Journal of the American College of Radiology : JACR最新文献

{"title":"Wait-They Had Another Miss?","authors":"Jennifer Nathan, Frank Lexa","doi":"10.1016/j.jacr.2025.08.050","DOIUrl":"10.1016/j.jacr.2025.08.050","url":null,"abstract":"","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145006931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing Health Care Delivery in Community, Rural, and Safety-Net Settings Radiologists as Equity Advocates: The Colombian Perspective.","authors":"Valeria Del Castillo, Laura Manuela Olarte Bermúdez, Valeria Noguera, Gustavo Triana, Hernan Dario Paez Rueda","doi":"10.1016/j.jacr.2025.08.049","DOIUrl":"https://doi.org/10.1016/j.jacr.2025.08.049","url":null,"abstract":"","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144994658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance of Artificial Intelligence Models in Predicting Responsiveness of Hepatocellular Carcinoma to Transarterial Chemoembolization (TACE): A Systematic Review and Meta-Analysis.","authors":"Iman Kiani, Iman Razeghian, Parya Valizadeh, Yasmin Esmaeilian, Payam Jannatdoust, Bardia Khosravi","doi":"10.1016/j.jacr.2025.08.028","DOIUrl":"10.1016/j.jacr.2025.08.028","url":null,"abstract":"<p><strong>Background: </strong>Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality worldwide. Handcrafted radiomics (HCR) and deep learning (DL) models have emerged as promising predictive tools extracting granular insights from images.</p><p><strong>Objective: </strong>This systematic review and meta-analysis aims to evaluate the predictive performance of these artificial intelligence models in predicting treatment efficacy in patients with HCC who are undergoing transarterial chemoembolization (TACE).</p><p><strong>Methods: </strong>A comprehensive search was conducted on PubMed, Embase, Web of Science, and Cochrane Library databases up to June 15, 2024. Inclusion criteria encompassed studies involving patients with confirmed HCC undergoing TACE. Random-effects diagnostic test accuracy meta-analyses were performed using bivariate modeling. Methodological quality was assessed using the Prediction model Risk of Bias Assessment Tool.</p><p><strong>Results: </strong>Twenty-seven studies were included in this review. The overall meta-analysis of models for TACE treatment response included 11 studies. The pooled area under the receiver operating characteristic curve was 0.89 (95% confidence interval: 0.81-0.93) for internal validation and 0.81 (95% confidence interval: 0.80-0.92) for external validation, with no significant differences (P = .66). Moreover, no significant differences were found between DL and HCR models (P = .21) or between models with and without clinical data (P = .78).</p><p><strong>Conclusion: </strong>Artificial intelligence models, including DL and HCR, show potential for predicting treatment outcomes in patients with HCC who are undergoing TACE. However, the observed heterogeneity stresses the need for further research.</p>","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144981421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Keeping Up With Communication: A Call to Involve Trainees.","authors":"Alankrit Shatadal, Allison Grayev","doi":"10.1016/j.jacr.2025.08.025","DOIUrl":"10.1016/j.jacr.2025.08.025","url":null,"abstract":"","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144981407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Repeat Imaging Rates for Office-Based Imaging Studies Interpreted by Nonphysician Practitioners Compared With Radiologists.","authors":"Eric W Christensen, Alexandra R Drake, Stella K Kang, Elizabeth Y Rula, Andrew B Rosenkrantz","doi":"10.1016/j.jacr.2025.07.030","DOIUrl":"https://doi.org/10.1016/j.jacr.2025.07.030","url":null,"abstract":"<p><strong>Purpose: </strong>As differences in imaging patterns may indicate unnecessary care, this study examined differences in repeat imaging rates between imaging studies interpreted by a nonphysician practitioner (NPP) versus a radiologist.</p><p><strong>Methods: </strong>This multiyear (2013-2022) retrospective study evaluated imaging performed on Medicare fee-for-service beneficiaries using a CMS Research Identifiable File. Imaging studies, grouped by anatomic region and modality (eg, shoulder radiography [XR]) with ≥30 repeat studies within 90 days for both NPP-interpreted and radiologist-interpreted index studies, were included. Logistic regression was used to assess the likelihood of repeat imaging within 90 days for NPP-interpreted versus radiologist-interpreted index studies, adjusted for patient gender, age, race or ethnicity, comorbidities, urbanicity, and community income.</p><p><strong>Results: </strong>There were 1,397,002 imaging studies that met the selection criteria. Of these, repeat imaging occurred for 12.5%. Unadjusted repeat imaging rates were higher for NPP-interpreted versus radiologist-interpreted imaging for XR (20.4% versus 14.6%), ultrasound (11.6% versus 4.5%), and MR (8.8% versus 3.8%). Adjusted for covariates, the odds ratio (OR) for repeat imaging was higher for NPP-interpreted versus radiologist-interpreted imaging: 1.35 (95% confidence interval [CI]: 1.33-1.37) for XR, 2.41 (95% CI: 2.21-2.63) for ultrasound, and 2.56 (95% CI: 1.81-3.64) for MR. By anatomic region-modality, these ORs ranged from 1.39 (95% CI: 1.34-1.44) for shoulder XR to 3.40 (95% CI: 2.80-4.14) for abdominal ultrasound, but was not significantly different for knee XR (OR: 1.01, 95% CI: 0.99-1.04).</p><p><strong>Conclusion: </strong>Among Medicare beneficiaries, imaging studies are more likely to be repeated when interpreted by a NPP than when interpreted by a radiologist. Potential excess reimaging has implications for unnecessary care.</p>","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145076732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Can Artificial Intelligence Cure Baumol's Cost Disease?","authors":"Amy L Kotsenas, Ajay Malhotra, David Seidenwurm","doi":"10.1016/j.jacr.2025.08.023","DOIUrl":"10.1016/j.jacr.2025.08.023","url":null,"abstract":"","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144981386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","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®: Chronic Knee Pain.","authors":"Sania Choudhary, Shari T Jawetz","doi":"10.1016/j.jacr.2025.08.022","DOIUrl":"10.1016/j.jacr.2025.08.022","url":null,"abstract":"","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144981392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sarcopenia Still Underdiagnosed: Leveraging Abdominal CT and Artificial Intelligence for Early Detection.","authors":"Taimour Mushtaq, Mubariz Ali, Brijesh Sathian, Javed Iqbal, Ayesha Parvaiz Malik","doi":"10.1016/j.jacr.2025.08.013","DOIUrl":"10.1016/j.jacr.2025.08.013","url":null,"abstract":"","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144981435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Defining the Pediatric Radiology Workforce in the United States Using Insurance Claims Data.","authors":"Tatiana Morales-Tisnés, Casey E Pelzl, Eric W Christensen, Elizabeth Y Rula, Hansel J Otero","doi":"10.1016/j.jacr.2025.07.029","DOIUrl":"https://doi.org/10.1016/j.jacr.2025.07.029","url":null,"abstract":"<p><strong>Objective: </strong>To determine the number of pediatric radiologists in the United States using professional imaging claims of children between 2016 and 2023 in a private payor claims database.</p><p><strong>Methods: </strong>From 2016 to 2023, using private payer claims data (Inovalon Insights, LLC), all claim lines for radiology professional services billed by radiologists were identified. Each claim was assigned a work relative value unit (wRVU) in accordance with the CMS valuation for the claim year. Each radiologist's pediatric and adult wRVUs were calculated annually (radiologist-year) and overall. Pediatric radiologists were defined by ≥50% pediatric wRVUs, with ≥25% and ≥75% thresholds tested as lower and upper limits.</p><p><strong>Results: </strong>Among 109,077 unique radiologists (mean 46,315 unique practicing radiologists per year), 9,198 (8.4%) unique providers met the ≥50% pediatric wRVU threshold over the entire study period. Annual unique pediatric radiologist count decreased from 2,190 (2016) to 2,032 (2023), as did their proportion as a percentage of all radiologists, declining from 6.4% to 4.6%. Using the ≥25% and ≥75% thresholds, the number of unique pediatric radiologists identified for the entire study period was 13,532 (12.4%) and 7,456 (6.8%), respectively. The median percentage of wRVUs attributed to pediatric radiology among nonpediatric radiologists never rose above 0%, across all years and when using any of the three threshold percentages.</p><p><strong>Conclusions: </strong>Using a large claims database, we identified US radiologists dedicating ≥50% of their professional effort to pediatric patients, revealing the number of pediatric radiologists declined from 2016 to 2023, despite rising demand for imaging services.</p>","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145024855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Can Obsolescence Be Cured?","authors":"Jack Smith, Elliot K Fishman, Steven P Rowe, Linda C Chu, Charles K Crawford","doi":"10.1016/j.jacr.2025.08.012","DOIUrl":"10.1016/j.jacr.2025.08.012","url":null,"abstract":"","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144981366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}