Radiology最新文献

{"title":"MRI-based Middle Neck Involvement in Stage N1-N2 Nasopharyngeal Carcinoma: A Marker for Risk Stratification.","authors":"Guan-Jie Qin, Wei Jiang, Wu-Qi Zhang, Wen-Fei Li, Gao-Yuan Wang, Yan-Ping Mao, Shun-Xin Wang, Zhe Dong, Yu-Pei Chen, Cheng Xu, Kai-Bin Yang, Yuan Zhang, Ying-Qi Lu, Na Liu, Lei Chen, Rui Guo, Ling-Long Tang, Ying Sun, Ji-Bin Li, Li-Zhi Liu, Xiao-Jing Du, Jun Ma","doi":"10.1148/radiol.243399","DOIUrl":"https://doi.org/10.1148/radiol.243399","url":null,"abstract":"<p><p>Background The prognostic implications of middle neck involvement, defined as cervical lymph node metastasis between the caudal border of the hyoid bone and the cricoid cartilage, remain unclear in nasopharyngeal carcinoma (NPC). Purpose To investigate the prognostic significance of middle neck involvement in patients with N1 or N2 NPC. Materials and Methods This retrospective analysis included patients with N1 or N2 NPC without distant metastasis treated between April 2009 and December 2017. Patients were categorized according to the presence or absence of middle neck involvement, as determined at MRI. Survival analysis was performed by incorporating TN category and middle neck involvement. Kaplan-Meier curves and the log-rank test were used to compare survival outcomes. Results This study included 9795 patients (mean age, 45 years ± 11 [SD]; 7135 male). Middle neck involvement was identified in 17.0% of patients (1668 of 9795), with prevalence rates of 11.4% (848 of 7429) in the N1 subgroup and 34.7% (820 of 2366) in the N2 subgroup. Multivariable analysis revealed that middle neck involvement was an independent prognostic factor for reduced metastasis-free survival (MFS), overall survival (OS), and disease-free survival (DFS) in both the N1 (all <i>P</i> < .001) and N2 subgroups (<i>P</i> = .001, .02, and .04, respectively). Patients with T1-T2 N1 NPC with middle neck involvement exhibited survival outcomes comparable to those in patients with T1-T2 N2 NPC (all <i>P</i> > .05). Conversely, patients with T3N1 disease without middle neck involvement had better 5-year MFS (91.7% vs 84.6%; <i>P</i> < .001), OS (90.6% vs 84.3%; <i>P</i> = .003), and DFS (83.6% vs 74.4%; <i>P</i> < .001) than those with middle neck involvement. Conclusion Middle neck involvement serves as a critical factor for risk stratification in N1 and N2 NPC. It helps identify patients at high risk within the T1-T2 N1 subgroup and those with T3N1 disease. © RSNA, 2025 <i>Supplemental material is available for this article.</i> See also the editorial by Jabehdar Maralani and Kang in this issue.</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"316 2","pages":"e243399"},"PeriodicalIF":15.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144966632","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":"Paraspinal Muscle Loss Correlates with Nerve Rootlet Avulsion in Brachial Plexus Injury.","authors":"Darryl B Sneag, Falko Ensle","doi":"10.1148/radiol.252527","DOIUrl":"10.1148/radiol.252527","url":null,"abstract":"<p><p></p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"316 2","pages":"e252527"},"PeriodicalIF":15.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144966621","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":"Liquid Biopsy and Imaging: Does Correlation Tell the Whole Story or Are We Missing the Bigger Picture?","authors":"Lukas Müller,Matteo Ligorio,Roman Kloeckner","doi":"10.1148/radiol.243871","DOIUrl":"https://doi.org/10.1148/radiol.243871","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"29 1","pages":"e243871"},"PeriodicalIF":19.7,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144778021","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 Evaluation of Liver Fat and Iron after Glucagon-like Peptide-1 Receptor and Glucagon Receptor Dual-Agonist Treatment in a High-Fat Diet-induced Mouse Model.","authors":"Huimin Xia, Yuqin Min, Yuhua Wang, Siyu Gao, Hailing Wang, Fuhua Yan, Ruixin Liu, Jiqiu Wang, Xuejiang Gu, Tingting Bo","doi":"10.1148/radiol.243780","DOIUrl":"10.1148/radiol.243780","url":null,"abstract":"<p><p>Background Glucagon-like peptide-1 receptor (GLP-1R) and glucagon receptor (GCGR) dual agonist, along with GLP-1R monoagonist, show promise in treating metabolic dysfunction-associated steatotic liver disease (MASLD). Liver fat and iron content are important surrogate markers for disease assessment. However, it remains unclear whether dual agonists provide superior therapeutic benefit over monoagonists for hepatic fat and iron regulation. Purpose To evaluate whether a GLP-1R/GCGR dual agonist offers greater therapeutic efficacy in reducing hepatic fat and iron content compared with a GLP-1R monoagonist in a high-fat diet mouse model using quantitative 9.4-T MRI. Materials and Methods Forty-two male mice were fed a high-fat diet for 13 weeks and then were treated subcutaneously with GLP-1R/GCGR dual agonist (mazdutide), GLP-1R monoagonist (semaglutide), or phosphate-buffered saline (control) every 3 days for 4 weeks. The control group included 14 age-matched male mice that received a standard chow diet and phosphate-buffered saline treatment. MRI scans and tissue samples were obtained at baseline and at 1 and 4 weeks after treatment. MRI-derived proton density fat fraction (PDFF), quantifying hepatic fat content, and R2*, quantifying hepatic iron content, were derived with a 9.4-T MRI scanner. Reductions in PDFF and R2* were compared among the groups using analysis of covariance and Student <i>t</i> tests. Correlations between imaging parameters and histologic analyses were evaluated using Pearson or Spearman correlation coefficients. Results After 4 weeks of treatment, mice treated with the dual agonist showed a greater reduction in PDFF from baseline values compared with mice treated with the monoagonist (median change, -5.59% [IQR, -6.80, -3.84] vs -3.30% [IQR, -3.80, -2.82]; <i>P</i> = .02). At 1 week after treatment, there was no evidence of a difference in PDFF reduction from baseline between the two groups (median change, -2.15% [IQR, -5.10, -1.69] vs -1.24% [IQR, -2.95, -0.78]; <i>P</i> = .19). Decreases in R2* values from baseline were also not significantly different between the groups at 1 week (median change, -53.86 Hz [IQR, -76.79, -43.19] vs -46.17 Hz [IQR, -68.01, -35.04]; <i>P</i> = .50) and 4 weeks (median change, -67.00 Hz [IQR, -79.33, -44.66] vs -57.18 Hz [IQR -78.51, -12.85]; <i>P</i> = .41) after treatment. Liver PDFF was positively correlated with hepatic triglyceride levels (<i>r</i> = 0.82; <i>P</i> < .001) and histologic steatosis scores (<i>r</i> = 0.81; <i>P</i> < .001), as well as R2* values (<i>r</i> = 0.69; <i>P</i> < .001). Conclusion Ultrahigh-field-strength MRI combined with histologic analyses demonstrated that the GLP-1R/GCGR dual agonist more effectively reduced hepatic fat accumulation compared with the GLP-1R monoagonist in a high-fat diet mouse model. MRI-derived liver PDFF and R2* values were correlated with histologic findings. Published under a CC BY 4.0 license. <i>Supplemental material is availabl","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"316 2","pages":"e243780"},"PeriodicalIF":15.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144874920","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":"Multisite Involvement in Klippel-Trénaunay Syndrome Using Photon-counting CT.","authors":"Xiaoxu Guo, Yuhan Zhou","doi":"10.1148/radiol.243867","DOIUrl":"10.1148/radiol.243867","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"316 2","pages":"e243867"},"PeriodicalIF":15.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144874921","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":"Cinematic Rendering of Klippel-Trénaunay Syndrome.","authors":"Guowei Chen, Peng Peng","doi":"10.1148/radiol.250230","DOIUrl":"10.1148/radiol.250230","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"316 2","pages":"e250230"},"PeriodicalIF":15.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144874917","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":"Evolving Multimodal Large Language Models in Radiology: A Year of Diagnostic Progress.","authors":"Chong Hyun Suh, Pae Sun Suh","doi":"10.1148/radiol.252282","DOIUrl":"10.1148/radiol.252282","url":null,"abstract":"<p><p></p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"316 2","pages":"e252282"},"PeriodicalIF":15.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144822390","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 Ovarian Lesion Classification via a Foundation Segmentation Model and Multimodal Analysis: A Multicenter Study.","authors":"Wen-Chi Hsu,Yuli Wang,Yu-Fu Wu,Ruohua Chen,Shadi Afyouni,Jhehong Liu,Somasundaram Vin,Victoria Shi,Maliha Imami,Jill S Chotiyanonta,Ghazal Zandieh,Yeyu Cai,Jeffrey P Leal,Kenichi Oishi,Atif Zaheer,Robert C Ward,Paul J L Zhang,Jing Wu,Zhicheng Jiao,Ihab R Kamel,Gigin Lin,Harrison X Bai","doi":"10.1148/radiol.243412","DOIUrl":"https://doi.org/10.1148/radiol.243412","url":null,"abstract":"Background Artificial intelligence may enhance diagnostic accuracy in classifying ovarian lesions on MRI scans; however, its applicability across diverse datasets is uncertain. Purpose To develop an efficient, generalizable pipeline for MRI-based ovarian lesion characterization. Materials and Methods In this retrospective study, multiparametric MRI datasets of patients with ovarian lesions from a primary institution (January 2008 to January 2019) and two external institutions (January 2010 to October 2020) were analyzed. Lesions were automatically segmented using Meta's Segment Anything Model (SAM). A DenseNet-121 deep learning (DL) model incorporating both imaging and clinical data was then trained and validated externally for ovarian lesion classification. Lesions were evaluated by radiologists using the Ovarian-Adnexal Reporting and Data System for MRI and subjective assessment, classifying them as benign or malignant. The classification performances of the DL model and radiologists were compared using the DeLong test. Results The primary dataset included 534 lesions from 448 women (mean age, 52 years ± 15 [SD]) from institution A (United States), whereas the external datasets included 58 lesions from 55 women (mean age, 51 years ± 19) from institution B (United States) and 29 lesions from 29 women (mean age, 49 years ± 10) from institution C (Taiwan). SAM-assisted segmentation had a Dice coefficient of 0.86-0.88, reducing the processing time per lesion by 4 minutes compared with manual segmentation. The DL classification model achieved an area under the receiver operating characteristic curve (AUC) of 0.85 (95% CI: 0.85, 0.85) on the internal test and 0.79 (95% CI: 0.79, 0.79 and 0.78, 0.79) across both external datasets with SAM-segmented images, comparable with the radiologists' performance (AUC: 0.84-0.93; all P > .05). Conclusion These results describe an accurate, efficient pipeline that integrates SAM with DL-based classification for differentiating malignant from benign ovarian lesions on MRI scans. It reduced segmentation time and achieved classification performance comparable with that of radiologists. © RSNA, 2025 Supplemental material is available for this article. See also the editorial by Bhayana and Wang in this issue.","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"39 1","pages":"e243412"},"PeriodicalIF":19.7,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144778018","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":"Detection of Oligometastatic Disease in Breast Cancer: It's the Little Things That Count.","authors":"Gary A Ulaner","doi":"10.1148/radiol.251797","DOIUrl":"https://doi.org/10.1148/radiol.251797","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"15 1","pages":"e251797"},"PeriodicalIF":19.7,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144778019","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":"CT Hyperrealistic Rendering for Posterior Mediastinal Dedifferentiated Liposarcoma.","authors":"Miaomiao Liu, Qingyu Ji","doi":"10.1148/radiol.250683","DOIUrl":"10.1148/radiol.250683","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"316 2","pages":"e250683"},"PeriodicalIF":15.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144822389","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}