American Journal of Roentgenology最新文献

{"title":"Editor's Notebook: July 2025.","authors":"Andrew B Rosenkrantz","doi":"10.2214/AJR.25.33312","DOIUrl":"https://doi.org/10.2214/AJR.25.33312","url":null,"abstract":"","PeriodicalId":55529,"journal":{"name":"American Journal of Roentgenology","volume":" ","pages":"1-2"},"PeriodicalIF":6.1,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144755195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding the Dramatic Shift in Reported Lung Cancer Screening Rates: Methodologic Changes in the American Lung Association's State of Lung Cancer Report.","authors":"Peter R Gunderman, Zach Jump, Nasser H Hanna","doi":"10.2214/AJR.25.32931","DOIUrl":"10.2214/AJR.25.32931","url":null,"abstract":"<p><p>The American Lung Association's state of lung cancer report showed a dramatic increase in national lung cancer screening rates from 4.5% in 2023 to 16.0% in 2024. This apparent improvement stems from a methodologic change-switching from the American College of Radiology Lung Cancer Screening Registry to the Behavioral Risk Factor Surveillance System. This Viewpoint examines this transition, discusses research and policy implications, and highlights the importance of understanding methodologic changes when interpreting screening statistics.</p>","PeriodicalId":55529,"journal":{"name":"American Journal of Roentgenology","volume":" ","pages":"1-2"},"PeriodicalIF":6.1,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143765997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reply to \"Have Your Cake and Eat It Too: Artificial Intelligence and Tailored Cardiac MRI Protocols\".","authors":"Bradley D Allen, Csilla Celeng, Brian B Ghoshhajra","doi":"10.2214/AJR.25.33561","DOIUrl":"https://doi.org/10.2214/AJR.25.33561","url":null,"abstract":"","PeriodicalId":55529,"journal":{"name":"American Journal of Roentgenology","volume":" ","pages":""},"PeriodicalIF":6.1,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144746116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LI-RADS CT/MRI Radiation Treatment Response Assessment Version 2024: Category Redistribution and Short-Term Outcomes in Patients Undergoing ⁹⁰Y Radioembolization for HCC.","authors":"Sung-Hua Chiu, Andrew Kesselman, Luke Yoon, Aya Kamaya, Justin R Tse","doi":"10.2214/AJR.25.32745","DOIUrl":"10.2214/AJR.25.32745","url":null,"abstract":"<p><p><b>BACKGROUND</b>. LI-RADS CT/MRI Radiation Treatment Response Assessment (TRA) algorithm version 2024 (v2024) addresses a pitfall of the earlier algorithm that relates to the expected persistent enhancement of hepatocellular carcinoma (HCC) responding to radiation. v2024 removes LR-TR Equivocal, introduces LR-TR Nonprogressing (stable or decreasing masslike enhancement), and more narrowly defines LR-TR Viable (new or increasing masslike enhancement). <b>OBJECTIVE</b>. The purpose of this study was to evaluate in patients with HCC treated by ⁹⁰Y radioembolization the redistribution of categories in LI-RADS CT/MRI Radiation TRA v2024 compared with LI-RADS CT/MRI TRA version 2018 (v2018) and to assess the short-term outcomes for patients with HCC assessed using v2024 categories. <b>METHODS</b>. This retrospective study included 242 patients (57 women and 185 men; median age, 65 years) with 319 HCCs treated by ⁹⁰Y radioembolization from February 2011 to March 2022 and evaluated by initial 3-month posttreatment CT or MRI examinations. Two radiologists assigned v2018 and v2024 categories; a third radiologist resolved discrepancies. The radiologists also assessed available second posttreatment CT or MRI examinations by use of v2024. Overall survival (OS) was determined. <b>RESULTS</b>. On initial follow-up, by use of v2018, 18 lesions (5.6%) were LR-TR Nonviable, 21 (6.6%) were LR-TR Equivocal, and 280 (87.8%) were LR-TR Viable; by use of v2024, 18 (5.6%) were LR-TR Nonviable, 182 (57.1%) were LR-TR Nonprogressing, and 119 (37.3%) were LR-TR Viable. All LR-TR Equivocal and 161 LR-TR Viable lesions (57.5%) categorized by v2018 were recategorized as LR-TR Nonprogressing by v2024. Of 96 LR-TR Nonprogressing lesions with second follow-up, 63 (65.6%) remained LR-TR Nonprogressing, 19 (19.8%) transitioned to LR-TR Nonviable, and 14 (14.6%) transitioned to LR-TR Viable. Of 29 LR-TR Viable lesions categorized by use of v2024 that had second follow-up, 23 (79.3%) remained LR-TR Viable, and six (20.7%) transitioned to LR-TR Nonprogressing. By Kaplan-Meier analysis using initial categories, OS showed no significant difference between LR-TR Equivocal and LR-TR Viable for v2018 (<i>p</i> = .05) but was significantly worse for LR-TR Viable than LR-TR Nonprogressing for v2024 (<i>p</i> < .001). <b>CONCLUSION</b>. LR-TR Viable was substantially less frequent for v2024 than for v2018, and the majority of lesions were assigned to the LR-TR Nonprogressing category. Using v2024, most LR-TR Viable lesions and the majority of LR-TR Nonprogressing lesions on initial follow-up remained as such on later imaging. Initial v2024 categories were associated with OS. <b>CLINICAL IMPACT</b>. The findings support the revisions in v2024.</p>","PeriodicalId":55529,"journal":{"name":"American Journal of Roentgenology","volume":" ","pages":"1-12"},"PeriodicalIF":6.1,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144046095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Have Your Cake and Eat It Too: Artificial Intelligence and Tailored Cardiac MRI Protocols.","authors":"Hein P Stallmann, Alie Vegter","doi":"10.2214/AJR.25.33306","DOIUrl":"https://doi.org/10.2214/AJR.25.33306","url":null,"abstract":"","PeriodicalId":55529,"journal":{"name":"American Journal of Roentgenology","volume":" ","pages":""},"PeriodicalIF":6.1,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144746114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How to Prepare for, Survive, and Recover From a Cybersecurity Attack: A Guide for Radiology Practices-<i>AJR</i> Expert Panel Narrative Review.","authors":"Benoit Desjardins, Marla B K Sammer, Alexander J Towbin, Patricia Balthazar, Richard Staynings, Po-Hao Chen","doi":"10.2214/AJR.25.33354","DOIUrl":"https://doi.org/10.2214/AJR.25.33354","url":null,"abstract":"<p><p>In an era of persistent and evolving cyberthreats that pose serious risks to patient safety, institutional integrity, and regulatory compliance, healthcare organizations, particularly radiology departments, must adopt a proactive stance toward cybersecurity. Radiology departments are particularly vulnerable to cyberattacks due to their dependence on often legacy and insecure digital imaging systems, as well as a reliance on network connectivity and specialized software. This <i>AJR</i> Expert Panel Narrative Review offers a strategic roadmap for healthcare institutions to prepare for and survive cybersecurity attacks, with a focus on the unique vulnerabilities within medical imaging systems that radiology departments must address. Real-world threats, ranging from PACS network exploitation to DICOM data manipulation, ransomware disruptions, and the consequences of inaction are examined. Emphasis is placed on practical defense mechanisms including layered security architecture, regular vulnerability assessments, employee training, and incident response simulations. The insights are intended to inform a defense-indepth strategy incorporating physical, technical, and administrative safeguards aligned with HIPAA and other regulatory standards. Overall, this guide for radiology practices seeks to align technical controls with operational resilience, to aid practices in detecting, containing, and recovering from cyber-incidents with minimal disruption to patient care.</p>","PeriodicalId":55529,"journal":{"name":"American Journal of Roentgenology","volume":" ","pages":""},"PeriodicalIF":6.1,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144746115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Baseline and Early Treatment MRI Model for Predicting Complete Pathologic Response to Neoadjuvant Chemoimmunotherapy in Patients With Triple-Negative Breast Cancer.","authors":"Bin Liu, Lei Wu, Chunling Liu, Xiaoyu Long, Shan Hu, Linyu Zhang, Zaiyi Liu, Changhong Liang","doi":"10.2214/AJR.25.33178","DOIUrl":"https://doi.org/10.2214/AJR.25.33178","url":null,"abstract":"<p><p><b>Background:</b> Triple-negative breast cancer (TNBC) is a highly aggressive breast cancer subtype lacking targeted therapies. Neoadjuvant chemoimmunotherapy (NACI) improves pathologic complete response (pCR) rates, although patient selection is challenging. <b>Objective:</b> To develop and test a model incorporating baseline and early-treatment MRI features, including dynamic contrast enhancement (DCE) features, for predicting pCR in patients with TNBC undergoing NACI. <b>Methods:</b> This retrospective study included patients with TNBC undergoing NACI and who underwent breast MRI including DCE before treatment and after the first treatment cycle (i.e., early NACI), including a single-center training set of 90 women (mean age, 49 years; January 2018 to September 2024) and an external test set of 29 women (mean age, 46 years; date range unavailable) from publicly available trial data. Two radiologists evaluated MRI features including percentage enhancement (PE) reduction, representing semiquantitative assessment of relative expansion of intralesional nonenhancing components after early NACI. A model for predicting pCR on definitive surgery after NACI completion was constructed in the training set using independent predictors from multivariable logistic regression analysis and was evaluated in the external test set. Shapley additive explanations (SHAP) analysis was used to identify features' contributions to model predictions in the training set <b>Results:</b> Independent predictors of pCR in the training set were tumor unifocality (OR=7.2, p=.001) on pretreatment MRI and early tumor shrinkage (ETS) ≥37% (OR=9.7, p<.001) and PE reduction (OR=9.7, p<.001) on early-NACI MRI. A model incorporating these parameters achieved in the external test set AUC of 0.88, sensitivity of 74%, and specificity of 90% for predicting pCR. In the external test set, calibration curves showed strong concordance between model-predicted and observed pCR outcomes, and the Hosmer-Lemeshow test showed satisfactory model fit (p=.67). In SHAP analysis, global importance for model predictions was highest for PE reduction (mean absolute SHAP value, 0.42), followed by ETS (0.32) and unifocality (0.21). <b>Conclusion:</b> A clinically practical model was created for early pCR prediction in patients undergoing NACI for TNBC. <b>Clinical Impact:</b> This MRI-based predictive model could facilitate timely tailoring of clinical regimens after immunotherapy initiation by informing optimal de-escalation strategies for responders while prompting therapeutic adaptations for nonresponders.</p>","PeriodicalId":55529,"journal":{"name":"American Journal of Roentgenology","volume":" ","pages":""},"PeriodicalIF":6.1,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144746113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Navigating the Noise: Thoughtful Artificial Intelligence Engagement in Medicine and Radiology.","authors":"Nicholas Dietrich","doi":"10.2214/AJR.25.32918","DOIUrl":"10.2214/AJR.25.32918","url":null,"abstract":"","PeriodicalId":55529,"journal":{"name":"American Journal of Roentgenology","volume":" ","pages":"1"},"PeriodicalIF":6.1,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143659961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Noncontrast MRA for Quantitative Flow Evaluation.","authors":"Zonghao Dai, Yan Xi","doi":"10.2214/AJR.25.32771","DOIUrl":"10.2214/AJR.25.32771","url":null,"abstract":"","PeriodicalId":55529,"journal":{"name":"American Journal of Roentgenology","volume":" ","pages":"1"},"PeriodicalIF":6.1,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143765990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reply to \"The Use of Ultrasound Attenuation Measurements to Evaluate Hepatic Steatosis\".","authors":"Giovanna Ferraioli, Davide Roccarina, Richard G Barr","doi":"10.2214/AJR.25.33189","DOIUrl":"10.2214/AJR.25.33189","url":null,"abstract":"","PeriodicalId":55529,"journal":{"name":"American Journal of Roentgenology","volume":" ","pages":"1-2"},"PeriodicalIF":6.1,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143998184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}