European Radiology最新文献

{"title":"Prognostic impact of remote myocardium changes using T1 mapping in patients with ST-segment elevation myocardial infarction.","authors":"Fritz Oberhollenzer, Ivan Lechner, Martin Reindl, Christina Tiller, Magdalena Holzknecht, Franziska Roithinger, Can Gollmann-Tepeköylü, Agnes Mayr, Axel Bauer, Bernhard Metzler, Sebastian J Reinstadler","doi":"10.1007/s00330-025-11711-0","DOIUrl":"https://doi.org/10.1007/s00330-025-11711-0","url":null,"abstract":"<p><strong>Objectives: </strong>The aim of this study was to investigate the prognostic relevance of remote myocardium alterations assessed by cardiac magnetic resonance (CMR) T1 mapping (native T1 and extracellular volume (ECV)) in patients after ST-segment elevation myocardial infarction (STEMI).</p><p><strong>Materials and methods: </strong>This retrospective study analyzed STEMI patients treated with primary percutaneous coronary intervention (PCI) who were included in the prospective MARINA-STEMI study. CMR images were analyzed for left ventricular (LV) function, standard infarct characteristics, as well as native remote T1 and remote ECV. The primary clinical endpoint was the composite of all-cause mortality, re-infarction and new congestive heart failure (Major adverse cardiac events (MACE)).</p><p><strong>Results: </strong>Four hundred ninety-one patients (median age 58 [Interquartile range (IQR): 52-66 years], 91 (19%) female) patients underwent CMR imaging at 4 ([IQR: 3-5]) days after PCI. Over a median follow-up of 12 months ([IQR: 12-13]) after STEMI, 42 MACE outcomes occurred. Higher native remote T1-times (1018.5 [IQR: 997.0-1064.0] ms vs. 1007.0 [IQR: 977.0-1041.5] ms, p = 0.033) as well as higher remote ECV values (28.07 vs. 26.27%, p = 0.009) were observed in patients with MACE. Multivariable Cox-regression analysis demonstrated that remote ECV (hazard ratio (HR): 1.53 [confidence interval (CI): 1.07-2.19], p = 0.020) but not native remote T1 is independently associated with MACE.</p><p><strong>Conclusions: </strong>A comprehensive assessment of the remote myocardium with native T1 and ECV provides prognostic information in a contemporary cohort of low-risk STEMI patients. Remote ECV, but not remote native T1, was found to be an independent predictor of MACE.</p><p><strong>Key points: </strong>Question The prognostic implications of remote myocardium changes assessed by cardiac magnetic resonance (CMR) T1 mapping in patients after ST-segment elevation myocardial infarction (STEMI) are not fully understood. Findings After STEMI, for patients treated with primary percutaneous coronary intervention, remote extracellular volume (ECV) was found to be an independent predictor of major adverse cardiac events. Clinical relevance This study underlines the importance of remote myocardium characterization as a promising diagnostic tool. In particular, the quantification of ECV changes in the remote myocardium could be relevant for optimized CMR-imaging-based risk stratification in patients after STEMI.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144215371","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":"Additive value of perivascular fat density to CT angiography characteristics of carotid plaques in predicting symptomatic carotid plaques.","authors":"Wei Luo, Peng Lv, Ranying Zhang, Qixuan Qiu, Jiang Lin","doi":"10.1007/s00330-025-11713-y","DOIUrl":"https://doi.org/10.1007/s00330-025-11713-y","url":null,"abstract":"<p><strong>Objectives: </strong>This study aimed to investigate the relationship between perivascular fat density (PFD) and carotid plaque characteristics while exploring the additive diagnostic value of PFD in predicting symptomatic carotid plaques.</p><p><strong>Materials and methods: </strong>In a single-center retrospective case-control study, 315 patients with unilateral carotid atherosclerosis were classified into symptomatic and asymptomatic groups based on the presence of acute ischemic stroke or transient ischemic attack (TIA) within 2 weeks before carotid computed tomography angiography (CTA). Plaque CTA features and PFD were assessed, and their relationship was analyzed using Spearman's rank correlation. Logistic regression analysis was employed to identify risk factors for symptomatic carotid plaques, and predictive models were subsequently developed. The performance of these models was further evaluated.</p><p><strong>Results: </strong>A positive linear correlation was found between PFD and plaque CTA characteristics (p < 0.05). PFD, degree of stenosis, plaque burden, and soft plaque thickness were identified as predictors of symptomatic carotid plaques. Receiver operating characteristic (ROC) curves showed that the areas under the curves (AUC) increased from 0.631 to 0.846 with the addition of plaque burden, soft plaque thickness, and PFD to a degree of stenosis. The calibration curves of the combined model with PFD and plaque risk features demonstrated good predictive consistency. Decision curve analysis suggested that the combined model provided higher clinical benefit.</p><p><strong>Conclusions: </strong>PFD may serve as a valuable imaging marker for vulnerable plaques, providing additional diagnostic value in risk assessment. The combination of PFD with plaque risk features may further enhance the predictive performance of symptomatic carotid plaques.</p><p><strong>Key points: </strong>Question Does perivascular fat density (PFD) add additional predictive value to plaque risk features on CT angiography (CTA) in the assessment of symptomatic carotid plaques? Findings PFD provides additive predictive value for symptomatic carotid plaques. Combining PFD with stenosis severity and plaque CTA features improves prediction. Clinical relevance PFD may serve as a valuable imaging biomarker for risk stratification and clinical decision-making in carotid atherosclerosis.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144208021","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":"Effect of contrast enhancement on diagnosis of interstitial lung abnormality in automatic quantitative CT measurement.","authors":"Jaeyeon Choi, Yura Ahn, Youngjae Kim, Han Na Noh, Kyung-Hyun Do, Joon Beom Seo, Sang Min Lee","doi":"10.1007/s00330-025-11715-w","DOIUrl":"https://doi.org/10.1007/s00330-025-11715-w","url":null,"abstract":"<p><strong>Objective: </strong>To investigate the effect of contrast enhancement on the diagnosis of interstitial lung abnormalities (ILA) in automatic quantitative CT measurement in patients with paired pre- and post-contrast scans.</p><p><strong>Materials and methods: </strong>Patients who underwent chest CT for thoracic surgery between April 2017 and December 2020 were retrospectively analyzed. ILA quantification was performed using deep learning-based automated software. Cases were categorized as ILA or non-ILA according to the Fleischner Society's definition, based on the quantification results or radiologist assessment (reference standard). Measurement variability, agreement, and diagnostic performance between the pre- and post-contrast scans were evaluated.</p><p><strong>Results: </strong>In 1134 included patients, post-contrast scans quantified a slightly larger volume of nonfibrotic ILA (mean difference: -0.2%), due to increased ground-glass opacity and reticulation volumes (-0.2% and -0.1%), whereas the fibrotic ILA volume remained unchanged (0.0%). ILA was diagnosed in 15 (1.3%), 22 (1.9%), and 40 (3.5%) patients by pre- and post-contrast scans and radiologists, respectively. The agreement between the pre- and post-contrast scans was substantial (κ = 0.75), but both pre-contrast (κ = 0.46) and post-contrast (κ = 0.54) scans demonstrated moderate agreement with the radiologist. The sensitivity for ILA (32.5% vs. 42.5%, p = 0.221) and specificity for non-ILA (99.8% vs. 99.5%, p = 0.248) were comparable between pre- and post-contrast scans. Radiologist's reclassification for equivocal ILA due to unilateral abnormalities increased the sensitivity for ILA (67.5% and 75.0%, respectively) in both pre- and post-contrast scans.</p><p><strong>Conclusion: </strong>Applying automated quantification on post-contrast scans appears to be acceptable in terms of agreement and diagnostic performance; however, radiologists may need to improve sensitivity reclassifying equivocal ILA.</p><p><strong>Key points: </strong>Question The effect of contrast enhancement on the automated quantification of interstitial lung abnormality (ILA) remains unknown. Findings Automated quantification measured slightly larger ground-glass opacity and reticulation volumes on post-contrast scans than on pre-contrast scans; however, contrast enhancement did not affect the sensitivity for interstitial lung abnormality. Clinical relevance Applying automated quantification on post-contrast scans appears to be acceptable in terms of agreement and diagnostic performance.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144208023","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 do medical institutions co-create artificial intelligence solutions with commercial startups?","authors":"Willem Grootjans, Uliana Krainska, Mohammad H Rezazade Mehrizi","doi":"10.1007/s00330-025-11672-4","DOIUrl":"https://doi.org/10.1007/s00330-025-11672-4","url":null,"abstract":"<p><strong>Objectives: </strong>As many radiology departments embark on adopting artificial intelligence (AI) solutions in their clinical practice, they face the challenge that commercial applications often do not fit with their needs. As a result, they engage in a co-creation process with technology companies to collaboratively develop and implement AI solutions. Despite its importance, the process of co-creating AI solutions is under-researched, particularly regarding the range of challenges that may occur and how medical and technological parties can monitor, assess, and guide their co-creation process through an effective collaboration framework.</p><p><strong>Materials and methods: </strong>Drawing on the multi-case study of three co-creation projects at an academic medical center in the Netherlands, we examine how co-creation processes happen through different scenarios, depending on the extent to which the two parties engage in \"resourcing,\" \"adaptation,\" and \"reconfiguration.\"</p><p><strong>Results: </strong>We offer a relational framework that helps involved parties monitor, assess, and guide their collaborations in co-creating AI solutions. The framework allows them to discover novel use-cases and reconsider their established assumptions and practices for developing AI solutions, also for redesigning their technological systems, clinical workflow, and their legal and organizational arrangements. Using the proposed framework, we identified distinct co-creation journeys with varying outcomes, which could be mapped onto the framework to diagnose, monitor, and guide collaborations toward desired results.</p><p><strong>Conclusion: </strong>The outcomes of co-creation can vary widely. The proposed framework enables medical institutions and technology companies to assess challenges and make adjustments. It can assist in steering their collaboration toward desired goals.</p><p><strong>Key points: </strong>Question How can medical institutions and AI startups effectively co-create AI solutions for radiology, ensuring alignment with clinical needs while steering collaboration effectively? Findings This study provides a co-creation framework allowing assessment of project progress, stakeholder engagement, as well as guidelines for radiology departments to steer co-creation of AI. Clinical relevance By actively involving radiology professionals in AI co-creation, this study demonstrates how co-creation helps bridge the gap between clinical needs and AI development, leading to clinically relevant, user-friendly solutions that enhance the radiology workflow.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144208026","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":"Spectral computed tomography-based quantitative parameters combined with extracellular volume fraction to predict lymph node metastases in gastric cancer.","authors":"Xiuling Zhang, Leping Peng, Fang Ma, Fan Zhang, Xiaoyue Zhang, Xiaoqin Liang, Zhaokun Wei, Xinli Li, Yaqiong Ma, Gang Huang, Lili Wang","doi":"10.1007/s00330-025-11705-y","DOIUrl":"https://doi.org/10.1007/s00330-025-11705-y","url":null,"abstract":"<p><strong>Objectives: </strong>Preoperative prediction of lymph node metastasis (LNM) is important for gastric cancer (GC) diagnosis, treatment and prognosis. This study aimed to predict LNM risk in GC using quantitative parameters and extracellular volume fraction (ECV%) derived from spectral computed tomography (CT).</p><p><strong>Materials and methods: </strong>Data from 230 lymph nodes (LNs) (97 nonmetastatic, 133 metastatic) were collected from 70 GC patients and were randomly divided into a training cohort and a test cohort (6:4 ratio). LN qualitative features (including edge, shape and degree of enhancement), spectral CT-derived quantitative parameters and ECV% were assessed. Multivariate logistic regression analysis with the forward variable selection method was used to build 3 models: Model 1 (traditional features: LN edge, short axis diameter), Model 2 (spectral CT parameters: iodine concentration in arterial and delayed phases), and Model 3 (spectral CT parameters and ECV%). Diagnostic performance was evaluated using AUC and compared with the Delong test.</p><p><strong>Results: </strong>In both cohorts, a significant difference in ECV% was observed between positive and negative LNs (p < 0.001), and the diagnostic efficacy of ECV% (AUC = 0.823 and 0.803, respectively, both p < 0.001) was higher than that of other parameters. Model 3 demonstrated significantly higher diagnostic efficacy than Models 1 and 2 in both cohorts (AUC = 0.858 and 0.881, respectively; both p < 0.001).</p><p><strong>Conclusion: </strong>ECV% can help diagnose LNM in GC, and combining the spectral CT quantitative features with ECV% can further improve diagnosis. This finding enables accurate preoperative prediction of LNM and the GC prognosis so that patients receive personalized treatment.</p><p><strong>Key points: </strong>Question Predicting lymph node metastasis (LNM) based on the LN remains a challenge; can spectral CT combined with the Extracellular volume fraction (ECV%) model predict regional LNM? Findings Based on the LN level, the spectral CT combined ECV% model could more accurately predict regional LNM in the training and test cohort. Clinical relevance Accurate and non-invasive preoperative prediction of LNM in each region is important for the individualized treatment and prognosis of gastric cancer. Assisting physicians in selecting the most appropriate treatment approaches to optimize patient outcomes.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144215372","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":"Deep learning-based automatic segmentation of arterial vessel walls and plaques in MR vessel wall images for quantitative assessment.","authors":"Long Yang, Xiong Yang, Zhenhuan Gong, Yufei Mao, Shan-Shan Lu, Chengcheng Zhu, Liwen Wan, Junhui Huang, Mohd Halim Mohd Noor, Ke Wu, Cheng Li, Guanxun Cheng, Ye Li, Dong Liang, Xin Liu, Hairong Zheng, Zhanli Hu, Na Zhang","doi":"10.1007/s00330-025-11697-9","DOIUrl":"https://doi.org/10.1007/s00330-025-11697-9","url":null,"abstract":"<p><strong>Objectives: </strong>To develop and validate a deep-learning-based automatic method for vessel walls and atherosclerotic plaques segmentation for quantitative evaluation in MR vessel wall images.</p><p><strong>Materials and methods: </strong>A total of 193 patients (107 patients for training and validation, 39 patients for internal test, 47 patients for external test) with atherosclerotic plaque from five centers underwent T1-weighted MRI scans and were included in the dataset. The first step of the proposed method was constructing a purely learning-based convolutional neural network (CNN) named Vessel-SegNet to segment the lumen and the vessel wall. The second step is using the vessel wall priors (including manual prior and Tversky-loss-based automatic prior) to improve the plaque segmentation, which utilizes the morphological similarity between the vessel wall and the plaque. The Dice similarity coefficient (DSC), intraclass correlation coefficient (ICC), etc., were used to evaluate the similarity, agreement, and correlations.</p><p><strong>Results: </strong>Most of the DSCs for lumen and vessel wall segmentation were above 90%. The introduction of vessel wall priors can increase the DSC for plaque segmentation by over 10%, reaching 88.45%. Compared to dice-loss-based vessel wall priors, the Tversky-loss-based priors can further improve DSC by nearly 3%, reaching 82.84%. Most of the ICC values between the Vessel-SegNet and manual methods in the 6 quantitative measurements are greater than 85% (p-value < 0.001).</p><p><strong>Conclusion: </strong>The proposed CNN-based segmentation model can quickly and accurately segment vessel walls and plaques for quantitative evaluation. Due to the lack of testing with other equipment, populations, and anatomical studies, the reliability of the research results still requires further exploration.</p><p><strong>Key points: </strong>Question How can the accuracy and efficiency of vessel component segmentation for quantification, including the lumen, vessel wall, and plaque, be improved? Findings Improved CNN models, manual/automatic vessel wall priors, and Tversky loss can improve the performance of semi-automatic/automatic vessel components segmentation for quantification. Clinical relevance Manual segmentation of vessel components is a time-consuming yet important process. Rapid and accurate segmentation of the lumen, vessel walls, and plaques for quantification assessment helps patients obtain more accurate, efficient, and timely stroke risk assessments and clinical recommendations.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144208022","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":"Advancing MS care: Are QReports the next leap in imaging?","authors":"Neus Mongay-Ochoa","doi":"10.1007/s00330-025-11716-9","DOIUrl":"10.1007/s00330-025-11716-9","url":null,"abstract":"","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144198629","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":"ESR Bridges: imaging of cancers in pregnancy-a multidisciplinary view.","authors":"Gabriele Masselli, Martina Derme, Giuseppe Rizzo, Carlo Catalano","doi":"10.1007/s00330-025-11598-x","DOIUrl":"https://doi.org/10.1007/s00330-025-11598-x","url":null,"abstract":"","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144208024","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":"Automatic patient centering in computed tomography: a systematic review and meta-analysis.","authors":"Yasser H Hadi, Lauren Keaney, Andrew England, Niamh Moore, Mark McEntee","doi":"10.1007/s00330-024-11170-z","DOIUrl":"10.1007/s00330-024-11170-z","url":null,"abstract":"<p><strong>Objective: </strong>To comprehensively examine the influence of auto-patient centering technologies on positioning accuracy, radiation dose, image quality, and time efficiency of computed tomography (CT) scans.</p><p><strong>Materials and methods: </strong>A systematic search of peer-reviewed English publications was performed between January 2000 and November 2023 in PubMed, Embase, CINAHL, Scopus, and Web of Science. Two postgraduate students and an academic lecturer independently reviewed the articles to verify adherence to the inclusion criteria. The QUADAS-2 tool was employed to evaluate study quality. We derived summary estimates on positioning accuracy, radiation dose reduction, image quality, and time efficiency using proportion and meta-analysis methodologies.</p><p><strong>Results: </strong>Nine studies were identified comparing automatic and manual CT positioning. Automatic positioning improved accuracy by reducing vertical offsets to 7 mm and 4 mm for thorax and abdominal CTs, compared to 19 mm and 18 mm with manual methods. Most studies showed significant reductions in radiation dose, ranging from 5.71 to 31%. Image quality results were mixed, automatic methods generally produced images with less noise, but differences were minimal. Time efficiency was better, with automatic positioning reducing preparation time from 0.48 min versus 0.67 min for manual positioning.</p><p><strong>Conclusions: </strong>This review confirms that automatic patient-centering technologies enhance positioning accuracy and decrease preparation times in CT scans. While reductions in radiation doses and some improvements in image quality were observed, the evidence remains mixed. Findings support integrating these technologies into clinical practice to optimize patient care.</p><p><strong>Key points: </strong>Question Does automatic patient centering in CT enhance positioning accuracy, reduce radiation exposure, and improve image quality? Findings Findings indicate that automatic centering can optimize image quality, reduce examination times and contribute to overall improvements in imaging efficiency. Clinical relevance Automatic patient centering in CT improves positioning accuracy, minimizes radiation exposure, enhances image quality, and accelerates imaging workflows, contributing to safer, more efficient imaging procedures that benefit patient care.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"3486-3498"},"PeriodicalIF":4.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142681309","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":"ESR Bridges: renal cell carcinoma-a multidisciplinary view.","authors":"Rebeca Mirón Mombiela, Frederik Ferløv Thomsen, Dan Fuglø, Thomas Hasselager, Anne Kirstine Møller","doi":"10.1007/s00330-024-11270-w","DOIUrl":"10.1007/s00330-024-11270-w","url":null,"abstract":"","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"3107-3109"},"PeriodicalIF":4.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142806089","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}