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":"Contrast-enhanced mammography in the preoperative assessment of primary operable breast cancer: Results from a prospective, two-centre study","authors":"Anna Grażyńska ,&nbsp;Joanna Szczudło-Chraścina ,&nbsp;Michał Hrapkowicz ,&nbsp;Urszula Szulc ,&nbsp;Aleksandra Kuźbińska ,&nbsp;Andrzej Lorek ,&nbsp;Michał Nycz ,&nbsp;Karolina Snopek-Miśta ,&nbsp;Agnieszka Niewiadomska ,&nbsp;Kamil Krupa ,&nbsp;Anna Barczyk-Gutkowska ,&nbsp;Agnieszka Bobola ,&nbsp;Iwona Gisterek-Grocholska ,&nbsp;Katarzyna Steinhof-Radwańska","doi":"10.1016/j.ejro.2026.100768","DOIUrl":"10.1016/j.ejro.2026.100768","url":null,"abstract":"<div><h3>Purpose</h3><div>To evaluate the performance of contrast-enhanced mammography (CEM) in the preoperative assessment of patients with primary operable breast cancer and in the detection of multifocal or multicentric carcinoma (MFMCC). Secondary aims were to determine the added value of recombined images (CEM-RI) relative to low-energy images (CEM-LEI) in detecting MFMCC.</div></div><div><h3>Materials and Methods</h3><div>In this prospective multicenter study, 184 patients with newly diagnosed breast cancer were included. All patients underwent CEM, and findings from CEM-LEI and CEM-RI were analysed separately for the detection of MFMCC, using histopathology as the reference standard. The influence of CEM-RI findings on surgical decision-making was evaluated. All patients completed a 12-month clinical and imaging follow-up to assess residual disease and recurrence.</div></div><div><h3>Results</h3><div>Histopathology confirmed MFMCC in 30/184 patients (16.30%). Sensitivity for MFMCC detection was significantly higher for CEM-RI than for CEM-LEI (86.67% vs. 53.33%, p), while specificity remained high for both techniques (91.56% vs. 90.26%, p). When both image types were assessed together, overall sensitivity increased to 93.33%. CEM-LEI sensitivity decreased with increasing breast density (63.16% for American College of Radiology (ACR) A + B vs. 36.36% for ACR C + D), whereas CEM-RI maintained high sensitivity irrespective of density (78.95% for ACR A+B vs. 100% for ACR C+D). CEM-RI findings led to modification of the planned surgical procedure in 23 of 184 patients (12.43%). The reoperation rate due to R1 resection after breast-conserving therapy was 3.80%.</div></div><div><h3>Conclusion</h3><div>CEM-RI demonstrates significantly greater sensitivity than CEM-LEI for detecting MFMCC and contributes to clinically meaningful changes in surgical management.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"16 ","pages":"Article 100768"},"PeriodicalIF":2.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148177982","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":"Standardized contrast-enhanced CT-based radiomics for non-invasive prediction of TTF-1 status in lung adenocarcinoma: A cross-validated single-center study","authors":"Martin Nüssel ,&nbsp;Jan-Niklas Trinks ,&nbsp;Stefan Förster ,&nbsp;Jens Krugmann ,&nbsp;Thorsten Klink","doi":"10.1016/j.ejro.2026.100770","DOIUrl":"10.1016/j.ejro.2026.100770","url":null,"abstract":"<div><h3>Objective</h3><div>Thyroid transcription factor-1 (TTF-1) is a clinically relevant immunohistochemical marker in lung adenocarcinoma with diagnostic, prognostic, and therapeutic implications. As tissue sampling is invasive and often limited by small biopsy specimens, non-invasive imaging biomarkers are of increasing interest. This study aimed to develop and internally validate a standardized contrast-enhanced CT-based radiomics workflow for prediction of TTF-1 expression, with emphasis on reproducibility and methodological robustness.</div></div><div><h3>Methods</h3><div>This retrospective single-center study included 164 treatment-naive patients with histologically confirmed lung adenocarcinoma and available TTF-1 immunohistochemistry. TTF-1 expression was assessed using routine immunohistochemical staining on biopsy or resection specimens, with binary classification based on nuclear staining. Primary tumors were semi-automatically segmented on contrast-enhanced CT using IBSI-compliant preprocessing. A total of 107 radiomic features were extracted. Interobserver reproducibility was evaluated in a subset of 55 tumors using intraclass correlation coefficients. Logistic regression, random forest, and XGBoost models were trained using five-fold stratified cross-validation. Performance was evaluated using AUC.</div></div><div><h3>Results</h3><div>Among 164 patients, 115 tumors (70.1%) were TTF-1 positive. XGBoost achieved the highest performance (mean AUC = 0.83 ± 0.04), significantly outperforming other models (p &lt; 0.01). Radiomic features showed good interobserver reproducibility, particularly for shape and first-order features. SHAP analysis identified sphericity, entropy, and mean CT attenuation as key predictors.</div></div><div><h3>Conclusion</h3><div>A standardized CT-based radiomics approach shows promising performance for non-invasive prediction of TTF-1 status in lung adenocarcinoma. Given the retrospective single-center design and lack of external validation, further multicenter studies are required.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"16 ","pages":"Article 100770"},"PeriodicalIF":2.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148177918","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}

{"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":"2026-06-01","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}