Radiology最新文献

{"title":"Determinants of Reader-LLM Interaction in Thoracic Radiology: Impact of Model Confidence and Reader Expertise.","authors":"Jiyoung Song, Won Gi Jeong, Dae Hee Han, Hongseok Ko, Soon Ho Yoon, Hyungjin Kim, Phakhanith Naruetook, Wai Ling Leong, Soojin Kim, Hee Eun Moon, Ji Yeong An, So Jung Koo, Meesun Lee, Eui Jin Hwang, Taehee Lee","doi":"10.1148/radiol.253397","DOIUrl":"https://doi.org/10.1148/radiol.253397","url":null,"abstract":"<p><p>Background Large language models (LLMs) provide diagnostic suggestions and rationales, but determinants of successful reader-LLM interaction remain unclear. Purpose To determine how LLM attributes and reader expertise independently and jointly influence the selective integration of diagnostic advice in human-LLM collaboration. Materials and Methods In this retrospective study, 10 readers evaluated 100 chest imaging cases (radiography, CT, MRI, or PET) from the Korean Society of Thoracic Radiology Weekly Case platform (January 2018 to December 2020), unaided (session 1) and randomized to LLM-assisted setups (session 2) at the reader-case level: high accuracy (76% [379 of 500 reader-case pairs]) using OpenAI's GPT-5 or low accuracy (27% [133 of 500]) using OpenAI's GPT-4o (August 2025), providing ranked diagnostic options with rationales against a reference standard established by the case author. The primary outcome was adequate interaction (accepting correct or rejecting incorrect suggestions). Data were analyzed using multivariable generalized estimating equations, adjusted for reader expertise, diagnostic correctness and reader confidence (session 1), model confidence (score assigned to the correct diagnosis), and reference panel-assessed rationale quality. Results A total of 100 patients were included (mean age, 50.0 years ± 16.3 [SD]; 59 male). After multivariable adjustment, model confidence (odds ratio [OR], 3.82 [95% CI: 1.58, 9.25]; <i>P</i> = .003) and reader expertise (OR, 2.06 [95% CI: 1.38, 3.07]; <i>P</i> < .001) were independently associated with adequate interaction, with a weaker effect of confidence among experts (OR, 0.79 [95% CI: 0.67, 0.94]; <i>P</i> = .008). Higher rationale quality reduced rejection of correct suggestions (OR, 0.79 [95% CI: 0.67, 0.93]; <i>P</i> = .005) but increased acceptance of incorrect suggestions (OR, 1.71 [95% CI: 1.47, 1.99]; <i>P</i> < .001). Higher reader expertise (OR, 0.54 [95% CI: 0.41, 0.70]; <i>P</i> < .001) and reader confidence (OR, 0.80 [95% CI: 0.67, 0.94]; <i>P</i> = .007) were protective, reducing acceptance of incorrect suggestions. Conclusion Successful reader-LLM collaboration is associated with model confidence and reader expertise, with rationale quality facilitating correct advice uptake but increasing overreliance on incorrect suggestions. © RSNA, 2026 <i>Supplemental material is available for this article.</i></p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"320 2","pages":"e253397"},"PeriodicalIF":17.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148707675","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":"Radiologically Relevant Clinical History Summarization with Large Language Models: A Multireader Performance Study.","authors":"Adrian Serapio, Timothy L Chen, Brian Tangsombatvisit, Brandon K K Fields, Yannan Yu, Yue Guo, Soo Kyung Kim, Brenda Y Miao, Madhumita Sushil, Christopher P Hess, Sharmila Majumdar, Jae Ho Sohn","doi":"10.1148/radiol.253238","DOIUrl":"https://doi.org/10.1148/radiol.253238","url":null,"abstract":"<p><p>Background Clinical histories accompanying imaging orders guide protocol selection and diagnostic focus. However, they are often incomplete, potentially compromising diagnostic accuracy and workflow efficiency. Purpose To evaluate whether large language models (LLMs) can improve the clinical utility of provided imaging indications by leveraging clinical notes. Materials and Methods This retrospective study curated a dataset from deidentified electronic health records at the University of California San Francisco (January 2012 to August 2024), consisting of radiology reports with paired referring clinician-provided and radiologist-curated indications linked to clinical notes. The dataset was stratified across five body systems and five pathophysiologic categories to derive LLM selection and reader study internal test sets. For the reader study, 20 radiologists with 2-25 years of experience compared indications from the referring clinician, radiologist, and best-performing LLMs. Readers scored comprehensiveness, factuality, and conciseness and ranked indications for usefulness in protocoling, usefulness in interpretation, and overall ranking. Models and clinicians were compared using cumulative link mixed models with Tukey-adjusted post hoc comparisons. Results From 28 313 patients (mean age, 59 years ± 20.6 [SD]; 14 912 women), 250 examinations from 247 patients were sampled for the reader study. After nine exclusions, 241 examinations were analyzed, yielding 482 reader-examination evaluations. Indications from the best-performing proprietary (Claude 3.5 Sonnet; Anthropic) and open-source (Qwen 2.5-7B Instruct; Alibaba) LLM were rated as more comprehensive (Likert rating of 5: 37.14% and 28.42%, respectively; both <i>P</i> < .001) and factual (68.05% and 59.75%; both <i>P</i> < .001) than referring clinician indications. The proprietary LLM ranked most useful in protocoling (rank 1: 40.87%; all <i>P</i> < .001), useful in interpretation (44.61%; all <i>P</i> < .001), and overall ranking (44.19%, all <i>P</i> < .001). Comprehensiveness (65.77% of ratings; both <i>P</i> < .001) most strongly influenced overall rankings. Conclusion LLMs generated radiology-relevant indications from clinical notes that were more comprehensive and factual than clinician indications, and when generated by the proprietary LLM, were ranked most useful in protocoling and imaging interpretation. © RSNA, 2026 <i>Supplemental material is available for this article.</i> See also the editorial by Yilmaz and Cardoza-Ochoa in this issue.</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"320 2","pages":"e253238"},"PeriodicalIF":17.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148670590","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":"Contrast-enhanced CT Radiomics for High-Grade Pattern Identification and Prognostic Stratification in Lung Adenocarcinoma with Consolidation-to-Tumor Ratio of 25% or More.","authors":"Jiahui E, Liuqing Kang, Fan Liu, Dianzhe Wang, Jingyi Yang, Xiaoyan Lu, Yong Huang, Jing Li, Yicai Zhang, Qiliang Wang, Xiaoting Cai, Bole Gao, Zhuo Ning, Ying Liu","doi":"10.1148/radiol.260264","DOIUrl":"10.1148/radiol.260264","url":null,"abstract":"<p><p>Background Radiomics may preoperatively identify high-grade patterns (HGPs) in lung adenocarcinoma (ADC) and assist in clinical decision-making. Purpose To develop and evaluate a machine learning model based on preoperative contrast-enhanced CT images to predict HGPs and explore the model's prognostic value. Materials and Methods Patients with clinical stage I invasive ADC who underwent surgery (January 2017 to May 2025) were retrospectively enrolled from three centers. Binary (low risk: HGPs < 20%; high risk: HGPs ≥ 20%) and ternary (HGP0: HGPs = 0; HGP1: 0 < HGPs < 20%; HGP2: HGPs ≥ 20%) classification analyses were performed based on the proportion of HGPs. Multivariable logistic regression analysis was used to determine independent predictors of HGPs. XGBoost classifier-based radiomic models and combined models (radiomics-predicted probabilities plus clinical variables plus CT semantic features) were constructed and evaluated for discriminability, calibration ability, and clinical utility. Kaplan-Meier and Cox regression analyses were conducted to identify prognostic factors for overall survival (OS) and recurrence-free survival (RFS). Results A total of 1181 patients (median age, 61 years [IQR, 54-66 years]; 694 female) were allocated to the training (<i>n</i> = 667), internal test (<i>n</i> = 279), and external test (<i>n</i> = 235) sets. The combined model achieved the best discrimination in both binary (training: area under the receiver operating characteristic curve [AUC], 0.87 [95% CI: 0.84, 0.90]; internal test: AUC, 0.80 [95% CI: 0.74, 0.85]; external test: AUC, 0.84 [95% CI: 0.78, 0.90]) and ternary (training: microaverage AUC, 0.80 [95% CI: 0.78, 0.82]; internal test: microaverage AUC, 0.74 [95% CI: 0.70, 0.77]; external test: microaverage AUC, 0.72 [95% CI: 0.68, 0.76]) classification analyses. Model-predicted high-risk group was an independent prognostic factor for both OS (binary: hazard ratio [HR] = 1.98, <i>P</i> =.04; ternary: HR = 2.93, <i>P</i> =.03) and RFS (binary: HR = 3.33, <i>P</i> < .001; ternary: HR = 5.10, <i>P</i> < .001) and was consistently confirmed across subgroup analyses. Conclusion The combined model, integrating clinical variables, CT semantic features, and radiomics-predicted probabilities, effectively predicted high-grade patterns in lung ADC and showed strong potential for prognostic risk stratification. © RSNA, 2026 <i>Supplemental material is available for this article.</i> See also the editorial by Arita and Kocak in this issue.</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"320 2","pages":"e260264"},"PeriodicalIF":17.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148707585","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":"\"Targeted\" Imaging: The Inevitable Fate of Cancer Therapy.","authors":"Egesta Lopci","doi":"10.1148/radiol.262021","DOIUrl":"https://doi.org/10.1148/radiol.262021","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"320 2","pages":"e262021"},"PeriodicalIF":17.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148797492","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 Value of Elastography, Microvascularity, and Classic US Findings in Evaluating Soft Tissue Tumors.","authors":"Kenneth S Lee, Zachary E Stewart","doi":"10.1148/radiol.262155","DOIUrl":"https://doi.org/10.1148/radiol.262155","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"320 2","pages":"e262155"},"PeriodicalIF":17.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148813964","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":"A Vascular Perspective on Osteoarthritis Progression.","authors":"Mohamed Jarraya, Hamid Harandi","doi":"10.1148/radiol.261915","DOIUrl":"https://doi.org/10.1148/radiol.261915","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"320 2","pages":"e261915"},"PeriodicalIF":17.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148670410","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":"Photon-counting CT Extracellular Volume Fraction for Liver Fibrosis Assessment: Validation against MR Elastography and Histopathology.","authors":"Xinyu Wu, Jinzhe Li, Rong Deng, Xinxin Xu, Jiahao Zhou, Huanhuan Chong, Weixia Li, Peng Wu, Yongjun Chen, Yuchen Yang, Rui Chang, Haipeng Dong, Ruokun Li, Ingolf Sack, Jing Guo, Zhihan Xu, Bernhard Schmidt, Amir A Borhani, Fuhua Yan, Huimin Lin","doi":"10.1148/radiol.253979","DOIUrl":"https://doi.org/10.1148/radiol.253979","url":null,"abstract":"<p><p>Background Photon-counting CT (PCCT) can theoretically be used to quantify extracellular volume fraction (ECV) for liver fibrosis assessment, but its performance remains unclear. Purpose To determine the feasibility of PCCT-derived ECV for staging liver fibrosis by assessing its correlation with MR elastography-derived liver stiffness measurement (LSM) and comparing their diagnostic performance, using histopathologic findings as the reference standard. Materials and Methods Between July 2024 and July 2025, 157 participants with suspected hepatic malignancies were prospectively enrolled and underwent PCCT and MRI at Ruijin Hospital. Intraparticipant comparisons of ECV and LSM were performed, and their diagnostic performance in staging liver fibrosis was evaluated using histopathologic findings as the reference standard. Subgroup analyses of fibrosis staging using ECV were performed among participants with coexisting steatosis or inflammation and participants with a body mass index of 25 or higher. Correlation, receiver operating characteristic, and equivalence analyses were performed. Results Ultimately, 139 participants (mean age, 62 years ± 8 [SD]; 114 male) were included. ECV was strongly correlated with LSM (Spearman ρ = 0.84; <i>P</i> < .001) in an intraparticipant-level comparison. When histopathologic examination was used as the reference standard, the area under the receiver operating characteristic curve (AUC) values for ECV were 0.99 (95% CI: 0.98, 1.00), 0.98 (95% CI: 0.96, 1.00), and 0.98 (95% CI: 0.96, 1.00) for participants with fibrosis stages F2 or higher, F3 or higher, and F4, respectively. Equivalence analysis revealed comparable diagnostic performance between ECV and LSM (95% CI differences in AUCs: 0.005, 0.055 for stage F2 or higher; -0.015, 0.027 for stage F3 or higher; and -0.021, 0.012 for stage F4). With use of the cutoffs derived from the whole-group analysis, ECV showed excellent agreement with histopathologic stage overall and across each subgroup (all weighted κ coefficients ≥0.86; <i>P</i> < .001 for all). Conclusion PCCT-derived ECV was strongly correlated with MR elastography-derived LSM and showed equivalent, clinically feasible performance for liver fibrosis staging. © RSNA, 2026 <i>Supplemental material is available for this article.</i> See also the editorial by Wu and Shi in this issue.</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"320 2","pages":"e253979"},"PeriodicalIF":17.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148670458","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 Airway Osteochondroma with a \"Centipede-like\" Appearance.","authors":"Xilai Chen, Yu Deng","doi":"10.1148/radiol.260896","DOIUrl":"https://doi.org/10.1148/radiol.260896","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"320 2","pages":"e260896"},"PeriodicalIF":17.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148707603","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":"T2 Mapping Reaches the Joint: A Solution to a Classic Bedside Dilemma.","authors":"Roque Oca Pernas","doi":"10.1148/radiol.261981","DOIUrl":"https://doi.org/10.1148/radiol.261981","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"320 2","pages":"e261981"},"PeriodicalIF":17.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148707189","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":"Pseudoprogression with Bone Erosion.","authors":"Petr Szturz, Vincent Dunet","doi":"10.1148/radiol.260123","DOIUrl":"https://doi.org/10.1148/radiol.260123","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"320 2","pages":"e260123"},"PeriodicalIF":17.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148797462","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}