European Journal of Radiology Open最新文献_第4页

Diagnostic accuracy of AI in chest radiography for pneumonia and lung cancer: A meta-analysis 人工智能在肺炎和肺癌胸片诊断中的准确性：一项荟萃分析

IF 2.9 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Open

Pub Date : 2025-11-03 DOI: 10.1016/j.ejro.2025.100701

Mohammed Elmujtba Adam Essa

Rationale and Objectives

Chest radiography (CXR) is the most common imaging test worldwide for evaluating pulmonary disease, yet its sensitivity for pneumonia and lung cancer is limited. Artificial intelligence (AI) based image analysis has shown promise to aid radiographic diagnosis. A meta-analysis was performed for AI algorithms evaluation for detecting pneumonia or lung nodules on CXR, and AI performance was compared to human readers.

Materials and Methods

Following PRISMA guidelines, searched PubMed/Medline, Embase, Cochrane, and IEEE Xplore (Jan 2017–July 2025) for diagnostic accuracy studies of AI on CXR. Eligible studies included any prospective or retrospective design reporting sensitivity and specificity for AI-based pneumonia or lung nodule detection, with an independent reference standard. Data were extracted using a standardized form and quality was assessed with QUADAS2.

Results

Fifteen studies (≈12,000 CXRs) met inclusion criteria; individual AI algorithms achieved sensitivities of ∼70–97 % and specificities of ∼85–95 %. Meta-analysis yielded a pooled sensitivity of 88 % and specificity of 90 % for AI pneumonia detection. For lung nodules, pooled AI sensitivity was ≈ 72 % and specificity ≈ 95 %. One representative deep-learning model for detecting nodules. AI tended to miss very small or central nodules but detected ∼90 % of larger nodules. Crucially, using AI as a second reader improved radiologist performance, increasing sensitivity by approximately 9–10 %age points.

Conclusion

AI algorithms demonstrate high diagnostic accuracy for pneumonia on CXR and can markedly increase the detection of occult lung nodules when used as a second reader. However, performance varies by lesion characteristics. Overall, AI has strong potential to enhance clinical chest radiograph interpretation.

理由和目的胸部x线摄影（CXR）是世界范围内评估肺部疾病最常用的影像学检查，但其对肺炎和肺癌的敏感性有限。基于人工智能（AI）的图像分析显示出帮助放射诊断的前景。对在CXR上检测肺炎或肺结节的人工智能算法评价进行了荟萃分析，并将人工智能的表现与人类读者进行了比较。材料和方法：按照PRISMA指南，检索PubMed/Medline、Embase、Cochrane和IEEE Xplore（2017年1月- 2025年7月），获取人工智能在CXR上的诊断准确性研究。符合条件的研究包括任何前瞻性或回顾性设计，报告基于人工智能的肺炎或肺结节检测的敏感性和特异性，并有独立的参考标准。使用标准化表格提取数据，并使用QUADAS2评估质量。结果15项研究（约12,000例cxr）符合纳入标准；单个AI算法的灵敏度为~ 70-97 %，特异性为~ 85-95 %。荟萃分析得出AI肺炎检测的总敏感性为88 %，特异性为90 %。对于肺结节，人工智能综合敏感性≈ 72 %，特异性≈ 95 %。一种典型的结节检测深度学习模型。AI往往会遗漏非常小或中心的结节，但能检测到~ 90% %的较大结节。至关重要的是，使用人工智能作为第二阅读器提高了放射科医生的表现，将灵敏度提高了大约9 - 10%。结论人工智能算法对CXR肺炎具有较高的诊断准确性，作为第二阅读器可显著提高肺隐性结节的检出率。然而，表现因病变特征而异。总的来说，人工智能在增强临床胸片解释方面具有很强的潜力。

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引用次数: 0

Dual-metric Bayesian optimization of B-spline mesh size for 4DCT lung registration 4DCT肺配准b样条网格尺寸双度量贝叶斯优化

IF 2.9 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Open

Pub Date : 2025-11-01 DOI: 10.1016/j.ejro.2025.100698

Liang Tan, Liyuan Chen, Huanli Luo, Xin Yang, Bin Feng, Fu Jin

Objectives

We aim to optimize the patient-specific mesh size (N) in the B-spline deformable image registration method, enhancing the computational efficiency of 4DCT lung image registration.

Methods

This study included 37 subjects (10 from the DIRLAB public dataset and 27 from a private 4DCT cohort), each consisting of 10 respiratory phases. A Bayesian optimization (BO) framework was proposed to determine patient-specific N within [2, 50]. Registration accuracy was evaluated using the Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD). To further validate registration robustness, extreme-phase registrations were additionally tested, and inverse consistency error (ICE) was calculated to assess deformation invertibility. A global evaluation approach was also applied across the full respiratory cycle, and the computational cost of the traditional grid search (GS) was analyzed for comparison.

Results

BO efficiently determined patient-specific N, with optimal values ranging from 6 to 15 (overall mean = 10.4 ± 2.6), achieving DSC = 0.976 and HD = 0.814. In the extreme-phase tests, registration performance remained stable between forward and reverse directions, with DSC > 0.94, HD₉₅< 3 mm, and small ICE differences (ICE₉₅ = 0.467 ± 0.230 mm), indicating strong inverse consistency and deformation stability. Compared with GS, BO achieved 50.7 %–99.4 % time savings, while GS showed a power-law increase in runtime (exponent = 2.53).

Conclusions

The proposed BO framework efficiently optimized patient-specific mesh sizes, achieving high registration accuracy and significantly reduced computation time, thereby offering a promising tool to improve efficiency in adaptive radiotherapy and motion-compensated treatment planning.

目的优化b样条可变形图像配准方法中患者特异性网格尺寸(N)，提高4DCT肺部图像配准的计算效率。方法本研究包括37名受试者（10名来自DIRLAB公共数据集，27名来自私人4DCT队列），每个受试者由10个呼吸期组成。提出了一个贝叶斯优化（BO）框架来确定患者特异性N[2,50]。采用Dice Similarity Coefficient （DSC）和Hausdorff Distance （HD）评价配准精度。为了进一步验证配准的稳健性，我们对极端相位配准进行了额外的测试，并计算了逆一致性误差（ICE）来评估变形可逆性。采用全呼吸周期的全局评价方法，对比分析了传统网格搜索方法的计算成本。结果bo能有效测定患者特异性N，最优值为6 ~ 15（总平均值= 10.4 ± 2.6），DSC = 0.976,HD = 0.814。在极相试验中，正反方向配准性能保持稳定，DSC >； 0.94,HD95<； 3 mm， ICE差异较小（ICE95 = 0.467 ± 0.230 mm），具有较强的逆一致性和变形稳定性。与GS相比，BO节省了50.7 % -99.4 %的时间，而GS在运行时间上呈幂律增长（指数= 2.53）。结论提出的BO框架有效地优化了患者特异性网格尺寸，实现了高配准精度，显著减少了计算时间，从而为提高自适应放疗和运动补偿治疗计划的效率提供了一个有希望的工具。

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引用次数: 0

Improvement of machine learning models for predicting high-grade subtypes of lung adenocarcinoma based on delta radiomics: A multicenter cohort study 基于放射组学的预测肺腺癌高级别亚型的机器学习模型的改进：一项多中心队列研究

IF 2.9 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Open

Pub Date : 2025-11-01 DOI: 10.1016/j.ejro.2025.100699

Feiyang Zhong , Ting Li , Wenping Li , Lijun Wu , Pengju Zhang , Pengxin Yu , Yuan Fang , Meiyan Liao , Shaohong Zhao

Objectives

To evaluate the effectiveness of delta radiomics in predicting high-grade components in lung adenocarcinoma and to develop a robust machine learning model for clinical application.

Methods

This retrospective multi-center cohort study included lung cancer patients from three hospitals who had pre-surgery CT follow-up scans. Training (n = 491) and validation (n = 210) were performed using cases from Center 1, and testing was conducted using cases from Centers 2 and 3 (n = 92). Radiomic features were extracted from baseline and follow-up CT images, and delta radiomic features were calculated. The LASSO algorithm was used for radiomic feature selection, and rad-score and delta rad-score were constructed. Significant clinical and radiomic features were combined to build the final machine learning model. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), DeLong test, decision curve analysis (DCA), and integrated discrimination improvement (IDI) analysis.

Results

In the external test cohort, the integrated machine learning model constructed based on clinical features (CTR, smoking status, maximum diameter of the solid component), rad-score, and delta rad-score showed that the random forest model performed the best, with an AUC of 0.91. The random forest model outperformed the clinical model (AUC = 0.80), rad-score (AUC = 0.79), and delta rad-score (AUC = 0.81). DCA and IDI indicated that the random forest model provides superior clinical benefit and improvement.

Conclusion

Delta radiomics significantly aids in identifying high-grade subtypes of lung adenocarcinoma. The integrated machine learning model offers an effective approach for prediction of high-grade components, with potential clinical implications.

Clinical Relevance Statement

This study presents a novel application of delta radiomics to predict high-grade lung adenocarcinoma, which may influence surgical management and improve patient outcomes.

目的评估δ放射组学在预测肺腺癌高级别成分中的有效性，并为临床应用开发一个强大的机器学习模型。方法回顾性多中心队列研究纳入三家医院的肺癌患者术前CT随访扫描。使用中心1的病例进行训练（n = 491）和验证（n = 210），使用中心2和3的病例进行测试（n = 92）。从基线和随访CT图像中提取放射学特征，并计算δ放射学特征。采用LASSO算法进行放射学特征选择，并构造rad-score和delta rad-score。将重要的临床和放射学特征结合起来构建最终的机器学习模型。采用受试者工作特征曲线下面积（AUC）、DeLong检验、决策曲线分析（DCA）和综合判别改进（IDI）分析对模型性能进行评价。结果在外部测试队列中，基于临床特征（CTR、吸烟状况、实体成分最大直径）、rad-score和delta rad-score构建的综合机器学习模型显示随机森林模型表现最好，AUC为0.91。随机森林模型优于临床模型（AUC = 0.80）、rad-score （AUC = 0.79）和delta rad-score （AUC = 0.81）。DCA和IDI表明随机森林模型具有较好的临床疗效和改善效果。结论delta放射组学在鉴别肺腺癌高级别亚型中具有重要的辅助作用。集成的机器学习模型为预测高级成分提供了有效的方法，具有潜在的临床意义。临床相关性声明本研究提出了delta放射组学预测高级别肺腺癌的新应用，这可能会影响手术治疗并改善患者预后。

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引用次数: 0

Development of a hybrid 2.5D deep learning model for glioma survival prediction using T1-weighted MRI from the CGGA database 开发混合2.5D深度学习模型，利用CGGA数据库的t1加权MRI预测胶质瘤生存

IF 2.9 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Open

Pub Date : 2025-10-26 DOI: 10.1016/j.ejro.2025.100697

Kai Jin, Caixing Sun, Liang Xia

Background

Current glioma survival prediction relies on invasive molecular profiling. To overcome this, a non-invasive deep learning framework using T1-weighted contrast-enhanced MRI (T1CE) was developed to predict overall survival. This framework addresses computational limitations associated with the volumetric analysis while preserving important spatial information.

Methods

We designed a hybrid 2.5D convolutional neural network to process multi-slice inputs, including the center slice and its adjacent slices, from 217 patients in the CGGA database. Transfer learning using ResNet and DenseNet architectures were employed to initialize the models. These models were subsequently fine-tuned with the Cox proportional hazards loss function. After the fine-tuning process was completed, the imaging signature was combined with clinical and molecular variables, including IDH and 1p19q status, to build an integrated model. Performance was evaluated via C-index, time-dependent AUC, and Kaplan-Meier analysis in independent training (70 %) and testing (30 %) cohorts.

Results

The Combined model achieved superior discrimination, with a training C-index of 0.819 (95 % CI: 0.758–0.880) and a testing C-index of 0.804 (95 % CI: 0.708–0.900). It significantly outperformed the isolated Radiomic, deep learning (2D and 2.5D), and Clinical models (all p < 0.05). Moreover, time-dependent ROC analysis demonstrated consistent model performance over 1–5 years, with AUC values ranging from 0.851 to 0.906. The stratified survival curves clearly revealed distinct prognostic groups (log-rank p < 0.001).

Conclusions

The 2.5D multi-source framework provides a clinically feasible, non-invasive tool for preoperative survival prediction, enabling personalized therapeutic strategies for glioma patients.

目前的胶质瘤生存预测依赖于侵入性分子谱分析。为了克服这一问题，研究人员开发了一种使用t1加权对比增强MRI （T1CE）的非侵入性深度学习框架来预测总生存率。该框架解决了与体积分析相关的计算限制，同时保留了重要的空间信息。方法设计一种混合2.5D卷积神经网络，对217例CGGA患者的中心切片及其相邻切片进行多片输入处理。使用ResNet和DenseNet架构进行迁移学习来初始化模型。这些模型随后用Cox比例风险损失函数进行微调。在微调过程完成后，结合临床和分子变量，包括IDH和1p19q状态，构建集成模型。在独立训练（70 %）和测试（30 %）队列中，通过c指数、时间相关AUC和Kaplan-Meier分析来评估表现。结果联合模型的训练c -指数为0.819(95 % CI: 0.758 ~ 0.880)，检验c -指数为0.804(95 % CI: 0.708 ~ 0.900)，具有较好的判别性。它明显优于孤立的Radiomic、深度学习（2D和2.5D）和临床模型（均p <； 0.05）。此外，时间相关的ROC分析显示，模型在1-5年内的表现一致，AUC值在0.851 ~ 0.906之间。分层生存曲线清楚地显示了不同的预后组（log-rank p <； 0.001）。结论2.5D多源框架为胶质瘤患者的术前生存预测提供了一种临床可行的、无创的工具，可为胶质瘤患者提供个性化的治疗策略。

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引用次数: 0

Enhancing accuracy of detecting left atrial dilatation on CT pulmonary angiography 提高CT肺血管造影检测左房扩张的准确性

IF 2.9 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Open

Pub Date : 2025-10-15 DOI: 10.1016/j.ejro.2025.100696

Louis Tapper , Samer Alabed , Ahmed Maiter , Andrzej Lejawka , Mahan Salehi , Krit Dwivedi , Pankaj Garg , David G. Kiely , Peter Metherall , Rob J. van der Geest , Kavita Karunasaagarar , Michael Sharkey , Andrew J. Swift

Introduction

Left atrial (LA) dilatation predicts several cardiovascular disorders. Identifying LA dilatation on computed tomography pulmonary angiography (CTPA) could aid diagnosis of cardiovascular disease. This study assessed an artificial intelligence (AI) segmentation model’s performance at detecting LA dilatation on CTPA.

Methods

Patients with suspected pulmonary hypertension (PH) who underwent CTPA and cardiac MRI (CMR) were retrospectively identified from a single centre registry. The LA was segmented by an AI tool for CTPA and a validated AI tool for CMR. LA volume measurements were categorised for LA dilatation based on existing threshold values. The expert radiologist's reports of the CTPA studies were also categorised for LA dilatation. Automated CTPA LA volumes and corresponding radiologist reports were compared against the reference standard of CMR.

Results

451 patients were included (mean age 64 ± 13 years, 62.5 % female, 85.8 % white). Automated LA volume measurements on CTPA showed strong positive correlation with those on CMR (ρ = 0.92, p < 0.001) with minimal bias on Bland-Altman analysis (-4 mL, 95 %CI −39 to +31 mL). Automated LA measurements on CTPA showed higher agreement with those on CMR (κ = 0.80) than the radiologist reports (κ = 0.62). Automated LA measurements on CTPA showed higher accuracy metrics (sensitivity 81.0 %, specificity 96.8 %, positive predictive value (PPV) 88.5 %, negative predictive value (NPV) 94.4 %) than the radiologist reports (sensitivity 66.7 %, specificity 93.1 %, PPV 74.5 %, NPV 90.2 %).

Conclusion

Deep learning increases the accuracy of LA volume measurements on non-ECG gated CTPA, improving radiologist performance in detecting LA dilatation.

左心房（LA）扩张可预测几种心血管疾病。通过ct肺血管造影（CTPA）识别LA扩张有助于心血管疾病的诊断。本研究评估了人工智能（AI）分割模型在检测CTPA上LA扩张方面的性能。方法回顾性分析单中心登记的疑似肺动脉高压（PH）患者行CTPA和心脏MRI （CMR）检查。LA通过CTPA的人工智能工具和CMR的经过验证的人工智能工具进行分割。根据现有阈值对LA扩张的容积测量进行分类。放射科专家的CTPA研究报告也被归类为LA扩张。自动CTPA LA体积和相应的放射科医生报告与CMR参考标准进行比较。结果纳入451例患者（平均年龄64岁 ± 13岁，女性62.5% %，白人85.8% %）。CTPA的自动LA体积测量与CMR的体积测量显示出很强的正相关（ρ = 0.92, p <； 0.001），Bland-Altman分析的偏差最小（-4 mL， 95% %CI - 39至+31 mL）。CTPA的自动LA测量值与CMR的一致性（κ = 0.80）高于放射科医生报告的一致性（κ = 0.62）。自动LA测量CTPA的准确性指标（敏感性81.0 %，特异性96.8 %，阳性预测值（PPV） 88.5 %，阴性预测值（NPV） 94.4 %）高于放射科医生报告的准确性指标（敏感性66.7 %，特异性93.1 %,PPV 74.5 %,NPV 90.2 %）。结论深度学习提高了非ecg门控CTPA左室容积测量的准确性，提高了放射科医生检测左室扩张的能力。

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A systematic review and meta-analysis of imaging characteristics and upgrade rates in noninvasive lobular neoplasia of the breast 对乳腺非侵袭性小叶瘤的影像学特征和升级率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 : 2025-10-15 DOI: 10.1016/j.ejro.2025.100691

Fatemeh Shakki Katouli , Negin Salehi , Faezeh Soveyzi , Mina Abedi , Parya Valizadeh , Hamed Ghorani , Seyedeh Melika Hashemi , Jayran Zebardast , Madjid Shakiba , Sadaf Alipour

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