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

{"title":"Partnerships Between Radiology and Industry Are Essential to Address Climate Change.","authors":"Reed A Omary, Jeff DiLullo, Catherine Estrampes, Christopher P Hess, David Pacitti, Thomas M Grist","doi":"10.1016/j.jacr.2025.02.049","DOIUrl":"10.1016/j.jacr.2025.02.049","url":null,"abstract":"","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143627116","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":"Comparing Artificial Intelligence and Traditional Regression Models in Lung Cancer Risk Prediction: A Systematic Review and Meta-Analysis.","authors":"Sierra Leonard, Meet A Patel, Zili Zhou, Ha Le, Prosanta Mondal, Scott J Adams","doi":"10.1016/j.jacr.2025.02.042","DOIUrl":"10.1016/j.jacr.2025.02.042","url":null,"abstract":"<p><strong>Purpose: </strong>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.</p><p><strong>Methods: </strong>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).</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143574974","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":"Visual Emphysema as a Category Modifier in Lung-RADS: Secondary Analysis of National Lung Screening Trial.","authors":"Sowon Jang, Jihang Kim, Seungjae Lee, Yeon Wook Kim, Junghoon Kim, Kyung Won Lee, Choon-Taek Lee","doi":"10.1016/j.jacr.2025.02.041","DOIUrl":"10.1016/j.jacr.2025.02.041","url":null,"abstract":"<p><strong>Objective: </strong>The Lung CT Reporting and Data System (Lung-RADS) does not consider emphysema, a lung cancer risk factor detectable on CT, when assessing nodule risk. This study aimed to evaluate the impact of incorporating emphysema into Lung-RADS on lung cancer diagnosis.</p><p><strong>Methods: </strong>In this secondary analysis of the National Lung Screening Trial data, CT arm participants with noncalcified nodules were assigned to Lung-RADS categories, and their emphysema severity was visually dichotomized. Lung cancer rates within each Lung-RADS category were compared based on emphysema severity. A modified Lung-RADS, reclassifying nodules with significant emphysema into a higher category, was evaluated against standard Lung-RADS.</p><p><strong>Results: </strong>A study of 9,444 participants (782 [8.3%] with lung cancer) revealed difference in lung cancer rates across Lung-RADS categories based on visual emphysema severity: category 2 (2.6% versus 4.9%; P = .007), 3 (4.9% versus 9.0%; P < .001), 4A (9.2% versus 15.5%; P = .01), 4B (16.1% versus 24.1%; P = .12), and 4X (25.3% versus 33.2%; P = .008) without or with significant emphysema. Compared with standard Lung-RADS, modified Lung-RADS demonstrated a comparable area under the curve (0.73 versus 0.74, P = .009), increased sensitivity (61.3% versus 67.6%, P < .001), decreased specificity (77.2% versus 71.4%, P < .001), and improved goodness of fit (P = .008) for predicting lung cancer.</p><p><strong>Discussion: </strong>Lung cancer rates differ by emphysema severity within Lung-RADS categories. Using the visual emphysema severity as a category modifier in Lung-RADS increased sensitivity while achieving comparable area under the curve for lung cancer.</p>","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143574975","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":"Positive Screens Are More Likely in a National Lung Cancer Screening Registry Than the National Lung Screening Trial.","authors":"Tina D Tailor, Roee Gutman, Na An, Richard M Hoffman, Caroline Chiles, Ruth C Carlos, JoRean D Sicks, Ilana F Gareen","doi":"10.1016/j.jacr.2025.02.012","DOIUrl":"10.1016/j.jacr.2025.02.012","url":null,"abstract":"<p><strong>Purpose: </strong>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).</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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); P < .001) and included more female patients (47.9% versus 40.9%; P < .001), and its patients were more likely to be currently smoking (61.5% versus 48.1%; P < .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 > 1 for Lung-RADS scores 2, 3, 4A, 4B, 4X relative to Lung-RADS 1).</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143538120","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":"Spaced Repetition in Radiology: Bridging the Gap Between Knowledge and Retention.","authors":"Valeria Del Castillo, Valeria Noguera, Laura Manuela Olarte Bermúdez, Javier Andres Romero","doi":"10.1016/j.jacr.2025.02.040","DOIUrl":"10.1016/j.jacr.2025.02.040","url":null,"abstract":"","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143538121","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":"Sustainability in Radiology: Position Paper and Call to Action from ACR, AOSR, ASR, CAR, CIR, ESR, ESRNM, ISR, IS3R, RANZCR, and RSNA.","authors":"Andrea G Rockall, Bibb Allen, Maura J Brown, Tarek El-Diasty, Jan Fletcher, Rachel F Gerson, Stacy Goergen, Amanda P Marrero González, Thomas M Grist, Kate Hanneman, Christopher P Hess, Evelyn Lai Ming Ho, Dina H Salama, Julia Schoen, Sarah Sheard","doi":"10.1016/j.jacr.2025.02.009","DOIUrl":"https://doi.org/10.1016/j.jacr.2025.02.009","url":null,"abstract":"<p><p>The urgency for climate action is recognized by international government and health care organizations, including the United Nations and World Health Organization. Climate change, biodiversity loss, and pollution negatively impact all life on earth. All populations are impacted but not equally; the most vulnerable are at highest risk, an inequity further exacerbated by differences in access to health care globally. The delivery of health care exacerbates the planetary health crisis through greenhouse gas emissions, largely due to combustion of fossil fuels for medical equipment production and operation, creation of medical and non-medical waste, and contamination of water supplies. As representatives of radiology societies from across the globe who work closely with industry, and both governmental and non-governmental leaders in multiple capacities, we advocate together for urgent, impactful, and measurable changes to the way we deliver care by further engaging our members, policymakers, industry partners, and our patients. Simultaneous challenges including global health disparities, resource allocation, and access to care must inform these efforts. Climate literacy should be increasingly added to radiology training programs. More research is required to understand and measure the environmental impact of radiological services and inform mitigation, adaptation, and monitoring efforts. Deeper collaboration with industry partners is necessary to support innovations in the supply chain, energy utilization, and circular economy. Many solutions have been proposed and are already available, but we must understand and address barriers to implementation of current and future sustainable innovations. Finally, there is a compelling need to partner with patients, to ensure that trust in the excellence of clinical care is maintained during the transition to sustainable radiology. By fostering a culture of global cooperation and rapid sharing of solutions among the broader imaging community, we can transform radiological practice to mitigate its environmental impact, adapt and develop resilience to current and future climate and environmental threats, and simultaneously improve access to care.</p>","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143525439","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":"Insurance-Based Differences in Treatment Patterns for Uterine Fibroids.","authors":"Pratik A Shukla, Alexandra R Drake, Antony Sare, Elizabeth Y Rula, Eric W Christensen","doi":"10.1016/j.jacr.2025.02.011","DOIUrl":"10.1016/j.jacr.2025.02.011","url":null,"abstract":"<p><strong>Purpose: </strong>The aim of this study was to examine whether Medicaid versus commercial insurance and reimbursement are associated with uterine artery embolization (UAE) utilization rates for uterine fibroid treatment.</p><p><strong>Methods: </strong>This retrospective (October 2015 to September 2023) study of women aged 30 to 59 years who underwent procedures for the treatment of uterine fibroids (hysterectomy, myomectomy, or UAE) was based on the Inovalon Insights dataset for those with Medicaid or commercial insurance. Differences in the receipt of UAE versus hysterectomy or myomectomy by insurance type and relative reimbursement were assessed using logistic regression controlling for patient characteristics and geographic differences in treatment patterns. For women with either hysterectomy or myomectomy, differences in the receipt of these procedures laparoscopically or not were assessed by insurance type and relative reimbursement controlling for patient characteristics and geographic differences in treatment patterns.</p><p><strong>Results: </strong>Medicaid compared with commercial insurance was associated with 38% higher odds of UAE (odds ratio [OR], 1.38; 95% confidence interval [CI], 1.34-1.42). States with higher Medicaid reimbursement for hysterectomy were associated with lower odds for UAE (OR, 0.95; 95% CI, 0.92-0.98). For women with hysterectomy or myomectomy, those with Medicaid versus commercial insurance had 20% lower odds (OR, 0.80; 95% CI, 0.79-0.82) of undergoing the procedure laparoscopically.</p><p><strong>Conclusions: </strong>Women insured by Medicaid versus commercial insurance were more likely to undergo the less invasive UAE procedure. Conversely, Medicaid patients who underwent hysterectomy or myomectomy were less likely to undergo the procedure laparoscopically. Both results are consistent with the notion that insurance status may influence both physician referral patterns and treatment options available to patients.</p>","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143473378","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®: Radiologic Management of Urinary Tract Obstruction.","authors":"Sania Choudhary, Sherry S Wang","doi":"10.1016/j.jacr.2025.02.010","DOIUrl":"10.1016/j.jacr.2025.02.010","url":null,"abstract":"","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143416497","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®: Movement Disorders and Neurodegenerative Diseases.","authors":"Christian P Haskett, Vincent M Timpone","doi":"10.1016/j.jacr.2025.02.003","DOIUrl":"10.1016/j.jacr.2025.02.003","url":null,"abstract":"","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143384338","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":"AI in the Era of GPT: Transforming the Future of Work and Discovery.","authors":"Juan M Lavista Ferres, Elliot K Fishman, Linda C Chu, Felipe Lopez-Ramirez, Charles K Crawford, Steven P Rowe","doi":"10.1016/j.jacr.2025.02.007","DOIUrl":"10.1016/j.jacr.2025.02.007","url":null,"abstract":"","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143384269","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}