Radiology最新文献

{"title":"A New Era for Quantitative MRI Biomarkers of the Liver: A Challenge and Opportunity for the Radiology Community.","authors":"Claude B Sirlin, Scott B Reeder","doi":"10.1148/radiol.241876","DOIUrl":"https://doi.org/10.1148/radiol.241876","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"313 3","pages":"e241876"},"PeriodicalIF":12.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142771785","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":"Comparing Large Language Model and Human Reader Accuracy with <i>New England Journal of Medicine</i> Image Challenge Case Image Inputs.","authors":"Pae Sun Suh, Woo Hyun Shim, Chong Hyun Suh, Hwon Heo, Kye Jin Park, Pyeong Hwa Kim, Se Jin Choi, Yura Ahn, Sohee Park, Ho Young Park, Na Eun Oh, Min Woo Han, Sung Tan Cho, Chang-Yun Woo, Hyungjun Park","doi":"10.1148/radiol.241668","DOIUrl":"https://doi.org/10.1148/radiol.241668","url":null,"abstract":"<p><p>Background Application of multimodal large language models (LLMs) with both textual and visual capabilities has been steadily increasing, but their ability to interpret radiologic images is still doubted. Purpose To evaluate the accuracy of LLMs and compare it with that of human readers with varying levels of experience and to assess the factors affecting LLM accuracy in answering <i>New England Journal of Medicine</i> Image Challenge cases. Materials and Methods Radiologic images of cases from October 13, 2005, to April 18, 2024, were retrospectively reviewed. Using text and image inputs, LLMs (Open AI's GPT-4 Turbo with Vision [GPT-4V] and GPT-4 Omni [GPT-4o], Google's DeepMind Gemini 1.5 Pro, and Anthropic's Claude 3) provided answers. Human readers (seven junior faculty radiologists, two clinicians, one in-training radiologist, and one medical student), blinded to the published answers, also answered. LLM accuracy with and without image inputs and short (cases from 2005 to 2015) versus long text inputs (from 2016 to 2024) was evaluated in subgroup analysis to determine the effect of these factors. Factor analysis was assessed using multivariable logistic regression. Accuracy was compared with generalized estimating equations, with multiple comparisons adjusted by using Bonferroni correction. Results A total of 272 cases were included. GPT-4o achieved the highest overall accuracy among LLMs (59.6%; 162 of 272), outperforming a medical student (47.1%; 128 of 272; <i>P</i> < .001) but not junior faculty (80.9%; 220 of 272; <i>P</i> < .001) or the in-training radiologist (70.2%; 191 of 272; <i>P</i> = .003). GPT-4o exhibited similar accuracy regardless of image inputs (without images vs with images, 54.0% [147 of 272] vs 59.6% [162 of 272], respectively; <i>P</i> = .59). Human reader accuracy was unaffected by text length, whereas LLMs demonstrated higher accuracy with long text inputs (all <i>P</i> < .001). Text input length affected LLM accuracy (odds ratio range, 3.2 [95% CI: 1.9, 5.5] to 6.6 [95% CI: 3.7, 12.0]). Conclusion LLMs demonstrated substantial accuracy with text and image inputs, outperforming a medical student. However, their accuracy decreased with shorter text lengths, regardless of image input. © RSNA, 2024 <i>Supplemental material is available for this article.</i></p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"313 3","pages":"e241668"},"PeriodicalIF":12.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142802203","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":"Distant-Stage Breast Cancer Incidence Is Increasing in U.S. Women across Age Groups and Race and Ethnicity Groups.","authors":"Eric Kim, Linda Moy","doi":"10.1148/radiol.242716","DOIUrl":"https://doi.org/10.1148/radiol.242716","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"313 3","pages":"e242716"},"PeriodicalIF":12.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142802205","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":"Family History of Lung Cancer in Women Who Have Never Smoked Is a Recognizable Risk Factor in Lung Cancer Screening.","authors":"Yeun-Chung Chang","doi":"10.1148/radiol.243281","DOIUrl":"https://doi.org/10.1148/radiol.243281","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"313 3","pages":"e243281"},"PeriodicalIF":12.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142802206","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":"Dynamic CT Myocardial Perfusion Imaging for Coronary Artery Stent Assessment.","authors":"Michelle C Williams","doi":"10.1148/radiol.243475","DOIUrl":"https://doi.org/10.1148/radiol.243475","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"313 3","pages":"e243475"},"PeriodicalIF":12.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142839113","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":"Intraspinal and Intracranial Melanotic Perivascular Epithelioid Cell Tumor.","authors":"Brecht Van Berkel, Johannes Devos","doi":"10.1148/radiol.241612","DOIUrl":"https://doi.org/10.1148/radiol.241612","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"313 3","pages":"e241612"},"PeriodicalIF":12.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142839116","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":"Glioma Imaging: A Road Map to Standardized Imaging.","authors":"Keith R Peters","doi":"10.1148/radiol.241235","DOIUrl":"https://doi.org/10.1148/radiol.241235","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"313 3","pages":"e241235"},"PeriodicalIF":12.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142802208","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":"Diagnostic Accuracy of Dynamic Stress Myocardial CT Perfusion Compared with Invasive Physiology in Patients with Stents: The Advantage 2 Study.","authors":"Daniele Andreini, Saima Mushtaq, Daniela Trabattoni, Edoardo Conte, Jeroen Sonck, Gerardo Lorusso, Stefano Galli, Giovanni Monizzi, Marta Belmonte, Luca Grancini, Giovanni Teruzzi, Sarah Troiano, Sebastiano Gili, Piero Montorsi, Paolo Olivares, Vincenzo Mallia, Davide Marchetti, Matteo Schillaci, Emanuele Gallinoro, Pasquale Paolisso, Carlo Gigante, Eleonora Melotti, Andrea Baggiano, Maria Elisabetta Mancini, Andrea Annoni, Alberto Formenti, Koshiro Sakai, Takuya Mizukami, Gianluca Pontone, Lorenza Zanotto, Antonio L Bartorelli, Carlos Collet","doi":"10.1148/radiol.232225","DOIUrl":"https://doi.org/10.1148/radiol.232225","url":null,"abstract":"<p><p>Background The detection of in-stent restenosis (ISR) with coronary CT angiography (CCTA) is challenging, but CT perfusion (CTP) has demonstrated improved diagnostic accuracy over CCTA in patients with stents. However, there are limited data on the performance of dynamic CTP, which allows noninvasive adjudication of regional myocardial blood flow. Purpose To compare the diagnostic performance of regadenoson-stress dynamic CTP with that of CCTA, using fractional flow reserve (FFR) and the index of microvascular resistance (IMR) as reference standards for epicardial coronary circulation and coronary microcirculation, respectively. Materials and Methods Between January 2021 and June 2022, this prospective study enrolled patients with stents with indication for invasive coronary angiography due to suspicion of ISR or coronary artery disease progression. Participants underwent dynamic stress myocardial CTP and rest CTP plus CCTA. A wide coverage (z-axis coverage, 16 cm) and fast (gantry rotation time, 0.28 second) scanner was used. During invasive coronary angiography, FFR and IMR were obtained. The diagnostic rate (number of interpretable territories divided by number of evaluated territories) and accuracy of CCTA and CTP were evaluated in a territory-based analysis and compared with FFR and IMR (primary end points of the study). Results The study included 156 consecutive patients (136 men [87%]; mean age, 63.1 years ± 8.2 [SD]) with 504 stents. The diagnostic rate was higher for CTP than for CCTA (98.7% [789 of 799 territories] vs 95.6% [764 of 799 territories], <i>P</i> < .001). With use of FFR as the reference standard, sensitivity, specificity, and diagnostic accuracy were higher for CTP than for CCTA (89.0%, 82.8%, and 84.7%, respectively, vs 60.0%, 61.9%, and 61.5%; <i>P</i> < .001). With use of IMR as the reference standard, sensitivity, specificity, and diagnostic accuracy were higher with CTP than with CCTA (76.5%, 85.9%, and 82.9%, respectively, vs 48.2%, 63.5%, and 59.3%; <i>P</i> < .01). The mean effective dose of stress CTP plus CCTA was 10.4 mSv ± 2.7. Conclusion In patients with coronary stents, dynamic CTP improves the diagnostic performance of CCTA in the detection of territory-based ischemia. © RSNA, 2024 <i>Supplemental material is available for this article.</i> See also the editorial by Williams in this issue.</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"313 3","pages":"e232225"},"PeriodicalIF":12.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142839111","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":"Large Language Model Ability to Translate CT and MRI Free-Text Radiology Reports Into Multiple Languages.","authors":"Aymen Meddeb, Sophia Lüken, Felix Busch, Lisa Adams, Lorenzo Ugga, Emmanouil Koltsakis, Antonios Tzortzakakis, Soumaya Jelassi, Insaf Dkhil, Michail E Klontzas, Matthaios Triantafyllou, Burak Kocak, Sabahattin Yüzkan, Longjiang Zhang, Bin Hu, Anna Andreychenko, Efimtcev Alexander Yurievich, Tatiana Logunova, Wipawee Morakote, Salita Angkurawaranon, Marcus R Makowski, Mike P Wattjes, Renato Cuocolo, Keno Bressem","doi":"10.1148/radiol.241736","DOIUrl":"10.1148/radiol.241736","url":null,"abstract":"<p><p>Background High-quality translations of radiology reports are essential for optimal patient care. Because of limited availability of human translators with medical expertise, large language models (LLMs) are a promising solution, but their ability to translate radiology reports remains largely unexplored. Purpose To evaluate the accuracy and quality of various LLMs in translating radiology reports across high-resource languages (English, Italian, French, German, and Chinese) and low-resource languages (Swedish, Turkish, Russian, Greek, and Thai). Materials and Methods A dataset of 100 synthetic free-text radiology reports from CT and MRI scans was translated by 18 radiologists between January 14 and May 2, 2024, into nine target languages. Ten LLMs, including GPT-4 (OpenAI), Llama 3 (Meta), and Mixtral models (Mistral AI), were used for automated translation. Translation accuracy and quality were assessed with use of BiLingual Evaluation Understudy (BLEU) score, translation error rate (TER), and CHaRacter-level F-score (chrF++) metrics. Statistical significance was evaluated with use of paired <i>t</i> tests with Holm-Bonferroni corrections. Radiologists also conducted a qualitative evaluation of translations with use of a standardized questionnaire. Results GPT-4 demonstrated the best overall translation quality, particularly from English to German (BLEU score: 35.0 ± 16.3 [SD]; TER: 61.7 ± 21.2; chrF++: 70.6 ± 9.4), to Greek (BLEU: 32.6 ± 10.1; TER: 52.4 ± 10.6; chrF++: 62.8 ± 6.4), to Thai (BLEU: 53.2 ± 7.3; TER: 74.3 ± 5.2; chrF++: 48.4 ± 6.6), and to Turkish (BLEU: 35.5 ± 6.6; TER: 52.7 ± 7.4; chrF++: 70.7 ± 3.7). GPT-3.5 showed highest accuracy in translations from English to French, and Qwen1.5 excelled in English-to-Chinese translations, whereas Mixtral 8x22B performed best in Italian-to-English translations. The qualitative evaluation revealed that LLMs excelled in clarity, readability, and consistency with the original meaning but showed moderate medical terminology accuracy. Conclusion LLMs showed high accuracy and quality for translating radiology reports, although results varied by model and language pair. © RSNA, 2024 <i>Supplemental material is available for this article.</i></p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"313 3","pages":"e241736"},"PeriodicalIF":12.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142839117","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":"2024 Top Images in <i>Radiology</i>: <i>Radiology</i> In Training Editors' Choices.","authors":"Mickael Tordjman, Alessia Guarnera, Carolyn Horst, Aileen O'Shea, Frank Yuan, Kuan Zhang, Francis Deng, Victoria Chernyak, Linda Moy, Simon Lennartz","doi":"10.1148/radiol.243310","DOIUrl":"https://doi.org/10.1148/radiol.243310","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"313 3","pages":"e243310"},"PeriodicalIF":12.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142771767","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}