Radiology最新文献

{"title":"Three-Dimensional Vector MR Elastography for Evaluating Tissue Mechanical Heterogeneity to Assess Liver Disease Progression.","authors":"Hao Wu, Zheng Zhu, Jiahui Li, Caixin Qiu, Peng Xu, Kevin J Glaser, Matthew C Murphy, Sudhakar K Venkatesh, Usman Yaqoob, Rondell Graham, Taofic Mounajjed, Armando Manduca, Christopher T Winkelmann, Hiroaki Yashiro, Rohan Manohar, Alina M Allen, Vijay H Shah, Richard L Ehman, Meng Yin","doi":"10.1148/radiol.242349","DOIUrl":"https://doi.org/10.1148/radiol.242349","url":null,"abstract":"<p><p>Background Metabolic dysfunction-associated steatotic liver disease (MASLD) is a growing global health challenge, with evidence indicating that hepatic inflammation and fibrosis are heterogeneous processes. Purpose To measure liver mechanical property heterogeneity using MR elastography (MRE) and evaluate its potential as a biomarker for tissue inflammation and fibrosis in patients with MASLD. Materials and Methods Mechanical tissue heterogeneity in MASLD was assessed at three-dimensional vector MRE pixel-wise histogram analysis of shear stiffness and loss modulus in preclinical and clinical studies. The preclinical study involved 25 rats that were examined monthly, whereas the clinical study analyzed data from 179 participants across two prospective studies (September 2015 to November 2022), including some who underwent bariatric surgery at pretreatment and posttreatment MRE examinations. Mean and coefficient of variation (CV) of shear stiffness and loss modulus were calculated for each examination. Nonparametric tests and Spearman correlation coefficient were used to compare MRE-derived tissue mechanics with biopsy-confirmed fibrosis and inflammation and assess correlations with portal pressure and histopathologic hepatic fibrosis. Results The preclinical study showed that, in cirrhotic livers, CV of loss modulus positively correlated with portal pressure and fibrosis area ratio variation (ρ = 0.52 [<i>P</i> = .008] and 0.55 [<i>P</i> = .005], respectively). The clinical study showed that, in 10 healthy volunteers (median age, 36.5 years; IQR, 34.0-38.8 years; five females) and 169 participants with MASLD (median age, 50.1 years; IQR, 41.0-58.2 years; 118 females), CV of sheer stiffness (from 0.12 to 0.30 in healthy participants to participants with stage 4 fibrosis) and loss modulus (from 0.31 to 0.51 in healthy participants to participants with grade 3 inflammation) increased with increasing severity of fibrosis and inflammation, respectively. In 36 participants who underwent bariatric surgery, the CV of sheer stiffness decreased at the 1-year follow-up, from 0.16 (IQR, 0.14-0.18) to 0.14 (IQR, 0.12-0.16) (<i>P</i> = .009). Conclusion Tissue mechanical heterogeneity assessed at MRE positively correlated with progression of MASLD, demonstrating potential as a biomarker for liver disease severity and therapeutic intervention. ClinicalTrials.gov Identifier: NCT02565446 Published under a CC BY 4.0 license. <i>Supplemental material is available for this article.</i> See also the editorial by Moura Cunha in this issue.</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"315 1","pages":"e242349"},"PeriodicalIF":12.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143754341","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":"Evaluating Whether Standalone AI Really Stands Up.","authors":"Liane E Philpotts","doi":"10.1148/radiol.250364","DOIUrl":"https://doi.org/10.1148/radiol.250364","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"315 1","pages":"e250364"},"PeriodicalIF":12.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143804174","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":"\"Fill States\": PET-derived Markers of the Spatial Extent of Alzheimer Disease Pathology.","authors":"Elena Doering, Merle C Hoenig, Kathrin Giehl, Verena Dzialas, Grégory Andrassy, Abdelmajid Bader, Andreas Bauer, David Elmenhorst, Johannes Ermert, Silke Frensch, Elena Jäger, Frank Jessen, Philipp Krapf, Tina Kroll, Christoph Lerche, Julia Lothmann, Andreas Matusch, Bernd Neumaier, Oezguer A Onur, Alfredo Ramirez, Nils Richter, Frederik Sand, Lutz Tellmann, Hendrik Theis, Philip Zeyen, Thilo van Eimeren, Alexander Drzezga, Gérard N Bischof","doi":"10.1148/radiol.241482","DOIUrl":"10.1148/radiol.241482","url":null,"abstract":"<p><p>Background Alzheimer disease (AD) progression can be monitored by tracking intensity changes in PET standardized uptake value (SUV) ratios of amyloid, tau, and neurodegeneration. The spatial extent (\"fill state\") of these three hallmark pathologic abnormalities may serve as critical pathophysiologic information, pending further investigation. Purpose To examine the clinical utility and increase the accessibility of PET-derived fill states. Materials and Methods This secondary analysis of two prospective studies used data from two independent cohorts: the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Tau Propagation over Time study (T-POT). Each cohort comprised amyloid-negative cognitively normal individuals (controls) and patients with subjective cognitive decline, mild cognitive impairment, or probable-AD dementia. Fill states of amyloid, tau, and neurodegeneration were computed as the percentages of significantly abnormal voxels relative to controls across PET scans. Fill states and SUV ratios were compared across stages (Kruskal-Wallis <i>H</i> test, area under the receiver operating characteristic curve analysis) and tested for association with the severity of cognitive impairment (Spearman correlation, multivariate regression analysis). Additionally, a convolutional neural network (CNN) was developed to estimate fill states from patients' PET scans without requiring controls. Results The ADNI cohort included 324 individuals (mean age, 72 years ± 6.8 [SD]; 173 [53%] female), and the T-POT cohort comprised 99 individuals (mean age, 66 years ± 8.7; 63 [64%] female). Higher fill states were associated with higher stages of cognitive impairment (<i>P</i> < .001), and tau and neurodegeneration fill states showed higher diagnostic performance for cognitive impairment compared with SUV ratio (<i>P</i> < .05) across cohorts. Similarly, all fill states were negatively correlated with cognitive performance (<i>P</i> < .001) and uniquely characterized the degree of cognitive impairment even after adjustment for SUV ratio (<i>P</i> < .05). The CNN estimated amyloid and tau accurately, but not neurodegeneration fill states. Conclusion Fill states provided reliable markers of AD progression, potentially improving early detection, staging, and monitoring of AD in clinical practice and trials beyond SUV ratio. Clinical trial registration no. NCT00106899 © RSNA, 2025 <i>Supplemental material is available for this article.</i> See also the editorial by Yun and Kim in this issue.</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"314 3","pages":"e241482"},"PeriodicalIF":12.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11950890/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143701251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI in Prostate Cancer Diagnosis with MRI: A Friend Not a Foe.","authors":"Carla Harmath","doi":"10.1148/radiol.250525","DOIUrl":"https://doi.org/10.1148/radiol.250525","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"314 3","pages":"e250525"},"PeriodicalIF":12.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143701256","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":"Efficacy of Traditional Epidural Patching versus Patching within Spinal Longitudinal Extradural Collections for Ventral Dural Cerebrospinal Fluid Leaks.","authors":"Andrew L Callen, Samantha L Pisani Petrucci, Peter Lennarson, Mark F Sedrak, Adriana Gutierrez, Mark D Mamlouk","doi":"10.1148/radiol.242194","DOIUrl":"https://doi.org/10.1148/radiol.242194","url":null,"abstract":"<p><p>Background Epidural blood patching is frequently used to treat spontaneous intracranial hypotension (SIH) due to cerebrospinal fluid leaks. However, its effectiveness in sealing ventral dural tears, particularly in chronic cases with organized spinal longitudinal extradural collections (SLECs), is not well documented. Purpose To assess the efficacy of intra-SLEC patching compared with traditional patching for treatment of ventral dural tears. Materials and Methods This two-site retrospective cross-sectional study conducted between January 2019 and July 2024 included patients with SIH due to a ventral dural tear who underwent epidural patching. Organized SLECs, characterized by sharply demarcated, convex edges and confined to the ventral epidural space, were distinguished from unorganized SLECs, which show fluid distribution in both ventral and dorsal spaces. The Fisher exact test was used to compare the complication rate between treatment groups, and the χ² test was used to compare the proportion of patients with SLEC resolution between treatment groups. Results Fifty-two patients (mean age, 44.9 years ± 9.5 [SD]; 30 male patients) were included; before treatment, 39 had organized SLECs and 13 had unorganized SLECs. Overall, 25% (13 of 52) of patients had SLEC resolution after treatment. Organized SLECs were less likely to resolve than unorganized SLECs (six of 39 [15%] vs seven of 13 [54%]; <i>P</i> = .02). In patients with organized SLECs, intra-SLEC patching had a higher success rate (33%; five of 15) than traditional patching (4%; one of 24; <i>P</i> = .046). Multivariable analysis showed that intra-SLEC patching (odds ratio, 13.24 [95% CI: 1, 149]; <i>P</i> = .04) and unorganized SLECs (odds ratio, 21.47 [95% CI: 2, 216]; <i>P</i> = .009) were associated with higher odds of SLEC resolution. Conclusion In 25% of patients with SIH, MRI performed after epidural blood patching showed resolution of the SLEC. Intra-SLEC patching was more effective than traditional patching for treating organized SLECs. © RSNA, 2025 See also the editorial by Urbach in this issue.</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"314 3","pages":"e242194"},"PeriodicalIF":12.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143701311","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":"Multiparametric MRI for Bladder Cancer: A Practical Approach to the Clinical Application of VI-RADS.","authors":"Martina Pecoraro, Stefano Cipollari, Emanuele Messina, Ludovica Laschena, Ailin Dehghanpour, Antonella Borrelli, Francesco Del Giudice, Valdair Francisco Muglia, Hebert Alberto Vargas, Valeria Panebianco","doi":"10.1148/radiol.233459","DOIUrl":"https://doi.org/10.1148/radiol.233459","url":null,"abstract":"<p><p>Multiparametric MRI of the bladder is highly accurate in the detection and local staging of bladder cancer. The Vesical Imaging Reporting and Data System (VI-RADS) scoring system has improved the diagnostic accuracy, reproducibility, and interpretability of bladder MRI in the assessment of the invasion of the muscularis propria. There are several technical details concerning bladder MRI that need to be strictly applied to obtain the highest possible diagnostic potential from the MRI. In addition, image evaluation, accurate interpretation, and reporting need to be standardized to optimize diagnostic accuracy and interreader agreement. This review describes the patient population for bladder MRI and discusses, with a practical approach, the correct acquisition protocol for optimal image quality using VI-RADS with reporting tips, pitfalls, and challenges for its clinical application. This review also discusses the latest evidence, clinical implications, current controversies, and future challenges, including gaps in knowledge, of the VI-RADS scoring system.</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"314 3","pages":"e233459"},"PeriodicalIF":12.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143543188","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":"Editor's Note 2024: The Year in Review for <i>Radiology</i>.","authors":"Linda Moy","doi":"10.1148/radiol.250376","DOIUrl":"https://doi.org/10.1148/radiol.250376","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"314 3","pages":"e250376"},"PeriodicalIF":12.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143656884","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 AI Reporting of Chest Radiographs: Promise and Perspectives.","authors":"Brent P Little","doi":"10.1148/radiol.250234","DOIUrl":"https://doi.org/10.1148/radiol.250234","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"314 3","pages":"e250234"},"PeriodicalIF":12.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143701339","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":"Radiologists Were Wrong to Mistrust the Machines.","authors":"Lars J Grimm","doi":"10.1148/radiol.250402","DOIUrl":"https://doi.org/10.1148/radiol.250402","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"314 3","pages":"e250402"},"PeriodicalIF":12.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143658178","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 and Clinical Value of a Domain-specific Multimodal Generative AI Model for Chest Radiograph Report Generation.","authors":"Eun Kyoung Hong, Jiyeon Ham, Byungseok Roh, Jawook Gu, Beomhee Park, Sunghun Kang, Kihyun You, Jihwan Eom, Byeonguk Bae, Jae-Bock Jo, Ok Kyu Song, Woong Bae, Ro Woon Lee, Chong Hyun Suh, Chan Ho Park, Seong Jun Choi, Jai Soung Park, Jae-Hyeong Park, Hyun Jeong Jeon, Jeong-Ho Hong, Dosang Cho, Han Seok Choi, Tae Hee Kim","doi":"10.1148/radiol.241476","DOIUrl":"https://doi.org/10.1148/radiol.241476","url":null,"abstract":"<p><p>Background Generative artificial intelligence (AI) is anticipated to alter radiology workflows, requiring a clinical value assessment for frequent examinations like chest radiograph interpretation. Purpose To develop and evaluate the diagnostic accuracy and clinical value of a domain-specific multimodal generative AI model for providing preliminary interpretations of chest radiographs. Materials and Methods For training, consecutive radiograph-report pairs from frontal chest radiography were retrospectively collected from 42 hospitals (2005-2023). The trained domain-specific AI model generated radiology reports for the radiographs. The test set included public datasets (PadChest, Open-i, VinDr-CXR, and MIMIC-CXR-JPG) and radiographs excluded from training. The sensitivity and specificity of the model-generated reports for 13 radiographic findings, compared with radiologist annotations (reference standard), were calculated (with 95% CIs). Four radiologists evaluated the subjective quality of the reports in terms of acceptability, agreement score, quality score, and comparative ranking of reports from <i>(a)</i> the domain-specific AI model, <i>(b)</i> radiologists, and <i>(c)</i> a general-purpose large language model (GPT-4Vision). Acceptability was defined as whether the radiologist would endorse the report as their own without changes. Agreement scores from 1 (clinically significant discrepancy) to 5 (complete agreement) were assigned using RADPEER; quality scores were on a 5-point Likert scale from 1 (very poor) to 5 (excellent). Results A total of 8 838 719 radiograph-report pairs (training) and 2145 radiographs (testing) were included (anonymized with respect to sex and gender). Reports generated by the domain-specific AI model demonstrated high sensitivity for detecting two critical radiographic findings: 95.3% (181 of 190) for pneumothorax and 92.6% (138 of 149) for subcutaneous emphysema. Acceptance rate, evaluated by four radiologists, was 70.5% (6047 of 8680), 73.3% (6288 of 8580), and 29.6% (2536 of 8580) for model-generated, radiologist, and GPT-4Vision reports, respectively. Agreement scores were highest for the model-generated reports (median = 4 [IQR, 3-5]) and lowest for GPT-4Vision reports (median = 1 [IQR, 1-3]; <i>P</i> < .001). Quality scores were also highest for the model-generated reports (median = 4 [IQR, 3-5]) and lowest for the GPT-4Vision reports (median = 2 [IQR, 1-3]; <i>P</i> < .001). From the ranking analysis, model-generated reports were most frequently ranked the highest (60.0%; 5146 of 8580), and GPT-4Vision reports were most frequently ranked the lowest (73.6%; 6312 of 8580). Conclusion A domain-specific multimodal generative AI model demonstrated potential for high diagnostic accuracy and clinical value in providing preliminary interpretations of chest radiographs for radiologists. © RSNA, 2025 <i>Supplemental material is available for this article.</i> See also the editorial by Little in this issue.</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"314 3","pages":"e241476"},"PeriodicalIF":12.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143701323","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}