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

{"title":"Characterization and Targeting of the Main Drivers of Elective Imaging Utilization: A Cross-Sectional Study Applying Conjoint Analysis.","authors":"Cindy Yuan, Hongxian Huang, Douglas Dirschl, Pradeep Chintagunta, David Paushter","doi":"10.1016/j.jacr.2025.01.009","DOIUrl":"10.1016/j.jacr.2025.01.009","url":null,"abstract":"<p><strong>Objective: </strong>To characterize patient and referring physician preferences when selecting where to have elective imaging performed.</p><p><strong>Methods: </strong>In this institutional research board-approved survey-based study, online surveys were completed by 393 patients who had recently had a noncontrast MRI of the shoulder, lumbar spine, or knee within the past 2 years and 168 physicians who had ordered at least 12 such MRIs in the past year. The survey data included explicit questions about preferences as well as a set of conjoint choice screens. Conjoint analysis used a logistic model estimated using maximum likelihood estimation. Additionally, latent class analysis was performed to evaluate segmentation of the market.</p><p><strong>Results: </strong>Both patients and referring physicians placed most value on copay and cost, with both groups containing a large segment that places almost all value on low costs. Patients also valued the recommendation of their referring physician and convenient appointment time and location. Overall, physicians also valued image quality and a trusted radiologist, with one physician segment placing more value on these traits than cost. As expected, there was some variation between the relative importance of traits depending on whether respondents were ranking, rating, or implicitly valuing characteristics.</p><p><strong>Discussion: </strong>Overall, both patients and referring physicians place greatest importance on copay and cost but other nonmonetary characteristics have significant utility values, which could be leveraged to help radiology practices offer better services for their patients and referring physicians.</p>","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143017749","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®: Supplemental Breast Cancer Screening Based on Breast Density: 2024 Update.","authors":"Avani Shinde, Samantha L Heller","doi":"10.1016/j.jacr.2025.01.005","DOIUrl":"10.1016/j.jacr.2025.01.005","url":null,"abstract":"","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143017750","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":"Artificial Intelligence in Radiology: A Leadership Survey.","authors":"Elizabeth S Burnside, Thomas M Grist, Michael R Lasarev, John W Garrett, Elizabeth A Morris","doi":"10.1016/j.jacr.2025.01.006","DOIUrl":"10.1016/j.jacr.2025.01.006","url":null,"abstract":"<p><strong>Purpose: </strong>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.</p><p><strong>Materials and methods: </strong>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. P values are provided only for descriptive purposes and have not been adjusted for multiple testing in this exploratory research.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>Radiology chairs are optimistic about AI and poised to tackle departmental challenges. Concerns about generative AI and workforce replacement are minimal.</p>","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142973851","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":"Impact of Perceived Discrimination and Pandemic Attitudes on Cancer Screening Behaviors Among Asian American Women: A Sequential Explanatory Mixed-Methods Study.","authors":"Chau D Vo, Bingjing Mao, Alexander W Burns, Jordan Neil, Nathaniel Mercaldo, Qi Yan, Yasmine Nousari, Lauren Ballini, Nikki López-Suárez, Aayan V Khasgiwala, Arlin Arias Castro, Kelly Irwin, Elyse R Park, Ruth C Carlos, Efrén J Flores","doi":"10.1016/j.jacr.2024.11.032","DOIUrl":"https://doi.org/10.1016/j.jacr.2024.11.032","url":null,"abstract":"<p><strong>Purpose: </strong>The aim of this study was to assess how pandemic-related health concerns and discrimination affected cancer screenings among Asian American women (AAW).</p><p><strong>Methods: </strong>A two-phase explanatory mixed-methods study was conducted. In phase 1, a survey was distributed among AAW eligible for lung, breast, or colorectal cancer screening to assess delays during the pandemic, concerns about contracting coronavirus disease 2019 (COVID-19), barriers to care, and experiences of discrimination. In phase 2, four qualitative focus group discussions, stratified by generational status and history of delayed cancer screening, were conducted to explore a priori domains of interest on cancer screening during the pandemic and perceived discrimination. Logistic regression analyses were performed to identify factors associated with delays, followed by deductive thematic qualitative analysis.</p><p><strong>Results: </strong>In phase 1, of 225 participants recruited, 166 met the inclusion criteria (74%). Most participants were first-generation Americans (67%), were employed (65%), and had higher education (67% with a master's degree or higher). Forty percent reported delays in cancer screening. Factors associated with delays included worry about contracting COVID-19 (adjusted odds ratio, 2.57; 95% confidence interval, 1.05-6.28; P = .038) and spreading it to family or friends (adjusted odds ratio, 5.78; 95% confidence interval, 1.50-22.3; P = .011). No association between discrimination and delayed cancer screening was found. In phase 2, focus group discussions (n = 19) revealed that first-generation women who delayed screening faced barriers due to a fear of infection and perceived discrimination. Perceived discrimination affected daily behaviors and sense of safety, though less so in medical settings.</p><p><strong>Conclusions: </strong>Delays in cancer screening among AAW were associated with patients' concerns about contracting and spreading COVID-19. Radiology practices can lead collaborative efforts on cancer screening campaigns that emphasize early detection and promote psychological safety.</p>","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143366958","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":"Has Anyone Seen Our Chief?","authors":"Jennifer Nathan, Frank Lexa, Kenneth Hite, Candice Johnstone, Serena Liebengood","doi":"10.1016/j.jacr.2024.12.013","DOIUrl":"10.1016/j.jacr.2024.12.013","url":null,"abstract":"","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142900987","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":"Bidirectional Encoder Representations From Transformers, Bias, and Babel's Tower in Radiology.","authors":"Teodoro Martín-Noguerol, Pilar López-Úbeda, Antonio Luna","doi":"10.1016/j.jacr.2024.12.012","DOIUrl":"10.1016/j.jacr.2024.12.012","url":null,"abstract":"","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142900985","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":"External Validation of the Neiman Imaging Comorbidity Index in Medicare, Medicaid, and Private Payer Claims Data.","authors":"Casey E Pelzl, Alexandra Drake, Andrew B Rosenkrantz, Elizabeth Y Rula, Eric W Christensen","doi":"10.1016/j.jacr.2024.11.007","DOIUrl":"10.1016/j.jacr.2024.11.007","url":null,"abstract":"<p><strong>Objective: </strong>The Neiman Imaging Comorbidity Index (NICI) was developed and validated in a claims dataset encompassing >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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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; P < .001), Medicaid (AUC: 0.6876, 95% CI: 0.6874-0.6878 versus AUC: 0.6798 95% CI: 0.6796-0.6800]; P < .001), and private insurance data (AUC: 0.6658, 95% CI: 0.6656-0.6660 versus AUC: 0.6479, 95% CI: 0.6477-0.6481; P < .001). NICI outperformed CCI in adjusted models and in nearly all age strata across the three cohorts.</p><p><strong>Discussion: </strong>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.</p>","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142873606","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":"Female Representation in Integrated Interventional Radiology Residency: Trends, Underrepresentation, and Modest Growth Over 5 Years.","authors":"Emily R Hunt, Sophie Vo, Andrea Benson, Sherron Thomas, Harika Dabbara, Rebecca T Le","doi":"10.1016/j.jacr.2024.12.008","DOIUrl":"10.1016/j.jacr.2024.12.008","url":null,"abstract":"<p><strong>Objective: </strong>Women remain a minority of trainees in interventional radiology (IR) since the residency's inception in 2014. Similar phenomena have been observed in other surgical specialties. Our study aims to quantify changes in female trainee representation in integrated IR over a 5-year period from the 2018-2019 to 2022-2023 academic years and to compare with trends in other specialties.</p><p><strong>Methods: </strong>We conducted a retrospective review of survey data collected from the National Graduate Medical Education database. The eight selected specialties, including IR, were chosen due to qualitative similarities with IR training or due to less than 30% female trainee representation in 2018-2019. In these data, gender was categorized as male or female. Percentage of female trainees in each specialty was collected in the study period. Analysis was conducted using analysis of variance, linear regression, and Tukey honest significant difference (P < .05).</p><p><strong>Results: </strong>In the 5-year study period, female representation in integrated IR residency grew by 16.6% at a mean relative growth rate of 4.0% annually. By 2022-2023, integrated vascular surgery residency had the highest female trainee representation (38.4%), whereas integrated IR had the second lowest (22.4%), ahead of orthopedic surgery (20.4%).</p><p><strong>Discussion: </strong>Despite observed growth, female trainee representation in integrated IR lags behind other procedural and radiology specialties. Our analysis emphasizes the need for continued recruitment of female applicants to bolster gender parity. Supporting known ongoing initiatives like female mentorship programs and developing new strategies to support female interest in IR should be a priority for the field.</p>","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142878838","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":"It's time to retire \"you guys\" from professional conversations.","authors":"Patrik Rogalla","doi":"10.1016/j.jacr.2024.12.007","DOIUrl":"https://doi.org/10.1016/j.jacr.2024.12.007","url":null,"abstract":"","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142878841","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":"Screening for Percutaneous Lung Cryoablation Adverse Event Risk: A Single-Center Comparative Analysis to Surgical Risk Estimates.","authors":"Prisha Patel, Koustav Pal, Hadi Ahmed, Bill Tang, Iwan Paolucci, Mohammad Khavandi, Peiman Habibollahi, Ketan Shah, Steven Y Huang, Bruno C Odisio, Sanjay Gupta, Kamran Ahrar, Steven Yevich, Joshua D Kuban, Alda Tam, Rahul A Sheth","doi":"10.1016/j.jacr.2024.12.006","DOIUrl":"10.1016/j.jacr.2024.12.006","url":null,"abstract":"<p><strong>Objective: </strong>To evaluate the relevance of established surgical risk calculators for predicting complications in patients undergoing percutaneous lung cryoablation (PLC).</p><p><strong>Methods: </strong>The institution's database was queried for PLC procedures from March 2015 to May 2024, excluding those patients with concomitant local therapies or five or more lesions treated in a single setting. Demographics, frailty metrics as defined by the surgical literature, and procedural variables were collected. To evaluate the suitability of surgical risk estimate calculators, the requisite demographic data were input into the American College of Surgery surgical risk calculator; estimates for length of stay (LOS), serious complications, 30-day readmission, and mortality were calculated to determine the comparative risk profile were the patients to have undergone surgical wedge resection instead of PLC. Additionally, to evaluate the suitability of imaging predictors of complications, the volume of emphysematous lung was calculated using a machine learning algorithm and incorporated into a generalized estimating equation logistic regression analysis of other demographic and technical variables.</p><p><strong>Results: </strong>The study included 217 patients who underwent 314 procedures. Chest tubes were placed in 49% of procedures. The median LOS was 1 day (interquartile range: 1-1, range: 0-13). The median percentage of emphysema within the lungs was 5.9% (interquartile range: 2.4%-12.1%, range: 0.01%-50.3%). The median predicted surgical rates for serious complications (13.5%), 30-day readmission (12%), and 30-day mortality (5.9%) were all greater than actual rates after PLC (1.6%, 4.8%, and 0.3%, respectively). The estimated surgical LOS differed significantly from the actual PLC LOS (5 days versus 1 day, P < .001). In univariable analysis, the number of probes the number of tumors ablated (odds ratio 1.90, 95% confidence interval 1.18-3.05, P = .008) and the number of probes used (odds ratio 1.44, 95% confidence interval 1.06-1.96, P = .021) were significantly associated with increased LOS, but demographic and emphysema details were not.</p><p><strong>Conclusion: </strong>Complications after PLC are significantly less frequent than the estimated complication risks for wedge resection in the same patient population. Risk estimate calculators tailored to PLC would help screen for high complication risks related to this procedure.</p>","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142873608","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}