Radiology最新文献

{"title":"The Updated ACR Manual on MR Safety and How It Will Affect Your Practice.","authors":"Emanuel Kanal","doi":"10.1148/radiol.242954","DOIUrl":"https://doi.org/10.1148/radiol.242954","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"315 1","pages":"e242954"},"PeriodicalIF":12.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143754337","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":"American College of Radiology Manual on MR Safety: 2024 Update and Revisions.","authors":"Ivan Pedrosa, David A Altman, Jonathan R Dillman, Michael N Hoff, Alexander M McKinney, Scott B Reeder, Jeffrey M Rogg, R Jason Stafford, James A Webb, Dina L Hernandez, Robert E Watson","doi":"10.1148/radiol.241405","DOIUrl":"https://doi.org/10.1148/radiol.241405","url":null,"abstract":"<p><p>Since the introduction of the American College of Radiology (ACR) MRI safety guidelines in 2002, the indications for use of MRI in clinical care and research have continued to expand. Similarly, MRI technologies have evolved, with multiple field strengths now available for human imaging. While several publications have updated the ACR recommendations since the first guidelines, a single source in a structured format was lacking. Accordingly, the ACR Committee on MR Safety recently updated the online ACR Manual on MR Safety that compiles ACR recommendations for safe use of MRI equipment in humans into a single document. This review describes the new structure of the ACR Manual on MR Safety, discusses new content, indicates gaps in knowledge that require further research, and explains the rationale for the Committee on MR Safety recommendations on certain topics, such as remote operation of MRI systems.</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"315 1","pages":"e241405"},"PeriodicalIF":12.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143754252","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 Markers for Differentiating Hepatic Inflammation in Chronic Liver Disease: Still a Long Way to Go.","authors":"Caixin Qiu","doi":"10.1148/radiol.242559","DOIUrl":"https://doi.org/10.1148/radiol.242559","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"315 1","pages":"e242559"},"PeriodicalIF":12.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143754345","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":"Spatial Viscoelastic Information in Metabolic Dysfunction-associated Steatotic Liver Disease.","authors":"Guilherme Moura Cunha","doi":"10.1148/radiol.250559","DOIUrl":"https://doi.org/10.1148/radiol.250559","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"315 1","pages":"e250559"},"PeriodicalIF":12.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143754336","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":"Distinct Functional MRI Connectivity Patterns and Cortical Volume Variations Associated with Repetitive Blast Exposure in Special Operations Forces Members.","authors":"Andrea Diociasi, Mary A Iaccarino, Scott Sorg, Emily J Lubin, Caroline Wisialowski, Amol Dua, Can Ozan Tan, Rajiv Gupta","doi":"10.1148/radiol.233264","DOIUrl":"https://doi.org/10.1148/radiol.233264","url":null,"abstract":"&lt;p&gt;&lt;p&gt;Background Special operations forces members often face multiple blast injuries and have a higher risk of traumatic brain injury. However, the relationship between neuroimaging markers, the cumulative severity of injury, and long-term symptoms has not previously been well-established in the literature. Purpose To determine the relationship between the frequency of blast injuries, persistent clinical symptoms, and related cortical volumetric and functional connectivity (FC) changes observed at brain MRI in special operations forces members. Materials and Methods A cohort of 220 service members from a prospective study between January 2021 and May 2023 with a history of repetitive blast exposure underwent psychodiagnostics and a comprehensive neuroimaging evaluation, including structural and resting-state functional MRI (fMRI). Of these, 212 met the inclusion criteria. Participants were split into two datasets for model development and validation, and each dataset was divided into high- and low-exposure groups based on participants' exposure to various explosives. Differences in FC were analyzed using a general linear model, and cortical gray matter volumes were compared using the Mann-Whitney &lt;i&gt;U&lt;/i&gt; test. An external age- and sex-matched healthy control group of 212 participants was extracted from the SRPBS Multidisorder MRI Dataset for volumetric analyses. A multiple linear regression model was used to assess correlations between clinical scores and FC, while a logistic regression model was used to predict exposure group from fMRI scans. Results In the 212 participants (mean age, 43.0 years ± 8.6 [SD]; 160 male [99.5%]) divided into groups with low or high blast exposure, the high-exposure group had higher scores for the Neurobehavioral Symptom Inventory (NSI) (&lt;i&gt;t&lt;/i&gt; = 3.16, &lt;i&gt;P&lt;/i&gt; &lt; .001) and Posttraumatic Stress Disorder Checklist for &lt;i&gt;Diagnostic and Statistical Manual of Mental Disorders&lt;/i&gt; (Fifth Edition) (PCL-5) (&lt;i&gt;t&lt;/i&gt; = 2.72, &lt;i&gt;P&lt;/i&gt; = .01). FC differences were identified in the bilateral superior and inferior lateral occipital cortex (LOC) (&lt;i&gt;P&lt;/i&gt; value range, .001-.04), frontal medial cortex (&lt;i&gt;P&lt;/i&gt; &lt; .001), left superior frontal gyrus (&lt;i&gt;P&lt;/i&gt; &lt; .001), and precuneus (&lt;i&gt;P&lt;/i&gt; value range, .02-.03). Clinical scores from NSI and PCL-5 were inversely correlated with FC in the LOC, superior parietal lobule, precuneus, and default mode networks (&lt;i&gt;r&lt;/i&gt; = -0.163 to -0.384; &lt;i&gt;P&lt;/i&gt; value range, &lt;.001 to .04). The high-exposure group showed increased cortical volume in regions of the LOC compared with healthy controls and the low-exposure group (&lt;i&gt;P&lt;/i&gt; value range, .01-.04). The predictive model helped accurately classify participants into high- and low-exposure groups based on fMRI data with 88.00 sensitivity (95% CI: 78.00, 98.00), 67% specificity (95% CI: 53.00, 81.00), and 73% accuracy (95% CI: 60.00, 86.00). Conclusion Repetitive blast exposure leads to distinct alterations in FC and cortical volume, which corr","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"315 1","pages":"e233264"},"PeriodicalIF":12.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143754266","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":"Accuracy of Dual-Energy CT-derived Fat Maps and Bone Marrow Edema Maps in Pedal Osteomyelitis Diagnosis.","authors":"Christoph Stern, Andrea B Rosskopf, Adrian A Marth, Georg C Feuerriegel, Martin C Berli, Benjamin Fritz, Reto Sutter","doi":"10.1148/radiol.232900","DOIUrl":"https://doi.org/10.1148/radiol.232900","url":null,"abstract":"<p><p><i>\"Just Accepted\" papers have undergone full peer review and have been accepted for publication in <i>Radiology</i>. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content.</i> Background In patients who cannot undergo MRI, dual-energy CT (DECT) with bone marrow edema (BME) maps are used as an approach for diagnosing pedal osteomyelitis, but with lower accuracy. Purpose To compare the diagnostic accuracy of additional bone marrow fat maps with that of DECT with BME maps and MRI for pedal osteomyelitis detection. Materials and Methods In this prospective study, thirty-one participants with clinically suspected osteomyelitis of the mid- and forefoot underwent noncontrast DECT (80 kV/140 kV) and MRI between October 2020 and February 2022. With image postprocessing, DECT-derived BME and fat maps were generated. Four independent readers evaluated 3 different image sets for osteomyelitis: DECT and BME maps (set 1); DECT, BME maps and fat maps (set 2); and MRI (set 3). Sensitivity, specificity and accuracy were calculated for each image set, with clinical and microbiological data as the reference standards. In a subanalysis, the DECT BME map, DECT fat map and DECT erosion map were analyzed for their accuracy in predicting bone marrow fat loss at T1-weighted MRI. Results Of the 31 participants included in the study (mean age, 61.7 years ±14.6 [SD]; 21 males) 17 (55%) had osteomyelitis. Sensitivity, specificity and accuracy for detecting osteomyelitis were 47% (8/17), 79% (11/14), and 61% (19/31) (set 1); 77% (13/17), 86% (12/14) and 81% (25/31) (set 2); and 82% (14/17), 93% (13/14) and 87% (27/31) (set 3), respectively. Thirty-one of 661 individual bones (0.5%) showed bone marrow fat loss on T1-weighted MRI; in the subanalysis, DECT fat map specificity was higher than that of the DECT BME map for predicting bone marrow fat loss in individual bones (97% (612/630) vs. 89% (560/630)) (P<.001). Conclusion Pedal osteomyelitis detection with novel DECT-derived fat map imaging in addition to DECT and BME maps was accurate. See also the editorial by Khurana in this issue.</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"315 1","pages":"e232900"},"PeriodicalIF":12.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143754332","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":"MRI-based Radiomic Features for Risk Stratification of Ductal Carcinoma in Situ in a Multicenter Setting (ECOG-ACRIN E4112 Trial).","authors":"Kalina P Slavkova, Ruya Kang, Anum S Kazerouni, Debosmita Biswas, Vivian Belenky, Rhea Chitalia, Hannah Horng, Michael Hirano, Jennifer Xiao, Ralph L Corsetti, Sara H Javid, Derrick W Spell, Antonio C Wolff, Joseph A Sparano, Seema A Khan, Christopher E Comstock, Justin Romanoff, Constantine Gatsonis, Constance D Lehman, Savannah C Partridge, Jon Steingrimsson, Despina Kontos, Habib Rahbar","doi":"10.1148/radiol.241628","DOIUrl":"https://doi.org/10.1148/radiol.241628","url":null,"abstract":"<p><p>Background Ductal carcinoma in situ (DCIS) is a nonlethal, preinvasive breast cancer for which breast MRI is best suited for accurate disease extent characterization. DCIS is often overtreated, necessitating robust models for improved risk stratification. Purpose To develop logistic regression models using clinical and MRI-based radiomic features of DCIS and to evaluate the performance of such models in predicting disease upstaging at surgery and DCIS score. Materials and Methods This study is a secondary analysis of dynamic contrast-enhanced (DCE) MRI data from the Eastern Cooperative Oncology Group-American College of Radiology Imaging Network, or ECOG-ACRIN, E4112 trial. Primary analysis focused on predicting disease upstaging (<i>n</i> = 295), and secondary analysis focused on predicting DCIS score (<i>n</i> = 174). Radiologist-drawn lesion segmentations and publicly available Cancer Phenomics Toolkit, or CaPTk, software was used to compute 65 radiomic features. Participants were clustered into groups based on their radiomic features (ie, radiomic phenotypes), and principal component analysis was used to summarize the feature space. Clinical information and qualitative MRI features were available. Associations were tested using χ² and likelihood ratio tests. Data were split into training and test sets with a 3:2 ratio, and model performance was assessed on the test set using the area under the receiver operating characteristic curve (AUC). Results Data from 297 female participants with median age of 60 years (IQR, 51-67 years) were analyzed. Two radiomic phenotypes were identified that were associated with disease upstaging (<i>P</i> = .007). For predicting disease upstaging, the top three radiomic principal components combined with clinical and qualitative MRI predictors yielded the highest AUC of 0.77 (95% CI: 0.65, 0.88) among all tested models (<i>P</i> = .007), identifying 25% more true-negative (49 of 93 true-negative findings, 53% specificity) findings, compared with using clinical information alone (23 of 93 true-negative findings, 28% specificity). Radiomic models were not predictive of the DCIS score (<i>P</i> > .05). Conclusion In patients with DCIS, combining radiomic metrics with clinical information improved prediction of disease upstaging but not DCIS score. ClinicalTrials.gov Identifier: NCT02352883 <i>Supplemental material is available for this article.</i> ©RSNA, 2025 See also the editorial by Kim and Woo in this issue.</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"315 1","pages":"e241628"},"PeriodicalIF":12.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143754271","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":"Agenesis of the Intrahepatic Inferior Vena Cava with Pulmonary Venous Fistula.","authors":"Xiaoxu Guo, Yuhan Zhou","doi":"10.1148/radiol.242069","DOIUrl":"https://doi.org/10.1148/radiol.242069","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"315 1","pages":"e242069"},"PeriodicalIF":12.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143754249","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":"MRI-based Radiomics for Ductal Carcinoma in Situ: Enhancing Its Role in Risk Stratification.","authors":"Soo-Yeon Kim, Ok Hee Woo","doi":"10.1148/radiol.250085","DOIUrl":"https://doi.org/10.1148/radiol.250085","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"315 1","pages":"e250085"},"PeriodicalIF":12.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143754275","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":"Beyond Proprietary Models: The Potential of Open-Source Large Language Models in Radiology.","authors":"Satvik Tripathi, Ali S Tejani, Tessa S Cook","doi":"10.1148/radiol.242454","DOIUrl":"https://doi.org/10.1148/radiol.242454","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"315 1","pages":"e242454"},"PeriodicalIF":12.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143754254","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}