European Journal of Radiology最新文献

{"title":"Expression of concern \"Role of contrast-enhanced ultrasound in decision support for diagnosis and treatment of hepatic artery thrombosis after liver transplantation\" [Eur. J. Radiol. 81(3) (2012) e338-e343].","authors":"Qiang Lu, Xiao Fei Zhong, Zi Xing Huang, Bo Yang Yu, Bu Yun Ma, Wen Wu Ling, Hong Wu, Jia Ying Yang, Yan Luo","doi":"10.1016/j.ejrad.2025.112404","DOIUrl":"https://doi.org/10.1016/j.ejrad.2025.112404","url":null,"abstract":"","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"192 ","pages":"112404"},"PeriodicalIF":3.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145148338","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":"EXPRESSION OF CONCERN - ORGAN PROVENANCE CASE \"The use of coronary stent in hepatic artery stenosis after orthotopic liver transplantation\". [Mingsheng Huang et al. EJR 60(3) (2006) 425-430].","authors":"","doi":"10.1016/j.ejrad.2025.112394","DOIUrl":"https://doi.org/10.1016/j.ejrad.2025.112394","url":null,"abstract":"","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"192 ","pages":"112394"},"PeriodicalIF":3.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145148416","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":"Differences in advanced radiologic features of glioma subtypes under the 2021 WHO classification of tumors of the central Nervous system.","authors":"Siddhant Kumarapuram, Quirin D Strotzer, Otto Rapalino, Rajiv Gupta","doi":"10.1016/j.ejrad.2025.112474","DOIUrl":"https://doi.org/10.1016/j.ejrad.2025.112474","url":null,"abstract":"<p><p>The 2021 World Health Organization (WHO) Classification of Tumors of the Central Nervous System (CNS) redefined glioma subtypes in the 5th edition of revised guidelines pertaining to histologic and molecular grading for CNS Tumors. Neuroradiologists' understanding of the tumor subtypes is key to effectively assisting with diagnosis. Although it is helpful to understand the characteristics of these tumors on standard imaging modalities, including T1, T2, and FLAIR MRI Sequences, new literature reports find characteristic features of different glioma subtypes can be further enhanced with advanced imaging techniques, including ADC, SWI, MRS, and FDG-PET. In this report, we review and illustrate advanced imaging characteristics of glioma subtypes within the 2021 WHO classification. These tools will provide neuroradiologists with more data points to hypothesize brain tumor subtypes before invasive approaches are pursued.</p>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"194 ","pages":"112474"},"PeriodicalIF":3.3,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145318250","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":"CT-based habitat radiomics for differentiating papillary thyroid carcinoma from nodular goiter: a two-center study.","authors":"Xiaocui Shen, Caiying Tang, Haibing Xu, Tong Li, Lixu Xin, Wei Li, Mengmeng Yang","doi":"10.1016/j.ejrad.2025.112464","DOIUrl":"https://doi.org/10.1016/j.ejrad.2025.112464","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>To develop habitat-based radiomics signatures for distinguishing papillary thyroid carcinoma (PTC) from nodular goiter (NG).</p><p><strong>Material and methods: </strong>A retrospective study was conducted on PTC and NG patients from two centers. Univariable and multivariable logistic regression analyses were performed to identify independent risk factors for developing the clinical model. Tumor and ablation regions of interest (ROI) were split into three spatial habitats through K-means clustering algorithm and dilated with 2 mm, 4 mm 6 mm, and 8 mm thicknesses. Radiomics signatures of intratumor, peritumor, and habitat were developed using the features extracted from preoperative CT images. A nomogram was developed by integrating the optimal model and clinical predictors. The model performance and benefit were assessed using the area under the receiver operating characteristic curve (AUC), net reclassification index (NRI), and integrated discrimination improvement (IDI).</p><p><strong>Results: </strong>A total of 382 eligible patients were included in the analysis. Two clinical variables (age and gender) were identified and used to construct the clinical model. The habitat-based radiomics model demonstrated superior discriminatory performance in differentiating PTC from NG, with AUCs of 0.948 (95% confidence interval [CI]: 0.923-0.973) and 0.941 (0.941, 95% CI: 0.896-0.985) in the training and validation sets, respectively. The combined radiomics nomogram achieved the highest predictive accuracy, with AUCs of 0.953 (95% CI: 0.930-0.976, training) and 0.950 (95% CI: 0.909-0.991, validation). Decision curve analysis (DCA) showed that the nomogram provided a higher net benefit than other radiomics models, supported by positive NRI and IDI values.</p><p><strong>Conclusions: </strong>CT-based habitat radiomics had the potential to differentiate PTC from NG. The nomogram combined with Peri4mm and habitat signature had the best performance and good model gains for identifying PTC patients.</p>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"193 ","pages":"112464"},"PeriodicalIF":3.3,"publicationDate":"2025-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145307334","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":"Development of predictive models to identify the intracranial aneurysm responsible for subarachnoid hemorrhage in patients with multiple saccular aneurysms.","authors":"Hua Wang, Jun-Feng Kong, Li Wen, Xiao-Jing Wang, Wen-Tao Zhang, Zhi-Qing Wang, Lu Zeng, Yan-Tao Huang, Shi-Hai Yang, Man Li, Tian-Wu Chen, Jun Liu, Guang-Xian Wang","doi":"10.1016/j.ejrad.2025.112466","DOIUrl":"https://doi.org/10.1016/j.ejrad.2025.112466","url":null,"abstract":"<p><strong>Purpose: </strong>To develop and test machine learning (ML) models using computed tomography angiography to identify the intracranial aneurysm (IA) responsible for subarachnoid hemorrhage (SAH) accurately in patients with multiple saccular IAs and to determine whether these models outperform traditional predictive markers.</p><p><strong>Materials and methods: </strong>Two hundred seven SAH patients with 460 IAs from four hospitals were included from May 2018-December 2023 and randomly divided into training (80%) and internal validation (20%) sets. Additionally, an external validation set comprising 65 patients with 147 IAs from other four hospitals was used. The predictive models were developed using ML methods that integrated the morphological features of IAs (e.g., size and shape) to identify the responsible IA. These models were then compared with traditional predictive markers that relies on hemorrhage patterns and the maximum IA size.</p><p><strong>Results: </strong>The areas under the curves (AUCs) for the hemorrhage patterns and the maximum IA size were 0.496-0.505, 0.502-0.523, and 0.488-0.498 in the training, internal validation, and external validation sets, respectively. Among the 13 ML models, the best-performing models were the Gaussian process, logistic regression, and quadratic discriminant analysis models, with AUCs of 0.912 [95 % confidence interval (CI): 0.881-0.943], 0.894 (95 % CI: 0.861-0.928), and 0.890 (95 % CI: 0.756-0.924), respectively, for the training set; 0.869 (95 % CI: 0.798-0.941), 0.872 (95 % CI: 0.802-0.942), and 0.853 (95 % CI: 0.778-0.929), respectively, for the internal validation set; and 0.898 (95 % CI: 0.848-0.947), 0.892 (95 % CI: 0.840-0.943), and 0.897 (95 % CI: 0.847-0.947), respectively, for the external validation set. DeLong tests revealed no significant differences among these models, but all the models outperformed traditional predictive markers (P < 0.001).</p><p><strong>Conclusion: </strong>ML models that integrate multiple morphological features can predict the IA responsible for SAH accurately in patients with multiple IAs. These models outperform traditional predictive markers in identifying the responsible IA, thereby facilitating prompt and effective treatment.</p>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"193 ","pages":"112466"},"PeriodicalIF":3.3,"publicationDate":"2025-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145307295","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":"Multimodal deep learning model for predicting microsatellite instability in colorectal cancer by contrast-enhanced computed tomography and histopathology.","authors":"Kai Tang, Ruiling She, Guangyuan Chen, Zhuoyao Xie, Tao Li, Dexuan Chen, Weihong Huang, Qianjin Feng, Yinghua Zhao, Yubao Liu","doi":"10.1016/j.ejrad.2025.112468","DOIUrl":"https://doi.org/10.1016/j.ejrad.2025.112468","url":null,"abstract":"<p><strong>Objectives: </strong>To develop and validate a multimodal deep learning (DL) model that integrates preoperative contrast-enhanced computed tomography (CECT) and postoperative whole-slide images (WSIs) to predict microsatellite instability (MSI) status in colorectal cancer (CRC).</p><p><strong>Materials and methods: </strong>This retrospective, multicenter study enrolled 305 CRC patients with paired CECT and WSIs. Patients from Center I and II were allocated to the training (n = 169) and internal validation (n = 85) sets, while those from Center III formed the external test set (n = 51). Pathology-based DL (PathDL) and venous-phase CECT (VPDL) models were constructed using EfficientNet-b0 and ResNet 101 architectures, respectively. A fusion model (F-VP-PathDL, Fusion of venous phase CT and pathology with deep learning) was developed using an adaptive residual network to integrate features from both modalities. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and F1 score.</p><p><strong>Results: </strong>The F-VP-PathDL model achieved strong performance on the internal validation set, with an AUC of 0.883 (95 % CI: 0.732-0.967). On the external test set, the model achieved an AUC of 0.905 (95 % CI: 0.831-0.945), outperforming single-modality and alternative fusion models (PathDL: 0.794; VPDL: 0.858; APDL: 0.802; F-AVPDL: 0.813). The model also demonstrated robust accuracy (84.2 %, 95 % CI: 69.1 %-92.8 %), sensitivity (80.3 %, 95 % CI: 28.4 %-98.7 %), specificity (83.7 %, 95% CI: 68.8 %-93.9 %) and F1 score (0.837, 95 % CI: 0.326-0.999) on the external test set.</p><p><strong>Conclusions: </strong>The F-VP-PathDL model demonstrates robust generalizability across centers and offers a clinically scalable tool for MSI prediction in CRC, supporting patient stratification and informing immunotherapy decisions.</p>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"193 ","pages":"112468"},"PeriodicalIF":3.3,"publicationDate":"2025-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145307256","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":"Multiparametric breast MRI to problem-solve mammographically detected suspicious calcifications.","authors":"Vivian Y Park, Daniel S Hippe, Anum S Kazerouni, Debosmita Biswas, Mary Lynn Bryant, Isabella Li, Sara H Javid, Mark Kilgore, Janice Kim, Andrew G Kim, John R Scheel, Kathryn P Lowry, Diana L Lam, Savannah Partridge, Habib Rahbar","doi":"10.1016/j.ejrad.2025.112467","DOIUrl":"https://doi.org/10.1016/j.ejrad.2025.112467","url":null,"abstract":"<p><strong>Objective: </strong>To evaluate the performance of multiparametric breast MRI to problem-solve mammographically-detected suspicious calcifications.</p><p><strong>Materials and methods: </strong>Participants with mammographically-detected suspicious calcifications were prospectively enrolled between August 2017 to May 2023. Pre-biopsy multiparametric MRI (standard and high-temporal resolution dynamic contrast enhanced [DCE]-MRI acquisitions and diffusion-weighted imaging [DWI]) was performed. The associations of MRI features with outcomes - (1) any malignancy and (2) invasive or high-grade ductal carcinoma in situ [DCIS] only - were analyzed using univariable logistic regression. Multivariable models, sequentially incorporating clinical/mammographic, qualitative, and quantitative features, were developed using penalized logistic regression with the least absolute shrinkage and selection operator. Area under the receiver operating characteristic curves (AUC) were estimated via cross-validation and compared between models using bootstrap methods.</p><p><strong>Results: </strong>81 women (mean age, 55 years ± 10 [standard deviation]) with 86 calcifications were included; 29 % (25/86) were malignant. Malignancy rates for non-enhancing mammographic Breast Imaging Reporting and Data System (BI-RADS) category 4a and 4b calcifications were 3.8 % (1/26) and 8.7 % (2/23), respectively, with no invasive cancer or high-grade DCIS. Mammographic BI-RADS category, MRI BI-RADS category, visibility on DWI, peak percent enhancement, functional tumor volume, and K^trans values were associated with both outcomes (all p < 0.05). Multivariable models, including qualitative DCE-MRI assessments, showed higher AUCs (malignancy: 0.71-0.76; invasive cancer or high-grade DCIS: 0.78-0.91) than when including only clinical and mammographic features (malignancy: 0.57; invasive cancer or high-grade DCIS: 0.61, all p < 0.05). Further incorporation of DWI or quantitative MRI features did not improve AUCs (all ΔAUC ≤ 0).</p><p><strong>Conclusion: </strong>Breast DCE-MRI aids in evaluating mammographic BI-RADS category 4a/4b calcifications without biopsy. DWI or quantitative MRI features may not further improve diagnostic performance.</p>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"194 ","pages":"112467"},"PeriodicalIF":3.3,"publicationDate":"2025-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145318287","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":"Machine learning outperforms deep learning in adhesive capsulitis diagnosis: a clinical-radiomics model bridging PD-T2 MRI and multimodal data fusion.","authors":"Yang Yang, Ting Pan, Cong Zhang","doi":"10.1016/j.ejrad.2025.112470","DOIUrl":"https://doi.org/10.1016/j.ejrad.2025.112470","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Purpose: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"193 ","pages":"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, Alexandre Ben Cheikh, Mylène Seux, Eric Stéphant, Julien May, Olivier Preteseille, Adrien Vague, Frédéric Nativel, Matthieu Daniel, Johan Etievant, Jessica Aristizabal, Antoine Perrey, Guillaume Gorincour","doi":"10.1016/j.ejrad.2025.112469","DOIUrl":"https://doi.org/10.1016/j.ejrad.2025.112469","url":null,"abstract":"<p><strong>Objectives: </strong>To compare the performance of artificial intelligence (AI)-aided radiologists, emergency physicians and two AI solutions for diagnosing bone fractures.</p><p><strong>Materials and methods: </strong>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).</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"194 ","pages":"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}