80-0.85), and the pooled AUC for traditional regression models was 0.73 (95% CI, 0.72-0.74). In a subgroup analysis, AI models that included LDCT had a pooled AUC of 0.85 (95% CI, 0.82-0.88). Overall risk of bias was high for both AI and traditional models.</div></div><div><h3>Conclusion</h3><div>AI-based models, particularly those using imaging data, show promise for improving lung cancer risk prediction over traditional regression models. Future research should focus on prospective validation of AI models and direct comparisons with traditional methods in diverse populations.</div></div>","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"22 6","pages":"Pages 675-690"},"PeriodicalIF":4.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143574974","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":"Patient-Friendly Summary of the ACR Appropriateness Criteria®: Radiologic Management of Urinary Tract Obstruction","authors":"Sania Choudhary ,&nbsp;Sherry S. Wang MBBS","doi":"10.1016/j.jacr.2025.02.010","DOIUrl":"10.1016/j.jacr.2025.02.010","url":null,"abstract":"","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"22 6","pages":"Page 709"},"PeriodicalIF":4.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143416497","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":"2022 and 2023 ACR/RBMA Workforce Surveys: Focus on Pediatric Radiology","authors":"Shannon G. Farmakis MD ,&nbsp;Eric Rubin MD ,&nbsp;Dominick Parris ,&nbsp;Jo Tarrant ,&nbsp;Dorothy Bulas MD ,&nbsp;Richard A. Barth MD","doi":"10.1016/j.jacr.2025.02.050","DOIUrl":"10.1016/j.jacr.2025.02.050","url":null,"abstract":"","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"22 6","pages":"Pages 670-674"},"PeriodicalIF":4.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143627105","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":"Practice Expense and Its Impact on Radiology Reimbursement","authors":"Guilherme Dabus MD, MHL ,&nbsp;Michael T. Booker MD, MBA ,&nbsp;Ezequiel Silva III MD ,&nbsp;Joshua A. Hirsch MD","doi":"10.1016/j.jacr.2025.02.047","DOIUrl":"10.1016/j.jacr.2025.02.047","url":null,"abstract":"<div><div>Practice expense (PE) is one of three components that contribute to total relative value units (RVUs). Across the Medicare Physician Fee Schedule, PE accounts for nearly half of total RVUs. In radiology, however, this number is even higher, largely because of the nature and particularities of the specialty. This makes radiology especially sensitive to changes and updates in the inputs, methodology, and pricing used to calculate PE. In this review, the authors explore the Resource-Based Relative Value Scale RVU system with an emphasis on PE, its direct and indirect components, and its impact on radiology reimbursement. The authors explore how calculation methodologies as well as budget-neutrality adjustment mechanisms have the potential to significantly affect radiology reimbursement and provide practical considerations.</div></div>","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"22 6","pages":"Pages 665-669"},"PeriodicalIF":4.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143627124","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}