European Journal of Radiology Open最新文献_第2页

Contemporary 0.55 T MRI to visualize interstitial lung disease – An exploratory study 当代0.55 T MRI显示间质性肺疾病-一项探索性研究

IF 2.9 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Open

Pub Date : 2026-01-05 DOI: 10.1016/j.ejro.2025.100720

Nadine Bayerl , Claudius S. Mathy , Christina Bergmann , Tobias Bäuerle , Lisa C. Adams , Armin M. Nagel , Jörg H.W. Distler , Teresa Gerhalter , Michael Uder , Rafael Heiss , Stephan Ellmann

Purpose

To evaluate the feasibility of contemporary 0.55 T MRI for visualizing interstitial lung disease (ILD) compared to high-resolution computed tomography (HRCT) in an exploratory first-experience study.

Materials and methods

Thirty participants (mean age 60 ± 13 years; 13 females) with rheumatologic ILD underwent HRCT and 0.55 T MRI within 31 days. MRI protocols included proton-density-weighted turbo-spin-echo sequences (transverse) and T2-weighted short-tau inversion recovery sequences (coronal). Three blinded radiologists independently assessed overall disease extent, ground-glass opacity (GGO), reticulation, and emphysema using a semi-quantitative scoring system. Differences between modalities were tested using Wilcoxon signed-rank tests, and Bland-Altman analysis evaluated systematic bias.

Results

Overall disease extent showed no statistically significant difference between low-field MRI and HRCT (median 22.5 % vs. 24.5 %), with excellent interobserver agreement (MRI ICC = 0.94; HRCT ICC = 0.97). MRI significantly overestimated GGO (13.1 % vs. 9.7 %) and underestimated reticulation (8.1 % vs. 11.4 %) compared to HRCT. Bland-Altman analysis confirmed no systematic bias for overall disease extent but consistent overestimation of GGO and underestimation of reticulation on MRI.

Conclusions

Contemporary 0.55 T MRI showed no statistically significant difference in overall ILD extent compared to HRCT but tended to overestimate GGO and underestimate reticulation. Despite these limitations, 0.55 T MRI represents a promising candidate for future development as a radiation-free alternative for gross disease burden assessment in ILD, warranting further technical refinement before routine clinical use.

目的在一项探索性的首次体验研究中，与高分辨率计算机断层扫描（HRCT）相比，评估当代0.55 T MRI在观察间质性肺疾病（ILD）方面的可行性。材料和方法30例风湿病ILD患者（平均年龄60岁 ± 13岁；13例女性）在31天内行HRCT和0.55 T MRI检查。MRI方案包括质子密度加权涡轮自旋回波序列（横向）和t2加权短tau反转恢复序列（冠状）。三位盲法放射科医师使用半定量评分系统独立评估总体疾病程度、毛玻璃混浊（GGO）、网状和肺气肿。使用Wilcoxon符号秩检验检验模式之间的差异，Bland-Altman分析评估系统偏倚。结果低场MRI和HRCT的总体疾病程度差异无统计学意义（中位数分别为22.5 %和24.5 %），观察者间一致性极好（MRI ICC = 0.94; HRCT ICC = 0.97）。与HRCT相比，MRI显著高估了GGO（13.1 % vs. 9.7 %）和低估了网状（8.1 % vs. 11.4 %）。Bland-Altman分析证实，总体疾病程度没有系统性偏倚，但MRI上一致高估GGO和低估网状。结论与HRCT相比，当代0.55 T MRI在ILD总体范围上无统计学差异，但倾向于高估GGO而低估网状。尽管存在这些限制，0.55 T MRI代表了未来发展的一个有希望的候选方案，作为ILD总疾病负担评估的无辐射替代方案，在常规临床应用之前需要进一步的技术改进。

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引用次数: 0

Performance of deep-learning reconstruction combined with metal artifact reduction algorithm for dual-energy computed tomography angiography in intracranial aneurysm coil embolization 深度学习重建联合金属伪影还原算法在双能ct血管造影颅内动脉瘤线圈栓塞中的应用

IF 2.9 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Open

Pub Date : 2025-12-12 DOI: 10.1016/j.ejro.2025.100715

Lina Tao , Yuhan Zhou , Limin Lei, Yajie Wang, Xiaoxu Guo, Yifan Guo, Songwei Yue

Purposes

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.

Methods

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.

Results

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 (p < 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.

Conclusion

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.

目的评价基于深度学习图像重建（DLIR）的虚拟单能图像（VMI）联合金属伪影还原（MAR）算法在脑动脉瘤栓塞线圈随访中的诊断置信度，选择最优方案。方法采用自适应统计迭代重建- veo （ASIR-V50 %）、DLIR中高水平（DLIR- m和H）对54例患者的CTA数据库进行前瞻性组装和重构。在40-140 keV范围内，以10 keV的间隔生成VMIs，有或没有mar。测量人工指标（AI）、信噪比（SNR）和噪声对比比（CNR）等客观参数。主观评价采用李克特量表评分法。栓塞后的治疗效果通过动脉瘤颈部、载动脉和术后并发症来评估。结果首先，80 至90 keV为伪影还原和血管显示之间的平衡提供了最佳的客观和主观评分。其次，DLIR-H+MAR组合在80 keV至90 keV之间表现出最高的CNR，同时也获得了最好的主观得分。此外，与DSA相比，MAR组在动脉瘤颈长度和双侧载动脉直径上的差异明显小于非MAR组（p <； 0.001）。重要的是，MAR组有2例动脉瘤复发，4例残余填充，10例载瘤动脉狭窄，4例动脉瘤破裂未被非MAR组发现。结论dlir - h +MAR在80 ~ 90 keV范围内是脑动脉显像和减轻金属伪影的最佳方法。同时，显著提高了栓塞后动脉瘤的疗效评估和并发症的发现。

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引用次数: 0

Dual-parameter risk stratification based on device landing zone calcification and aortic annular perimeter for paravalvular regurgitation after self-expanding TAVR 基于器械着陆区钙化和主动脉环周长的双参数风险分层对自扩张TAVR后瓣旁反流的影响

IF 2.9 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Open

Pub Date : 2025-12-10 DOI: 10.1016/j.ejro.2025.100719

Jun Shu , Didi Wen , Jingji Xu , Yu Mao , Hui Ma , Jing Zhang , Yao Zhao , Jian Yang , Minwen Zheng

Purpose

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.

Methods

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.

Results

Mild/moderate PVR occurred in 24.0 % of patients. Independent predictors included aortic annular perimeter (OR:1.067, P = 0.015), device landing zone calcific volume (OR:1.006 per 10 mm³, P = 0.025), and presence of sealing skirt (OR:0.412, P = 0.010). The combination of these predictors had a higher discriminative performance (AUC=0.779) than single predictors (P = 0.036, 0.007, and <0.001, respectively), with significant integrated discrimination improvement (integrated discrimination improvement=5.4–6.7 %, P < 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 (P = 0.003) and Group B vs. C (P < 0.001). Sealing skirts significantly reduced PVR in Groups A (P = 0.042) but not in Group B and C (P = 0.082 and 0.342).

Conclusion

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.

目的探讨经导管主动脉瓣置换术（SE-TAVR）后瓣旁反流（PVR）的独立预测因素，建立双参数风险分层模型。方法回顾性研究292例重度主动脉瓣狭窄患者行SE-TAVR。出院前评估PVR严重程度。多因素logistic回归确定轻度/中度PVR的独立预测因子，采用受试者工作特征（ROC）曲线分析确定重要解剖参数的最佳截止值。随后根据这些阈值将患者分为三个危险组。结果轻/中度PVR发生率为24.0 %。独立预测因素包括主动脉环周长（OR:1.067, P = 0.015）、器械着陆区钙化体积（OR:1.006 / 10 mm³，P = 0.025）和密封裙的存在（OR:0.412, P = 0.010）。这些预测因子组合比单一预测因子具有更高的判别性能（AUC=0.779）（P = 0.036,0.007和<；0.001），具有显著的综合判别改善（综合判别改善= 5.4-6.7 %,P <； 0.001）。roc衍生阈值（器械着陆区钙化体积≥1240 mm³，主动脉环周长≥77 mm）将PVR发病率逐渐增加的患者分为三个危险组：A组（均未升高）：8.4 %；B组（任一升高）：23.7 %；C组（均升高）：48.7 %。两两比较证实了A组与B组（P = 0.003）和B组与C组（P <； 0.001）之间的差异。封裙显著降低了A组的PVR (P = 0.042)，而B组和C组无显著降低（P = 0.082和0.342）。结论基于器械着陆区钙化和主动脉环周长的双参数模型可显著增强SE-TAVR术后PVR风险分层。双阈值模型为术前风险分层和个性化瓣膜选择提供了临床可操作的工具。

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引用次数: 0

Machine Learning for diagnosis of malignant thyroid nodules based on thyroid ultrasound: Systematic review and meta-analysis of studies with external datasets 基于甲状腺超声的机器学习诊断恶性甲状腺结节：外部数据集研究的系统回顾和荟萃分析

IF 2.9 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Open

Pub Date : 2025-12-10 DOI: 10.1016/j.ejro.2025.100716

Elisa Gatta , Roberto Gatta , Riccardo Morandi , Samuele Isoli , Sara Corvaglia , Simone Vetrugno , Virginia Maltese , Ilenia Pirola , Claudio Casella , Carlo Cappelli

Introduction

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.

Methods

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.

Results

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).

Conclusion

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.

Systematic review registration

PROSPERO (CRD420251108149)

优化甲状腺结节的诊断方法仍然是一个关键的挑战。超声风险分层系统如EU-TIRADS显示出合理的敏感性和特异性。因此，我们进行了一项系统综述和荟萃分析，以评估人工智能（AI）模型在超声数据鉴别甲状腺结节良恶性方面的诊断性能。方法综合检索截至2025年1月1日的PubMed/MEDLINE、Scopus、Web of Science数据库。符合条件的研究包括接受超声检查的甲状腺结节患者，其中基于人工智能的模型与细胞学或组织学结果进行了验证。人工智能算法是使用不同类型的超声衍生数据开发的，包括b模式图像，放射组学特征。使用分级汇总接收者工作特征（HSROC）模型估计合并敏感性、特异性和曲线下面积（AUC）。结果27项研究，146332例患者，60多万张超声图像符合纳入标准。总体而言，合并敏感性为87 %(95 % CI: 84-89 %)，特异性为83 %（95 % CI: 79-86 %）。总结操作点灵敏度为88 %，特异性为83 %，AUC为91.9 %（95 % CI: 90.0 ~ 93.2 %）。虽然亚组分析表明，与组织学相比，细胞学作为参考标准的准确性更高，但混合效应荟萃回归并没有证实统计学上显著的关联（p = 0.238敏感性；p = 0.188特异性）。结论基于人工智能的算法在区分甲状腺结节良恶性方面表现出优异的诊断性能，在外部数据集上具有鲁棒性验证。这些发现支持人工智能在临床甲状腺结节治疗中的潜在整合，尽管进一步的多中心、非亚洲和基于组织学的研究是有保证的。系统评价注册号prospero （CRD420251108149）

{"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 , Roberto Gatta , Riccardo Morandi , Samuele Isoli , Sara Corvaglia , Simone Vetrugno , Virginia Maltese , Ilenia Pirola , Claudio Casella , 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":"2025-12-10","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}

引用次数: 0

Automated assessment of right heart function by artificial intelligence: A systematic review and meta-analysis 用人工智能自动评估右心功能：一项系统综述和荟萃分析

IF 2.9 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Open

Pub Date : 2025-12-08 DOI: 10.1016/j.ejro.2025.100713

Pooya Eini , Homa serpoush , Mohammad Rezayee , Jason Tremblay

Background

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.

Methods

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.

Results

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.

Conclusion

AI models show promising diagnostic accuracy for RV assessment, but high heterogeneity and moderate evidence certainty necessitate cautious interpretation and further research.

背景：准确评估右心室（RV）的大小和功能对心脏疾病的治疗至关重要，但传统超声心动图的局限性对其提出了挑战。人工智能（AI）模型为改进RV评估提供了潜力，但其诊断准确性仍不确定。本系统综述和荟萃分析评估了人工智能模型在预测RV大小和功能、综合性能指标和评估证据质量方面的诊断准确性。方法按照PRISMA指南，使用MeSH和Emtree检索截至2025年6月的5个数据库中的“人工智能”、“右心室功能”和“右心室功能障碍”。两名审稿人筛选研究，提取数据并使用PROBAST+AI评估质量。使用带有MIDAS和METADATA模块的STATA 18计算汇总估计值。通过亚组分析、meta回归和敏感性分析探讨异质性。采用漏斗图评估发表偏倚。结果25项研究中，18项提供了荟萃分析的数据，合并敏感性为0.85(95 % CI: 0.73-0.92)，特异性为0.81(95 % CI: 0.72-0.88)， AUROC为0.89（95 % CI: 0.86-0.92）。高异质性（敏感性I²= 71.63 %，特异性I²= 73.51 %）部分由算法类型和研究国家解释。GRADE评估显示，在25% %的研究中，由于异质性和偏倚，证据具有中等确定性。结论人工智能模型对RV评估具有较好的诊断准确性，但异质性高，证据确定性不高，需要谨慎解释和进一步研究。

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引用次数: 0

The value of modified time-of-flight magnetic resonance venography in evaluating anatomical variations of the internal iliac vein 改良飞行时间磁共振静脉造影在评估髂内静脉解剖变异中的价值

IF 2.9 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Open

Pub Date : 2025-12-08 DOI: 10.1016/j.ejro.2025.100717

Ziyu Zuo , Xiaoyu Zhang , Wei Zhu , Chengxin Wan , Yong Xu , Zhiwei Zhang , Yu Zhao , Dechuan Zhang , Li Tao

Objective

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).

Methods

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).

Results

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 %).

Conclusion

The mTOF-MRV clearly visualizes IIV anatomy and variations. The proposed classification system aids preoperative planning and postoperative hemodynamic evaluation for pelvic venous disorders.

目的探讨应用改良飞行时间磁共振静脉成像（mTOF-MRV）评价髂内静脉（IIV）解剖变异的可行性。方法本回顾性研究纳入了158例疑似髂静脉压迫综合征（IVCS）的患者，这些患者于2021年6月至2024年3月期间接受了盆腔mTOF-MRV。排除14例血栓形成后综合征（PTS）患者，留下144例符合条件的患者（男性52例，女性92例，平均年龄53 ± 16岁）。两名放射科医生使用4分制独立评估图像质量，并通过多平面重建（MPR）、最大强度投影（MIP）和体绘制（VR）技术分析iv解剖特征。采用Cohen’s kappa系数和类内相关系数（ICC）评估观察者间的一致性。结果观察者间图像质量一致性好（K=0.893），客观测量一致性好（ICC[95 %置信区间]:0.893[0.845-0.941]）。确定了四种IIV解剖变异类型：I型（单侧单一IIV引流至同侧CIV， 30.56% %），II型（一个/两个盆腔有两个IIV引流至同侧CIV， 55.56% %），III型（一个IIV引流至同侧CIV，另一个引流至对侧CIV， 11.80% %）和IV型（其他变异，2.08 %）。左CIV压迫最为常见（86.11 %）。结论mTOF-MRV能清晰显示iv的解剖结构和变异。所提出的分类系统有助于盆腔静脉疾病的术前规划和术后血流动力学评估。

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引用次数: 0

Editorial for the special issue “Pancreatic imaging: Recent advancements and new frontiers” 特刊《胰腺影像学：最新进展与新前沿》社论

IF 2.9 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Open

Pub Date : 2025-12-01 DOI: 10.1016/j.ejro.2025.100702

Piero Boraschi

引用次数: 0

MRI-derived extracellular volume fraction as a prognostic biomarker for early recurrence after R0 resection of pancreatic ductal adenocarcinoma mri衍生的细胞外体积分数作为胰腺导管腺癌R0切除术后早期复发的预后生物标志物

IF 2.9 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Open

Pub Date : 2025-12-01 DOI: 10.1016/j.ejro.2025.100711

Jing-Yi Liu , Qi Wang , Yi-Tong Lu , Yue-Luan Jiang , Dominik Nichel , Robert Grimm , Liang Zhu , Meng-Hua Dai

Objectives

Pancreatic ductal adenocarcinoma (PDAC) has a high recurrence risk after R0 resection. This study aimed to evaluate the prognostic value of preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) pharmacokinetic parameters, including volume transfer constant (K^trans), rate constant (k_ep), extracellular extravascular volume fraction (v_e), and MRI-derived extracellular volume (ECV) fraction, for predicting early recurrence and survival after surgery.

Methods

In this retrospective cohort study, 61 patients (mean age 60.7 ± 9.7 years; 35 males) with histologically confirmed PDAC underwent preoperative 3 T MRI between January 2017 and May 2024, including DCE-MRI and dual-time-point T1 mapping. Two radiologists independently measured K^trans, k_ep, v_e, and ECV. Inter-observer agreement (ICC), associations with pathological features, and prognostic value for recurrence-free survival (RFS) and overall survival (OS) were analyzed using Cox regression, Kaplan–Meier analysis with log-rank tests, and time-dependent ROC curves.

Results

ECV demonstrated excellent reproducibility (ICC = 0.99) and independently predicted shorter RFS (HR = 1.020; 95 % CI: 1.003–1.038; p = 0.022). An optimal ECV cutoff of 31.99 % effectively stratified patients into high- and low-risk groups with significantly different median RFS (10.9 vs. 17.4 months, p = 0.012). However, other DCE-MRI parameters (K^trans, k_ep, v_e) showed poor reproducibility and lacked independent prognostic value for RFS or OS.

Conclusion

MRI-derived tumor ECV is a robust, reproducible biomarker for predicting early recurrence after R0 resection in PDAC patients, potentially assisting in preoperative risk stratification.

目的探讨胰腺导管腺癌（PDAC）在R0切除术后复发的危险性。本研究旨在评估术前动态对比增强磁共振成像（DCE-MRI）药代动力学参数，包括体积转移常数（Ktrans）、速率常数（keep）、细胞外血管外体积分数（ve）和mri衍生的细胞外体积分数（ECV）对预测术后早期复发和生存的预后价值。方法回顾性队列研究，在2017年1月至2024年5月期间，61例经组织学证实的PDAC患者（平均年龄60.7 ± 9.7岁，男性35例）行术前3次 T MRI检查，包括DCE-MRI和双时间点T1定位。两名放射科医生独立测量了Ktrans、kep、ve和ECV。采用Cox回归、Kaplan-Meier分析（log-rank检验）和随时间变化的ROC曲线分析观察者间一致性（ICC）、与病理特征的相关性以及对无复发生存期（RFS）和总生存期（OS）的预后价值。结果secv具有良好的重现性（ICC = 0.99），独立预测较短的RFS （HR = 1.020; 95 % CI: 1.003 ~ 1.038; p = 0.022）。最佳ECV截止值为31.99 %，有效地将患者分为高危组和低危组，中位RFS差异显著（10.9个月vs 17.4个月，p = 0.012）。然而，其他DCE-MRI参数（Ktrans、keep、ve）的重现性较差，对RFS或OS缺乏独立的预后价值。结论mri来源的肿瘤ECV是预测PDAC患者R0切除术后早期复发的可靠、可重复的生物标志物，可能有助于术前风险分层。

{"title":"MRI-derived extracellular volume fraction as a prognostic biomarker for early recurrence after R0 resection of pancreatic ductal adenocarcinoma","authors":"Jing-Yi Liu , Qi Wang , Yi-Tong Lu , Yue-Luan Jiang , Dominik Nichel , Robert Grimm , Liang Zhu , Meng-Hua Dai","doi":"10.1016/j.ejro.2025.100711","DOIUrl":"10.1016/j.ejro.2025.100711","url":null,"abstract":"<div><h3>Objectives</h3><div>Pancreatic ductal adenocarcinoma (PDAC) has a high recurrence risk after R0 resection. This study aimed to evaluate the prognostic value of preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) pharmacokinetic parameters, including volume transfer constant (K<sup>trans</sup>), rate constant (k<sub>ep</sub>), extracellular extravascular volume fraction (v<sub>e</sub>), and MRI-derived extracellular volume (ECV) fraction, for predicting early recurrence and survival after surgery.</div></div><div><h3>Methods</h3><div>In this retrospective cohort study, 61 patients (mean age 60.7 ± 9.7 years; 35 males) with histologically confirmed PDAC underwent preoperative 3 T MRI between January 2017 and May 2024, including DCE-MRI and dual-time-point T1 mapping. Two radiologists independently measured K<sup>trans</sup>, k<sub>ep</sub>, v<sub>e</sub>, and ECV. Inter-observer agreement (ICC), associations with pathological features, and prognostic value for recurrence-free survival (RFS) and overall survival (OS) were analyzed using Cox regression, Kaplan–Meier analysis with log-rank tests, and time-dependent ROC curves.</div></div><div><h3>Results</h3><div>ECV demonstrated excellent reproducibility (ICC = 0.99) and independently predicted shorter RFS (HR = 1.020; 95 % CI: 1.003–1.038; p = 0.022). An optimal ECV cutoff of 31.99 % effectively stratified patients into high- and low-risk groups with significantly different median RFS (10.9 vs. 17.4 months, p = 0.012). However, other DCE-MRI parameters (K<sup>trans</sup>, k<sub>ep</sub>, v<sub>e</sub>) showed poor reproducibility and lacked independent prognostic value for RFS or OS.</div></div><div><h3>Conclusion</h3><div>MRI-derived tumor ECV is a robust, reproducible biomarker for predicting early recurrence after R0 resection in PDAC patients, potentially assisting in preoperative risk stratification.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"15 ","pages":"Article 100711"},"PeriodicalIF":2.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145614350","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}

引用次数: 0

Comparative evaluation of ADC and ADC ratio in differentiating Gleason Score 7 in prostate cancer imaging 前列腺癌影像中ADC与ADC比值鉴别Gleason评分7的比较评价

IF 2.9 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Open

Pub Date : 2025-11-20 DOI: 10.1016/j.ejro.2025.100710

D. Samaras , G. Agrotis , D. Tsivaka , M. Vakalopoulou , K. Vassiou , V. Tzortzis , M. Vlychou , I. Tsougos

Background

Multiparametric Magnetic Resonance Imaging (mpMRI), including diffusion-weighted imaging (DWI), plays a key role in detecting and stratifying prostate cancer (PCa). The Apparent Diffusion Coefficient (ADC) aids in tissue characterization and may assist in distinguishing between Gleason score 7 subgroups (GS 3 +4 and GS 4 +3). This study aimed to evaluate mean ADC and ADC_ratio (ratio of tumor-to-normal ADC) in differentiating these subgroups and to assess the influence of magnetic field strength (1.5 T vs. 3.0 T) on diagnostic performance.

Methods

This retrospective study included 48 patients with histologically confirmed GS 7 PCa who underwent mpMRI at 1.5 T or 3.0 T. Two experienced radiologists independently and blindly delineated tumor and reference ROIs. Inter-observer agreement was evaluated using the intraclass correlation coefficient (ICC). Correlation, t-tests, and receiver operating characteristic (ROC) analyses were used to assess relationships, group differences, and optimal cut-off values.

Results

Excellent inter-observer agreement was found for both mean ADC and ADC_ratio (ICC

>

0.9). At 3.0 T, mean ADC (10.9 ×10⁻⁴ mm²/s vs 8.55 ×10⁻⁴ mm²/s, p = 0.011) and ADC_ratio (0.68 vs 0.54, p = 0.003) significantly distinguished GS 3 + 4 from GS 4 + 3, while no differences were observed at 1.5 T. Spearman’s ρ confirmed stronger correlations at 3.0 T (ADCratio ρ=−0.510; p = 0.004). For the combined (3.0 T + 1.5 T) dataset, ADC_ratio achieved an AUC of 0.748 and mean ADC an AUC of 0.718 (p > 0.05).

Conclusion

ADC_ratio demonstrated high reproducibility and stable diagnostic performance across scanners, supporting its potential as a reliable imaging biomarker for prostate cancer grading.

多参数磁共振成像（mpMRI），包括弥散加权成像（DWI），在前列腺癌（PCa）的检测和分层中起着关键作用。表观扩散系数（ADC）有助于组织表征，并有助于区分Gleason评分7个亚组（GS 3 +4和GS 4 +3）。本研究旨在评估这些亚组的平均ADC和adratio（肿瘤与正常ADC之比），并评估磁场强度（1.5 T vs. 3.0 T）对诊断性能的影响。方法回顾性研究48例组织学证实的GS - 7型前列腺癌患者，在1.5 T或3.0 T行mpMRI检查。两位经验丰富的放射科医生独立、盲目地描绘肿瘤和参考roi。使用类内相关系数（ICC）评估观察者间的一致性。使用相关性、t检验和受试者工作特征（ROC）分析来评估相关性、组间差异和最佳临界值。结果平均ADC和ADCratio的观察者间一致性很好（ICC>0.9）。在3.0 T时，平均ADC （10.9 ×10⁻⁴mm²/s vs 8.55 ×10⁻⁴mm²/s, p = 0.011）和ADCratio （0.68 vs 0.54, p = 0.003）显着区分了GS 3 + 4和GS 4 + 3，而在1.5 T时没有观察到差异。Spearman 's ρ在3.0 T时证实了更强的相关性（ADCratio ρ= - 0.510; p = 0.004）。对于组合（3.0 T + 1.5 T）数据集，adcreatio的AUC为0.748，平均ADC和AUC为0.718 （p > 0.05）。结论adcratio具有高重复性和稳定的诊断性能，支持其作为前列腺癌分级的可靠成像生物标志物的潜力。

{"title":"Comparative evaluation of ADC and ADC ratio in differentiating Gleason Score 7 in prostate cancer imaging","authors":"D. Samaras , G. Agrotis , D. Tsivaka , M. Vakalopoulou , K. Vassiou , V. Tzortzis , M. Vlychou , I. Tsougos","doi":"10.1016/j.ejro.2025.100710","DOIUrl":"10.1016/j.ejro.2025.100710","url":null,"abstract":"<div><h3>Background</h3><div>Multiparametric Magnetic Resonance Imaging (mpMRI), including diffusion-weighted imaging (DWI), plays a key role in detecting and stratifying prostate cancer (PCa). The Apparent Diffusion Coefficient (ADC) aids in tissue characterization and may assist in distinguishing between Gleason score 7 subgroups (GS 3 +4 and GS 4 +3). This study aimed to evaluate mean ADC and ADC<sub>ratio</sub> (ratio of tumor-to-normal ADC) in differentiating these subgroups and to assess the influence of magnetic field strength (1.5 T vs. 3.0 T) on diagnostic performance.</div></div><div><h3>Methods</h3><div>This retrospective study included 48 patients with histologically confirmed GS 7 PCa who underwent mpMRI at 1.5 T or 3.0 T. Two experienced radiologists independently and blindly delineated tumor and reference ROIs. Inter-observer agreement was evaluated using the intraclass correlation coefficient (ICC). Correlation, t-tests, and receiver operating characteristic (ROC) analyses were used to assess relationships, group differences, and optimal cut-off values.</div></div><div><h3>Results</h3><div>Excellent inter-observer agreement was found for both mean ADC and ADC<sub>ratio</sub> (ICC<span><math><mo>></mo></math></span>0.9). At 3.0 T, mean ADC (10.9 ×10⁻⁴ mm²/s vs 8.55 ×10⁻⁴ mm²/s, p = 0.011) and ADC<sub>ratio</sub> (0.68 vs 0.54, p = 0.003) significantly distinguished GS 3 + 4 from GS 4 + 3, while no differences were observed at 1.5 T. Spearman’s ρ confirmed stronger correlations at 3.0 T (ADCratio ρ=−0.510; p = 0.004). For the combined (3.0 T + 1.5 T) dataset, ADC<sub>ratio</sub> achieved an AUC of 0.748 and mean ADC an AUC of 0.718 (p > 0.05).</div></div><div><h3>Conclusion</h3><div>ADC<sub>ratio</sub> demonstrated high reproducibility and stable diagnostic performance across scanners, supporting its potential as a reliable imaging biomarker for prostate cancer grading.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"15 ","pages":"Article 100710"},"PeriodicalIF":2.9,"publicationDate":"2025-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145568665","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}

引用次数: 0

Analysis of a clot-based combined radiomics model for predicting embolic etiology in acute ischemic stroke patients 基于凝块的联合放射组学模型预测急性缺血性脑卒中患者栓塞病因分析

IF 2.9 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Open

Pub Date : 2025-11-20 DOI: 10.1016/j.ejro.2025.100709

Ru Ding , Kun An , Fen Wang , Zirui Cao , Yan Zeng , Lili Guo

Background and objectives

Acute ischemic stroke (AIS) remains the primary cause of mortality and disability among adults in China. Different etiologies of acute ischemic stroke (AIS) are considered important factors affecting neurological function. The accurate etiological classification of AIS prior to surgery is crucial. To investigate and explore the predictive value of a clot-based combined radiomics model for identifying the etiological subtypes of acute ischemic stroke.

Materials and methods

A total of 263 patients with acute ischemic stroke caused by anterior circulation large artery occlusion were retrospectively enrolled. These were grouped into training (180), testing (45), and external validation cohorts (38). NCCT and CTA scans were adopted to segment region of interest (ROI) of clots. Feature selection was conducted to establish Clinical model, radiomics models (NCCT, CTA, and NCCT&CTA), and combined model.

Results

The AUCs of the clinical model and radiomics models (NCCT, CTA and NCCT&CTA) in the testing cohort were 0.8288(95 % CI: 0.7174–0.9403), 0.8133(95 % CI:0.6853–0.9414), 0.8075(95 % CI:0.6844–0.9307) and 0.8535(95 % CI:0.6774–1), respectively. The combined model achieved a greater AUC than the other four models in the testing cohort (0.9077 [95 % CI: 0.821–0.9944]). Clinical decision curve analysis (DCA) demonstrated that the radiomics (NCCT&CTA) model and combined model show better net benefits within a relatively wide range of threshold probabilities.

Conclusion

The combined radiomics model achieved good predictive efficacy for distinguishing the etiological subtypes of acute ischemic stroke and can provide valuable information for the precise selection of recanalization strategies in clinical practice.

背景和目的在中国，急性缺血性脑卒中（AIS）仍然是导致成人死亡和残疾的主要原因。急性缺血性脑卒中的不同病因被认为是影响神经功能的重要因素。手术前准确的AIS病因分类是至关重要的。探讨基于凝块的联合放射组学模型对急性缺血性脑卒中病因亚型的预测价值。材料与方法回顾性分析前循环大动脉闭塞致急性缺血性脑卒中患者263例。这些人被分为训练组（180人）、测试组（45人）和外部验证组（38人）。采用NCCT和CTA扫描对血栓感兴趣区域（ROI）进行分割。通过特征选择建立临床模型、放射组学模型（NCCT、CTA、NCCT& CTA）和联合模型。结果临床模型和放射组学模型（NCCT、CTA和NCCT& CTA）在检测队列中的auc分别为0.8288（95 % CI: 0.7174 ~ 0.9403）、0.8133（95 % CI:0.6853 ~ 0.9414）、0.8075（95 % CI:0.6844 ~ 0.9307）和0.8535（95 % CI:0.6774 ~ 1）。在测试队列中，联合模型的AUC高于其他四个模型（0.9077[95 % CI: 0.821-0.9944]）。临床决策曲线分析（DCA）表明，放射组学（NCCT&；CTA）模型和联合模型在相对较宽的阈值概率范围内显示出更好的净效益。结论联合放射组学模型对急性缺血性脑卒中病因亚型有较好的预测效果，可为临床精确选择再通策略提供有价值的信息。

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引用次数: 0