Journal of the American College of Radiology最新文献

{"title":"ACR Appropriateness Criteria® Suspected and Known Heart Failure: 2024 Update","authors":"Expert Panel on Cardiac Imaging,&nbsp;James Roberts MD, MSc ,&nbsp;Kate Hanneman MD, MPH ,&nbsp;Prabhakar Shantha Rajiah MD ,&nbsp;Shawn Ahmad MD, MBA ,&nbsp;Ryan Avery MD ,&nbsp;William M. Brown MD ,&nbsp;Ahmed H. El-Sherief MD ,&nbsp;Joe Y. Hsu MD ,&nbsp;Veronica Lenge de Rosen MD ,&nbsp;Fay Lin MD ,&nbsp;Gurusher Panjrath MBBS ,&nbsp;Rahul D. Renapurkar MD, MBBS ,&nbsp;James A. White MD ,&nbsp;Michael A. Bolen MD","doi":"10.1016/j.jacr.2025.02.021","DOIUrl":"10.1016/j.jacr.2025.02.021","url":null,"abstract":"<div><div>Heart failure (HF) is a prevalent and complex clinical syndrome with no single reference standard diagnostic test. Imaging has a supportive role in patients with suspected and known HF, including initial imaging assessment of an adult with suspected HF, but without history of HF, including evaluation of pulmonary edema and detection of left ventricular dysfunction (Variant 1). In adults with established diagnosis of HF but unknown etiology, imaging also has an important role in the assessment of the underlying disease process, including ischemic and nonischemic etiologies (Variant 2). In the course of continuing care for adult patients with an established diagnosis of HF without new symptoms, follow-up imaging is performed to assess for longitudinal changes in ventricular function, response to therapy and prognostication (Variant 3).</div><div>The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.</div></div>","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"22 5","pages":"Pages S424-S439"},"PeriodicalIF":4.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144106162","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":"ACR Appropriateness Criteria® Introduction to the JACR Appropriateness Criteria May 2025 Supplement","authors":"Ihab R. Kamel MD, PhD","doi":"10.1016/j.jacr.2025.02.017","DOIUrl":"10.1016/j.jacr.2025.02.017","url":null,"abstract":"","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"22 5","pages":"Pages S1-S2"},"PeriodicalIF":4.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144106455","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®: Urinary Tract Infection-Child","authors":"Anna Cernich DO ,&nbsp;Saadiya Sehareen DO","doi":"10.1016/j.jacr.2024.06.021","DOIUrl":"10.1016/j.jacr.2024.06.021","url":null,"abstract":"","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"22 5","pages":"Page 616"},"PeriodicalIF":4.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141545627","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 in Radiology: A Leadership Survey","authors":"Elizabeth S. Burnside MD, MPH, MS ,&nbsp;Thomas M. Grist MD ,&nbsp;Michael R. Lasarev MS ,&nbsp;John W. Garrett PhD ,&nbsp;Elizabeth A. Morris MD","doi":"10.1016/j.jacr.2025.01.006","DOIUrl":"10.1016/j.jacr.2025.01.006","url":null,"abstract":"<div><h3>Purpose</h3><div>Surveys to assess views about artificial intelligence (AI) of various diagnostic radiology constituencies have revealed interesting combinations of enthusiasm, caution, and implementation priorities. We surveyed academic radiology leaders about their views on AI and how they intend to approach AI implementation in their departments.</div></div><div><h3>Materials and methods</h3><div>We conducted a web survey of Society of Chairs of Academic Radiology Departments members between October 5 and October 31, 2023, to solicit optimism or pessimism about AI, target use cases, planned implementation, and perceptions of their workforce. <em>P</em> values are provided only for descriptive purposes and have not been adjusted for multiple testing in this exploratory research.</div></div><div><h3>Results</h3><div>The survey was sent to the 112 Society of Chairs of Academic Radiology Departments members and 43 responded (38%). Chairs were optimistic, with no statistical difference between views of AI in general versus generative AI. Chairs plan to implement AI to improve quality and efficiency (43 of 43, 100%), burnout (41 of 43, 95%), health care costs (22 of 43, 51%), and equity (27 of 43, 63%) and most likely will target the postprocessing (26 of 43, 60%), interpretation workflow (26 of 43, 60%), and image acquisition (18 of 43, 42%) steps in the imaging value chain. Chairs perceived that radiologists (36 of 43, 84%) and technologists (38 of 43, 88%) were not particularly worried about being displaced but saw trainees as slightly less confident (31 of 43, 72%). Free text responses revealed concerns about the cost of AI and emphasized trade-offs that needed to be balanced.</div></div><div><h3>Conclusion</h3><div>Radiology chairs are optimistic about AI and poised to tackle departmental challenges. Concerns about generative AI and workforce replacement are minimal.</div></div>","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"22 5","pages":"Pages 577-585"},"PeriodicalIF":4.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142973851","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":"ACR Appropriateness Criteria® Cervical Pain or Cervical Radiculopathy: 2024 Update","authors":"Expert Panel on Neurological Imaging,&nbsp;Rami W. Eldaya MD ,&nbsp;Matthew S. Parsons MD ,&nbsp;Troy A. Hutchins MD ,&nbsp;Ryan Avery MD ,&nbsp;Judah Burns MD ,&nbsp;Brent Griffith MD ,&nbsp;Alvand Hassankhani MD ,&nbsp;Majid A. Khan MD ,&nbsp;Henry Ng MD, MPH ,&nbsp;Noah M. Raizman MD ,&nbsp;Charles Reitman MD ,&nbsp;Vinil N. Shah MD ,&nbsp;Clint Sliker MD ,&nbsp;Hesham Soliman MD ,&nbsp;Vincent M. Timpone MD ,&nbsp;Christian A. Tomaszewski MD, MS, MBA ,&nbsp;Noushin Yahyavi-Firouz-Abadi MD, MBA ,&nbsp;Bruno Policeni MD, MBA","doi":"10.1016/j.jacr.2025.02.035","DOIUrl":"10.1016/j.jacr.2025.02.035","url":null,"abstract":"<div><div>Cervical spine pain is one of the most common reasons for seeking medical care as it ranks in the top 5 causes of global years lost to disability. The economic burden of cervical pain is also significant. Imaging is at the center of diagnosis of cervical pain and its causes. However, different symptoms and potential causes of cervical pain require different initial imaging to maximize the benefit of diagnostic usefulness of imaging. In this document we address different cervical pain variants with detailed assessment of the strengths and weaknesses of different modalities for addressing each specific variant.</div><div>The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.</div></div>","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"22 5","pages":"Pages S136-S162"},"PeriodicalIF":4.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144106334","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":"ACR Appropriateness Criteria® Evaluation of Coronary Artery Anomalies","authors":"Expert Panel on Cardiac Imaging,&nbsp;Cristina Fuss MD ,&nbsp;Raluca McCallum MD ,&nbsp;Brian B. Ghoshhajra MD, MBA ,&nbsp;Diana Litmanovich MD ,&nbsp;Prachi P. Agarwal MD ,&nbsp;Stephen Bloom MD ,&nbsp;William M. Brown MD ,&nbsp;Anjali Chelliah MD ,&nbsp;Carlo N. De Cecco MD, PhD ,&nbsp;Peter Frommelt MD ,&nbsp;Kimberly Kallianos MD ,&nbsp;Sachin B. Malik MD ,&nbsp;Constantine D. Mavroudis MD, MSc, MTR ,&nbsp;Nandini M. Meyersohn MD ,&nbsp;Sven Plein MD ,&nbsp;Tina D. Tailor MD ,&nbsp;Chadwick L. Wright MD, PhD ,&nbsp;Lynne M. Koweek MD","doi":"10.1016/j.jacr.2025.02.030","DOIUrl":"10.1016/j.jacr.2025.02.030","url":null,"abstract":"<div><div>Congenital anomalies of the coronary arteries are often discovered incidentally yet in the context of acute cardiac symptoms. The management and decision-making pathway of anomalous coronary arteries relies heavily on imaging to determine the type of anomaly, its clinical significance, and ultimately to plan any surgical interventions. Imaging also provides guidance for patients if no further interventions are needed, and watchful management is the right choice. Imaging also allows to anticipate potential high-risk scenarios during surgery or intervention by determining patients with high-risk features that already cause ischemia during stress. In summary, cardiac imaging determines, guides, and reassures patients and physicians in the managements of congenital coronary anomalies.</div><div>The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.</div></div>","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"22 5","pages":"Pages S234-S242"},"PeriodicalIF":4.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144106339","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®: Imaging of the Axilla","authors":"Christian P. Haskett PhD, AEMT ,&nbsp;Sonya Bhole MD","doi":"10.1016/j.jacr.2024.11.018","DOIUrl":"10.1016/j.jacr.2024.11.018","url":null,"abstract":"","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"22 4","pages":"Page 514"},"PeriodicalIF":4.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142752472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patient-Friendly Summary of the ACR Appropriateness Criteria®: Altered Mental Status, Coma, Delirium, and Psychosis: 2024 Update","authors":"Giorgia Villa BS ,&nbsp;Orit Ariel Glenn MD","doi":"10.1016/j.jacr.2024.11.019","DOIUrl":"10.1016/j.jacr.2024.11.019","url":null,"abstract":"","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"22 4","pages":"Page 513"},"PeriodicalIF":4.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142752515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"External Validation of the Neiman Imaging Comorbidity Index in Medicare, Medicaid, and Private Payer Claims Data","authors":"Casey E. Pelzl MPH ,&nbsp;Alexandra Drake MPH ,&nbsp;Andrew B. Rosenkrantz MD ,&nbsp;Elizabeth Y. Rula PhD ,&nbsp;Eric W. Christensen PhD","doi":"10.1016/j.jacr.2024.11.007","DOIUrl":"10.1016/j.jacr.2024.11.007","url":null,"abstract":"<div><h3>Objective</h3><div>The Neiman Imaging Comorbidity Index (NICI) was developed and validated in a claims dataset encompassing &gt;10 million privately insured beneficiaries, in which it outperformed the commonly used Charlson Comorbidity Index (CCI) in predicting advanced imaging use. This external validation assessed the broader generalizability of NICI for predicting receipt of advanced imaging in nationally representative populations, including patients insured by Medicare, Medicaid, and private payers.</div></div><div><h3>Methods</h3><div>All 2018 to 2019 patient-level claims from the CMS Medicare 5% Research Identifiable File, CMS Medicaid 100% Research Identifiable File, and private insurance (commercial and Medicare Advantage) claims from Inovalon Insights, LLC, were included. Using 2018 comorbidity data, beneficiaries were assigned CCI and NICI. Area under the receiver operator characteristic curves (AUCs) measured index performance predicting advanced imaging in 2019. AUCs for NICI and CCI were compared overall, across age groups, and after adjusting for age and sex.</div></div><div><h3>Results</h3><div>A total of 108,846,549 beneficiaries were included across Medicare (n = 2,536,403), Medicaid (n = 49,685,052), and private insurance (n = 56,625,094) datasets. NICI outperformed CCI in Medicare (AUC: 0.7709, 95 confidence interval [CI]: 0.7702-0.7716 versus AUC: 0.7503, 95% CI: 0.7496-0.7510; <em>P</em> &lt; .001), Medicaid (AUC: 0.6876, 95% CI: 0.6874-0.6878 versus AUC: 0.6798 95% CI: 0.6796-0.6800]; <em>P</em> &lt; .001), and private insurance data (AUC: 0.6658, 95% CI: 0.6656-0.6660 versus AUC: 0.6479, 95% CI: 0.6477-0.6481; <em>P</em> &lt; .001). NICI outperformed CCI in adjusted models and in nearly all age strata across the three cohorts.</div></div><div><h3>Discussion</h3><div>The NICI outperformed CCI in predicting advanced imaging in populations insured by numerous different payers. Validation data support NICI as the preferred index to adjust for patient comorbidities when studying advanced imaging as an outcome, but further investigations are warranted.</div></div>","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"22 4","pages":"Pages 436-443"},"PeriodicalIF":4.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142873606","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":"Incidental Detection of Previously Unknown Strokes on Head CT Examinations: An Untapped Opportunity for Secondary Prevention","authors":"Carys L. Kenny-Howell BS ,&nbsp;Irene Dixe de Oliveira Santo MD ,&nbsp;Charles Wira MD ,&nbsp;Adam de Havenon MD, MS ,&nbsp;Long H. Tu MD, PhD","doi":"10.1016/j.jacr.2024.11.010","DOIUrl":"10.1016/j.jacr.2024.11.010","url":null,"abstract":"<div><h3>Background</h3><div>The incidental discovery of previously unknown strokes on neuroimaging is an opportunity to implement secondary prevention, reducing the risk of recurrent strokes by up to 80%.</div></div><div><h3>Objective</h3><div>To evaluate the prevalence of previously unknown strokes on emergency department (ED) head CT imaging and identify associated patient and imaging factors.</div></div><div><h3>Methods</h3><div>Retrospective study of adult patients receiving head CT (age ≥ 18 years) at three EDs between July and December 2023. Old strokes on CT imaging were categorized as previously known or unknown. Patient and imaging factors associated with unknown strokes were assessed via univariable regression, multivariable regression, and decision tree analysis.</div></div><div><h3>Results</h3><div>In 21,985 ED encounters with head CT, 869 (4.0%) examinations demonstrated an old stroke (mean age, 74.4 years ± 14.0 [SD]), of which 372 (43%; 1.7% of all CTs) were unknown. Univariable analysis showed that unknown strokes were associated with greater age (odds ratio [OR], 1.03; 95% confidence interval [CI]: 1.02-1.04; <em>P</em> &lt; .001), a single site of old stroke (OR, 2.7; 95% CI: 2.06-3.58; <em>P</em> and &lt; .001), smaller strokes (OR, 1.8, 95% CI: 1.6-2.0, <em>P</em> &lt; .001), as well as gangliocapsular (OR, 2.8; 95% CI: 1.9-4.0; <em>P</em> &lt; .001) and cerebellar location (OR, 2.1; 95% CI: 1.4-3.2; <em>P</em> &lt; .001). Results of further analyses corroborated those of the univariable regression.</div></div><div><h3>Conclusion</h3><div>More than a third of old strokes on head CT imaging are unknown to patients and clinicians. Capturing this opportunity for secondary prevention could benefit 100,000 to 200,000 patients per year in the United States, based on trends in ED care.</div></div>","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"22 4","pages":"Pages 461-470"},"PeriodicalIF":4.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142689637","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}