European Journal of Radiology最新文献

{"title":"Streamlining liver iron assessment: accuracy and limitations of 3D qDixon MRI for liver iron overload quantification","authors":"Fei Peng ,&nbsp;Chaotian Luo ,&nbsp;Xiaojing Ning ,&nbsp;Linlin Liang ,&nbsp;Chaojie Huang ,&nbsp;Mingrui Yang ,&nbsp;Cheng Tang ,&nbsp;Huiting Zhang ,&nbsp;Peng Peng","doi":"10.1016/j.ejrad.2025.112237","DOIUrl":"10.1016/j.ejrad.2025.112237","url":null,"abstract":"<div><h3>Objective</h3><div>To prospectively evaluate the accuracy and limitations of the 1.5 T 3D-Dixon sequence (qDixon) in quantifying liver iron concentration (LIC), using FerriScan technology-determined LIC and the 1.5 T multi-echo GRE sequence (ME-GRE) as reference standards.</div></div><div><h3>Methods</h3><div>A total of 161 chronic transfusion-dependent patients, 83 men and 43 women with a mean age of 21.04 years, were scanned using ME-GRE and qDixon sequences; among them, 67 were scanned using FerriScan technology. R2* values (ME-GRE-R2* and qDixon-R2*) and LIC were obtained for both sequences. Comparative analysis was conducted using concordance correlation coefficients (CCC), Bland-Altman plots, and linear regression. A piecewise regression model was constructed to determine the upper limit for quantifying LIC using the qDixon.</div></div><div><h3>Results</h3><div>The qDixon-LIC and ME-GRE-LIC showed mean differences of 0.22, 0.01, −0.27, and 6.8 mg/g dry weight (dw) in normal, mild, moderate, and severe liver iron overload with corresponding CCC values of 0.986, 0.982, 0.956, and 0.216, respectively. The piecewise regression model established a 28.47 mg/g dw threshold for accurate liver iron quantification using qDixon, beyond which qDixon-R2* reached 892.86 s⁻¹. Below this threshold, qDixon demonstrated a strong linear correlation with FerriScan in estimating LIC (r = 0.82, p &lt; 0.001), with a negligible mean difference of 1.68 mg/g dw and a CCC of 0.984.</div></div><div><h3>Conclusion</h3><div>The 1.5 T qDixon sequence can be used to quantitatively assess LIC within a specific range (LIC &lt; 28.47 mg/g dw), simplifying clinical workflow procedures.</div></div>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"190 ","pages":"Article 112237"},"PeriodicalIF":3.2,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313884","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":"Nomogram based on radiomics and CT features for predicting visceral pleural invasion of invasive adenocarcinoma ≤ 2 cm: A multicenter study","authors":"Rikun Huang ,&nbsp;Chunli Zhao ,&nbsp;Jinhan Yang ,&nbsp;Bingfeng Lu ,&nbsp;Yi Dai ,&nbsp;Miaomiao Lin ,&nbsp;Xiang Zhao ,&nbsp;Haipeng Huang ,&nbsp;Xiaoyu Pan ,&nbsp;Liling Lu ,&nbsp;Lina Chen ,&nbsp;Kai Li","doi":"10.1016/j.ejrad.2025.112227","DOIUrl":"10.1016/j.ejrad.2025.112227","url":null,"abstract":"<div><h3>Objective</h3><div>To explore the value of a nomogram based on radiomics and computed tomography (CT) features for preoperative prediction of visceral pleural invasion (VPI) of subpleural, small (≤2 cm) invasive adenocarcinoma (IAC) of the lung.</div></div><div><h3>Methods</h3><div>For this retrospective study, 457 cases of invasive lung adenocarcinoma ≤ 2 cm were collected from three tertiary hospitals in Guangxi and used in a training group (n = 254), validation group (n = 112), and test group (n = 91). Risk factors for IAC VPI were screened by univariate and multivariate logistic regression analyses, and a CT model was constructed. Radiomics features of regions representing the gross tumor area (GTA), peritumor area (PTA), and gross peritumor area (GPTA) were extracted from CT images, and the optimal feature subsets based on radiomics score were selected to construct three radiomics models. A combination model was then constructed from the radiomics model with the optimal radiomics score and the CT model and visualized by nomogram. Model performance was analyzed by receiver operating characteristic curve analysis and DeLong test.</div></div><div><h3>Results</h3><div>Pleural indentation (<em>P &lt;</em> 0.05), pleural thickening (<em>P &lt;</em> 1e-04), and tumor diameter (<em>P &lt;</em> 0.001) were identified as risk factors of the CT model for predicting VPI of IAC. Among 1226 radiomics features, 5, 13, and 12 optimal features were selected for the GTA, PTA, and GPTA models, respectively, and the area under the curve (AUC) values did not differ among these models. Based on AUC values, the CT model and GPTA model features were combined to construct the predictive nomogram. Compared with the individual models, the nomogram exhibited better accuracy, specificity, and AUC values (AUC values for training, verification, and test groups were 0.86, 0.84, and 0.86, respectively). Calibration curve and decision curve analyses showed that the nomogram outperformed traditional CT features and radiomics studies, and could offer greater clinical benefit.</div></div><div><h3>Conclusions</h3><div>The developed nomogram combining CT and radiomics features shows high diagnostic value for VPI prediction of IAC of the lung.</div></div>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"190 ","pages":"Article 112227"},"PeriodicalIF":3.2,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144298210","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":"A machine learning approach for personalized breast radiation dosimetry in CT: Integrating radiomics and deep neural networks","authors":"Eleftherios Tzanis,&nbsp;John Stratakis,&nbsp;John Damilakis","doi":"10.1016/j.ejrad.2025.112236","DOIUrl":"10.1016/j.ejrad.2025.112236","url":null,"abstract":"<div><h3>Purpose</h3><div>To develop a machine learning-based workflow for patient-specific breast radiation dosimetry in CT.</div></div><div><h3>Materials and Methods</h3><div>Two hundred eighty-six chest CT examinations, with corresponding right and left breast contours, were retrospectively collected from the radiotherapy department at our institution to develop and validate breast segmentation U-Nets. Additionally, Monte Carlo simulations were performed for each CT scan to determine radiation doses to the breasts. The derived breast doses, along with predictors such as X-ray tube current and radiomic features, were then used to train deep neural networks (DNNs) for breast dose prediction.</div></div><div><h3>Results</h3><div>The breast segmentation models achieved a mean dice similarity coefficient of 0.92, with precision and sensitivity scores above 0.90 for both breasts, indicating high segmentation accuracy. The DNNs demonstrated close alignment with ground truth values, with mean predicted doses of 5.05 ± 0.50 mGy for the right breast and 5.06 ± 0.55 mGy for the left breast, compared to ground truth values of 5.03 ± 0.57 mGy and 5.02 ± 0.61 mGy, respectively. The mean absolute percentage errors were 4.01 % (range: 3.90 %–4.12 %) for the right breast and 4.82 % (range: 4.56 %–5.11 %) for the left breast. The mean inference time was 30.2 ± 4.3 s. Statistical analysis showed no significant differences between predicted and actual doses (p ≥ 0.07).</div></div><div><h3>Conclusion</h3><div>This study presents an automated, machine learning-based workflow for breast radiation dosimetry in CT, integrating segmentation and dose prediction models. The models and code are available at: https://github.com/eltzanis/ML-based-Breast-Radiation-Dosimetry-in-CT.</div></div>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"190 ","pages":"Article 112236"},"PeriodicalIF":3.2,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144279961","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":"Node-RADS 1.0 for lymph node involvement detection in renal cell carcinoma: A study on 2D and 3D CT imaging","authors":"Chunlei He ,&nbsp;Enyu Yuan ,&nbsp;Lei Ye ,&nbsp;Hui Xu ,&nbsp;Qiyou Wu ,&nbsp;Jin Yao ,&nbsp;Yuntian Chen ,&nbsp;Bin Song","doi":"10.1016/j.ejrad.2025.112239","DOIUrl":"10.1016/j.ejrad.2025.112239","url":null,"abstract":"<div><h3>Objectives</h3><div>To evaluate the diagnostic accuracy of the node reporting and data system 1.0 (Node-RADS) score in predicting lymph node (LN) involvement (LNI) in renal cell carcinoma (RCC) patients and to compare the diagnostic performance of Node-RADS score based on two-dimensional (2D) and three-dimensional (3D) modality.</div></div><div><h3>Methods</h3><div>From January 2012 to September 2024, patients with RCC and histologically confirmed LN status who underwent preoperative abdomen computed tomography imaging were retrospectively enrolled in the study. Radiological assessments were performed independently and blinded by three readers according to the Node-RADS, and both Node-RADS scores based on 2D and 3D modality were recorded. The gold reference of LNI was determined by the histological results after surgery. The sensitivity, specificity, area under receiver operating characteristic curve (AUC) and inter-agreements for different features were calculated.</div></div><div><h3>Results</h3><div>Finally, 375 patients (median age 55  years; interquartile range (IQR), 45–63 years), including 270 negative LNI and 105 positive LNI, were enrolled in the study. Younger patients showed higher LNI prevalence (<em>p</em> &lt; 0.001). Node-RADS score ≥ 3 yielded a sensitivity of 0.876, a specificity of 0.926 and an AUC of 0.901 (95% CI 0.866–0.937) on 2D, which was comparable to Node-RADS score on 3D (Delong test: <em>p</em> = 0.651). An excellent inter-reader agreement was observed for Node-RADS score between three readers based on 2D and 3D modality (Kendall’s W 0.889 and 0.869).</div></div><div><h3>Conclusions</h3><div>The Node-RADS score demonstrates high overall accuracy in identifying LNI in patients with RCC. Node-RADS score based on 2D and 3D modality demonstrate comparable diagnostic performance.</div></div>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"190 ","pages":"Article 112239"},"PeriodicalIF":3.2,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144279962","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":"An improved ultrasound-guided injection technique for treatment of plantar fasciitis","authors":"Hilmi Berkan Abacıoğlu,&nbsp;Murat Kara","doi":"10.1016/j.ejrad.2025.112233","DOIUrl":"10.1016/j.ejrad.2025.112233","url":null,"abstract":"","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"190 ","pages":"Article 112233"},"PeriodicalIF":3.2,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144270382","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-based post-hoc noise reduction improves quarter-radiation-dose coronary CT angiography","authors":"Tomoro Morikawa ,&nbsp;Tatsuya Nishii ,&nbsp;Yuki Tanabe ,&nbsp;Kazuki Yoshida ,&nbsp;Wataru Toshimori ,&nbsp;Naoki Fukuyama ,&nbsp;Hidetaka Toritani ,&nbsp;Hiroshi Suekuni ,&nbsp;Tetsuya Fukuda ,&nbsp;Teruhito Kido","doi":"10.1016/j.ejrad.2025.112232","DOIUrl":"10.1016/j.ejrad.2025.112232","url":null,"abstract":"<div><h3>Purpose</h3><div>To evaluate the impact of deep learning-based post-hoc noise reduction (DLNR) on image quality, coronary artery disease reporting and data system (CAD-RADS) assessment, and diagnostic performance in quarter-dose versus full-dose coronary CT angiography (CCTA) on external datasets.</div></div><div><h3>Materials and Methods</h3><div>We retrospectively reviewed 221 patients who underwent retrospective electrocardiogram-gated CCTA in 2022–2023. Using dose modulation, either mid-diastole or end-systole was scanned at full dose depending on heart rates, and the other phase at quarter dose. Only patients with motion-free coronaries in both phases were included. Images were acquired using iterative reconstruction, and a residual dense network trained on external datasets denoised the quarter-dose images. Image quality was assessed by comparing noise levels using Tukey’s test. Two radiologists independently assessed CAD-RADS, with agreement to full-dose images evaluated by Cohen’s kappa. Diagnostic performance for significant stenosis referencing full-dose images was compared between quarter-dose and denoised images by the area under the receiver operating characteristic curve (AUC) using the DeLong test.</div></div><div><h3>Results</h3><div>Among 40 cases (age, 71 ± 7 years; 24 males), DLNR reduced noise from 37 to 18 HU (P &lt; 0.001) in quarter-dose CCTA (full-dose images: 22 HU), and improved CAD-RADS agreement from moderate (0.60 [95 % CI: 0.41–0.78]) to excellent (0.82 [95 % CI: 0.66–0.94]). Denoised images demonstrated a superior AUC (0.97 [95 % CI: 0.95–1.00]) for diagnosing significant stenosis compared with original quarter-dose images (0.93 [95 % CI: 0.89–0.98]; P = 0.032).</div></div><div><h3>Conclusion</h3><div>DLNR for quarter-dose CCTA significantly improved image quality, CAD-RADS agreement, and diagnostic performance for detecting significant stenosis referencing full-dose images.</div></div>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"190 ","pages":"Article 112232"},"PeriodicalIF":3.2,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144261414","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":"Artificial intelligence-assisted detection of fractures on radiographs with BoneView: a systematic review","authors":"Robert M. Kwee ,&nbsp;Thomas C. Kwee","doi":"10.1016/j.ejrad.2025.112230","DOIUrl":"10.1016/j.ejrad.2025.112230","url":null,"abstract":"<div><h3>Purpose</h3><div>To systematically review the added value of the artificial intelligence tool BoneView in detecting fractures on radiographs.</div></div><div><h3>Method</h3><div>Medline and Embase were searched for original studies that reported the diagnostic performance of human reading in detecting fractures on radiographs with and without BoneView. Study quality was assessed. Diagnostic accuracy data and reading speed were extracted.</div></div><div><h3>Results</h3><div>Eight studies were included. There was high risk of bias with respect to patient selection (5 studies), reference standard (1 study), and flow and timing (3 studies). There was high concern regarding the applicability of the execution of the index test in one study. Sensitivities and specificities were heterogeneous (p ≤ 0.0001). Sensitivity was significantly higher (p &lt; 0.05) among the far majority of the readers in the included studies when radiographs were evaluated with BoneView. Specificities and diagnostic odds ratio results were mixed, with either no significant change or significant increase or decrease. Four studies assessed reporting time. In 3 studies, reading speed was faster with BoneView (mean of 5.3–15.7 s, p ≤ 0.046), whereas in one study there was no change (p = 0.12).</div></div><div><h3>Conclusion</h3><div>BoneView appears to improve sensitivity, whereas the results regarding specificity and overall diagnostic accuracy are mixed. There are methodological quality concerns in the existing literature and further research is needed to explore causes of heterogeneity. The use of BoneView appears not to compromise reading speed and may even improve it.</div></div>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"190 ","pages":"Article 112230"},"PeriodicalIF":3.2,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144366067","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 imaging parameters in the diagnosis of developmental dysplasia of the hip based on artificial intelligence: A perspective","authors":"Yu Zou,&nbsp;Shinong Pan,&nbsp;Qian Wang,&nbsp;Ying Zhang,&nbsp;Yue Gao,&nbsp;Ziwei Fu","doi":"10.1016/j.ejrad.2025.112231","DOIUrl":"10.1016/j.ejrad.2025.112231","url":null,"abstract":"<div><div>Developmental dysplasia of the Hip (DDH) is a common pediatric orthopedic disease, characterized primarily by abnormal development of the hip joint structure. The clinical objective is the early detection, diagnosis, and treatment. Current diagnostic strategies rely on a combination of physical examination and imaging assessment, with particular emphasis on the comprehensive assessment of multiple imaging parameters. In recent years, the integration of Artificial Intelligence (AI) with medical imaging has enhanced the accuracy and objectivity of DDH diagnosis and management. Nevertheless, current research still faces model limitations and variability in imaging acquisition protocols. This article provides a comprehensive overview of the anatomical foundations of DDH-related imaging parameters, their clinical significance, and the latest advancements in AI application. It initially details the measurement of hip joint imaging parameters, then discusses their quantitative contributions to clinical decision-making through predictive modeling, and finally explores the innovative use of AI in imaging interpretation while addressing existing technical constraints, with the aim of advancing precision medicine in the context of DDH.</div></div>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"190 ","pages":"Article 112231"},"PeriodicalIF":3.2,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144261415","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":"Diagnostic accuracy of radiomics in risk stratification of gastrointestinal stromal tumors: A systematic review and meta-analysis","authors":"Mohsen Salimi ,&nbsp;Hanieh Mohammadi ,&nbsp;Sahar Ghahramani ,&nbsp;Maryam Nemati ,&nbsp;Anita Ashari ,&nbsp;Amirhossein Imani ,&nbsp;Mohammad Hossein Imani","doi":"10.1016/j.ejrad.2025.112225","DOIUrl":"10.1016/j.ejrad.2025.112225","url":null,"abstract":"<div><h3>Rationale and objectives</h3><div>This systematic review and meta-analysis aimed to assess the diagnostic accuracy of radiomics in risk stratification of gastrointestinal stromal tumors (GISTs). It focused on evaluating radiomic models as a non-invasive tool in clinical practice.</div></div><div><h3>Materials and methods</h3><div>A comprehensive search was conducted across PubMed, Web of Science, EMBASE, Scopus, and Cochrane Library up to May 17, 2025. Studies involving preoperative imaging and radiomics-based risk stratification of GISTs were included. Quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool and Radiomics Quality Score (RQS). Pooled sensitivity, specificity, and area under the curve (AUC) were calculated using bivariate random-effects models. Meta-regression and subgroup analyses were performed to explore heterogeneity.</div></div><div><h3>Results</h3><div>A total of 29 studies were included, with 22 (76 %) based on computed tomography scans, while 2 (7 %) were based on endoscopic ultrasound, 3 (10 %) on magnetic resonance imaging, and 2 (7 %) on ultrasound. Of these, 18 studies provided sufficient data for meta-analysis. Pooled sensitivity, specificity, and AUC for radiomics-based GIST risk stratification were 0.84, 0.86, and 0.90 for training cohorts, and 0.84, 0.80, and 0.89 for validation cohorts. QUADAS-2 indicated some bias due to insufficient pre-specified thresholds. The mean RQS score was 13.14 ± 3.19.</div></div><div><h3>Conclusion</h3><div>Radiomics holds promise for non-invasive GIST risk stratification, particularly with advanced imaging techniques. However, radiomic models are still in the early stages of clinical adoption. Further research is needed to improve diagnostic accuracy and validate their role alongside conventional methods like biopsy or surgery.</div></div>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"190 ","pages":"Article 112225"},"PeriodicalIF":3.2,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144254212","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":"MRI for assessing abdominal organs iron concentration: A comparative study between the relaxometry methods","authors":"Chaotian Luo ,&nbsp;Fei Peng ,&nbsp;Xiaojing Ning ,&nbsp;Cheng Tang ,&nbsp;Mingrui Yang ,&nbsp;Linlin Liang ,&nbsp;Fangyan Xiao ,&nbsp;Yanyan Zhang ,&nbsp;Fuling Huang ,&nbsp;Peng Peng","doi":"10.1016/j.ejrad.2025.112226","DOIUrl":"10.1016/j.ejrad.2025.112226","url":null,"abstract":"<div><h3>Objective</h3><div>To compare magnetic resonance imaging (MRI) commercial 3D quantitative Dixon sequence (qDixon) and 2D multi-gradient recalled echo sequence (GRE) for iron quantification in multiple abdominal organs.</div></div><div><h3>Methods</h3><div>1.5T MRI GRE and qDixon data were collected on patients with 211 MR exams from 171 patients (86 males, 85 females; median age: 21 years). Compare the R2* values of liver, pancreas, spleen, and kidneys using Bland-Altman, intraclass correlation coefficients (ICC), concordance correlation coefficients (CCC), and linear regression. Iron overload (IO) diagnostic concordance was assessed using overall agreement and the Kappa coefficient.</div></div><div><h3>Results</h3><div>Bland-Altman analysis of liver, pancreas, spleen, and kidney R2* values between GRE and qDixon, resulted in a bias (absolute mean difference) of −11.3 1/s (LoA: 77.7 and −100.3), −11.2 1/s (LoA: 123.9 and −146.3), 5.1 1/s (LoA: 117.3 and −107.1), and 1.9 1/s (LoA: 14.7 and −10.9). The CCCs between GRE and qDixon R2* values for liver, pancreas, spleen, and kidneys were 0.98, 0.94, 0.96, and 0.95, the ICCs were 0.99, 0.95, 0.96, and 0.97, respectively. Linear regression analysis correlating abdominal organs R2* values of GRE and qDixon resulted in a coefficient of determination of 0.96, 0.89, 0.93, and 0.92 (all <em>P</em> &lt; 0.001). The overall agreement was 98.5 %, 94.8 %, 92.6 %, and 90.7 %; the Kappa value was 0.95, 0.89, 0.84, and 0.69 (all <em>P</em> &lt; 0.001).</div></div><div><h3>Conclusion</h3><div>The qDixon and GRE showed good agreement and significant positive in measuring R2* values for IO assessment in abdominal organs, with qDixon being an excellent adjunctive diagnostic method.</div></div>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"190 ","pages":"Article 112226"},"PeriodicalIF":3.2,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144261416","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}