Radiology最新文献

{"title":"Malpractice and Teleradiology: Let's See the Bottle as Half Full Rather than Half Empty….","authors":"Guillaume Gorincour, Mylène Seux, Patrick Malléa, Vivien Thomson, Amandine Crombé","doi":"10.1148/radiol.241280","DOIUrl":"https://doi.org/10.1148/radiol.241280","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"313 2","pages":"e241280"},"PeriodicalIF":12.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142584111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PiSlope: A New Tool to Evaluate Airway Remodeling.","authors":"David A Lynch","doi":"10.1148/radiol.243135","DOIUrl":"10.1148/radiol.243135","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"313 2","pages":"e243135"},"PeriodicalIF":12.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11605100/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142716995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Treasure Trove Hidden in Plain Sight: The Utility of GPT-4 in Chest Radiograph Evaluation.","authors":"Soroosh Tayebi Arasteh, Robert Siepmann, Marc Huppertz, Mahshad Lotfinia, Behrus Puladi, Christiane Kuhl, Daniel Truhn, Sven Nebelung","doi":"10.1148/radiol.233441","DOIUrl":"https://doi.org/10.1148/radiol.233441","url":null,"abstract":"<p><p>Background Limited statistical knowledge can slow critical engagement with and adoption of artificial intelligence (AI) tools for radiologists. Large language models (LLMs) such as OpenAI's GPT-4, and notably its Advanced Data Analysis (ADA) extension, may improve the adoption of AI in radiology. Purpose To validate GPT-4 ADA outputs when autonomously conducting analyses of varying complexity on a multisource clinical dataset. Materials and Methods In this retrospective study, unique itemized radiologic reports of bedside chest radiographs, associated demographic data, and laboratory markers of inflammation from patients in intensive care from January 2009 to December 2019 were evaluated. GPT-4 ADA, accessed between December 2023 and January 2024, was tasked with autonomously analyzing this dataset by plotting radiography usage rates, providing descriptive statistics measures, quantifying factors of pulmonary opacities, and setting up machine learning (ML) models to predict their presence. Three scientists with 6-10 years of ML experience validated the outputs by verifying the methodology, assessing coding quality, re-executing the provided code, and comparing ML models head-to-head with their human-developed counterparts (based on the area under the receiver operating characteristic curve [AUC], accuracy, sensitivity, and specificity). Statistical significance was evaluated using bootstrapping. Results A total of 43 788 radiograph reports, with their laboratory values, from University Hospital RWTH Aachen were evaluated from 43 788 patients (mean age, 66 years ± 15 [SD]; 26 804 male). While GPT-4 ADA provided largely appropriate visualizations, descriptive statistical measures, quantitative statistical associations based on logistic regression, and gradient boosting machines for the predictive task (AUC, 0.75), some statistical errors and inaccuracies were encountered. ML strategies were valid and based on consistent coding routines, resulting in valid outputs on par with human specialist-developed reference models (AUC, 0.80 [95% CI: 0.80, 0.81] vs 0.80 [95% CI: 0.80, 0.81]; <i>P</i> = .51) (accuracy, 79% [6910 of 8758 patients] vs 78% [6875 of 8758 patients], respectively; <i>P</i> = .27). Conclusion LLMs may facilitate data analysis in radiology, from basic statistics to advanced ML-based predictive modeling. © RSNA, 2024 <i>Supplemental material is available for this article.</i></p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"313 2","pages":"e233441"},"PeriodicalIF":12.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142626498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"US Volume Flow Assessment to Optimize Angioplasty of Dysfunctional Dialysis Access: The VOLA-II Multicenter Study.","authors":"Stavros Spiliopoulos, Panagiotis Filippou, Ioannis E Giannikouris, Konstantinos Katsanos, Konstantinos Palialexis, Stavros Grigoriadis, Panagiotis Kitrou, Elias Brountzos, Dimitrios Karnabatidis","doi":"10.1148/radiol.233076","DOIUrl":"10.1148/radiol.233076","url":null,"abstract":"<p><p>Background Digital subtraction angiography and thrill palpation demonstrate limitations when used to assess the outcomes of angioplasty in autologous arteriovenous fistulas (AVFs). Purpose To investigate a new functional quantifiable index of successful angioplasty for failing AVFs using intraprocedural percutaneous US volume flow (VF) measurements. Materials and Methods This prospective, multicenter, single-arm, cohort clinical trial included consecutive patients with indications for fluoroscopically guided balloon angioplasty due to AVF dysfunction between June 2020 and May 2022. Intraprocedural VF measurements were obtained before the procedure and after balloon dilation using transcutaneous duplex US. The primary end points were the quantification of VF-guided angioplasty outcomes and assessment of the association between postprocedural VF and freedom from target lesion reintervention (TLR) due to recurrence of access failure. Cox multivariate regression analysis was performed to identify predictors of TLR-free survival, which was estimated using Kaplan-Meier methods. Receiver operating characteristic curve analysis was used to determine the optimal cutoff VF value. Results In total, 100 participants (mean age, 67 years ± 12 [SD]; 84 male) were evaluated. The rate of freedom from TLR was 73% at 6 months. The mean pre- and postintervention VF values were 353 mL/min ± 199 and 1045 mL/min ± 413, respectively. Higher VF at completion of angioplasty (hazard ratio, 0.89 per 100 mL/min [95% CI: 0.82, 0.98]; <i>P</i> = .01) and forearm versus upper arm AVF (hazard ratio, 0.51 [95% CI: 0.27, 0.95]; <i>P</i> = .03) were independent predictors of increased freedom from TLR. A postprocedural VF of 720 mL/min (<i>P</i> < .001) was identified as the optimal cutoff point for predicting increased fistula freedom from reintervention and freedom of restenosis for radiocephalic AVFs, compared with an optimal cutoff point of 1120 mL/min (<i>P</i> = .03) for upper arm AVFs. Conclusion Intraprocedural VF measurement is a quantifiable functional index and postprocedural predictor of outcomes following angioplasty for failing dialysis AVFs. ClinicalTrials.gov identifier: NCT04694287 © RSNA, 2024 <i>Supplemental material is available for this article.</i> See also the editorial by Prince et al this issue.</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"313 2","pages":"e233076"},"PeriodicalIF":12.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142716998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interstitial Lung Abnormalities at Clinical CT: Insights and Implications from a Large-Scale Study.","authors":"Akinori Hata","doi":"10.1148/radiol.243020","DOIUrl":"10.1148/radiol.243020","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"313 2","pages":"e243020"},"PeriodicalIF":12.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142669068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prognostic AI Model for Precise Surgical Decision-making in Non-Small Cell Lung Cancer.","authors":"Haoji Yan, Kenji Suzuki","doi":"10.1148/radiol.241370","DOIUrl":"https://doi.org/10.1148/radiol.241370","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"313 2","pages":"e241370"},"PeriodicalIF":12.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142716996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nonmass Lesions at US: Almost Ready for Prime Time.","authors":"Lars J Grimm","doi":"10.1148/radiol.242490","DOIUrl":"10.1148/radiol.242490","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"313 2","pages":"e242490"},"PeriodicalIF":12.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11605101/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142584113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CT/MRI LI-RADS 2024 Update: Treatment Response Assessment.","authors":"Anum Aslam, Victoria Chernyak, Frank H Miller, Mustafa Bashir, Richard Do, Claude Sirlin, Robert J Lewandowski, Charles Y Kim, Ania Zofia Kielar, Avinash R Kambadakone, Hooman Yarmohammadi, Edward Kim, Dawn Owen, Resmi A Charalel, Anuradha Shenoy-Bhangle, Lauren M Burke, Mishal Mendiratta-Lala","doi":"10.1148/radiol.232408","DOIUrl":"10.1148/radiol.232408","url":null,"abstract":"<p><p>With the rising incidence of hepatocellular carcinoma, there has been increasing use of local-regional therapy (LRT) to downstage or bridge to transplant, for definitive treatment, and for palliation. The CT/MRI Liver Imaging Reporting and Data System (LI-RADS) Treatment Response Assessment (TRA) algorithm provides guidance for step-by-step tumor assessment after LRT and standardized reporting. Current evidence suggests that the algorithm performs well in the assessment of tumor response to arterial embolic and loco-ablative therapies and fair when assessing response to radiation-based therapies, with limited data to validate the latter. Both evidence-based and expert-based refinements of the algorithm are needed to improve its diagnostic accuracy after varying types of LRT. This review provides an overview of the challenges and limitations of the LI-RADS TRA algorithm version 2017 and discusses the refinements introduced in the updated 2024 LI-RADS algorithm for CT/MRI.</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"313 2","pages":"e232408"},"PeriodicalIF":12.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11605109/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142626431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Myocardial Fibrosis Assessment at 3-T versus 5-T Myocardial Late Gadolinium Enhancement MRI: Early Results.","authors":"Yubo Guo, Lu Lin, Shihai Zhao, Gan Sun, Yuyan Chen, Ke Xue, Yuxin Yang, Shuo Chen, Yan Zhang, Guobin Li, Yanjie Zhu, Rozemarijn Vliegenthart, Yining Wang","doi":"10.1148/radiol.233424","DOIUrl":"https://doi.org/10.1148/radiol.233424","url":null,"abstract":"<p><p>Background Cardiac MRI at 5 T has recently become available and potentially improves tissue contrast enhancement at gadolinium chelate-enhanced T1-weighted imaging. Purpose To evaluate the feasibility of 5-T myocardial late gadolinium enhancement (LGE) MRI in assessing myocardial fibrosis by comparing image quality and LGE quantification with reference-standard 3-T myocardial LGE MRI. Materials and Methods Consecutive patients with confirmed myocardial fibrosis on previous 3-T MRI scans between January 2023 and July 2023 prospectively underwent follow-up imaging from August 2023 to November 2023. Each participant underwent follow-up 5-T imaging using an identical dose of contrast agent. Radiologist scoring of image quality using a Likert scale (range, 1-5), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), contrast ratio, and semiautomatic quantitative LGE assessment were obtained and reported as medians and IQRs. Paired Wilcoxon signed rank tests were used to compare characteristics derived at 3-T and 5-T imaging. Results A total of 18 participants (mean age, 49 years ± 17 [SD]; nine male participants) were included, with a mean interval of 6.2 months ± 2.3 between undergoing 3-T and 5-T MRI. Median image quality scores were 4.0 (IQR, 3.0-4.2) at 3 T and 4.0 (IQR, 3.0-4.4) at 5 T (<i>P</i> = .45). SNR at 5 T was higher than at 3 T (183.7 [IQR, 147.2-255.9] vs 125.8 [IQR, 108.2-171.6], respectively; <i>P</i> = .002). Median CNR at 5 T was higher than at 3 T in normal myocardium (50.8 [IQR, 35.4-67.9] vs 16.5 [IQR, 11.3-24.6], respectively) and pericardial fat (21.4 [IQR, 7.1-29.3] vs -5.0 [IQR, -16.4 to -2.3], respectively) (both <i>P</i> < .001). There was no evidence of a difference in the percentage of LGE quantified between 5 T and 3 T (median, 11.8% [IQR, 7.7%-20.5%] vs 12.6% [IQR, 6.6%-20.4%], respectively; <i>P</i> = .81). Conclusion Myocardial LGE MRI at 5 T was found to be feasible, with no evidence of differences in subjective image quality and myocardial fibrosis quantification compared with 3-T myocardial LGE MRI. Furthermore, with use of identical contrast agent doses, SNRs and CNRs were improved at 5 T. Published under a CC BY 4.0 license. <i>Supplemental material is available for this article.</i> See also the editorial by Czum in this issue.</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"313 2","pages":"e233424"},"PeriodicalIF":12.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142626369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Risk Factors for Pneumothorax Following Lung Biopsy: Another Peek at Air Leak.","authors":"Darryl A Zuckerman","doi":"10.1148/radiol.242504","DOIUrl":"https://doi.org/10.1148/radiol.242504","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"313 2","pages":"e242504"},"PeriodicalIF":12.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142584091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}