Radiology最新文献

{"title":"AI to Improve Bone Tumor Malignancy Risk Stratification Reporting.","authors":"Mickael Tordjman, Mark D Murphey","doi":"10.1148/radiol.243176","DOIUrl":"https://doi.org/10.1148/radiol.243176","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"315 1","pages":"e243176"},"PeriodicalIF":12.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144018527","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":"Breast Cancer Detection with Standalone AI versus Radiologist Interpretation of Unilateral Surveillance Mammography after Mastectomy.","authors":"Su Min Ha, Janie M Lee, Myoung-Jin Jang, Hong-Kyu Kim, Jung Min Chang","doi":"10.1148/radiol.242955","DOIUrl":"10.1148/radiol.242955","url":null,"abstract":"<p><p>Background Limited data are available regarding the accuracy of artificial intelligence (AI) algorithms trained on bilateral mammograms for second breast cancer surveillance in patients with a personal history of breast cancer treated with unilateral mastectomy. Purpose To compare the performance of standalone AI for second breast cancer surveillance on unilateral mammograms with that of radiologists reading mammograms without AI assistance. Materials and Methods In this retrospective institutional database study, patients who were diagnosed with breast cancer between January 2001 and December 2018 and underwent postmastectomy surveillance mammography from January 2011 to March 2023 were included. Radiologists' mammogram interpretations without AI assistance were collected from these records and compared with AI interpretations of the same mammograms. The reference standards were histologic examination and 1-year follow-up data. The cancer detection rate per 1000 screening examinations, sensitivity, and specificity of standalone AI and the radiologists' interpretations without AI were compared using the McNemar test. Results Among the 4184 asymptomatic female patients (mean age, 52 years), 111 (2.7%) had contralateral second breast cancer. The cancer detection rate (17.4 per 1000 examinations [73 of 4184]; 95% CI: 13.7, 21.9) and sensitivity (65.8% [73 of 111]; 95% CI: 56.2, 74.5) were greater for standalone AI than for radiologists (14.6 per 1000 examinations [61 of 4184]; 95% CI: 11.2, 18.7; <i>P</i> = .01; 55.0% [61 of 111]; 95% CI: 45.2, 64.4; <i>P</i> = .01). The specificity was lower for standalone AI than for radiologists (91.5% [3725 of 4073]; 95% CI: 90.6, 92.3 vs 98.1% [3996 of 4073]; 95% CI: 97.6, 98.5; <i>P</i> < .001). AI detected 16 of 50 (32%) cancers missed by radiologists; however, 34 of 111 (30.6%) breast cancers were missed by both radiologists and AI. Conclusion Standalone AI for surveillance mammography showed higher sensitivity with lower specificity for contralateral breast cancer detection in patients treated with unilateral mastectomy than radiologists without AI assistance. © RSNA, 2025 <i>Supplemental material is available for this article.</i> See also the editorial by Philpotts in this issue.</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"315 1","pages":"e242955"},"PeriodicalIF":12.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143804169","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":"Generative Artificial Intelligence to Improve Ultralow-Field-Strength MRI Scan Quality.","authors":"Shuncong Wang, Greg Zaharchuk","doi":"10.1148/radiol.250932","DOIUrl":"https://doi.org/10.1148/radiol.250932","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"315 1","pages":"e250932"},"PeriodicalIF":12.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144053754","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":"Search for a New <i>Radiology</i> Editor.","authors":"Jeffrey S Klein","doi":"10.1148/radiol.259007","DOIUrl":"https://doi.org/10.1148/radiol.259007","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"315 1","pages":"e259007"},"PeriodicalIF":12.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144053786","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":"Impact of Preoperative MRI on Survival Outcomes in Patients with HER2-positive and Hormone Receptor-negative Breast Cancer.","authors":"Hee Jeong Kim, Woo Jung Choi, Hye Joung Eom, Eun Young Chae, Hee Jung Shin, Joo Hee Cha, Hak Hee Kim","doi":"10.1148/radiol.242712","DOIUrl":"https://doi.org/10.1148/radiol.242712","url":null,"abstract":"<p><p>Background Little is known regarding the impact of preoperative breast MRI on the long-term outcomes of patients with breast cancer that is human epidermal growth factor receptor 2 (HER2) positive and hormone receptor negative. Purpose To evaluate the impact of preoperative breast MRI on recurrence-free survival (RFS) and overall survival (OS) in patients with HER2-positive and hormone receptor-negative breast cancer by using propensity score matching. Materials and Methods This retrospective study included women diagnosed with HER2-positive and hormone receptor-negative invasive ductal carcinoma between January 2007 and December 2016. Patients who underwent preoperative MRI (the MRI group) were matched with those who did not (the no-MRI group) using propensity score matching based on 19 clinical-pathologic covariates. RFS and OS were compared using Kaplan-Meier estimates, Cox proportional hazards models, and inverse probability weighting. Results Among 1094 women (median age, 52 years; age range, 24-91 years), 47.81% (523 of 1094) underwent preoperative MRI. The rates of total recurrence and death were 14.3% (75 of 523) and 7.07% (37 of 523) in the MRI group, respectively, compared with 16.5% (94 of 571) and 13.1% (75 of 571) in the no-MRI group. In the propensity score-matched set, preoperative MRI was not associated with total recurrence (hazard ratio [HR], 0.69; 95% CI: 0.47, 1.02; <i>P</i> = .06), local-regional recurrence (HR, 0.94; 95% CI: 0.52, 1.70; <i>P</i> = .84), contralateral breast recurrence (HR, 0.55; 95% CI: 0.24, 1.25; <i>P</i> = .15), or distant recurrence (HR, 0.56; 95% CI: 0.31, 1.03; <i>P</i> = .06). OS was not higher with preoperative MRI (HR, 0.63; 95% CI: 0.39, 1.00; <i>P</i> = .05). At multivariable analysis, preoperative MRI was not associated with improved RFS (HR, 0.89; 95% CI: 0.67, 1.19; <i>P</i> = .44) or OS (HR, 0.73; 95% CI: 0.48, 1.10; <i>P</i> = .14). Conclusion Preoperative MRI did not improve RFS or OS in patients with HER2-positive and hormone receptor-negative breast cancer. © RSNA, 2025 <i>Supplemental material is available for this article.</i> See also the editorial by Imbriaco and Ponsiglione in this issue.</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"315 1","pages":"e242712"},"PeriodicalIF":12.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144020764","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":"Enhanced CT and MRI Focal Bone Tumor Classification with Machine Learning-based Stratification: A Multicenter Retrospective Study.","authors":"Astrée Lemore, Nora Vogt, Julien Oster, Edouard Germain, Marc Fauvel, Romain Gillet, François Sirveaux, Béatrice Marie, Nicolas Sans, Marie Faruch, Franck Lapègue, François Lafourcade, Sammy Badr, Anne Cotten, Fadila Mihoubi Bouvier, Sisi Yang, Jean-Luc Drapé, Maxime Pastor, Yann Thouvenin, Marie Pierre Baron, Catherine Cyteval, David Fadli, Claire Fournier, Olivier Hauger, Mariem Ben Haj Amor, Nicolas Stacoffe, Sophie Daubie, Jean-Baptiste Noel, Jean-Baptiste Pialat, Stéphane Cherix, Fabio Zanchi, Patrick Omoumi, Alain Blum, Gabriela Hossu, Pedro A Gondim Teixeira","doi":"10.1148/radiol.232834","DOIUrl":"https://doi.org/10.1148/radiol.232834","url":null,"abstract":"<p><p>Background Standardized bone tumor reporting is crucial for consistent, risk-aligned patient management. Current systems are based on expert consensus and/or lack multicenter validation. Purpose To evaluate a machine learning-based approach for differentiating between benign and malignant focal bone lesions and to propose a Bone Tumor Imaging Reporting and Data System (BTI-RADS) 2.0 for further risk stratification. Materials and Methods This retrospective multicenter trial included patients with solitary bone tumors undergoing <i>(a)</i> radiography or CT and <i>(b)</i> MRI at 10 centers from November 2009 to March 2022. Patients were divided into training and test datasets. Predefined radioclinical features were extracted. The training dataset was considered for bootstrapped χ² feature selection, and extreme gradient boosting (XGBoost) classifiers were optimized using nested cross-validation. Continuous classifier outputs were thresholded to stratify patients into seven malignancy risk classes (BTI-RADS 2.0), and malignancy rates were assessed for the test set. XGBoost and human expert performances were compared using the Wilcoxon signed-rank significance test with a significance level of .05. Results In total, 1113 patients (mean age, 39 years ± 22 [SD]; 623 men) were included: 298 in the training and 815 in the test datasets. Twenty-seven of 80 (34%) multimodal features were selected based on χ² analysis. Best classification performances were achieved by an XGBoost model trained on 27 features, with an F1 score of 0.81 (95% CI: 0.78, 0.84). This model performed slightly inferior to 28 experienced radiologists, who demonstrated an F1 score of 0.83 (95% CI: 0.80, 0.85; <i>P</i> < .001). BTI-RADS 2.0 risk grades II-V were associated with malignancy rates of 0% (0 of 102; 95% CI: 0, 0), 8.3% (14 of 168; 95% CI: 4, 13), 45% (121 of 271; 95% CI: 39, 50), and 92% (252 of 274; 95% CI: 89, 95), respectively, identifying malignant lesions with a sensitivity of 96% (373 of 387; 95% CI: 94, 98). Conclusion A machine learning algorithm and risk stratification system achieved accurate and standardized bone tumor malignancy grading. Clinical trial registration no. NCT04884048 © RSNA, 2025 <i>Supplemental material is available for this article.</i> See also the editorial by Tordjman and Murphey in this issue.</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"315 1","pages":"e232834"},"PeriodicalIF":12.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144042551","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":"Improving Effectiveness of Liver US Shear-Wave Elastography Using a Quality Improvement Framework.","authors":"Masoud Baikpour, David Hunt, Jyoti Narayanswami, Madhangi Parameswaran, Kelly R Young, Melanie Orlowski, Anthony E Samir, Theodore T Pierce","doi":"10.1148/radiol.243155","DOIUrl":"https://doi.org/10.1148/radiol.243155","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"315 1","pages":"e243155"},"PeriodicalIF":12.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144014689","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":"Silent Trauma: Neuroimaging Highlights Subtle Changes from Military Blast Exposure.","authors":"Siddhant Dogra, Yvonne W Lui","doi":"10.1148/radiol.250694","DOIUrl":"10.1148/radiol.250694","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"315 1","pages":"e250694"},"PeriodicalIF":12.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144026253","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":"Framework for Environmentally Sustainable Radiology: Call for Collaborative Action and a Health-Centered Focus.","authors":"Kate Hanneman, Isabelle Redenius, Marc Dewey, Ania Kielar, Julian Dobranowski, Marie-France Bellin, Jean-Pierre Tasu, Noriko Aida, Masahiro Jinzaki, Noryiuki Tomiyama, Katharine Halliday, Stephen Harden, Oliver Reichardt, Carlo Catalano, Konstantin Nikolaou, Christiane Kuhl, Curtis P Langlotz, Umar Mahmood, Nicoletta Gandolfo, Andrea Giovagnoni","doi":"10.1148/radiol.250070","DOIUrl":"https://doi.org/10.1148/radiol.250070","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> It is imperative that the entire medical imaging sector acts collectively and decisively to reduce its own environmental impact and prepare for the current and future effects of the climate crisis. The Radiology R7 meeting convened in Venice, Italy, on October 10-13, 2024 to discuss environmental sustainability and other key issues facing radiology and the patients served by medical imaging. Radiology R7 delegates agree that collaborative action is urgently needed to transform radiology systems to be climate-resilient, equitable, low-carbon, and sustainable. This special report highlights priorities and outlines a framework for environmentally sustainable radiology, centered on eight collaborative action areas. A health-centered response reinforces the role of radiologists as physicians, emphasizes the opportunity for medical imaging to improve health, and will be essential to engage key partners in climate action. Effective leadership and governance are needed to ensure that radiology services are accessible, equitable, affordable, high quality and sustainable. Collaboration and partnership are essential to achieve meaningful change. Health equity should be prioritized to increase global access to high quality radiology services while minimizing the environmental impact. Multiple climate response pathways should be implemented in parallel including mitigation strategies to reduce the use of energy, finite resources and waste and adaptation strategies to build resilience to the effects of climate change. Innovation and research are necessary to develop, validate, and implement sustainable solutions. Finally, knowledge sharing, education, and training are needed to disseminate information on actions toward environmentally sustainable radiology practices. We all have a role to play and must work together to achieve these aims quickly by identifying the problem, setting goals, implementing a plan, measuring impact, sharing results, and celebrating successes.</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"315 1","pages":"e250070"},"PeriodicalIF":12.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143993461","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":"Intravenous Thrombolysis before Mechanical Thrombectomy in Patients with Acute Ischemic Stroke Due to Medium Vessel Occlusion.","authors":"Yu Guo, Wenmiao Luo","doi":"10.1148/radiol.242671","DOIUrl":"https://doi.org/10.1148/radiol.242671","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"315 1","pages":"e242671"},"PeriodicalIF":12.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144021634","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}