European Journal of Radiology Open最新文献

{"title":"Workflow-embedded AI as a cognitive scaffold: A randomized trial on knowledge retention and diagnostic competency in undergraduate radiology education","authors":"Jing Li ,&nbsp;Haiyan Zhao","doi":"10.1016/j.ejro.2026.100724","DOIUrl":"10.1016/j.ejro.2026.100724","url":null,"abstract":"<div><h3>Background</h3><div>Traditional didactic methods in medical imaging education, predominantly reliant on static images (non-augmented, traditional PACS workflow that requires manual, unguided search and interpretation), consistently fail to bridge the theory-practice divide, contributing to high diagnostic error rates. While the integration of artificial intelligence (AI) with Picture Archiving and Communication Systems (PACS+AI) offers transformative potential, robust evidence quantifying its impact on longitudinal competency development remains scarce.</div></div><div><h3>Objective</h3><div>This study aims to quantitatively evaluate the efficacy of a cognitively optimized PACS+AI framework versus conventional PACS in enhancing radiology education across four critical domains: theoretical knowledge, clinical decision-making competencies, AI acceptance, and knowledge retention.</div></div><div><h3>Methods</h3><div>In a prospective single-blind randomized controlled trial (RCT), 110 medical imaging undergraduates were randomized to PACS+AI (n = 55) or standard PACS (n = 55) groups. Theoretical knowledge was assessed using validated item-bank assessments; clinical decision-making competencies were evaluated through lesion detection, anatomical localization, diagnostic accuracy, and report completeness; AI acceptance was measured using the Technology Acceptance Model (TAM); and knowledge retention was tracked through immediate, 1-month, and 3-month follow-up assessments. The PACS+AI framework provided three core cognitive support functions: automated lesion annotation, structured diagnostic prompting, and workflow-contextualized feedback.</div></div><div><h3>Results</h3><div>The PACS+AI group demonstrated significantly superior outcomes across all domains: theoretical knowledge retention was substantially higher (79.3 % vs. 19.7 % at 3 months, P &lt; 0.001, d=1.95); clinical decision-making competencies showed progressive improvement with large effect sizes (Δ=12.4–18.1, all P &lt; 0.001, d=1.88–2.48); AI acceptance scores were significantly elevated across all TAM constructs (all P &lt; 0.001, d&gt;1.9); and knowledge retention was maintained longitudinally with amplified effects over time.</div></div><div><h3>Conclusion</h3><div>The PACS+AI framework significantly enhances radiology education by optimizing cognitive load distribution, resulting in sustained knowledge retention, superior clinical decision-making competencies, and heightened AI acceptance. This integrated teaching model effectively bridges the gap between theory and practice, cultivates professionals adaptable to the artificial intelligence environment, and aligns with the core needs of the new generation of medical education.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"16 ","pages":"Article 100724"},"PeriodicalIF":2.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145939272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated assessment of right heart function by artificial intelligence: A systematic review and meta-analysis","authors":"Pooya Eini ,&nbsp;Homa serpoush ,&nbsp;Mohammad Rezayee ,&nbsp;Jason Tremblay","doi":"10.1016/j.ejro.2025.100713","DOIUrl":"10.1016/j.ejro.2025.100713","url":null,"abstract":"<div><h3>Background</h3><div>Accurate assessment of right ventricular (RV) size and function is critical for managing cardiac diseases but is challenged by the limitations of traditional echocardiography. Artificial intelligence (AI) models offer potential for improving RV assessment, yet their diagnostic accuracy remains uncertain. This systematic review and meta-analysis evaluates the diagnostic accuracy of AI models for predicting RV size and function, synthesizing performance metrics and assessing evidence quality.</div></div><div><h3>Methods</h3><div>Adhering to PRISMA guidelines, we searched 5 databases up to June 2025 using MeSH and Emtree terms for \"Artificial Intelligence,\" \"Right Ventricular Function,\" and \"Right Ventricular Dysfunction.\" Two reviewers screened studies, extracted data and assessed quality using PROBAST+AI. Pooled estimates were calculated using STATA 18 with MIDAS and METADATA modules. Heterogeneity was explored via subgroup analyses, meta-regression, and sensitivity analyses. Publication bias was assessed using funnel plot.</div></div><div><h3>Results</h3><div>From 25 studies, 18 provided data for meta-analysis, yielding a pooled sensitivity of 0.85 (95 % CI: 0.73–0.92), specificity of 0.81 (95 % CI: 0.72–0.88), and AUROC of 0.89 (95 % CI: 0.86–0.92). High heterogeneity (I² = 71.63 % for sensitivity, 73.51 % for specificity) was partially explained by algorithm type and study country. The GRADE assessment indicated moderate certainty of evidence due to heterogeneity and bias in 25 % of studies.</div></div><div><h3>Conclusion</h3><div>AI models show promising diagnostic accuracy for RV assessment, but high heterogeneity and moderate evidence certainty necessitate cautious interpretation and further research.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"16 ","pages":"Article 100713"},"PeriodicalIF":2.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145692986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning for diagnosis of malignant thyroid nodules based on thyroid ultrasound: Systematic review and meta-analysis of studies with external datasets","authors":"Elisa Gatta ,&nbsp;Roberto Gatta ,&nbsp;Riccardo Morandi ,&nbsp;Samuele Isoli ,&nbsp;Sara Corvaglia ,&nbsp;Simone Vetrugno ,&nbsp;Virginia Maltese ,&nbsp;Ilenia Pirola ,&nbsp;Claudio Casella ,&nbsp;Carlo Cappelli","doi":"10.1016/j.ejro.2025.100716","DOIUrl":"10.1016/j.ejro.2025.100716","url":null,"abstract":"<div><h3>Introduction</h3><div>Optimizing the diagnostic approach to thyroid nodules remains a crucial challenge. Ultrasound-based risk stratification systems such as EU-TIRADS have shown reasonable sensitivity and specificity. Therefore, we conducted a systematic review and meta-analysis to assess the diagnostic performance of Artificial Intelligence (AI) models in differentiating benign from malignant thyroid nodules on ultrasound data.</div></div><div><h3>Methods</h3><div>A comprehensive search of PubMed/MEDLINE, Scopus, and Web of Science was performed up to January 1, 2025. Eligible studies included patients with thyroid nodules undergoing ultrasound, where AI-based models were validated against cytological or histological findings. The AI algorithms were developed using different types of ultrasound-derived data, including B-mode images, radiomics features. Pooled sensitivity, specificity, and area under the curve (AUC) were estimated using a hierarchical summary receiver operating characteristic (HSROC) model.</div></div><div><h3>Results</h3><div>Twenty-seven studies comprising 146,332 patients and over 600,000 ultrasound images met inclusion criteria. Overall, pooled sensitivity was 87 % (95 % CI: 84–89 %) and specificity 83 % (95 % CI: 79–86 %). The summary operating point indicated a sensitivity of 88 % and specificity of 83 %, with an AUC of 91.9 % (95 % CI: 90.0–93.2 %). Although subgroup analysis suggested higher accuracy when cytology was used as the reference standard compared to histology, the mixed-effects meta-regression did not confirm a statistically significant association (p = 0.238 for sensitivity; p = 0.188 for specificity).</div></div><div><h3>Conclusion</h3><div>AI-based algorithms show excellent diagnostic performance in distinguishing benign from malignant thyroid nodules, with robust validation across external datasets. These findings support the potential integration of AI into clinical thyroid nodule management, although further multicenter, non-Asian, and histology-based studies are warrantee.</div></div><div><h3>Systematic review registration</h3><div>PROSPERO (CRD420251108149)</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"16 ","pages":"Article 100716"},"PeriodicalIF":2.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145749597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual-parameter risk stratification based on device landing zone calcification and aortic annular perimeter for paravalvular regurgitation after self-expanding TAVR","authors":"Jun Shu ,&nbsp;Didi Wen ,&nbsp;Jingji Xu ,&nbsp;Yu Mao ,&nbsp;Hui Ma ,&nbsp;Jing Zhang ,&nbsp;Yao Zhao ,&nbsp;Jian Yang ,&nbsp;Minwen Zheng","doi":"10.1016/j.ejro.2025.100719","DOIUrl":"10.1016/j.ejro.2025.100719","url":null,"abstract":"<div><h3>Purpose</h3><div>The study aimed to identify independent predictors associated with paravalvular regurgitation (PVR) after self-expanding transcatheter aortic valve replacement (SE-TAVR) and to develop a dual-parameter risk stratification model.</div></div><div><h3>Methods</h3><div>This retrospective study enrolled 292 severe aortic stenosis patients underwent SE-TAVR. PVR severity was assessed pre-discharge. Multivariate logistic regression identified independent predictors of mild/moderate PVR, optimal cutoff values for significant anatomical parameters were determined using receiver operating characteristic (ROC) curve analysis. Patients were subsequently stratified into three risk groups based on these thresholds.</div></div><div><h3>Results</h3><div>Mild/moderate PVR occurred in 24.0 % of patients. Independent predictors included aortic annular perimeter (OR:1.067, <em>P</em> = 0.015), device landing zone calcific volume (OR:1.006 per 10 mm³, <em>P</em> = 0.025), and presence of sealing skirt (OR:0.412, <em>P</em> = 0.010). The combination of these predictors had a higher discriminative performance (AUC=0.779) than single predictors (<em>P</em> = 0.036, 0.007, and &lt;0.001, respectively), with significant integrated discrimination improvement (integrated discrimination improvement=5.4–6.7 %, <em>P</em> &lt; 0.001). ROC-derived thresholds (device landing zone calcific volume≥1240 mm³ and aortic annular perimeter≥77 mm) stratified patients into three risk groups with progressively increasing PVR incidence: Group A (neither elevate):8.4 %; Group B (either elevated):23.7 %; and Group C (both elevated):48.7 %. Pairwise comparisons confirming differences between Group A vs. B (<em>P</em> = 0.003) and Group B vs. C (<em>P</em> &lt; 0.001). Sealing skirts significantly reduced PVR in Groups A (<em>P</em> = 0.042) but not in Group B and C (<em>P</em> = 0.082 and 0.342).</div></div><div><h3>Conclusion</h3><div>The dual-parameter model based on device landing zone calcification and aortic annular perimeter significantly enhances PVR risk stratification after SE-TAVR. The dual-threshold model provides a clinically actionable tool for pre-procedural risk stratification and personalized valve selection.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"16 ","pages":"Article 100719"},"PeriodicalIF":2.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145749553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-based multi-class classification of bladder pathologies using fused 3D CT radiomic and 3D auto-encoder deep features","authors":"Hongwei Xiao ,&nbsp;Weihao Liu ,&nbsp;Huancheng Yang ,&nbsp;Zexin Huang ,&nbsp;Yangguang Yuan ,&nbsp;Tianyu Wang ,&nbsp;Hanlin Liu ,&nbsp;Kai Wu","doi":"10.1016/j.ejro.2026.100728","DOIUrl":"10.1016/j.ejro.2026.100728","url":null,"abstract":"<div><h3>Objective</h3><div>To develop an automated analytical framework that integrates hybrid radiomics and deep learning features from non-contrast CT images for the multi-class classification of bladder pathologies.</div></div><div><h3>Methods</h3><div>This retrospective study analyzed 902 CT scans (584 normal, 142 calculi, 66 cancers, 110 cystitis). An integrated pipeline was implemented, comprising: 1) automatic bladder segmentation using a 3D-UNet, 2) hybrid feature extraction combining 100 radiomics features and 256 deep features from a 3D convolutional autoencoder, 3) feature selection via variance thresholding and LASSO regression, and 4) final classification using an XGBoost classifier. The dataset was split into training (80 %) and validation (20 %) sets. Performance was evaluated using the area under the receiver operating characteristic curve (AUROC) with a one-vs-rest strategy for multi-class classification. Model stability was assessed via stratified five-fold cross-validation, and interpretability was analyzed with SHapley Additive exPlanations (SHAP).</div></div><div><h3>Results</h3><div>The framework achieved one-vs-rest AUROCs of 0.94 (95 % CI: 0.89–0.99) for calculi, 0.92 (0.85–0.99) for cancer, 0.90 (0.84–0.95) for normal bladder, and 0.83 (0.75–0.91) for cystitis. The micro-average AUROC for four-class discrimination was 0.94 (0.92–0.96). Binary normal/abnormal classification demonstrated stable performance across cross-validation folds (AUROC range: 0.89–0.92). SHAP analysis revealed that radiomic features dominated decisions for calculi/normal differentiation, while deep features were critical for distinguishing cancer and cystitis.</div></div><div><h3>Conclusion</h3><div>The proposed hybrid CT analysis framework achieves clinically relevant performance in the automated, multi-class classification of bladder pathologies, excelling particularly in calculi detection. The complementary roles of radiomic and deep features provide an interpretable diagnostic aid, demonstrating potential for integration into clinical workflows to support differential diagnosis.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"16 ","pages":"Article 100728"},"PeriodicalIF":2.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145939415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Muscle and fat matter: Automated CT-based body composition analysis predicts survival in Hepatocellular carcinoma patients undergoing radioembolization","authors":"Hannah L. Steinberg-Vorhoff ,&nbsp;Anneke Ketelsen ,&nbsp;Tabea Schuch ,&nbsp;Jens M. Theysohn ,&nbsp;Benedikt M. Schaarschmidt ,&nbsp;Johannes Haubold ,&nbsp;Farroch Vahidi Noghani ,&nbsp;Matthias Jeschke ,&nbsp;Leonie Jochheim ,&nbsp;Johannes M. Ludwig","doi":"10.1016/j.ejro.2025.100721","DOIUrl":"10.1016/j.ejro.2025.100721","url":null,"abstract":"<div><h3>Purpose</h3><div>This study aimed to assess the prognostic significance of pretreatment CT-based body composition markers in patients with <em>Hepatocellular carcinoma</em> (HCC) treated with radioembolization.</div></div><div><h3>Material and methods</h3><div>Automated analysis of baseline CT scans was performed to retrospectively evaluate body composition (BCA) parameters in 198 patients from a prospective registry database, including skeletal muscle (SM) and bone (B) volumes. BCA parameters and ratios were dichotomized using a maximally selected log-rank approach. Kaplan-Meier and uni- (UVA) and multivariate (MVA) Cox-proportional-hazard ratio (HR) survival analyses were performed.</div></div><div><h3>Results</h3><div>The median survival time was 18.5 months. In UVA, lower BCLC stage, ≦ 70 years of age, normal serum albumin, non-elevated C-reactive protein, normal aspartate aminotransferase (ASAT), normal alkaline phosphatase, normal gamma-glutamyl transaminase (GGT), absence of portal vein thrombosis and various BCA parameters were statistically significant with the skeletal muscle to bone ratio (SM/B) demonstrating the strongest survival discrimination with a median survival of 23.6 months for high and 12.0 months for low SM/B (HR: 0.65, 95 %CI: 0.46–0.9; p = 0.0001). In MVA, SM/B, BCLC stage, ASAT, and GGT remained independently significant. Patients with higher SM/B ratios demonstrated a significantly higher disease control rate during the initial imaging follow-up after three months (74.4 % vs. 54.0 %, p = 0.017).</div></div><div><h3>Conclusion</h3><div>These findings suggest that fully automated, CT-based measurement of BCA parameters — particularly the SM/B ratio — can serve as an independent prognostic factor for survival and disease control in patients with <em>Hepatocellular carcinoma</em> (HCC) undergoing radioembolization. This could potentially facilitate the identification of patients who would benefit most from this treatment.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"16 ","pages":"Article 100721"},"PeriodicalIF":2.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146038031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance of deep-learning reconstruction combined with metal artifact reduction algorithm for dual-energy computed tomography angiography in intracranial aneurysm coil embolization","authors":"Lina Tao ,&nbsp;Yuhan Zhou ,&nbsp;Limin Lei,&nbsp;Yajie Wang,&nbsp;Xiaoxu Guo,&nbsp;Yifan Guo,&nbsp;Songwei Yue","doi":"10.1016/j.ejro.2025.100715","DOIUrl":"10.1016/j.ejro.2025.100715","url":null,"abstract":"<div><h3>Purposes</h3><div>To evaluate the diagnostic confidence in cerebral aneurysm embolization coil follow-up using the deep learning image reconstruction (DLIR) based virtual monoenergetic images (VMI) combined with metal artifact reduction (MAR) algorithm, with a focus on selecting the most optimal scheme.</div></div><div><h3>Methods</h3><div>A CTA database of 54 patients was prospectively assembled and reconstructed utilizing adaptive statistical iterative reconstruction-Veo(ASIR-V50 %), DLIR at medium and high levels (DLIR-M and H). VMIs were generated within the 40–140 keV range at 10 keV intervals, both with or without MAR. Objective parameters such as artifact index (AI), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured. Subjective evaluation was assessed according to the Likert scale scoring method. The post-embolization therapeutic efficacy was assessed by the aneurysm neck, parent artery, and postprocedural complications.</div></div><div><h3>Results</h3><div>Firstly, 80 keV to 90 keV provided the best objective and subjective scores for a balance between artifact reduction and vascular display. Secondly, the DLIR-H+MAR combination exhibited the highest CNR at 80 keV to 90 keV, while also receiving the best subjective scores. Moreover, the MAR group showed significantly smaller discrepancies in aneurysm neck length and bilateral parent artery diameters compared to the non-MAR group when compared to DSA (<em>p</em> &lt; 0.001). Importantly, the MAR group demonstrated two cases of aneurysm recurrence, four cases of residual filling, ten cases of parent artery stenosis, and four cases of aneurysmal rupture that were undetected by the non-MAR group.</div></div><div><h3>Conclusion</h3><div>DLIR-H+MAR at 80 keV to 90 keV proved to be the optimal method for visualizing cerebral arteries and mitigating metal artifacts. Simultaneously, it significantly enhanced the efficacy assessment and complication detection of post-embolization aneurysm.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"16 ","pages":"Article 100715"},"PeriodicalIF":2.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145749552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The value of modified time-of-flight magnetic resonance venography in evaluating anatomical variations of the internal iliac vein","authors":"Ziyu Zuo ,&nbsp;Xiaoyu Zhang ,&nbsp;Wei Zhu ,&nbsp;Chengxin Wan ,&nbsp;Yong Xu ,&nbsp;Zhiwei Zhang ,&nbsp;Yu Zhao ,&nbsp;Dechuan Zhang ,&nbsp;Li Tao","doi":"10.1016/j.ejro.2025.100717","DOIUrl":"10.1016/j.ejro.2025.100717","url":null,"abstract":"<div><h3>Objective</h3><div>To investigate the feasibility of using modified time-of-flight magnetic resonance venography (mTOF-MRV) to evaluate the anatomical variations of the internal iliac vein (IIV).</div></div><div><h3>Methods</h3><div>This retrospective study included 158 patients suspected of iliac vein compression syndrome (IVCS) who underwent pelvic mTOF-MRV between June 2021 and March 2024. Fourteen patients with post-thrombotic syndrome (PTS) were excluded, leaving 144 eligible patients (52 males, 92 females; mean age 53 ± 16 years). Two radiologists independently evaluated image quality using a 4-point scale and analyzed IIV anatomical features via multiplanar reconstruction (MPR), maximum intensity projection (MIP), and volume rendering (VR) techniques. Inter-observer agreement was assessed using Cohen’s kappa coefficient and intraclass correlation coefficient (ICC).</div></div><div><h3>Results</h3><div>Inter-observer agreement for image quality was good (K=0.893), and for objective measurements was excellent (ICC [95 % confidence interval]: 0.893 [0.845–0.941]). Four IIV anatomical variation types were identified: Type I (unilateral single IIV draining to ipsilateral CIV bilaterally, 30.56 %), Type II (one/both pelvic cavities with two IIVs draining to ipsilateral CIV, 55.56 %), Type III (one IIV draining to ipsilateral CIV and the other to contralateral CIV, 11.80 %), and Type IV (other variations, 2.08 %). Left CIV compression was the most common (86.11 %).</div></div><div><h3>Conclusion</h3><div>The mTOF-MRV clearly visualizes IIV anatomy and variations. The proposed classification system aids preoperative planning and postoperative hemodynamic evaluation for pelvic venous disorders.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"16 ","pages":"Article 100717"},"PeriodicalIF":2.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145749598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Submucosal laryngeal lesions: A puzzling diagnostic conundrum","authors":"Melissa Shuhui Lee ,&nbsp;Jean Lee ,&nbsp;Richard Wiggins","doi":"10.1016/j.ejro.2026.100735","DOIUrl":"10.1016/j.ejro.2026.100735","url":null,"abstract":"<div><div>Laryngeal mucosal masses are commonly squamous cell carcinomas, easily identified and biopsied on scope. In contrast, a submucosal laryngeal mass has a broad differential diagnosis, including benign and malignant epithelial and non-epithelial neoplasms as well as other non-neoplastic abnormalities including vascular malformations, infective or inflammatory pathologies, submucosal hematoma, rare depositional diseases such as amyloidosis, and other benign lesions such as laryngoceles. Due to a lack of visible mucosal abnormality, biopsy of these lesions are often challenging with higher rates of false negatives or inadequate sampling. Whilst radiological imaging features of submucosal laryngeal lesions may be non-specific, there are some lesions which may exhibit typical imaging features which could help radiologists to narrow the differential diagnosis and direct diagnostic workup and clinical management more effectively. In this article, we will illustrate a spectrum of submucosal laryngeal lesions, with an emphasis on helpful imaging features to help distinguish pathologies, and an overview of appropriate workup and management aspects which the radiologist needs to know to contribute effectively to patient care.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"16 ","pages":"Article 100735"},"PeriodicalIF":2.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence in breast cancer screening: A systematic review and meta-analysis of integration strategies","authors":"Eloïse Sossavi,&nbsp;Catherine Roy,&nbsp;Sébastien Molière","doi":"10.1016/j.ejro.2026.100727","DOIUrl":"10.1016/j.ejro.2026.100727","url":null,"abstract":"<div><h3>Objective</h3><div>To compare AI-augmented and conventional double reading in organised breast-cancer screening with respect to cancer-detection rate (CDR), recall rate, and radiologist workload.</div></div><div><h3>Methods</h3><div>We conducted a systematic review and random-effects meta-analysis of 13 prospective and retrospective studies (1.03 million screens) from 2017 to 2024 that embedded commercial or research AI into population-based digital mammography or tomosynthesis programmes. Eligible studies included ≥ 10,000 screens (or ≥100 cancers) and reported CDR, recalls, and/or workload metrics. We extracted cancer and recall counts and calculated risk ratios (RRs) for AI-augmented versus double reading, overall and by integration model: independent second reader, gate-keeper/decision-referral triage, and concurrent overlay.</div></div><div><h3>Results</h3><div>Overall, AI-augmented protocols achieved CDR parity (RR 1.01; 95 % CI 0.96–1.07) and no significant change in recalls (RR 1.00; 95 % CI 0.88–1.15). Triage models preserved CDR (RR 1.02; 95 % CI 0.98–1.07) while reducing recalls by 11 % (RR 0.89; 95 % CI 0.82–0.96) and cutting initial reads by 44–70 %. Independent-reader workflows maintained CDR (RR 0.98; 95 % CI 0.92–1.05) but showed variable recall effects (RR 1.12; 95 % CI 0.90–1.39) driven by arbitration logic and threshold choices. Concurrent overlay (two studies) indicated possible sensitivity gains (RR 1.31; 95 % CI 0.90–1.91) without higher recall rates, though precision was limited.</div></div><div><h3>Conclusions</h3><div>AI integration can match conventional double reading in detection performance, but its impact on workflow depends on the chosen model. Triage-based approaches consistently lower radiologist workload and recalls without compromising sensitivity, whereas replacing a second reader may simply shift effort to arbitration. Future implementation should focus on workflow-aware metrics and prospective threshold validation.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"16 ","pages":"Article 100727"},"PeriodicalIF":2.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145939567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}