European Journal of Radiology Open最新文献

{"title":"The value of virtual non-contrast images derived from dual-energy spectral CT in the short-term efficacy assessment of hepatocellular carcinoma after TACE","authors":"Mingzi Gao ,&nbsp;Taoming Du ,&nbsp;Kai Zhang ,&nbsp;Cheng Yan ,&nbsp;Changchun Liu ,&nbsp;Can Su ,&nbsp;Jingwen Zhang ,&nbsp;Yingxuan Wang ,&nbsp;Jing Han ,&nbsp;Mingxin Zhang ,&nbsp;Yujie Chen ,&nbsp;Jinghui Dong ,&nbsp;Liqin Zhao","doi":"10.1016/j.ejro.2026.100730","DOIUrl":"10.1016/j.ejro.2026.100730","url":null,"abstract":"<div><h3>Purpose</h3><div>To explore the clinical utility of virtual non-contrast (VNC) images from dual-energy spectral CT (DEsCT) in short-term follow-up of hepatocellular carcinoma (HCC) after transarterial chemoembolization (TACE).</div></div><div><h3>Methods</h3><div>66 HCC patients with DEsCT 4–6 weeks post-TACE were retrospectively enrolled. VNC images were generated from arterial (VNC_AP) and portal venous phase (VNC_PVP) images. Beam-hardening artifacts surrounding lipiodol were assessed on true non-contrast (TNC) and both VNC images. Lipiodol removal degree was classified into 4 grades at 25 % intervals on both VNC images. Residual viable tumor (RVT) was diagnosed using contrast-enhanced CT or MRI. CT attenuation values of RVT, adjacent normal hepatic parenchyma (ANHP), and lipiodol removal area (LRA) were compared among TNC and VNC images. Diagnostic performance of CT attenuation values on VNC images was compared for the three areas.</div></div><div><h3>Results</h3><div>34 patients showed beam-hardening artifacts surrounding lipiodol on TNC images, which were reduced or eliminated on VNC_AP and VNC_PVP images in 28 and 26 cases. All HCCs showed good lipiodol removal on both VNC images, with 7 lesions at Grade 3 and 59 lesions at Grade 4. On TNC images, significant CT attenuation value differences were found between LRA and ANHP, and LRA and RVT (P &lt; 0.001), but not between RVT and ANHP (P &gt; 0.05). Both VNC images showed good diagnostic efficacy for these three areas, with LRA having the lowest value.</div></div><div><h3>Conclusion</h3><div>VNC images demonstrate superior lipiodol removal efficacy and beam-hardening artifacts reduction, facilitating precise RVT delineation and TACE-induced necrosis assessment, complementing contrast-enhanced CT for TACE efficacy assessment in HCC.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"16 ","pages":"Article 100730"},"PeriodicalIF":2.9,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977584","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":"Association of preoperative co-occurring intervertebral disc-related degenerative features with one-year lumbar discectomy outcomes: A proposal for and preliminary testing of a novel MRI-based criterion","authors":"Tero Korhonen ,&nbsp;Jyri Järvinen ,&nbsp;Juha Pesälä ,&nbsp;Marianne Haapea ,&nbsp;Pietari Kinnunen ,&nbsp;Jaakko Niinimäki","doi":"10.1016/j.ejro.2026.100729","DOIUrl":"10.1016/j.ejro.2026.100729","url":null,"abstract":"<div><h3>Purpose</h3><div>This study developed a criterion for preoperative co-occurring intervertebral disc (IVD)-related degenerative features and evaluated its association with one-year outcomes following single-level lumbar discectomy.</div></div><div><h3>Methods</h3><div>The novel literature-based criterion, termed “Advanced Preoperative Degeneration” (APD), required the operated segment to exhibit preoperatively at least two advanced-level phenotypes from endplate damage (EPD), Modic changes (MC), and IVD degeneration. Subsequently, a retrospective single-center register-based study of patients treated with single-level micro- or endoscopic lumbar discectomy at a tertiary-level hospital between 2017 and 2022 was performed. The patients were categorized into three groups, APD-positive, APD1/3, and APD0, based on the presence of two or more, one, or none of the required phenotypes, respectively. A mixed-effects model was employed to assess between-group differences in improvement of LBP and leg pain (0–100 VAS), disability (ODI), and quality of life (EQ-5D-3L) from baseline to the one-year postoperative time point.</div></div><div><h3>Results</h3><div>The cohort consisted of 140 patients (mean age: 45.3 years; 81 [57.9 %] male). Overall, the patients exhibited significant improvements in all PROMs after discectomy. However, at the one-year follow-up, the APD-positive group exhibited significantly higher leg pain and disability levels than the APD0 group, with mean scores of 31.4 versus 19.6 for leg pain and 20.6 versus 12.0 for ODI, respectively.</div></div><div><h3>Conclusion</h3><div>This study introduces a novel approach by integrating preoperative co-occurring IVD-related degenerative features into a composite APD criterion. Meeting the APD criterion was associated with significantly poorer one-year outcomes for leg pain and disability following lumbar discectomy.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"16 ","pages":"Article 100729"},"PeriodicalIF":2.9,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977545","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":"Eye tracking as a tool to quantify the effects of CAD display on radiologists’ interpretation of chest radiographs","authors":"Daisuke Matsumoto ,&nbsp;Tomohiro Kikuchi ,&nbsp;Yusuke Takagi ,&nbsp;Soichiro Kojima ,&nbsp;Ryoma Kobayashi ,&nbsp;Daiju Ueda ,&nbsp;Kohei Yamamoto ,&nbsp;Sho Kawabe ,&nbsp;Harushi Mori","doi":"10.1016/j.ejro.2026.100731","DOIUrl":"10.1016/j.ejro.2026.100731","url":null,"abstract":"<div><h3><em>Background:</em></h3><div>Computer-aided detection (CAD) systems for chest radiographs are widely used; however, concurrent reader displays such as bounding-box (BB) highlights may influence interpretation. This pilot study used eye tracking to examine which aspects of visual search were affected by these factors.</div></div><div><h3><em>Methods:</em></h3><div>We sampled 180 chest radiographs from the VinDR-CXR dataset: 120 with solitary pulmonary nodules or masses and 60 without. BBs were configured for 80 % display sensitivity and specificity. Three radiologists (with 11, 5, and 1 years of experience) interpreted each case twice—once with BBs visible and once without—after a ≥ 2-week washout. Eye movements were recorded using an EyeTech VT3 Mini. Metrics included interpretation time, time to first fixation, lesion dwell time, total gaze-path length, and lung-field coverage. Outcomes were modeled using a linear mixed model with the reading condition set as a fixed effect and case and reader as random intercepts. Primary analysis was restricted to true positives (n = 96).</div></div><div><h3><em>Results:</em></h3><div>Concurrent BB display prolonged interpretation time by 4.9 s (p &lt; 0.001) and increased lesion dwell time by 1.3 s (p &lt; 0.001). Total gaze-path length increased by 2076 pixels (p &lt; 0.001), and lung-field coverage increased by 10.5 % (p &lt; 0.001). The time to first fixation was reduced by 1.3 s (p &lt; 0.001).</div></div><div><h3><em>Conclusion:</em></h3><div>Eye tracking revealed measurable changes in search behavior associated with concurrent BB display during chest radiograph interpretation. These findings support this approach and highlight the need for larger studies across modalities and clinical contexts.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"16 ","pages":"Article 100731"},"PeriodicalIF":2.9,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977585","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 diagnosis of usual interstitial pneumonia on chest CT via the mean curvature of isophotes","authors":"Peter Savadjiev ,&nbsp;Morteza Rezanejad ,&nbsp;Sahir Bhatnagar ,&nbsp;David Camirand ,&nbsp;Claude Kauffmann ,&nbsp;Kaleem Siddiqi ,&nbsp;Ronald J. Dandurand ,&nbsp;Patrick Bourgouin ,&nbsp;Carl Chartrand-Lefebvre ,&nbsp;Alexandre Semionov","doi":"10.1016/j.ejro.2025.100723","DOIUrl":"10.1016/j.ejro.2025.100723","url":null,"abstract":"<div><h3>Purpose</h3><div>To test whether the mean curvature of isophotes (MCI), a geometric image transformation, can be used to improve automatic detection on chest CT of Usual Interstitial Pneumonia (UIP), a determining radiological pattern in the diagnosis of Interstitial Lung Diseases (ILD).</div></div><div><h3>Materials and methods</h3><div>This retrospective study included chest CT scans from 234 patients (123 female,111 male; mean age: 61.6 years; age range: 18–90 years) obtained at two independent institutions between 2007 and 2024.</div><div>Three different classification models were trained on the original CT images and separately on MCI-transformed CT images: (1) a previously published deep learning model for classifying fibrotic lung disease on chest CT, (2) a classification pipeline based on the EfficientNet-V2 convolutional neural network architecture, and (3) a non-deep-learning model based on the functional principal component analysis (FPCA) of density functions of voxel intensity.</div><div>All models were trained on data from the first institution and evaluated on data from the second institution with the recall-macro, precision-macro and F1-macro scores. Performance difference between classifier pairs was tested with the Stuart-Maxwell marginal homogeneity test.</div></div><div><h3>Results</h3><div>For a fixed model architecture and training algorithm, MCI-transformed images yield comparable or better classification performance than the original CT images. The best performance improvement achieved with MCI compared to CT was: recall-macro 0.83 vs 0.57, precision-macro 0.81 vs 0.50, F1-macro 0.80 vs 0.49, p = 4.2e-5.</div></div><div><h3>Conclusion</h3><div>MCI may be a valuable addition to existing AI systems for screening for UIP on chest CT.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"16 ","pages":"Article 100723"},"PeriodicalIF":2.9,"publicationDate":"2026-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145939566","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":"2026-01-10","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}

{"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":"2026-01-09","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":"Myocardial transit time mapping by CMR: A novel potential parameter of microcirculatory dysfunction in hypertrophic cardiomyopathy with and without atrial fibrillation","authors":"Junlin Yang ,&nbsp;Yong Cheng ,&nbsp;Jinxiu Yang ,&nbsp;Yuncheng Li ,&nbsp;Zhen Wang ,&nbsp;Shutian An ,&nbsp;Jun Wang ,&nbsp;Yongqiang Yu ,&nbsp;Ren Zhao ,&nbsp;Xiaohu Li","doi":"10.1016/j.ejro.2026.100726","DOIUrl":"10.1016/j.ejro.2026.100726","url":null,"abstract":"<div><h3>Purpose</h3><div>This study aimed to explore myocardial transit time (MyoTT) by cardiovascular magnetic resonance (CMR) as a potential parameter of coronary microvascular dysfunction (CMD) in hypertrophic cardiomyopathy (HCM) with and without atrial fibrillation (AF).</div></div><div><h3>Materials and Methods</h3><div>This study enrolled 50 patients with HCM and 50 healthy control subjects to assess cardiac function, native T1, extracellular volume (ECV), myocardial strain, and MyoTT. Factors associated with HCM status were estimated by logistic regression analyses, adjusting for potential confounders. Meanwhile, we evaluated the discriminative performance of the aforementioned parameters for HCM (vs control) and for AF status among HCM patients.</div></div><div><h3>Results</h3><div>In the HCM and control groups, the HCM group exhibited significant reductions in LV-GLS, RV-EDVI, and RV-ESVI (all P &lt; 0.05) and significant increases in LVMI, LVGPWT, native T1 values, ECV values, absolute MyoTT values and presence of LGE (all P &lt; 0.05). Logistic regression analysis revealed HCM was significantly associated with MyoTT, native T1, ECV and LV-GLS. In HCM with and without AF, the HCM with AF group showed nominally higher native T1 values and absolute MyoTT values before correction. Logistic regression analysis revealed HCM with AF was significantly associated with MyoTT and native T1. The AUCs for ECV, MyoTT, LV-GLS, and native T1 were 0.828, 0.848, 0.708, and 0.862, respectively (P &lt; 0.05). The AUCs for discriminating HCM with AF from HCM without AF for MyoTT and native T1 were 0.740 and 0.681, respectively (P &lt; 0.05).</div></div><div><h3>Conclusion</h3><div>MyoTT levels are elevated in patients with HCM, particularly when AF is present. This suggests that MyoTT may reflect CMD-related alterations in HCM, especially in those with AF.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"16 ","pages":"Article 100726"},"PeriodicalIF":2.9,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145939568","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 consequences of percutaneous transhepatic biliary drainage (PTBD) in patients with tumoral obstructive jaundice: A retrospective study and review of literature","authors":"Javad Jalili,&nbsp;Yaser Ahmadi,&nbsp;Mahsa Karbasi,&nbsp;Sarah Vaseghi,&nbsp;Mahdiyeh Baastani Khajeh,&nbsp;Sahar Rezaei,&nbsp;Esmaeil Gharepapagh,&nbsp;Alireza Motamedi","doi":"10.1016/j.ejro.2025.100722","DOIUrl":"10.1016/j.ejro.2025.100722","url":null,"abstract":"<div><h3>Background</h3><div>Percutaneous transhepatic biliary drainage (PTBD) is widely used in malignant obstructive jaundice (MOJ), but most series report aggregate complication rates without stratification by tumor type. This limits individualized risk counseling and hampers benchmarking across centers.</div></div><div><h3>Objective</h3><div>To evaluate short-term outcomes of PTBD in a large single-center cohort, with a focus on tumor-specific complication patterns using patient-level attribution and robust statistical methods.</div></div><div><h3>Methods</h3><div>We retrospectively analyzed 453 patients with MOJ undergoing PTBD (2017–2022). Complications within 30 days were recorded at the patient level, with downstream sequelae attributed to the index event. Exact tests and Firth penalized logistic regression were applied to mitigate sparse-data bias. Survival status was documented through 90 days.</div></div><div><h3>Results</h3><div>Technical success was 100 %, and mean bilirubin declined significantly within 48 h. Twenty-two complications occurred in 21 patients (4.6 %), lower than most published series. Catheter displacement (1.5 %) was the most frequent event, while severe bleeding (0.2 %) and biliary peritonitis (0.6 %) were rare and managed without surgery. No cholangitis was observed. Complications were most frequent in cholangiocarcinoma and pancreatic cancer, although differences across tumor types were not statistically significant. Follow-up was complete; no deaths occurred within 90 days, yielding 30-day and 90-day survival rates of 100 %.</div></div><div><h3>Conclusions</h3><div>PTBD is highly effective and safe in MOJ, with lower complication rates than many prior reports. Tumor-specific analysis revealed variation in complication subtypes but no significant differences in overall incidence. The rigorous methodology and complete follow-up provide a robust framework for individualized risk profiling and multicenter benchmarking.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"16 ","pages":"Article 100722"},"PeriodicalIF":2.9,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145939569","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":"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":"2026-01-08","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":"A biology-informed radiomics model for prognostication of hepatocellular carcinoma based on AKR1B10 expression","authors":"Hongan Ying ,&nbsp;Lili Huang ,&nbsp;Weiwen Hong","doi":"10.1016/j.ejro.2026.100725","DOIUrl":"10.1016/j.ejro.2026.100725","url":null,"abstract":"<div><h3>Background</h3><div>Current radiomic models for hepatocellular carcinoma (HCC) prognosis rely on direct correlations between imaging features and clinical outcomes, resulting in limited biological interpretability and restricted clinical applicability. This study explores a novel biology-driven radiomic strategy focusing on AKR1B10. AKR1B10 is a functionally established molecular driver of HCC progression, and the study aims to develop an interpretable prediction model bridging imaging phenotypes and underlying tumor biology.</div></div><div><h3>Methods</h3><div>We analyzed multi-institutional data from The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA). After confirming the prognostic significance of AKR1B10 expression through survival and enrichment analyses, we developed a radiomics model using a cohort of 34 patients with matched computed tomography (CT) images and genomic data. Tumor and peritumoral regions were segmented, and 107 radiomic features were extracted. Feature selection was performed using maximum-relevance-minimum-redundancy (mRMR) and recursive feature elimination (RFE) algorithms, with subsequent model building via logistic regression. The model was evaluated using ROC analysis, calibration curves, and decision curve analysis. Finally, we constructed a prognostic nomogram integrating the radiomics signature with clinical variables.</div></div><div><h3>Results</h3><div>AKR1B10 overexpression was significantly associated with poor overall survival (HR = 2.187, 95 % CI: 1.385–3.454, P &lt; 0.001) and characteristic activation of oncogenic pathways. The radiomics model demonstrated strong performance in predicting AKR1B10 status (AUC = 0.83, 95 % CI: 0.69–0.97), with significant difference in rad-scores between AKR1B10 high- and low-expression groups (P &lt; 0.001). The integrated nomogram showed excellent predictive accuracy for 3-year survival (AUC = 0.85) and provided clinical net benefit across threshold probabilities.</div></div><div><h3>Conclusions</h3><div>The biology-informed radiomics model based on AKR1B10 expression demonstrates strong prognostic performance in hepatocellular carcinoma. By directly linking imaging phenotypes to a key molecular driver of HCC, this approach provides a clinically applicable and biologically interpretable tool for pre-operative risk prediction.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"16 ","pages":"Article 100725"},"PeriodicalIF":2.9,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145939271","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}