Radiology最新文献

{"title":"Accuracy of Dual-Energy CT-derived Fat Maps and Bone Marrow Edema Maps in Pedal Osteomyelitis Diagnosis.","authors":"Christoph Stern, Andrea B Rosskopf, Adrian A Marth, Georg C Feuerriegel, Martin C Berli, Benjamin Fritz, Reto Sutter","doi":"10.1148/radiol.232900","DOIUrl":"10.1148/radiol.232900","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> Background In patients who cannot undergo MRI, dual-energy CT (DECT) with bone marrow edema (BME) maps are used as an approach for diagnosing pedal osteomyelitis, but with lower accuracy. Purpose To compare the diagnostic accuracy of additional bone marrow fat maps with that of DECT with BME maps and MRI for pedal osteomyelitis detection. Materials and Methods In this prospective study, thirty-one participants with clinically suspected osteomyelitis of the mid- and forefoot underwent noncontrast DECT (80 kV/140 kV) and MRI between October 2020 and February 2022. With image postprocessing, DECT-derived BME and fat maps were generated. Four independent readers evaluated 3 different image sets for osteomyelitis: DECT and BME maps (set 1); DECT, BME maps and fat maps (set 2); and MRI (set 3). Sensitivity, specificity and accuracy were calculated for each image set, with clinical and microbiological data as the reference standards. In a subanalysis, the DECT BME map, DECT fat map and DECT erosion map were analyzed for their accuracy in predicting bone marrow fat loss at T1-weighted MRI. Results Of the 31 participants included in the study (mean age, 61.7 years ±14.6 [SD]; 21 males) 17 (55%) had osteomyelitis. Sensitivity, specificity and accuracy for detecting osteomyelitis were 47% (8/17), 79% (11/14), and 61% (19/31) (set 1); 77% (13/17), 86% (12/14) and 81% (25/31) (set 2); and 82% (14/17), 93% (13/14) and 87% (27/31) (set 3), respectively. Thirty-one of 661 individual bones (0.5%) showed bone marrow fat loss on T1-weighted MRI; in the subanalysis, DECT fat map specificity was higher than that of the DECT BME map for predicting bone marrow fat loss in individual bones (97% (612/630) vs. 89% (560/630)) (P<.001). Conclusion Pedal osteomyelitis detection with novel DECT-derived fat map imaging in addition to DECT and BME maps was accurate. See also the editorial by Khurana in this issue.</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"315 1","pages":"e232900"},"PeriodicalIF":12.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143754332","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":"Distinct Functional MRI Connectivity Patterns and Cortical Volume Variations Associated with Repetitive Blast Exposure in Special Operations Forces Members.","authors":"Andrea Diociasi, Mary A Iaccarino, Scott Sorg, Emily J Lubin, Caroline Wisialowski, Amol Dua, Can Ozan Tan, Rajiv Gupta","doi":"10.1148/radiol.233264","DOIUrl":"10.1148/radiol.233264","url":null,"abstract":"&lt;p&gt;&lt;p&gt;Background Special operations forces members often face multiple blast injuries and have a higher risk of traumatic brain injury. However, the relationship between neuroimaging markers, the cumulative severity of injury, and long-term symptoms has not previously been well-established in the literature. Purpose To determine the relationship between the frequency of blast injuries, persistent clinical symptoms, and related cortical volumetric and functional connectivity (FC) changes observed at brain MRI in special operations forces members. Materials and Methods A cohort of 220 service members from a prospective study between January 2021 and May 2023 with a history of repetitive blast exposure underwent psychodiagnostics and a comprehensive neuroimaging evaluation, including structural and resting-state functional MRI (fMRI). Of these, 212 met the inclusion criteria. Participants were split into two datasets for model development and validation, and each dataset was divided into high- and low-exposure groups based on participants' exposure to various explosives. Differences in FC were analyzed using a general linear model, and cortical gray matter volumes were compared using the Mann-Whitney &lt;i&gt;U&lt;/i&gt; test. An external age- and sex-matched healthy control group of 212 participants was extracted from the SRPBS Multidisorder MRI Dataset for volumetric analyses. A multiple linear regression model was used to assess correlations between clinical scores and FC, while a logistic regression model was used to predict exposure group from fMRI scans. Results In the 212 participants (mean age, 43.0 years ± 8.6 [SD]; 160 male [99.5%]) divided into groups with low or high blast exposure, the high-exposure group had higher scores for the Neurobehavioral Symptom Inventory (NSI) (&lt;i&gt;t&lt;/i&gt; = 3.16, &lt;i&gt;P&lt;/i&gt; &lt; .001) and Posttraumatic Stress Disorder Checklist for &lt;i&gt;Diagnostic and Statistical Manual of Mental Disorders&lt;/i&gt; (Fifth Edition) (PCL-5) (&lt;i&gt;t&lt;/i&gt; = 2.72, &lt;i&gt;P&lt;/i&gt; = .01). FC differences were identified in the bilateral superior and inferior lateral occipital cortex (LOC) (&lt;i&gt;P&lt;/i&gt; value range, .001-.04), frontal medial cortex (&lt;i&gt;P&lt;/i&gt; &lt; .001), left superior frontal gyrus (&lt;i&gt;P&lt;/i&gt; &lt; .001), and precuneus (&lt;i&gt;P&lt;/i&gt; value range, .02-.03). Clinical scores from NSI and PCL-5 were inversely correlated with FC in the LOC, superior parietal lobule, precuneus, and default mode networks (&lt;i&gt;r&lt;/i&gt; = -0.163 to -0.384; &lt;i&gt;P&lt;/i&gt; value range, &lt;.001 to .04). The high-exposure group showed increased cortical volume in regions of the LOC compared with healthy controls and the low-exposure group (&lt;i&gt;P&lt;/i&gt; value range, .01-.04). The predictive model helped accurately classify participants into high- and low-exposure groups based on fMRI data with 88.00 sensitivity (95% CI: 78.00, 98.00), 67% specificity (95% CI: 53.00, 81.00), and 73% accuracy (95% CI: 60.00, 86.00). Conclusion Repetitive blast exposure leads to distinct alterations in FC and cortical volume, which corr","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"315 1","pages":"e233264"},"PeriodicalIF":12.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143754266","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 International Expert Consensus on US-guided Thermal Ablation for T1N0M0 Papillary Thyroid Cancer.","authors":"Zhen-Long Zhao, Shu-Rong Wang, Jennifer Kuo, Bülent Çekiç, Lei Liang, Hossam Arafa Ghazi, Shu-Hang Xu, Gerardo Amabile, Song-Song Wu, Ajit Yadav, Gang Dong, Ingo Janssen, Bo-Qiang Fan, Nobuhiro Fukunari, Jun-Feng He, Le Thanh Dung, Song-Yuan Yu, Sum Leong, Jian-Jun Yu, Yi-Hong Chou, Rafael De Cicco, Ying Che, Kai-Lun Cheng, Emad Kandil, Wei-Che Lin, Dong Xu, Jonathon Russell, Man Lu, Ralph H. Tufano, Lin-Xue Qian, Gregory W Randolph, Jian-Qiao Zhou, Giovanni Mauri, Hong-Hui Su, Marika Russell, Amr H. Abdelhamid Ahmed, Kaustubh Patel, Jung Hwan Baek, Ji-Hoon Kim, Ying Wei, Ming-An Yu","doi":"10.1148/radiol.240347","DOIUrl":"10.1148/radiol.240347","url":null,"abstract":"<p><p>Thermal ablation has started to gain acceptance as a therapeutic approach for the management of papillary thyroid cancer (PTC), mainly in the T1 stage (tumor size ≤ 2 cm in greatest dimension and limited to the thyroid). Despite its increasing popularity, a lack of uniformity in the technical details persists, and existing guidelines do not fully align with recent research advancements. To standardize the use of US-guided thermal ablation for T1N0M0 PTC, a panel of experts jointly issued this expert consensus on thermal ablation for PTC. This consensus was developed by experts with specific competence and expertise in this field through rounds of the modified Delphi method. An evidence-based approach incorporating the practical experience of the panelists was adopted. The consensus was developed through extensive discussions, systematic literature review with meta-analysis, and expert evaluations. In this consensus, a total of 27 recommendations are made, addressing the indications and contraindications for thermal ablation for PTC, physician training, preoperative preparation, technical procedures, complications, efficacy assessment, and follow-up strategies. This expert consensus provides up-to-date, high-quality, standardized guidance to harmonize treatment practices, enhance patient outcomes, and potentially shape future research and policy developments in the management of T1N0M0 PTC using US-guided thermal ablation.</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"315 1","pages":"e240347"},"PeriodicalIF":12.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143996290","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":"Deep Learning Applications in Imaging of Acute Ischemic Stroke: A Systematic Review and Narrative Summary.","authors":"Bin Jiang, Nancy Pham, Eric K van Staalduinen, Yongkai Liu, Sanaz Nazari-Farsani, Amirhossein Sanaat, Henk van Voorst, Ates Fettahoglu, Donghoon Kim, Jiahong Ouyang, Ashwin Kumar, Aditya Srivatsan, Ramy Hussein, Maarten G Lansberg, Fernando Boada, Greg Zaharchuk","doi":"10.1148/radiol.240775","DOIUrl":"10.1148/radiol.240775","url":null,"abstract":"<p><p>Background Acute ischemic stroke (AIS) is a major cause of morbidity and mortality, requiring swift and precise clinical decisions based on neuroimaging. Recent advances in deep learning-based computer vision and language artificial intelligence (AI) models have demonstrated transformative performance for several stroke-related applications. Purpose To evaluate deep learning applications for imaging in AIS in adult patients, providing a comprehensive overview of the current state of the technology and identifying opportunities for advancement. Materials and Methods A systematic literature review was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A comprehensive search of four databases from January 2016 to January 2024 was performed, targeting deep learning applications for imaging of AIS, including automated detection of large vessel occlusion and measurement of Alberta Stroke Program Early CT Score. Articles were selected based on predefined inclusion and exclusion criteria, focusing on convolutional neural networks and transformers. The top-represented areas were addressed, and the relevant information was extracted and summarized. Results Of 380 studies included, 171 (45.0%) focused on stroke lesion segmentation, 129 (33.9%) on classification and triage, 31 (8.2%) on outcome prediction, 15 (3.9%) on generative AI and large language models, and 11 (2.9%) on rapid or low-dose imaging specific to stroke applications. Detailed data extraction was performed for 68 studies. Public AIS datasets are also highlighted, for researchers developing AI models for stroke imaging. Conclusion Deep learning applications have permeated AIS imaging, particularly for stroke lesion segmentation. However, challenges remain, including the need for standardized protocols and test sets, larger public datasets, and performance validation in real-world settings. © RSNA, 2025 <i>Supplemental material is available for this article.</i></p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"315 1","pages":"e240775"},"PeriodicalIF":12.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143804172","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 New Step Forward in the Extraction of Appropriate Radiology Reports.","authors":"Koichiro Yasaka, Osamu Abe","doi":"10.1148/radiol.250867","DOIUrl":"https://doi.org/10.1148/radiol.250867","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"315 1","pages":"e250867"},"PeriodicalIF":12.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143977226","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":"Comparison of Deuterium Metabolic Imaging with FDG PET in Alzheimer Disease.","authors":"Nikolaj Bøgh, Malene Aastrup, Janne K Mortensen, Hanne Gottrup, Jakob U Blicher, Per Borghammer, Mattias H Kristensen, Esben S S Hansen, Michael Vaeggemose, Christoffer Laustsen","doi":"10.1148/radiol.241808","DOIUrl":"10.1148/radiol.241808","url":null,"abstract":"<p><p>Background The approval of amyloid-targeting therapies has made it increasingly important to differentiate Alzheimer disease (AD) from other causes of dementia. Dysfunctional glucose metabolism is a recognized pathophysiological element in AD that may be visualized with spectroscopic MRI of deuterated glucose and its metabolites, also known as deuterium metabolic imaging (DMI). Purpose To explore the potential of DMI as a diagnostic tool for AD. Materials and Methods In this prospective cross-sectional study, participants with newly diagnosed AD and age-matched controls were recruited from April to October 2023. DMI was performed with a 3-T system equipped with a proton/deuterium head coil following oral consumption of 75 g of deuterated glucose. Clinical fluorodeoxyglucose (FDG) PET data were acquired from patient records for comparison. The predefined primary outcome, the ratio between lactate and glutamine plus glutamate (Glx) at DMI, was analyzed using age-corrected linear mixed-effect models. Results Ten participants with AD (mean age, 72 years ± 6 [SD]; six women) and five age-matched healthy controls (mean age, 68 years ± 7; four men) were included. The primary analysis revealed no evidence of a difference in the ratio of lactate to Glx between participants with AD and controls (<i>P</i> = .24 across all regions of interest). Exploratory analyses revealed that participants with AD had reduced signals for medial temporal lactate (0.7 ± 0.2 vs 0.5 ± 0.1, <i>P</i> = .04) and Glx (0.5 ± 0.03 vs 0.48 ± 0.05, <i>P</i> = .03) compared with controls. Finally, a strong correlation (<i>r</i> = 0.73) was observed between DMI and FDG PET. Conclusion This study did not find evidence to support a shift from oxidative to anaerobic metabolism in AD. Exploratory analyses revealed a decrease in glucose metabolism in the medial temporal lobe. In extension hereof, a similar distribution of low DMI metabolism and decreased FDG PET glucose uptake was observed. © RSNA, 2025 <i>Supplemental material is available for this article.</i> See also the article by Liu et al in this issue. See also the editorial by Port in this issue.</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"315 1","pages":"e241808"},"PeriodicalIF":12.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143804170","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":"Deep Learning-based Quantitative CT Myocardial Perfusion Imaging and Risk Stratification of Coronary Artery Disease.","authors":"Yarong Yu, Dijia Wu, Jiajun Yuan, Lihua Yu, Xu Dai, Wenli Yang, Ziting Lan, Jiayu Wang, Ze Tao, Yiqiang Zhan, Runjianya Ling, Xiaomei Zhu, Yi Xu, Yuehua Li, Jiayin Zhang","doi":"10.1148/radiol.242570","DOIUrl":"https://doi.org/10.1148/radiol.242570","url":null,"abstract":"<p><p>Background Precise assessment of myocardial ischemia burden and cardiovascular risk stratification based on dynamic CT myocardial perfusion imaging (MPI) is lacking. Purpose To develop and validate a deep learning (DL) model for automated quantification of myocardial blood flow (MBF) and ischemic myocardial volume (IMV) percentage and to explore the prognostic value for major adverse cardiovascular events (MACE). Materials and Methods This multicenter study comprised three cohorts of patients with clinically indicated CT MPI and coronary CT angiography (CCTA). Cohorts 1 and 2 were retrospective cohorts (May 2021 to June 2023 and January 2018 to December 2022, respectively). Cohort 3 was prospectively included (November 2016 to December 2021). The DL model was developed in cohort 1 (training set: 211 patients, validation set: 57 patients, test set: 90 patients). The diagnostic performance of MBF derived from the DL model (MBF_DL) for myocardial ischemia was evaluated in cohort 2 based on the area under the receiver operating characteristic curve (AUC). The prognostic value of the DL model-derived IMV percentage was assessed in cohort 3 using multivariable Cox regression analyses. Results Across three cohorts, 1108 patients (mean age: 61 years ± 12 [SD]; 667 men) were included. MBF_DL showed excellent agreement with manual measurements in the test set (segment-level intraclass correlation coefficient = 0.928; 95% CI: 0.921, 0.935). MBF_DL showed higher diagnostic performance (vessel-based AUC: 0.97) over CT-derived fractional flow reserve (FFR) (vessel-based AUC: 0.87; <i>P</i> = .006) and CCTA-derived diameter stenosis (vessel-based AUC: 0.79; <i>P</i> < .001) for hemodynamically significant lesions, compared with invasive FFR. Over a mean follow-up of 39 months, MACE occurred in 94 (14.2%) of 660 patients. IMV percentage was an independent predictor of MACE (hazard ratio = 1.12, <i>P</i> = .003), with incremental prognostic value (C index: 0.86; 95% CI: 0.84, 0.88) over conventional risk factors and CCTA parameters (C index: 0.84; 95% CI: 0.82, 0.86; <i>P</i> = .02). Conclusion A DL model enabled automated CT MBF quantification and accurate diagnosis of myocardial ischemia. DL model-derived IMV percentage was an independent predictor of MACE and mildly improved cardiovascular risk stratification. © RSNA, 2025 <i>Supplemental material is available for this article.</i> See also the editorial by Zhu and Xu in this issue.</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"315 1","pages":"e242570"},"PeriodicalIF":12.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144041866","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":"Requirements for AI Development and Reporting for MRI Prostate Cancer Detection in Biopsy-Naive Men: PI-RADS Steering Committee, Version 1.0.","authors":"Baris Turkbey, Henkjan Huisman, Andriy Fedorov, Katarzyna J Macura, Daniel J Margolis, Valeria Panebianco, Aytekin Oto, Ivo G Schoots, M Minhaj Siddiqui, Caroline M Moore, Olivier Rouvière, Leonardo K Bittencourt, Anwar R Padhani, Clare M Tempany, Masoom A Haider","doi":"10.1148/radiol.240140","DOIUrl":"https://doi.org/10.1148/radiol.240140","url":null,"abstract":"<p><p>This document defines the key considerations for developing and reporting an artificial intelligence (AI) interpretation model for the detection of clinically significant prostate cancer (PCa) at MRI in biopsy-naive men with a positive clinical screening status. Specific data and performance metric requirements and a checklist are provided for this use case. Data requirements emphasize the need for sufficient information to provide transparency and characterization of training and test data. The definition of a true-negative examination (which includes a minimum of 2-year follow-up), the need for image quality assessments, and nonimaging metadata requirements are provided. Performance metrics ranges are included, such as a cancer detection rate of 40%-70% for Prostate Imaging Reporting and Data System, or PI-RADS, 4 or higher lesions and demonstration of equivalent or better than human performance using receiver operating characteristic and precision-recall curves. The use of open datasets such as those used in the AI challenge model is encouraged. The study design should include conformity with the Checklist for Artificial Intelligence in Medical Imaging requirements. This article should be taken in the context of the current and evolving regulatory landscape. This review provides guidance based on subspeciality expertise in prostate MRI and will hopefully accelerate the clinical translation of AI in PCa detection.</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"315 1","pages":"e240140"},"PeriodicalIF":12.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143995621","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":"MRI-based Radiomic Features for Risk Stratification of Ductal Carcinoma in Situ in a Multicenter Setting (ECOG-ACRIN E4112 Trial).","authors":"Kalina P Slavkova, Ruya Kang, Anum S Kazerouni, Debosmita Biswas, Vivian Belenky, Rhea Chitalia, Hannah Horng, Michael Hirano, Jennifer Xiao, Ralph L Corsetti, Sara H Javid, Derrick W Spell, Antonio C Wolff, Joseph A Sparano, Seema A Khan, Christopher E Comstock, Justin Romanoff, Constantine Gatsonis, Constance D Lehman, Savannah C Partridge, Jon Steingrimsson, Despina Kontos, Habib Rahbar","doi":"10.1148/radiol.241628","DOIUrl":"10.1148/radiol.241628","url":null,"abstract":"<p><p>Background Ductal carcinoma in situ (DCIS) is a nonlethal, preinvasive breast cancer for which breast MRI is best suited for accurate disease extent characterization. DCIS is often overtreated, necessitating robust models for improved risk stratification. Purpose To develop logistic regression models using clinical and MRI-based radiomic features of DCIS and to evaluate the performance of such models in predicting disease upstaging at surgery and DCIS score. Materials and Methods This study is a secondary analysis of dynamic contrast-enhanced (DCE) MRI data from the Eastern Cooperative Oncology Group-American College of Radiology Imaging Network, or ECOG-ACRIN, E4112 trial. Primary analysis focused on predicting disease upstaging (<i>n</i> = 295), and secondary analysis focused on predicting DCIS score (<i>n</i> = 174). Radiologist-drawn lesion segmentations and publicly available Cancer Phenomics Toolkit, or CaPTk, software was used to compute 65 radiomic features. Participants were clustered into groups based on their radiomic features (ie, radiomic phenotypes), and principal component analysis was used to summarize the feature space. Clinical information and qualitative MRI features were available. Associations were tested using χ² and likelihood ratio tests. Data were split into training and test sets with a 3:2 ratio, and model performance was assessed on the test set using the area under the receiver operating characteristic curve (AUC). Results Data from 297 female participants with median age of 60 years (IQR, 51-67 years) were analyzed. Two radiomic phenotypes were identified that were associated with disease upstaging (<i>P</i> = .007). For predicting disease upstaging, the top three radiomic principal components combined with clinical and qualitative MRI predictors yielded the highest AUC of 0.77 (95% CI: 0.65, 0.88) among all tested models (<i>P</i> = .007), identifying 25% more true-negative (49 of 93 true-negative findings, 53% specificity) findings, compared with using clinical information alone (23 of 93 true-negative findings, 28% specificity). Radiomic models were not predictive of the DCIS score (<i>P</i> > .05). Conclusion In patients with DCIS, combining radiomic metrics with clinical information improved prediction of disease upstaging but not DCIS score. ClinicalTrials.gov Identifier: NCT02352883 <i>Supplemental material is available for this article.</i> ©RSNA, 2025 See also the editorial by Kim and Woo in this issue.</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"315 1","pages":"e241628"},"PeriodicalIF":12.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143754271","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":"Agenesis of the Intrahepatic Inferior Vena Cava with Pulmonary Venous Fistula.","authors":"Xiaoxu Guo, Yuhan Zhou","doi":"10.1148/radiol.242069","DOIUrl":"https://doi.org/10.1148/radiol.242069","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"315 1","pages":"e242069"},"PeriodicalIF":12.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143754249","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}