European Journal of Radiology Open最新文献

Multidisciplinary quantitative and qualitative assessment of IDH-mutant gliomas with full diagnostic deep learning image reconstruction. idh突变胶质瘤的多学科定量和定性评估，充分诊断深度学习图像重建。

IF 1.8 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Open

Pub Date : 2024-12-04 eCollection Date: 2024-12-01 DOI: 10.1016/j.ejro.2024.100617

Christer Ruff, Paula Bombach, Constantin Roder, Eliane Weinbrenner, Christoph Artzner, Leonie Zerweck, Frank Paulsen, Till-Karsten Hauser, Ulrike Ernemann, Georg Gohla

Rationale and Objectives: Diagnostic accuracy and therapeutic decision-making for IDH-mutant gliomas in tumor board reviews are based on MRI and multidisciplinary interactions.

Materials and methods: This study explores the feasibility of deep learning-based reconstruction (DLR) in MRI for IDH-mutant gliomas. The research utilizes a multidisciplinary approach, engaging neuroradiologists, neurosurgeons, neuro-oncologists, and radiotherapists to evaluate qualitative aspects of DLR and conventional reconstructed (CR) sequences. Furthermore, quantitative image quality and tumor volumes according to Response Assessment in Neuro-Oncology (RANO) 2.0 standards were assessed.

Results: All DLR sequences consistently outperformed CR sequences (median of 4 for all) in qualitative image quality across all raters (p < 0.001 for all) and revealed higher SNR and CNR values (p < 0.001 for all). Preference for all DLR over CR was overwhelming, with ratings of 84 % from the neuroradiologist, 100 % from the neurosurgeon, 92 % from the neuro-oncologist, and 84 % from the radiation oncologist. The RANO 2.0 compliant measurements showed no significant difference between the CR and DRL sequences (p = 0.142).

Conclusion: This study demonstrates the clinical feasibility of DLR in MR imaging of IDH-mutant gliomas, with significant time savings of 29.6 % on average and non-inferior image quality to CR. DLR sequences received strong multidisciplinary preference, underscoring their potential for enhancing neuro-oncological decision-making and suitability for clinical implementation.

原理和目的：肿瘤委员会审查中idh突变胶质瘤的诊断准确性和治疗决策是基于MRI和多学科相互作用。材料与方法：本研究探讨了基于深度学习的MRI重建（DLR）在idh突变胶质瘤中的可行性。该研究采用多学科方法，让神经放射学家、神经外科医生、神经肿瘤学家和放射治疗师对DLR和传统重建（CR）序列的定性方面进行评估。此外，根据神经肿瘤反应评估（RANO） 2.0 标准对定量图像质量和肿瘤体积进行评估。结果：所有DLR序列在所有评分者的定性图像质量方面始终优于CR序列（所有序列的中位数为4）（p ）。本研究证明了DLR在idh突变胶质瘤MR成像中的临床可行性，平均节省时间29.6% %，图像质量不低于CR。DLR序列受到了强烈的多学科偏好，强调了它们在增强神经肿瘤学决策和临床实施适用性方面的潜力。

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引用次数: 0

Role of pre-procedure CCTA in predicting failed percutaneous coronary intervention for chronic total occlusions 术前CCTA在预测慢性全闭塞经皮冠状动脉介入治疗失败中的作用

IF 1.8 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Open

Pub Date : 2024-12-01 DOI: 10.1016/j.ejro.2024.100616

Hua Zhou , Xiaojun Fan , Mingyuan Yuan , Wei Wang, Qiyuan Wu

Purpose

This study aimed to identify major lesion characteristics of chronic total occlusions (CTOs) that predict failed percutaneous coronary intervention (PCI) using pre-procedure coronary computed tomography angiography (CCTA) in combination with conventional coronary angiography (CCA).

Methods

Consecutive patients with at least one CTO of the native coronary arteries received CCTA and CCA-guided PCI, with computed tomography performed before or during PCI.

Results

A total of 76 patients with CTO were included in this study. 76 patients were divided into successful and failed PCI groups based on their PCI outcome. There were 62 (81.58 %) patients in the successful PCI group and 14 (18.42 %) in the failed PCI group. The occlusion length ≥20 mm, ostial or bifurcation lesions, negative remodeling, microchannels, and good collateral vessels were the CCTA morphologic parameters associated with PCI outcome (P<0.05). In addition, the blunt stump, occlusion length ≥20 mm, and ostial or bifurcation lesions, were the CCA morphologic parameters associated with PCI outcome (P<0.05). The multivariate regression model showed that the three independent negative predictors: blunt stump on CCA (OR: 0.63; 95 % CI: 0.23–0.98; p =0.048), occlusion length ≥20 mm on CCTA (OR: 0.37; 95 % CI: 0.32–0.71; p <0.001) and negative remodeling on CCTA (OR: 0.26; 95 % CI: 0.28–0.44; p <0.001).

Conclusion

Our study demonstrated that combining CCTA and CCA morphologic characteristics could improve PCI outcome prediction in patients with CTO compared to CCTA morphologic features alone.

目的：本研究旨在通过术前冠状动脉计算机断层血管造影（CCTA）联合常规冠状动脉造影（CCA），确定慢性全闭塞（CTOs）的主要病变特征，以预测经皮冠状动脉介入治疗（PCI）失败。方法连续至少有一个原生冠状动脉CTO的患者接受CCTA和CCTA引导的PCI，在PCI之前或期间进行计算机断层扫描。结果本研究共纳入76例CTO患者。76例患者根据PCI结果分为PCI成功组和PCI失败组。PCI成功组62例（81.58 %），PCI失败组14例（18.42 %）。与PCI预后相关的CCTA形态学参数为闭塞长度≥20 mm、口或分叉病变、负重构、微通道和良好的侧支血管（P<0.05）。此外，钝残端、咬合长度≥20 mm、口或分叉病变是与PCI预后相关的CCA形态学参数（P<0.05）。多元回归模型显示，3个独立的负预测因子：钝残肢对CCA (OR: 0.63；95 % ci: 0.23-0.98；p =0.048)， CCTA上咬合长度≥20 mm (OR: 0.37；95 % ci: 0.32-0.71；p <0.001)和CCTA阴性重塑(OR: 0.26；95 % ci: 0.28-0.44；p & lt; 0.001)。结论本研究表明，与单独使用CCTA形态学特征相比，结合CCTA和CCA形态学特征可以提高CTO患者PCI预后的预测。

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引用次数: 0

Enhancing mortality prediction in patients with spontaneous intracerebral hemorrhage: Radiomics and supervised machine learning on non-contrast computed tomography 增强自发性脑出血患者的死亡率预测：非对比计算机断层扫描的放射组学和监督机器学习

IF 1.8 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Open

Pub Date : 2024-12-01 DOI: 10.1016/j.ejro.2024.100618

Antonio López-Rueda , María-Ángeles Rodríguez-Sánchez , Elena Serrano , Javier Moreno , Alejandro Rodríguez , Laura Llull , Sergi Amaro , Laura Oleaga

Purpose

This study aims to develop a Radiomics-based Supervised Machine-Learning model to predict mortality in patients with spontaneous intracerebral hemorrhage (sICH).

Methods

Retrospective analysis of a prospectively collected clinical registry of patients with sICH consecutively admitted at a single academic comprehensive stroke center between January-2016 and April-2018. We conducted an in-depth analysis of 105 radiomic features extracted from 105 patients. Following the identification and handling of missing values, radiomics values were scaled to 0–1 to train different classifiers. The sample was split into 80–20 % training-test and validation cohort in a stratified fashion. Random Forest(RF), K-Nearest Neighbor(KNN), and Support Vector Machine(SVM) classifiers were evaluated, along with several feature selection methods and hyperparameter optimization strategies, to classify the binary outcome of mortality or survival during hospital admission. A tenfold stratified cross-validation method was used to train the models, and average metrics were calculated.

Results

RF, KNN, and SVM, with the "DropOut+SelectKBest" feature selection strategy and no hyperparameter optimization, demonstrated the best performances with the least number of radiomic features and the most simplified models, achieving a sensitivity range between 0.90 and 0.95 and AUC range from 0.97 to 1 on the validation dataset. Regarding the confusion matrix, the SVM model did not predict any false negative test (negative predicted value 1).

Conclusion

Radiomics-based Supervised Machine Learning models can predict mortality during admission in patients with sICH. SVM with the "DropOut+SelectKBest" feature selection strategy and no hyperparameter optimization was the best simplified model to detect mortality during admission in patients with sICH.

本研究旨在建立基于放射组学的监督机器学习模型来预测自发性脑出血（siich）患者的死亡率。方法回顾性分析前瞻性收集的2016年1月至2018年4月在单一学术综合脑卒中中心连续入院的siich患者的临床登记。我们对105例患者的105个放射学特征进行了深入分析。在识别和处理缺失值之后，将放射组学值缩放到0-1，以训练不同的分类器。将样本分层分为80-20 %训练检验和验证队列。随机森林（RF）、k近邻（KNN）和支持向量机（SVM）分类器，以及几种特征选择方法和超参数优化策略，用于对住院期间死亡率或存活率的二元结果进行分类。采用十倍分层交叉验证法对模型进行训练，并计算平均指标。结果采用“DropOut+SelectKBest”特征选择策略、未进行超参数优化的rf、KNN和SVM在验证数据集上以最少的放射学特征和最简化的模型表现出最佳性能，灵敏度范围为0.90 ~ 0.95，AUC范围为0.97 ~ 1。对于混淆矩阵，SVM模型没有预测到任何假阴性检验（阴性预测值1）。结论基于放射组学的监督式机器学习模型可以预测sICH患者入院期间的死亡率。采用“DropOut+SelectKBest”特征选择策略且不进行超参数优化的SVM是检测siich患者入院死亡率的最佳简化模型。

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引用次数: 0

Different radiomics models in predicting the malignant potential of small intestinal stromal tumors 预测小肠间质瘤恶性潜能的不同放射组学模型

IF 1.8 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Open

Pub Date : 2024-11-25 DOI: 10.1016/j.ejro.2024.100615

Yuxin Xie , Chongfeng Duan , Xuzhe Zhou , Xiaoming Zhou , Qiulin Shao , Xin Wang , Shuai Zhang , Fang Liu , Zhenbo Sun , Ruirui Zhao , Gang Wang

Objectives

To explore the feasibility of different radiomics models for predicting the malignant potential of small intestinal stromal tumors (SISTs), and to select the best radiomics model.

Methods

A retrospective analysis of 140 patients with SISTs was conducted. Radiomics features were extracted from CT-enhanced images. Support vector machine (SVM), Decision tree (DT), Conditional inference trees (CIT), Random Forest (RF), K-nearest neighbors (KNN), Back-propagation neural network (BPNet), and Bayes were used to construct different radiomics models. The clinical data and CT performance were selected using univariate analysis and to construct clinical model. Nomogram model was developed by combining clinical data and radiomics features. Model performances were assessed by using the area under the receiver operator characteristic (ROC) curve (AUC). The models’ clinical values were assessed by decision curve analysis (DCA).

Results

A total of 1132 radiomics features were extracted. Among radiomics models, SVM was better than DT, CIT, RF, KNN, BPNet, Bayes because it had the highest AUC with a significant difference (P<0.05). The AUC of the clinical model was 0.781. The AUC of the radiomics model was 0.910. The AUC of nomogram model was 0.938. Clinical models had the lowest AUC. Nomogram AUC were slightly higher than radiomics model, but the difference was not significant (P=0.48). The DCA of the nomogram model and radiomics model showed optimal clinical efficacy.

Conclusions

The model constructed with SVM method was the best model for predicting the malignant potential of SISTs. Radiomics model and nomogram model showed high predictive value in predicting the malignant potential of SISTs.

方法对140例小肠间质瘤患者进行回顾性分析。从CT增强图像中提取放射组学特征。使用支持向量机（SVM）、决策树（DT）、条件推理树（CIT）、随机森林（RF）、K-近邻（KNN）、反向传播神经网络（BPNet）和贝叶斯构建不同的放射组学模型。通过单变量分析选择临床数据和 CT 性能，构建临床模型。结合临床数据和放射组学特征，建立了提名图模型。使用接收者操作特征曲线（ROC）下面积（AUC）评估模型性能。结果共提取了 1132 个放射组学特征。在放射组学模型中，SVM优于DT、CIT、RF、KNN、BPNet和Bayes，因为它的AUC最高且差异显著（P<0.05）。临床模型的 AUC 为 0.781。放射组学模型的 AUC 为 0.910。提名图模型的 AUC 为 0.938。临床模型的 AUC 最低。提名图 AUC 略高于放射组学模型，但差异不显著（P=0.48）。结论用 SVM 方法构建的模型是预测 SISTs 恶性潜能的最佳模型。放射组学模型和提名图模型在预测 SISTs 恶性潜能方面显示出较高的预测价值。

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引用次数: 0

Low-dose lung CT: Optimizing diagnostic radiation dose – A phantom study 低剂量肺部 CT：优化诊断辐射剂量 - 一项模型研究

IF 1.8 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Open

Pub Date : 2024-11-24 DOI: 10.1016/j.ejro.2024.100614

Michael Zellner , Sebastian Tschauner , Mathias S. Weyland , Peter Eggenberger Hotz , Stephan Scheidegger , Christian J. Kellenberger

Background/purpose

To investigate a quantitative method for assessing image quality of low dose lung computed tomography (CT) and find the lowest exposure dose providing diagnostic images.

Methods

Axial volumetric lung CT acquisitions (256 slice scanner) were performed on three different sized anthropomorphic phantoms at different dose levels. The maximum steepness of sigmoid curves fitted to line density profiles was measured at lung-to-pleura interfaces. For each phantom, image sharpness was calculated as the median of 468 measurements from 39 different locations. Diagnostic image quality for the adult and paediatric phantom was rated by three radiologists using 4-point Likert scales. The image sharpness cut-off for obtaining adequate image quality was determined from qualitative ratings.

Results

Adequate diagnostic image quality was reached at a median steepness of 713 HU/mm in the adult phantom with a corresponding CTDIvol of 0.14 mGy and an effective dose of 0.13 mSv at a dose level of 100 kVp and 10 mA. In the paediatric phantom diagnostic image quality was reached at a median steepness of 1139 HU/mm with a corresponding CTDIvol of 0.13 mGy and an effective dose of 0.08 mSv at a dose level of 100 kVp and 10 mA.

Conclusions

Determination of image sharpness on line density profiles can be used as quantitative measure for image quality of lung CT. Sufficient-quality lung CT can be achieved at effective radiation doses of 0.13 mSv (adult phantom) and 0.08 mSv (paediatric phantom). These findings suggest that substantial dose reduction is feasible without compromising diagnostic accuracy.

背景/目的研究一种评估低剂量肺部计算机断层扫描（CT）图像质量的定量方法，并找出能提供诊断图像的最低曝光剂量。方法在三个不同大小的拟人化模型上以不同剂量水平进行轴向容积肺部 CT 采集（256 片扫描仪）。在肺-胸膜界面测量了线密度剖面拟合的sigmoid曲线的最大陡度。每个模型的图像清晰度是根据 39 个不同位置 468 次测量结果的中位数计算得出的。成人和儿童模型的诊断图像质量由三位放射科医生使用 4 点李克特量表进行评分。结果成人模型的中位陡度为 713 HU/mm，相应的 CTDIvol 为 0.14 mGy，有效剂量为 0.13 mSv（剂量水平为 100 kVp 和 10 mA）时，诊断图像质量达到合格。在儿童模型中，诊断图像质量的中位陡度为 1139 HU/mm，相应的 CTDIvol 为 0.13 mGy，有效剂量为 0.08 mSv，剂量水平为 100 kVp 和 10 mA。在有效辐射剂量为 0.13 毫西弗特（成人模型）和 0.08 毫西弗特（儿童模型）的情况下，可以获得足够质量的肺部 CT。这些研究结果表明，在不影响诊断准确性的前提下大幅降低剂量是可行的。

{"title":"Low-dose lung CT: Optimizing diagnostic radiation dose – A phantom study","authors":"Michael Zellner , Sebastian Tschauner , Mathias S. Weyland , Peter Eggenberger Hotz , Stephan Scheidegger , Christian J. Kellenberger","doi":"10.1016/j.ejro.2024.100614","DOIUrl":"10.1016/j.ejro.2024.100614","url":null,"abstract":"<div><h3>Background/purpose</h3><div>To investigate a quantitative method for assessing image quality of low dose lung computed tomography (CT) and find the lowest exposure dose providing diagnostic images.</div></div><div><h3>Methods</h3><div>Axial volumetric lung CT acquisitions (256 slice scanner) were performed on three different sized anthropomorphic phantoms at different dose levels. The maximum steepness of sigmoid curves fitted to line density profiles was measured at lung-to-pleura interfaces. For each phantom, image sharpness was calculated as the median of 468 measurements from 39 different locations. Diagnostic image quality for the adult and paediatric phantom was rated by three radiologists using 4-point Likert scales. The image sharpness cut-off for obtaining adequate image quality was determined from qualitative ratings.</div></div><div><h3>Results</h3><div>Adequate diagnostic image quality was reached at a median steepness of 713 HU/mm in the adult phantom with a corresponding CTDIvol of 0.14 mGy and an effective dose of 0.13 mSv at a dose level of 100 kVp and 10 mA. In the paediatric phantom diagnostic image quality was reached at a median steepness of 1139 HU/mm with a corresponding CTDIvol of 0.13 mGy and an effective dose of 0.08 mSv at a dose level of 100 kVp and 10 mA.</div></div><div><h3>Conclusions</h3><div>Determination of image sharpness on line density profiles can be used as quantitative measure for image quality of lung CT. Sufficient-quality lung CT can be achieved at effective radiation doses of 0.13 mSv (adult phantom) and 0.08 mSv (paediatric phantom). These findings suggest that substantial dose reduction is feasible without compromising diagnostic accuracy.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"13 ","pages":"Article 100614"},"PeriodicalIF":1.8,"publicationDate":"2024-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Initial experience with Double-vein Embolization in Hungary 匈牙利双静脉栓塞术的初步经验

IF 1.8 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Open

Pub Date : 2024-11-21 DOI: 10.1016/j.ejro.2024.100613

David Adam Korda , Andras Bibok , Attila Doros , Denes Horvathy , Oszkar Hahn , Balint Kokas , Damjan Pekli , Anna Zsofia Meltzer , Attila Szijarto , Domonkos Nadasdy-Horvath , Pal Akos Deak

Introduction

In recent years several new techniques have emerged to induce hypertrophy of the future liver remnant prior to major hepatectomies. We aimed to summarize our initial experience with Double-vein Embolization as the first center in Hungary.

Methods

Between March 2023 and August 2024 a total of 16 Double-vein Embolization procedures were performed in Semmelweis University. Future liver remnant volume was calculated based on computed tomography scans obtained within 4 weeks prior and 2–3 weeks after the procedure. Tc-99m mebrofenin hepatobiliary scintigraphy results were available for 12/16 patients.

Results

Technical success rate was 100 %. No major complication was observed. Successful resection rate was 93.8 %. One patient died due to post-hepatectomy liver failure. Future liver remnant volume and ratio increased significantly after the procedure compared to baseline (433.1 ± 163.8 cm³ vs. 603.5 ± 201.8 cm³, p < 0.0001 and 27.2 ± 6.5 % vs. 37 ± 8.8 %, p < 0.0001, respectively). Future liver remnant clearance improved significantly 1 and 2 weeks after the procedure (1.68 ± 0.58 %/min/m² vs. 2.44 ± 0.64 %/min/m² and 2.39 ± 0.31 %/min/m², respectively). Mean function gain was 50.6 % after one week and 60.1% after two weeks, respectively.

Discussion

Volumetric and functional outcomes in the present study are comparable with results reported in the literature. Our findings provide further evidence that Double-vein Embolization is a safe procedure that offers sufficient volumetric and functional gain in most candidates for liver resection. However, further studies are needed to define the exact place of this new technique in clinical practice.

导言近年来出现了几种新技术，可在大肝切除术前诱导未来残肝肥大。方法2023 年 3 月至 2024 年 8 月，塞梅尔维斯大学共进行了 16 例双静脉栓塞手术。根据术前 4 周和术后 2-3 周内获得的计算机断层扫描结果计算未来肝脏残余体积。12/16例患者均获得了锝-99m勃吗啡肝胆闪烁扫描结果。无重大并发症。成功切除率为 93.8%。一名患者死于肝切除术后肝功能衰竭。与基线相比，术后未来残肝体积和比例明显增加（分别为 433.1 ± 163.8 cm3 vs. 603.5 ± 201.8 cm3，p < 0.0001 和 27.2 ± 6.5 % vs. 37 ± 8.8 %，p < 0.0001）。术后1周和2周，未来残肝清除率明显提高（分别为1.68 ± 0.58 %/min/m2 vs. 2.44 ± 0.64 %/min/m2 和 2.39 ± 0.31 %/min/m2）。本研究的血容量和功能结果与文献报道的结果相当。我们的研究结果进一步证明，双静脉栓塞术是一种安全的手术，能为大多数肝切除术候选者提供足够的体积和功能增益。然而，要确定这项新技术在临床实践中的确切地位，还需要进一步的研究。

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引用次数: 0

Coronary CT angiography: First comparison of model-based and hybrid iterative reconstruction with the reference standard invasive catheter angiography for CAD-RADS reporting 冠状动脉 CT 血管造影：基于模型的混合迭代重建与 CAD-RADS 报告参考标准侵入性导管血管造影的首次比较

IF 1.8 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Open

Pub Date : 2024-11-20 DOI: 10.1016/j.ejro.2024.100612

Aiste Matuleviciute-Stojanoska , Julia Sautier , Verena Bauer , Martin Nuessel , Volha Nizhnikava , Christian Stumpf , Thorsten Klink

Background

The purpose of this study was to compare CCTA images generated using HIR and IMR algorithm with the reference standard ICA, and to determine to what extend further improvements of IMR over HIR can be expected.

Methods

This retrospective study included 60 patients with low to intermediate CAD risk, who underwent coronary CTA (with HIR and IMR) and ICA. ICA was used as reference standard. Two independent and blinded readers evaluated 2226 segments, classifying stenosis with CAD-RADS (significant stenosis ≥3). Image quality was assessed with a 5-point scale, SNR in the ascending aorta, and FWHM of proximal LCA calibers. The impact of image noise, radiation dose, and BMI on diagnostic accuracy was evaluated using ROC curves and Fisher’s Exact Test. Quantitative plaque analysis was performed on 28 plaques.

Results

IMR showed higher image quality than HIR (IMR 4.4, HIR 3.97, p<0.001) with better SNR (21.4 vs. 13.28, p<0.001) and FWHM (4.44 vs. 4.55, p=0.003). IMR had better diagnostic accuracy (ROC AUC 0.967 vs. 0.948, p=0.16, performed better at higher radiation doses (p=0.02) and showed a larger minimum lumen area (p=0.022 and p=0.046).

Conclusion

IMR offers significantly superior image quality of CCTA, more precise measurements, and a stronger positive correlation with ICA. The overall diagnostic accuracy may be superior with IMR, although the differences were not statistically significant. However, in patients who are exposed to higher radiation doses during CCTA due to their constitution, IMR enables significantly better diagnostic accuracy than HIR thus providing a specific benefit for obese patients.

背景本研究的目的是比较使用 HIR 和 IMR 算法生成的 CCTA 图像与参考标准 ICA，并确定 IMR 与 HIR 相比的进一步改进程度。以 ICA 作为参考标准。两名独立的盲人读者对 2226 个节段进行了评估，并根据 CAD-RADS 对狭窄进行了分类（明显狭窄≥3）。图像质量采用 5 分制、升主动脉信噪比和 LCA 近端口径 FWHM 进行评估。使用 ROC 曲线和费雪精确检验评估了图像噪声、辐射剂量和 BMI 对诊断准确性的影响。结果IMR比HIR显示出更高的图像质量（IMR 4.4，HIR 3.97，p<0.001），具有更好的信噪比（21.4 vs. 13.28，p<0.001）和FWHM（4.44 vs. 4.55，p=0.003）。IMR具有更好的诊断准确性（ROC AUC 0.967 vs. 0.948，p=0.16），在更高辐射剂量下表现更好（p=0.02），显示的最小管腔面积更大（p=0.022 和 p=0.046）。IMR 的整体诊断准确性可能更佳，尽管差异在统计学上并不显著。然而，对于因体质而在 CCTA 过程中暴露于较高辐射剂量的患者，IMR 的诊断准确性明显优于 HIR，从而为肥胖患者带来了特殊的益处。

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引用次数: 0

Increased background parenchymal enhancement on peri-menopausal breast magnetic resonance imaging 围绝经期乳腺磁共振成像的背景实质增强增强

IF 1.8 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Open

Pub Date : 2024-11-18 DOI: 10.1016/j.ejro.2024.100611

Hidemi Okuma , Amro Masarwah , Aleksandr Istomin , Aki Nykänen , Juhana Hakumäki , Ritva Vanninen , Mazen Sudah

Objectives

To examine the background parenchymal enhancement (BPE) levels in peri-menopausal breast MRI compared with pre- and post-menopausal breast MRI.

Methods

This study included 562 patients (55.8±12.3 years) who underwent contrast-enhanced dynamic breast MRI between 2011 and 2015 for clinical indications. We evaluated the BPE level, amount of fibroglandular tissue (FGT), and social and clinical variables. The inter-reader agreement for the amount of FGT and the BPE level was evaluated using interclass correlation coefficients. Associations between the BPE level and body mass index (BMI), ages of menarche and menopause, childbirth history, number of children, and the amount of FGT were determined using Spearman’s correlation coefficients or Mann-Whitney U-test. Pearson’s χ² test was used to assess the difference in the frequency of BPE categories among the age-groups.

Results

The inter-reader agreement was 0.864 for the amount of FGT and 0.840 for the BPE level, both indicating almost perfect agreement. The BPE level showed a weak positive correlation with the amount of FGT (Spearman’s ρ=0.271, P<0.001). BPE was not significantly correlated with BMI, childbirth history, number of births, or ages of menarche or menopause. BPE was greater in the peri-menopausal age-group compared with the corresponding pre- and post-menopausal age-groups, both with benign and malignant lesions.

Conclusions

BPE was greater in the peri-menopausal stage than in the pre- and post-menopausal stages. Our results suggest that BPE showed a non-linear decrease with age and that the hormonal disbalance in the peri-menopausal period has a greater effect on the BPE level than was previously assumed.

目的研究围绝经期乳腺 MRI 与绝经前和绝经后乳腺 MRI 的背景实质增强（BPE）水平。我们评估了BPE水平、纤维腺体组织（FGT）数量以及社会和临床变量。我们使用类间相关系数评估了阅片者之间对 FGT 量和 BPE 水平的一致性。采用 Spearman 相关系数或 Mann-Whitney U 检验法确定 BPE 水平与体重指数（BMI）、初潮和绝经年龄、生育史、子女数和 FGT 量之间的关系。结果 FGT 量和 BPE 水平的读数间一致性分别为 0.864 和 0.840，两者几乎完全一致。BPE 水平与 FGT 量呈弱正相关（Spearman's ρ=0.271，P<0.001）。BPE 与体重指数、生育史、生育次数、初潮年龄或绝经年龄无明显相关性。与绝经前和绝经后的相应年龄组相比，围绝经期年龄组的良性和恶性病变的 BPE 都更大。我们的研究结果表明，BPE 随年龄呈非线性下降，围绝经期的荷尔蒙失衡对 BPE 水平的影响比以前假设的要大。

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引用次数: 0

Deep learning based on multiparametric MRI predicts early recurrence in hepatocellular carcinoma patients with solitary tumors ≤5 cm 基于多参数磁共振成像的深度学习可预测单发肿瘤≤5 厘米的肝细胞癌患者的早期复发

IF 1.8 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Open

Pub Date : 2024-11-15 DOI: 10.1016/j.ejro.2024.100610

Tingting Mu , Xinde Zheng , Danjun Song , Jiejun Chen , Xuewang Yue , Wentao Wang , Shengxiang Rao

Purpose

To evaluate the effectiveness of a constructed deep learning model in predicting early recurrence after surgery in hepatocellular carcinoma (HCC) patients with solitary tumors ≤5 cm.

Materials and methods

Our study included a total of 331 HCC patients who underwent curative resection, with all patients having preoperative dynamic contrast-enhanced MRI (DCE-MRI). Patients who recurred within two years after surgery were defined as early recurrence. The enrolled patients were randomly divided into the training group and the testing group. A ResNet-based deep learning model with eight conventional neural network branches was built to predict the early recurrence status of these patients. Patient characteristics and laboratory tests were further filtered by regression models and then integrated with deep learning models to improve the prediction performance.

Results

Among 331 HCC patients, 70 (21.1 %) experienced early recurrence. In multivariate Cox regression analysis, only tumor size (Hazard ratio (HR=1.394, 95 %CI:1.011–1.920, p value=0.043) and deep learning extracted image features (HR: 38440, 95 %CI:2321–636600, p value<0.001) were significant risk factors for early recurrence. In the training and testing cohort, the AUCs of the image-based deep learning prediction model were 0.839 and 0.833. By integrating tumor size with image-based deep learning model to construct a combined model, we found that the AUCs of the combined model to assess early recurrence in the training and validation cohort were 0.846 and 0.842. We further developed a nomogram to visualize the preoperative combined model, and the prediction performance of nomogram showed a good fitness in the testing cohort.

Conclusions

The proposed deep learning-based prediction model using DCE-MRI is useful for assessing early recurrence in HCC patients with single tumors ≤5 cm.

目的评估构建的深度学习模型在预测单发肿瘤≤5 cm的肝细胞癌（HCC）患者术后早期复发方面的有效性。术后两年内复发的患者被定义为早期复发。入组患者被随机分为训练组和测试组。建立了一个基于 ResNet 的深度学习模型，该模型有八个传统神经网络分支，用于预测这些患者的早期复发状况。通过回归模型对患者特征和实验室检查进行进一步筛选，然后与深度学习模型整合，以提高预测性能。在多变量考克斯回归分析中，只有肿瘤大小（危险比（HR=1.394，95 %CI：1.011-1.920，p 值=0.043）和深度学习提取的图像特征（HR：38440，95 %CI：2321-636600，p 值<0.001）是早期复发的显著风险因素。在训练队列和测试队列中，基于图像的深度学习预测模型的AUC分别为0.839和0.833。通过将肿瘤大小与基于图像的深度学习模型整合在一起构建组合模型，我们发现在训练队列和验证队列中，组合模型评估早期复发的AUC分别为0.846和0.842。我们进一步开发了一个提名图来直观显示术前组合模型，在测试队列中，提名图的预测性能显示出良好的适配性。结论所提出的基于深度学习的预测模型可用于评估单个肿瘤≤5 厘米的 HCC 患者的早期复发。

{"title":"Deep learning based on multiparametric MRI predicts early recurrence in hepatocellular carcinoma patients with solitary tumors ≤5 cm","authors":"Tingting Mu , Xinde Zheng , Danjun Song , Jiejun Chen , Xuewang Yue , Wentao Wang , Shengxiang Rao","doi":"10.1016/j.ejro.2024.100610","DOIUrl":"10.1016/j.ejro.2024.100610","url":null,"abstract":"<div><h3>Purpose</h3><div>To evaluate the effectiveness of a constructed deep learning model in predicting early recurrence after surgery in hepatocellular carcinoma (HCC) patients with solitary tumors ≤5 cm.</div></div><div><h3>Materials and methods</h3><div>Our study included a total of 331 HCC patients who underwent curative resection, with all patients having preoperative dynamic contrast-enhanced MRI (DCE-MRI). Patients who recurred within two years after surgery were defined as early recurrence. The enrolled patients were randomly divided into the training group and the testing group. A ResNet-based deep learning model with eight conventional neural network branches was built to predict the early recurrence status of these patients. Patient characteristics and laboratory tests were further filtered by regression models and then integrated with deep learning models to improve the prediction performance.</div></div><div><h3>Results</h3><div>Among 331 HCC patients, 70 (21.1 %) experienced early recurrence. In multivariate Cox regression analysis, only tumor size (Hazard ratio (HR=1.394, 95 %CI:1.011–1.920, p value=0.043) and deep learning extracted image features (HR: 38440, 95 %CI:2321–636600, p value<0.001) were significant risk factors for early recurrence. In the training and testing cohort, the AUCs of the image-based deep learning prediction model were 0.839 and 0.833. By integrating tumor size with image-based deep learning model to construct a combined model, we found that the AUCs of the combined model to assess early recurrence in the training and validation cohort were 0.846 and 0.842. We further developed a nomogram to visualize the preoperative combined model, and the prediction performance of nomogram showed a good fitness in the testing cohort.</div></div><div><h3>Conclusions</h3><div>The proposed deep learning-based prediction model using DCE-MRI is useful for assessing early recurrence in HCC patients with single tumors ≤5 cm.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"13 ","pages":"Article 100610"},"PeriodicalIF":1.8,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep learning model for diagnosis of thyroid nodules with size less than 1 cm: A multicenter, retrospective study 用于诊断小于 1 厘米甲状腺结节的深度学习模型：一项多中心回顾性研究

IF 1.8 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Open

Pub Date : 2024-10-31 DOI: 10.1016/j.ejro.2024.100609

Na Feng , Shanshan Zhao , Kai Wang , Peizhe Chen , Yunpeng Wang , Yuan Gao , Zhengping Wang , Yidan Lu , Chen Chen , Jincao Yao , Zhikai Lei , Dong Xu

Objective

To develop a ultrasound images based dual-channel deep learning model to achieve accurate early diagnosis of thyroid nodules less than 1 cm.

Methods

A dual-channel deep learning model called thyroid nodule transformer network (TNT-Net) was proposed. The model has two input channels for transverse and longitudinal ultrasound images of thyroid nodules, respectively. A total of 9649 nodules from 8455 patients across five hospitals were retrospectively collected. The data were divided into a training set (8453 nodules, 7369 patients), an internal test set (565 nodules, 512 patients), and an external test set (631 nodules, 574 patients).

Results

TNT-Net achieved an area under the curve (AUC) of 0.953 (95 % confidence interval (CI): 0.934, 0.969) on the internal test set and 0.941 (95 % CI: 0.921, 0.957) on the external test set, significantly outperforming traditional deep convolutional neural network models and single-channel swin transformer model, whose AUCs ranged from 0.800 (95 % CI: 0.759, 0.837) to 0.856 (95 % CI: 0.819, 0.881). Furthermore, feature heatmap visualization showed that TNT-Net could extract richer and more energetic malignant nodule patterns.

Conclusion

The proposed TNT-Net model significantly improved the recognition capability for thyroid nodules with size less than 1 cm. This model has the potential to reduce overdiagnosis and overtreatment of such nodules, providing essential support for precise management of thyroid nodules while complementing fine-needle aspiration biopsy.

方法提出了一种名为甲状腺结节变压器网络（TNT-Net）的双通道深度学习模型。该模型有两个输入通道，分别用于甲状腺结节的横向和纵向超声图像。研究人员回顾性收集了五家医院 8455 名患者的 9649 个甲状腺结节。数据分为训练集（8453 个结节，7369 名患者）、内部测试集（565 个结节，512 名患者）和外部测试集（631 个结节，574 名患者）。结果TNT-Net在内部测试集上的曲线下面积（AUC）为0.953（95 % 置信区间（CI）：0.934，0.969），在外部测试集上的曲线下面积（AUC）为0.941（95 % 置信区间（CI）：0.921，0.957），明显优于传统的深度卷积神经网络模型和单通道swin transformer模型，后者的AUC在0.800（95 % 置信区间（CI）：0.759，0.837）到0.856（95 % 置信区间（CI）：0.819，0.881）之间。此外，特征热图可视化显示 TNT-Net 能提取出更丰富、更有活力的恶性结节模式。该模型有望减少此类结节的过度诊断和过度治疗，为甲状腺结节的精确管理提供重要支持，同时也是对细针穿刺活检的补充。

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引用次数: 0