European Journal of Radiology最新文献

{"title":"Machine learning outperforms deep learning in adhesive capsulitis diagnosis: a clinical-radiomics model bridging PD-T2 MRI and multimodal data fusion","authors":"Yang Yang ,&nbsp;Ting Pan ,&nbsp;Cong Zhang","doi":"10.1016/j.ejrad.2025.112470","DOIUrl":"10.1016/j.ejrad.2025.112470","url":null,"abstract":"<div><h3>Background</h3><div>Adhesive Capsulitis of the Shoulder (ACS) is a chronic inflammatory condition characterized by capsular fibrosis, thickening, and restricted mobility. Early diagnosis remains challenging due to the limited sensitivity of traditional imaging and symptom-based methods.</div></div><div><h3>Purpose</h3><div>This study developed a clinical-multi-sequence radiomics model by integrating clinical data with magnetic resonance imaging (MRI) radiomics to enhance ACS detection and compared machine learning (ML) and deep learning (DL) approaches.</div></div><div><h3>Methods</h3><div>A total of 444 patients from two medical centers were retrospectively included and divided into a primary cohort (n = 387) and an external test cohort (n = 57). Radiomic features were extracted from proton density-weighted coronal (PD-COR) and T2-weighted sagittal (T2-SAG) MRI sequences using PyRadiomics, while deep learning features were obtained from ResNet-200 and Vision Transformer (ViT) models. ML models were developed using Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting machine (LightGBM). The clinical-multi-sequence radiomics model was constructed by integrating radiomic and clinical features, with performance assessed via the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and Brier Score.</div></div><div><h3>Results</h3><div>The PD_T2_LightGBM model achieved optimal performance (AUC: 0.975 training, 0.915 validation, 0.886 test), surpassing DL features models. The Clinical-Radiomics Combined model showed robust generalization (AUC: 0.981 training, 0.935 validation, 0.882 test). DL features models exhibited high sensitivity but reduced external validation accuracy.</div></div><div><h3>Conclusion</h3><div>Integrating clinical and radiomic features significantly improved diagnostic precision. While DL features models provide valuable feature extraction capabilities, traditional ML models like LightGBM exhibit superior stability and interpretability, making them suitable for clinical applications. Future efforts should prioritize larger datasets and advanced fusion techniques to refine ACS diagnosis.</div></div>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"193 ","pages":"Article 112470"},"PeriodicalIF":3.3,"publicationDate":"2025-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145299193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-life performance of AI-aided radiologists, emergency physicians and two AI solutions for diagnosing bone fractures in appendicular skeletal trauma","authors":"Amandine Crombé ,&nbsp;Alexandre Ben Cheikh ,&nbsp;Mylène Seux ,&nbsp;Eric Stéphant ,&nbsp;Julien May ,&nbsp;Olivier Preteseille ,&nbsp;Adrien Vague ,&nbsp;Frédéric Nativel ,&nbsp;Matthieu Daniel ,&nbsp;Johan Etievant ,&nbsp;Jessica Aristizabal ,&nbsp;Antoine Perrey ,&nbsp;Guillaume Gorincour","doi":"10.1016/j.ejrad.2025.112469","DOIUrl":"10.1016/j.ejrad.2025.112469","url":null,"abstract":"<div><h3>Objectives</h3><div>To compare the performance of artificial intelligence (AI)-aided radiologists, emergency physicians and two AI solutions for diagnosing bone fractures.</div></div><div><h3>Materials and Methods</h3><div>Consecutive patients treated at two centres for appendicular skeletal traumatic injury between January and April 2021 who underwent X-ray imaging and whose initial conclusions were available and prospectively encoded by emergency physicians, were also prospectively analysed via two AI solutions (BoneView and SmartUrgence) available for the real-life interpretation of AI-aided radiologists. The ground truth was retrospectively assessed by 5 senior musculoskeletal radiologists who were aware of all the clinical, radiological and AI data. Numbers of suspected fractures, true positives and false positives per AI were compared. Diagnostic performance metrics (sensitivity, specificity, positive and negative predictive values and accuracy with 95% confidence intervals) for detecting fractures were estimated for each interpretation (emergency physician, BoneView, SmartUrgence, AI-aided radiologist).</div></div><div><h3>Results</h3><div>969 patients with 1049 radiography sets were included, 287 of whom had fractures (27.4 %). The average number of any fracture and true positive fractures were greater with BoneView than with SmartUrgence (P = 0.0469 and P = 0.0022, respectively). The real-life sensitivity, specificity and accuracy for detecting fracture in the entire cohort were 93 %, 99 % and 97.6 % for AI-aided radiologists; 80.8 %, 97.6 % and 93 % for emergency physicians; 89.5 %, 93.8 % and 92.7 % for BoneView; and 85.7 %, 94.6 % and 92.2 % for SmartUrgence.</div></div><div><h3>Conclusion</h3><div>In a real-life emergency setting, the performance of AI-aided radiologists in diagnosing bone fractures was excellent, and these radiologists outperformed AI solutions alone regardless of age and location.</div></div>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"194 ","pages":"Article 112469"},"PeriodicalIF":3.3,"publicationDate":"2025-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145318217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Erratum to “Value of rectal MRI prior to endoscopic submucosal dissection (ESD): An exploratory study” [Eur. J. Radiol. 192C (2025) 112400]","authors":"Marc J. Gollub ,&nbsp;Maria Clara Fernandes ,&nbsp;Wyanne Law ,&nbsp;Makoto Nishimura ,&nbsp;Lee Rodriguez ,&nbsp;Sayaka Nagao ,&nbsp;Jinru Shia ,&nbsp;Julio Garcia-Aguilar ,&nbsp;Martin R. Weiser ,&nbsp;Junting Zheng ,&nbsp;Marinela Capanu","doi":"10.1016/j.ejrad.2025.112448","DOIUrl":"10.1016/j.ejrad.2025.112448","url":null,"abstract":"","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"193 ","pages":"Article 112448"},"PeriodicalIF":3.3,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prognostic value of coronary CTA-based AI plaque quantification in patients undergoing transcatheter aortic valve implantation","authors":"Kifah Hussain ,&nbsp;Kevin Lee ,&nbsp;Senthil S. Balasubramanian ,&nbsp;Lavisha Singh ,&nbsp;Natasha Vyas ,&nbsp;Fazal Hussain ,&nbsp;Lucas Wathen ,&nbsp;Mark J. Ricciardi ,&nbsp;Jonathon Leipsic ,&nbsp;Campbell Rogers ,&nbsp;David Davidson ,&nbsp;Amit Pursnani","doi":"10.1016/j.ejrad.2025.112445","DOIUrl":"10.1016/j.ejrad.2025.112445","url":null,"abstract":"<div><h3>Background</h3><div>Coronary artery disease can be concomitantly evaluated as part of the pre-transcatheter aortic valve implantation (TAVI) CT angiography (CTA). More recently it has been possible to perform quantitative plaque analysis on coronary CT angiography (CCTA). In this study, we aimed to assess the prognostic value of quantitative plaque volumes using Artificial Intelligence Quantified Coronary Plaque Analysis (AI-QCPA) on pre-TAVI CTA.</div></div><div><h3>Methods</h3><div>This observational cohort study included patients who underwent CTA for TAVI planning with concomitant evaluation for coronary atherosclerosis. Studies were performed on a 192-slice dual-source SIEMENS Force CT scanner without administration of pre-medication such as nitroglycerin or beta-blockers. We examined coronary plaque characteristics in TAVI patients with AI-QCPA and assessed differences in plaque volumes between patients who developed major adverse cardiac events (MACE) at one year versus those who did not. MACE was defined as a composite of all-cause mortality, stroke, myocardial infarction, heart failure hospitalization, or unplanned revascularization within 1-year post-TAVI.</div></div><div><h3>Results</h3><div>AI-QCPA was performed in 128 patients, with mean age of 80.1 ± 9.6 years (51.6 % male). A total of 24 patients developed MACE at 1 year. Median total plaque volume (TPV), calcified plaque volume (CPV), non-calcified plaque volume (NCPV), and low-attenuation plaque volume (LAPV) were significantly higher in patients who developed MACE. Multivariable regression analysis adjusting for Society of Thoracic Surgeons (STS) score, statin use, and revascularization pre-TAVI, showed that the highest quartile of NCPV (OR 4.56, 95 % CI 1.61–12.89; p = 0.004) and LAPV (OR 3.70, 95 % CI 1.29–10.60; p = 0.015) were independently associated with MACE. When adjusted for coronary stenosis total plaque and all plaque subtypes were independently associated with MACE.</div></div><div><h3>Conclusion</h3><div>There is high volume of coronary plaque in patients undergoing TAVI assessed by AI-QCPA which is independently associated with the development of MACE one year post-TAVI.</div></div>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"193 ","pages":"Article 112445"},"PeriodicalIF":3.3,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical value of CT enterography imaging features in predicting enteroscopy passage in Crohn's disease.","authors":"Wenjuan Wu, Dongyang Zhu, Houqiang Chen, Yan Jin, Ximing Wang, Lei Zhang","doi":"10.1016/j.ejrad.2025.112352","DOIUrl":"10.1016/j.ejrad.2025.112352","url":null,"abstract":"<p><strong>Rationale and objective: </strong>Small bowel strictures in Crohn's disease (CD) can hinder enteroscopy, thereby limiting its diagnostic utility. This study aimed to evaluate whether CT enterography (CTE) imaging features can predict enteroscopy passage in CD patients.</p><p><strong>Methods: </strong>This retrospective cohort study included 277CD patients who underwent both CTE and enteroscopy at two institutions between March 2021 and December 2024. CTE served as the index test, with enteroscopy passage as the reference standard. Patients were divided into a training cohort (151 patients, Center 1) and a validation cohort (126 patients, Center 2). CTE features, including stricture length, bowel wall thickness, and upstream bowel dilatation, were assessed. Logistic regression analysis identified independent predictors of enteroscopy passage. Receiver operating characteristic (ROC) curve analysis determined the optimal cutoff values and predictive performance.</p><p><strong>Results: </strong>The study included 176 men and 101 women (mean age, 40.2 ± 13.6 years; mean BMI, 21.56 ± 3.33 kg/m²). Interobserver variability for enteroscopy passage assessment showed kappa values of 0.690 and 0.807, indicating substantial to excellent consistency. Upstream bowel dilatation was the strongest predictor, with an optimal cutoff of 26.5 mm. The area under the curve (AUC) was 0.821 (95 % CI: 0.76-0.88, p < 0.05) in the training cohort and 0.808 (95 % CI: 0.74-0.88, p < 0.05) in the validation cohort.</p><p><strong>Conclusion: </strong>CTE imaging features, particularly upstream bowel dilatation, demonstrated strong predictive value for enteroscopy passage in CD patients and may aid clinical decision-making.</p>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"191 ","pages":"112352"},"PeriodicalIF":3.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144816147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Duration of diabetes, glycemic control and intracranial atherosclerotic plaque vulnerability.","authors":"Qin Huang, Wei Liu, Lili Sun, Lei Liu, Shuo Zhang, Yantong Yang, Xianjin Zhu, Zunjing Liu","doi":"10.1016/j.ejrad.2025.112347","DOIUrl":"10.1016/j.ejrad.2025.112347","url":null,"abstract":"<p><strong>Background: </strong>Intracranial vulnerable plaque, characterized by intraplaque hemorrhage (IPH), surface irregularity, positive remodeling, and plaque enhancement, is associated with cerebrovascular events. History of diabetes mellitus (DM) was reported to be a potential predictor of plaque vulnerability, but the predictive value of DM-related variables for it is still uncertain. The purpose of this study was to investigate the association of diabetes duration and glycemic control with symptomatic plaque vulnerability, in comparison to non-diabetic patients.</p><p><strong>Methods: </strong>We retrospectively included a total of 220 patients who presented with ischemic stroke or transient ischemic attacks (TIA) underwent 3 T high-resolution magnetic resonance vessel wall imaging within 4 weeks of the onset of symptoms. The culprit plaque characteristics including morphology (positive remodeling and surface irregularity) and activation (IPH and plaque enhancement) were evaluated. We used multivariable logistic regression analyses to assess the differences of duration of diabetes (≥10 years compared with < 10 years) and glycemic control (HbA1c values ≥ 7.0 % compared with < 7.0 %) in plaque characteristics.</p><p><strong>Results: </strong>The sample (n = 220) had a mean age of 56.6 ± 11.9 years and was 46.4 % of diabetes. Of the 102 DM individuals, 58.8 % had been with DM duration < 10 years (Short-DM group) and 41.2 % with duration ≥ 10 years (Long-DM group). The well-controlled group (HbA1c values < 7.0 %) and poor-controlled DM group (HbA1c values ≥ 7.0 %) included 31 (30.4 %) and 71 (69.6 %) patients, respectively. We found significant difference in positive remodeling between patients with DM duration < 10 years and ≥ 10 years (adjusted odds ratio [OR] = 2.9 [95 % CI, 1.2-6.9]). Compared with patients who had HbA1c < 7.0 %, poor glycemic control was significantly associated with an increased rate of IPH (adjusted OR = 9.14 [95 % CI, 2.66-31.33]), plaque enhancement (adjusted OR = 3.09 [95 % CI, 1.02-9.34]), and surface irregularity (adjusted OR = 2.72 [95 % CI, 1.04-7.11]). Both patients with duration ≥ 10 years (adjusted OR = 3.84 [95 % CI, 1.31-11.24]) and patients HbA1c ≥ 7.0 % (adjusted OR = 3.41 [95 % CI, 1.33-8.76]) had more often coexistence of all vulnerable plaque characteristics compared with nondiabetic patients.</p><p><strong>Conclusion: </strong>Increased durations of diabetes and poor glycemic control are both independently associated with existence and coexistence of intracranial atherosclerotic plaque vulnerability, which should be considered when stratifying risk among patients with intracranial atherosclerosis.</p>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"191 ","pages":"112347"},"PeriodicalIF":3.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144816148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of contrast-enhanced ultrasound in the diagnosis of liver solid-cystic lesions.","authors":"Zhiqiang Yuan, Jiawu Li, Yingyu Shi, Yang Pu, Yi Tao, Yan Luo","doi":"10.1016/j.ejrad.2025.112336","DOIUrl":"10.1016/j.ejrad.2025.112336","url":null,"abstract":"<p><strong>Background: </strong>Liver solid-cystic lesions are diverse in type, and accurate differential diagnosis is of great clinical significance. This study aimed retrospectively analyze the sonographic features of conventional ultrasound (US) and contrast-enhanced ultrasound (CEUS) in liver solid-cystic lesions, and to compare their diagnostic performance.</p><p><strong>Methods: </strong>A total of 157 patients with liver solid-cystic lesions diagnosed by US from January 2011 to November 2024 were retrospectively collected. The clinical features, US and CEUS features of 157 lesions were summarized. The diagnostic performance of CEUS was compared with US and contrast-enhanced computed tomography (CECT) in the evaluation of liver solid-cystic lesions.</p><p><strong>Results: </strong>This study included 157 lesions comprising 87 benign lesions (37 liver abscesses, 30 hepatic echinococcosis, 8 cysts, and 12 other benign lesions), 8 borderline tumors (5 intraductal papillary neoplasms of the bile duct and 3 mucinous cystic neoplasms), and 62 malignant tumors (24 metastatic carcinomas, 19 hepatocellular carcinomas, 14 intrahepatic cholangiocarcinomas, and 5 other malignant neoplasms).The diagnostic accuracy of US for liver solid-cystic lesions was 64.3 % (95 % confidence interval [CI]: 56.7 %- 72.0 %), while that of CEUS was 84.1 % (95 % CI: 77.9-90.5 %). The difference between the US and CEUS was statistically significant (P＜0.001). Comparative analysis of 142 lesions examined with both CEUS and CECT, CEUS showed non-inferior accuracy to CECT (85.2 % [95 % CI: 80.0-90.9 %] vs 81.0 % [95 % CI: 74.7-87.3 %], P = 0.21).</p><p><strong>Conclusion: </strong>CEUS demonstrates significant promise in the diagnosis of liver solid-cystic lesions. Compared with US, CEUS further enhances diagnostic accuracy and is not inferior to CECT in our study.</p>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"191 ","pages":"112336"},"PeriodicalIF":3.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144793834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to “Keeping AI on Track: Regular monitoring of algorithmic updates in mammography” [Eur. J. Radiol. 187 (2025) 112100]","authors":"Adnan G. Taib ,&nbsp;Jonathan J. James ,&nbsp;George J.W. Partridge ,&nbsp;Yan Chen","doi":"10.1016/j.ejrad.2025.112443","DOIUrl":"10.1016/j.ejrad.2025.112443","url":null,"abstract":"","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"193 ","pages":"Article 112443"},"PeriodicalIF":3.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145212071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diffusion tensor imaging has prognostic value on return to play in hamstring injuries: A prospective cohort study.","authors":"Milo J K Mokkenstorm, Jithsa R Monte, Jozef J M Suskens, Özgür Kilic, Frank F Smithuis, Mario Maas, Stan Buckens, Aart J Nederveen, Gustav J Strijkers, Susanne S Rauh, Martijn Froeling, Gustaaf Reurink, Melissa T Hooijmans, Johannes L Tol","doi":"10.1016/j.ejrad.2025.112350","DOIUrl":"10.1016/j.ejrad.2025.112350","url":null,"abstract":"<p><strong>Background: </strong>Predicting an accurate return to play (RTP) time after hamstring injury remains difficult. The ability of diffusion tensor imaging (DTI) to detect muscle micro-trauma may help to overcome this limitation.</p><p><strong>Purpose: </strong>To investigate the predictive value of DTI-derived parameters for RTP prognosis following hamstring injury.</p><p><strong>Materials and methods: </strong>In this single-centre prospective cohort study, athletes with an acute hamstring injury were included. Athletes underwent a 3 T MRI scan of the upper legs, including DTI acquisition within 7 days after injury. DTI parameters were calculated as the relative difference between legs, including the first, second and third eigenvalues, mean diffusivity, radial diffusivity and fractional anisotropy. RTP was defined as the self-reported time needed to return to full unrestricted training in days. Linear regression analysis was performed to determine the association between DTI parameters and RTP. To investigate the added value of DTI to conventional MRI. DTI parameters that met a p < 0.05 threshold in the univariate analyses were selected for multivariate linear regression. In the multivariate linear regression, the selected DTI parameters were individually combined with the conventional modified Peetrons classification.</p><p><strong>Results: </strong>From 116 athletes, 91 were included in the analysis. The median RTP time was 37 days (IQR 41). We found univariate associations between all DTI parameters and the RTP time, explaining between 5.4 % to 15.0 % of the variance in RTP time. Multivariate analysis showed that all DTI parameters except the first eigenvalue were independently associated with RTP time.</p><p><strong>Conclusion: </strong>Univariate associations between all six DTI parameters and RTP time were found, explaining between 5.4% and 15% of the variance in RTP time. Multivariate analysis showed DTI parameters that included information on diffusivity in the radial axes had the most added value in RTP prognosis.</p><p><strong>Summary: </strong>In athletes with an acute diagnosis of hamstring injury, multivariate linear regression analysis revealed associations between diffusor tensor imaging-derived parameters (eigenvalues, mean diffusivity, radial diffusivity, and fractional anisotropy) and prognosis for time to return to play prognosis.</p>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"191 ","pages":"112350"},"PeriodicalIF":3.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144811976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predictive performance of the perivascular adipose tissue for endovascular recanalization of chronic carotid artery occlusion.","authors":"Yongsheng Liu, Yuxiang Liu, Yongjian Liu, Mingyi Wang, Ke Li, Tao Pan, Xiangbo An, Feng Wang","doi":"10.1016/j.ejrad.2025.112348","DOIUrl":"10.1016/j.ejrad.2025.112348","url":null,"abstract":"<p><strong>Objectives: </strong>The purpose of this study was to investigate the association between pericarotid fat (PCF) and the success of endovascular recanalization for chronic internal carotid artery occlusion (CICAO), and to establish a scoring system to predict the feasibility of the procedure.</p><p><strong>Materials and methods: </strong>Sixty-six patients (66 carotid arteries) with CICAO who underwent endovascular recanalization were retrospectively analyzed. Patients' clinical information, the density of PCF, digital subtraction angiography (DSA) characteristics, and procedural results were recorded. Factors related to technical success were analyzed. A nomogram was developed and validated.</p><p><strong>Results: </strong>Successful recanalization was achieved in 41 (62.1 %) CICAO lesions. Patients in the successful group had a significantly higher PCF density (-42 HU vs. -74 HU; p < 0.001). The cut-off value of PCF density determined by the ROC curve was -61 HU. The multivariable model including conventional variables showed that tapered stump (OR = 4.92; p = 0.017), shorter occlusion length (≤50 mm) (OR = 14.07; p = 0.016), and shorter estimated occlusion duration (OR = 0.87; p = 0.019) were predictors of successful recanalization. The model including only PCF showed that higher PCF density (OR = 1.13; p < 0.001) was associated with successful recanalization. The combined model (including conventional variables and PCF) outperformed the conventional model in discriminating successful recanalization (area under the curve [AUC]: 0.960 vs. 0.854; p = 0.019). The decision curve analysis (DCA) showed that the combined model had good clinical value.</p><p><strong>Conclusions: </strong>PCF density may serve as a novel imaging biomarker associated with successful endovascular recanalization in symptomatic CICAO. When combined with conventional anatomical features, the proposed nomogram can be used to screen more suitable candidates for recanalization therapy.</p>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"191 ","pages":"112348"},"PeriodicalIF":3.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144811977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}