European Journal of Radiology最新文献

{"title":"Predicting brain metastases in EGFR-positive lung adenocarcinoma patients using pre-treatment CT lung imaging data","authors":"Xinliu He ,&nbsp;Chao Guan ,&nbsp;Ting Chen ,&nbsp;Houde Wu ,&nbsp;Liuchao Su ,&nbsp;Mingfang Zhao ,&nbsp;Li Guo","doi":"10.1016/j.ejrad.2025.112265","DOIUrl":"10.1016/j.ejrad.2025.112265","url":null,"abstract":"<div><h3>Objectives</h3><div>This study aims to establish a dual-feature fusion model integrating radiomic features with deep learning features, utilizing single-modality pre-treatment lung CT image data to achieve early warning of brain metastasis (BM) risk within 2 years in EGFR-positive lung adenocarcinoma.</div></div><div><h3>Materials and methods</h3><div>After rigorous screening of 362 EGFR-positive lung adenocarcinoma patients with pre-treatment lung CT images, 173 eligible participants were ultimately enrolled in this study, including 93 patients with BM and 80 without BM. Radiomic features were extracted from manually segmented lung nodule regions, and a selection of features was used to develop radiomics models. For deep learning, ROI-level CT images were processed using several deep learning networks, including the novel vision mamba, which was applied for the first time in this context. A feature-level fusion model was developed by combining radiomic and deep learning features. Model performance was assessed using receiver operating characteristic (ROC) curves and decision curve analysis (DCA), with statistical comparisons of area under the curve (AUC) values using the DeLong test.</div></div><div><h3>Results</h3><div>Among the models evaluated, the fused vision mamba model demonstrated the best classification performance, achieving an AUC of 0.86 (95% CI: 0.82–0.90), with a recall of 0.88, F1-score of 0.70, and accuracy of 0.76. This fusion model outperformed both radiomics-only and deep learning-only models, highlighting its superior predictive accuracy for early BM risk detection in EGFR-positive lung adenocarcinoma patients.</div></div><div><h3>Conclusion</h3><div>The fused vision mamba model, utilizing single CT imaging data, significantly enhances the prediction of brain metastasis within two years in EGFR-positive lung adenocarcinoma patients. This novel approach, combining radiomic and deep learning features, offers promising clinical value for early detection and personalized treatment.</div></div>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"190 ","pages":"Article 112265"},"PeriodicalIF":3.2,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144513557","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":"The clinical value of second-opinion reporting by subspecialist musculoskeletal radiologists","authors":"Ajay Patel ,&nbsp;Amanda Isaac","doi":"10.1016/j.ejrad.2025.112262","DOIUrl":"10.1016/j.ejrad.2025.112262","url":null,"abstract":"<div><h3>Introduction</h3><div>This systematic review aims to evaluate the added clinical value of secondary interpretations produced by specialist musculoskeletal radiologists. Additional aims are to identify clinical settings producing more discrepant cases between the initial and secondary interpreters.</div></div><div><h3>Methods</h3><div>A systematic search of the MEDLINE and Scopus databases was performed for original research studies, which included a discrepancy rate or a number of discordant reports between a primary interpreter of any training level and a secondary subspecialist musculoskeletal radiologist. Full texts included were screened by two reviewers to determine inclusion. A modified version of the QUADAS-2 tool was used to evaluate the risk of bias for each study.</div></div><div><h3>Results</h3><div>Eight studies with 11,186 initial imaging examinations reinterpreted by a specialist musculoskeletal radiologist met the inclusion criteria. Across the studies, clinically significant discrepancies were generally defined as discrepant cases impacting a patient’s management. Most initial reports were produced by radiologists of varying experience without musculoskeletal specialisation. The secondary reports were produced mainly by multiple experienced subspecialised musculoskeletal radiologists. The range of clinically significant discrepancies reported across the eight studies was between 1.4–27.9%. High discrepancy rates were seen in musculoskeletal oncologic cases, and lower discrepancy rates were seen in appendicular radiographs; however, it was concluded that both areas require greater awareness of the potential discrepancies.</div></div><div><h3>Conclusion</h3><div>Second opinion reports initially interpreted by a non-musculoskeletal radiologist and reinterpreted by a specialist musculoskeletal radiologist were established as beneficial for patients and impacted their management, especially in musculoskeletal oncology cases, fractures within the appendicular extremities and multiple myeloma focal lesion detection. Greater attention to these clinical settings can potentially advise policymaking to formalise second opinion reinterpretations, which could reduce the risk of misdiagnosis and enhance patient safety and survival. Findings highlight areas requiring greater focus in radiology education, guiding resource allocation to address knowledge gaps and enhance diagnostic accuracy.</div></div>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"190 ","pages":"Article 112262"},"PeriodicalIF":3.2,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144491983","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":"Identifying critical moments of patient anxiety throughout the MRI care pathway: From prescription to results","authors":"Matthieu L.G. Fernandez ,&nbsp;Pierrick Laulan ,&nbsp;Antonio Ribeiro ,&nbsp;Marie Becar ,&nbsp;David Higué ,&nbsp;Jérôme Dimet","doi":"10.1016/j.ejrad.2025.112264","DOIUrl":"10.1016/j.ejrad.2025.112264","url":null,"abstract":"<div><h3>Objectives</h3><div>To investigate peak anxiety timing during MRI patient management, identify factors associated with heightened anxiety levels, and evaluate their impact on examination processes, whilst developing targeted recommendations for improved anxiety management protocols.</div></div><div><h3>Methods</h3><div>We prospectively included 856 adult outpatients scheduled for MRI examination. Peak anxiety was measured using the Anxiety Thermometer. We related it to personal and situational factors of the MRI examinations. Statistical analyses were conducted in three stages: (1) distribution of maximum anxiety scores according to different key moments in patient management; (2) links between personal and situational factors and high anxiety in patients; (3) influence of high anxiety in patients on their management. To conduct these analyses, we used chi-square tests, pairwise comparisons and linear and logistic regressions.</div></div><div><h3>Results</h3><div>Approximately 28% of outpatients exhibited state anxiety. Women, patients receiving intravenous contrast injection and those undergoing head and neck MRI were particularly at risk of developing anxiety beyond the pathological threshold. The MRI examination itself, waiting for the results, and preparation’s phase are the moments most associated with anxiety peaks.</div></div><div><h3>Conclusions</h3><div>Undergoing an MRI, from prescription to results, is highly anxiety-inducing for many patients, with moments of high uncertainty presenting the greatest risk for peak anxiety. Identifying these moments and associated risk factors can guide the development of targeted strategies to reduce anxiety, improve patient experience, and optimize care.</div></div>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"190 ","pages":"Article 112264"},"PeriodicalIF":3.2,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144518884","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":"Enhancing MRI efficiency in musculoskeletal examinations: Impact of optimized facility design and workflow optimization efforts","authors":"Alexander Herold ,&nbsp;Wei-Ching Lo ,&nbsp;Andrew Sharp ,&nbsp;Barbara D. Wichtmann ,&nbsp;Sean P. Hartmann ,&nbsp;Arhaan Gupta-Rastogi ,&nbsp;Lauren M. Melski ,&nbsp;John Conklin ,&nbsp;Min Lang ,&nbsp;Bryan Clifford ,&nbsp;Shivraman Giri ,&nbsp;Michael Weber ,&nbsp;James A. Brink ,&nbsp;Oleg Pianykh ,&nbsp;Onofrio A. Catalano ,&nbsp;Susie Y. Huang ,&nbsp;Jad S. Husseini","doi":"10.1016/j.ejrad.2025.112263","DOIUrl":"10.1016/j.ejrad.2025.112263","url":null,"abstract":"<div><h3>Background</h3><div>To assess the workflow efficiency of an optimized MRI facility design compared to conventional facilities.</div></div><div><h3>Methods</h3><div>This retrospective study analyzed 7,164 non-contrast MRI examinations (3,951 knee, 2,246 shoulder, 967 ankle) performed between January 2021 and April 2024. We compared an optimized facility (OF) featuring three scanners, three dedicated preparation bays, and dockable tables to two reference facilities (RF) with traditional single-scanner/single-table setups. All scans were performed on 3 T scanners. Workflow metrics were extracted from scanner logs and electronic health records. Three-way ANOVA and chi-square tests assessed the impact of facility design, body region, and date on workflow metrics.</div></div><div><h3>Results</h3><div>The OF demonstrated a decrease of the mean total process cycle by 5.0–6.8 min (16.4–18.9 %) compared to RF (p &lt; 0.001), which was primarily attributed to reduced turnaround times (mean reduction: 4.2 min, p &lt; 0.001). Scans incorporating deep learning (DL)-based reconstruction showed 4.9–6.2 min shorter mean acquisition times at both facilities (p &lt; 0.001). The mean time interval from patient arrival to exam start was 6.6 min shorter at OF, and on-time performance was higher at OF (79.4 %) versus RF (63.4 %) (p &lt; 0.001).</div></div><div><h3>Conclusion</h3><div>This study demonstrates that optimized MRI facility design significantly enhances outpatient efficiency and increases patient throughput for non-contrast enhanced musculoskeletal examinations.</div></div>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"190 ","pages":"Article 112263"},"PeriodicalIF":3.2,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144511089","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":"Validation of MRI short axis analysis for predicting lymphovascular invasion in endometrial cancer patients","authors":"Amelia Favier ,&nbsp;Leo Razakamanantsoa ,&nbsp;Julie Mereaux ,&nbsp;Samia Lamrabet ,&nbsp;Edwige Pottier ,&nbsp;Claire Theodore ,&nbsp;Cyril Touboul ,&nbsp;Bassam Haddad ,&nbsp;Yohann Dabi ,&nbsp;Isabelle Thomassin-Naggara","doi":"10.1016/j.ejrad.2025.112259","DOIUrl":"10.1016/j.ejrad.2025.112259","url":null,"abstract":"<div><h3>Background</h3><div>In the context of FIGO classification updates in Endometrial Cancer (EC), lymphovascular space invasion (LVSI) is often either missing or wrongly assessed in preoperative histological analysis.</div></div><div><h3>Objective</h3><div>This retrospective study aimed to validate the diagnostic efficacy of systematic short-axis measurement on preoperative MRI for predicting lymphovascular space invasion (LVSI) in patients with EC.</div></div><div><h3>Materials</h3><div>A total of 116 patients who underwent preoperative pelvic MRI between January 2015 and December 2019 were included. Two expert radiologists specializing in female pelvic MRI measured the tumor’s short axis (previously described by Lavaud et al) on all sequences in sagittal axes T2-weighted and post-contrast T1-weighted images fat suppressed. MRI findings were compared with preoperative biopsy results and postoperative histopathology.</div></div><div><h3>Results</h3><div>The analysis revealed the highest discrepancies between preoperative histology combined with MRI images and final pathology in tumor grade (21.6 %), FIGO stage (39.6 %), and myometrial invasion (27.6 %). A 24 mm threshold for the anteroposterior measurement was used as a predictor of LVSI. The model utilizing this cutoff demonstrated good performance (AUC = 0.61, p &lt; 0.001) and correctly reclassified 19.8 % of patients with preoperative FIGO stage I tumors as FIGO stage II or more after surgery.</div></div><div><h3>Conclusion</h3><div>This approach may enhance the preoperative prediction of LVSI and improve the application of the updated FIGO classification in endometrial cancer. The results suggest that MRI-derived short-axis measurement could be a valuable tool for refining the preoperative assessment of LVSI in EC patients.</div></div>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"190 ","pages":"Article 112259"},"PeriodicalIF":3.2,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144366069","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 for predicting unsuccessful recanalization in refractory large vessel occlusion","authors":"Jesús D. González ,&nbsp;Pere Canals ,&nbsp;Marc Rodrigo-Gisbert ,&nbsp;Jordi Mayol ,&nbsp;Alvaro García-Tornel ,&nbsp;Marc Ribó","doi":"10.1016/j.ejrad.2025.112254","DOIUrl":"10.1016/j.ejrad.2025.112254","url":null,"abstract":"<div><div><strong><em>Purpose:</em></strong> This study explores a multi-modal deep learning approach that integrates pre-intervention neuroimaging and clinical data to predict endovascular therapy (EVT) outcomes in acute ischemic stroke patients. To this end, consecutive stroke patients undergoing EVT were included in the study, including patients with suspected Intracranial Atherosclerosis-related Large Vessel Occlusion ICAD-LVO and other refractory occlusions. <strong><em>Methods:</em></strong> A retrospective, single-center cohort of patients with anterior circulation LVO who underwent EVT between 2017–2023 was analyzed. Refractory LVO (rLVO) defined class, comprised patients who presented any of the following: final angiographic stenosis &gt; 50 %, unsuccessful recanalization (eTICI 0-2a) or required rescue treatments (angioplasty +/- stenting). Neuroimaging data included non-contrast CT and CTA volumes, automated vascular segmentation, and CT perfusion parameters. Clinical data included demographics, comorbidities and stroke severity. Imaging features were encoded using convolutional neural networks and fused with clinical data using a DAFT module. Data were split 80 % for training (with four-fold cross-validation) and 20 % for testing. Explainability methods were used to analyze the contribution of clinical variables and regions of interest in the images. <strong><em>Results:</em></strong> The final sample comprised 599 patients; 481 for training the model (77, 16.0 % rLVO), and 118 for testing (16, 13.6 % rLVO). The best model predicting rLVO using just imaging achieved an AUC of 0.53 ± 0.02 and F1 of 0.19 ± 0.05 while the proposed multimodal model achieved an AUC of 0.70 ± 0.02 and F1 of 0.39 ± 0.02 in testing. <strong><em>Conclusion:</em></strong> Combining vascular segmentation, clinical variables, and imaging data improved prediction performance over single-source models. This approach offers an early alert to procedural complexity, potentially guiding more tailored, timely intervention strategies in the EVT workflow.</div></div>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"190 ","pages":"Article 112254"},"PeriodicalIF":3.2,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144329580","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":"Image-based AI tools in peripheral nerves assessment: Current status and integration strategies − A narrative review","authors":"Teodoro Martín-Noguerol ,&nbsp;Carolina Díaz-Angulo ,&nbsp;Antonio Luna ,&nbsp;Fermín Segovia ,&nbsp;Manuel Gómez-Río ,&nbsp;Juan M. Górriz","doi":"10.1016/j.ejrad.2025.112255","DOIUrl":"10.1016/j.ejrad.2025.112255","url":null,"abstract":"<div><div>Peripheral Nerves (PNs) are traditionally evaluated using US or MRI, allowing radiologists to identify and classify them as normal or pathological based on imaging findings, symptoms, and electrophysiological tests. However, the anatomical complexity of PNs, coupled with their proximity to surrounding structures like vessels and muscles, presents significant challenges. Advanced imaging techniques, including MR-neurography and Diffusion-Weighted Imaging (DWI) neurography, have shown promise but are hindered by steep learning curves, operator dependency, and limited accessibility. Discrepancies between imaging findings and patient symptoms further complicate the evaluation of PNs, particularly in cases where imaging appears normal despite clinical indications of pathology. Additionally, demographic and clinical factors such as age, sex, comorbidities, and physical activity influence PN health but remain unquantifiable with current imaging methods.</div><div>Artificial Intelligence (AI) solutions have emerged as a transformative tool in PN evaluation. AI-based algorithms offer the potential to transition from qualitative to quantitative assessments, enabling precise segmentation, characterization, and threshold determination to distinguish healthy from pathological nerves. These advances could improve diagnostic accuracy and treatment monitoring.</div><div>This review highlights the latest advances in AI applications for PN imaging, discussing their potential to overcome the current limitations and opportunities to improve their integration into routine radiological practice.</div></div>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"190 ","pages":"Article 112255"},"PeriodicalIF":3.2,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144321967","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 radiomics models for predicting spread through air space in lung cancer: A systematic review and meta-analysis","authors":"Lihua Chen ,&nbsp;Xiaosong Lan ,&nbsp;Yao Huang ,&nbsp;Junli Tao ,&nbsp;Xuemei Huang ,&nbsp;Yangfan Su ,&nbsp;Daihong Liu ,&nbsp;Xiangming Fang ,&nbsp;Jiuquan Zhang","doi":"10.1016/j.ejrad.2025.112249","DOIUrl":"10.1016/j.ejrad.2025.112249","url":null,"abstract":"<div><h3>Rationale and objectives</h3><div>Numerous studies have developed and validated models to predict spread through air space (STAS) in lung cancer using preoperative computed tomography (CT), yielding inconsistent results. We aimed to estimate the diagnostic accuracy of CT-based radiomics for predicting spread through air space (STAS) for preoperative prediction of lung cancer.</div></div><div><h3>Materials and methods</h3><div>Original studies published prior to January 2024 were searched in various databases. Only studies that used CT-based radiomics to preoperatively predict STAS in lung cancer patients were included. Two researchers independently extracted data and assessed the methodological quality of the included studies. We estimated summary sensitivity (SEN), specificity (SPE), and the areas under the receiver operating characteristic curve (AUC) of CT-based radiomics for predicting STAS. A head-to-head comparison was performed to evaluate the efficacy of clinical and radiomics models.</div></div><div><h3>Results</h3><div>A total of 17 studies with 6254 participants were included, and the methodological quality was found to be moderate. The <em>meta</em>-analysis comprised 26 datasets and achieved a pooled SEN of 0.82 (95 % CI: 0.78, 0.86), SPE of 0.76 (95 % CI: 0.72, 0.80), and AUC of 0.86 (95 % CI: 0.83, 0.89). In 11 pairwise comparison datasets, the radiomics model outperformed the clinical model with a higher AUC of 0.86 (95 % CI: 0.83, 0.89) compared to 0.80 (95 % CI: 0.76, 0.85), p &lt; 0.001.</div></div><div><h3>Conclusions</h3><div>Due to its moderate diagnostic accuracy, widespread use, and low cost, CT-based radiomics can be used to predict STAS in lung cancer preoperatively. However, further research is required in a large, multicentre, and prospective design.</div></div>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"190 ","pages":"Article 112249"},"PeriodicalIF":3.2,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144321970","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":"Deep learning model using CT images for longitudinal prediction of benign and malignant ground-glass nodules","authors":"Xiaolong Yang ,&nbsp;Jiayang Wang ,&nbsp;Ping Wang ,&nbsp;Yingjie Li ,&nbsp;Zhubin Wen ,&nbsp;Jiming Shang ,&nbsp;Kaige Chen ,&nbsp;Chao Tang ,&nbsp;Shuang Liang ,&nbsp;Wei Meng","doi":"10.1016/j.ejrad.2025.112252","DOIUrl":"10.1016/j.ejrad.2025.112252","url":null,"abstract":"<div><h3>Objectives</h3><div>To develop and validate a CT image-based multiple time-series deep learning model for the longitudinal prediction of benign and malignant pulmonary ground-glass nodules (GGNs).</div></div><div><h3>Methods</h3><div>A total of 486 GGNs from an equal number of patients were included in this research, which took place at two medical centers. Each nodule underwent surgical removal and was confirmed pathologically. The patients were randomly assigned to a training set, validation set, and test set, following a distribution ratio of 7:2:1. We established a transformer-based deep learning framework that leverages multi-temporal CT images for the longitudinal prediction of GGNs, focusing on distinguishing between benign and malignant types. Additionally, we utilized 13 different machine learning algorithms to formulate clinical models, delta-radiomics models, and combined models that merge deep learning with CT semantic features. The predictive capabilities of the models were assessed using the receiver operating characteristic (ROC) curve and the area under the curve (AUC).</div></div><div><h3>Results</h3><div>The multiple time-series deep learning model based on CT images surpassed both the clinical model and the delta-radiomics model, showcasing strong predictive capabilities for GGNs across the training, validation, and test sets, with AUCs of 0.911 (95% CI, 0.879–0.939), 0.809 (95% CI,0.715–0.908), and 0.817 (95% CI,0.680–0.937), respectively. Furthermore, the models that integrated deep learning with CT semantic features achieved the highest performance, resulting in AUCs of 0.960 (95% CI, 0.912–0.977), 0.878 (95% CI,0.801–0.942), and 0.890(95% CI, 0.790–0.968).</div></div><div><h3>Conclusion</h3><div>The multiple time-series deep learning model utilizing CT images was effective in predicting benign and malignant GGNs.</div></div>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"190 ","pages":"Article 112252"},"PeriodicalIF":3.2,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144335791","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":"The role of neuroimaging in brain death diagnosis: a review of radiological protocols and the need for standardization","authors":"Giulia Iacobellis ,&nbsp;Alessia Leggio ,&nbsp;Cecilia Salzillo ,&nbsp;Miriam Solenne ,&nbsp;Andrea Marzullo","doi":"10.1016/j.ejrad.2025.112247","DOIUrl":"10.1016/j.ejrad.2025.112247","url":null,"abstract":"<div><div>Brain death, defined as the irreversible cessation of all brain functions, including the brainstem, is a critical concept in modern medicine, particularly in the context of organ transplantation. The diagnosis of brain death relies primarily on a thorough clinical neurological examination, which assesses the absence of brainstem reflexes, coma, and apnoea. This paper underscores the critical role of neuroimaging-based ancillary tests in enhancing the accuracy of brain death determination and calls for harmonization of protocols to address existing disparities and improve clinical practice worldwide. The use of ancillary tests, particularly neuroimaging techniques plays a crucial role in confirming the diagnosis, especially in cases where clinical examination is inconclusive or confounded by factors such as drug intoxication or hypothermia. These tests provide objective evidence of the absence of cerebral blood, thereby supporting the clinical determination of brain death. Despite global consensus on the importance of the clinical neurological examination in diagnosing brain death, significant variations exist between countries regarding the use of ancillary tests, the number of required clinical examinations, observation periods, and the number of physicians involved in the determination process. The discrepancies among international guidelines, highlight the need for standardized to ensure consistency and reliability in brain death diagnosis, particularly in the context of organ transplantation, where timely and accurate diagnosis is essential. This literature review examines brain death in the context of organ donation, highlighting the differences in diagnostic protocols worldwide. It emphasises the importance of clinical and neuroimaging tests for accurate diagnosis and the need to standardise international guidelines to improve clinical practice and ensure timely and reliable decisions, particularly in organ transplantation.</div></div>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"190 ","pages":"Article 112247"},"PeriodicalIF":3.2,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144364643","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}