European Journal of Radiology Open最新文献_第7页

Clinical and MRI variables associated with close or positive margins during breast-conserving surgery using MRI projection mapping in breast carcinoma with nonmass enhancement 在非肿块增强的乳腺癌保乳手术中，MRI投影成像与边缘闭合或阳性相关的临床和MRI变量

IF 2.9 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Open

Pub Date : 2025-12-01 Epub Date: 2025-09-01 DOI: 10.1016/j.ejro.2025.100681

Maki Amano , Jun Ozeki , Yumi Koyama , Xiaoyan Tang , Fumi Nozaki , Mayumi Tani , Yasuo Amano

Purpose

To evaluate the utility of a magnetic resonance imaging (MRI) projection mapping system (PMS) for determining the resection lines during breast-conserving surgery (BCS) in patients with breast cancer presenting with nonmass enhancement (NME) and identify the clinical or MRI variables associated with close or positive margins.

Materials and methods

Forty-one patients with breast cancer exhibiting NME were enrolled. In the operating room, a maximum intensity projection image generated from supine MRI was projected onto the breast using a PMS, which employed a structured light method to measure the surface of the breast. Cancer contours delineated on the MRI-PMS, with an additional safety margin, served as the resection lines for cylindrical BCS. Margins were pathologically categorized as negative (> 2 mm), close (≤ 2 mm), or positive. The association between margin status and clinical or MRI variables was analyzed.

Results

Surgical margins were negative in 24 patients (58.5 %), close in 15 (36.6 %), and positive in 2 (4.9 %). There were significant differences in the maximum diameter of nonmass components (NMCs) shown by pathology, that of NME on MRI, and the discrepancy between the two diameters between patients with negative margin and those with close or positive margin (< 0.05 for all). Receiver operating characteristics revealed that threshold of 40 mm for NMEs provided high specificity of 91.7 %.

Conclusion

The MRI-PMS led to a low rate of positive margins during BCS in patients with breast cancer with NMEs. Large NMCs and NMEs are associated with positive or close margin.

目的评估磁共振成像（MRI）投影成像系统（PMS）在乳腺癌保乳手术（BCS）期间确定非肿块增强（NME）患者切除线的效用，并确定与边缘闭合或阳性相关的临床或MRI变量。材料与方法入选41例表现为NME的乳腺癌患者。在手术室中，使用PMS将仰卧位MRI产生的最大强度投影图像投影到乳房上，PMS采用结构光法测量乳房表面。在MRI-PMS上划定的肿瘤轮廓，具有额外的安全裕度，作为圆柱形BCS的切除线。切缘病理分类为阴性（≤2 mm）、接近（≤2 mm）或阳性。分析了切缘状态与临床或MRI变量之间的关系。结果手术切缘阴性24例（58.5% %），闭合15例（36.6 %），阳性2例（4.9 %）。病理显示的非肿块成分（NMCs）最大直径与MRI显示的NME最大直径、切缘阴性患者与切缘相近或阳性患者的最大直径差异均有统计学意义（均为0.05）。接受者工作特征显示，NMEs的阈值为40 mm，特异性为91.7 %。结论MRI-PMS可导致合并NMEs的乳腺癌患者BCS阳性切缘率低。大型nmc和NMEs与正边际或近边际相关。

{"title":"Clinical and MRI variables associated with close or positive margins during breast-conserving surgery using MRI projection mapping in breast carcinoma with nonmass enhancement","authors":"Maki Amano , Jun Ozeki , Yumi Koyama , Xiaoyan Tang , Fumi Nozaki , Mayumi Tani , Yasuo Amano","doi":"10.1016/j.ejro.2025.100681","DOIUrl":"10.1016/j.ejro.2025.100681","url":null,"abstract":"<div><h3>Purpose</h3><div>To evaluate the utility of a magnetic resonance imaging (MRI) projection mapping system (PMS) for determining the resection lines during breast-conserving surgery (BCS) in patients with breast cancer presenting with nonmass enhancement (NME) and identify the clinical or MRI variables associated with close or positive margins.</div></div><div><h3>Materials and methods</h3><div>Forty-one patients with breast cancer exhibiting NME were enrolled. In the operating room, a maximum intensity projection image generated from supine MRI was projected onto the breast using a PMS, which employed a structured light method to measure the surface of the breast. Cancer contours delineated on the MRI-PMS, with an additional safety margin, served as the resection lines for cylindrical BCS. Margins were pathologically categorized as negative (> 2 mm), close (≤ 2 mm), or positive. The association between margin status and clinical or MRI variables was analyzed.</div></div><div><h3>Results</h3><div>Surgical margins were negative in 24 patients (58.5 %), close in 15 (36.6 %), and positive in 2 (4.9 %). There were significant differences in the maximum diameter of nonmass components (NMCs) shown by pathology, that of NME on MRI, and the discrepancy between the two diameters between patients with negative margin and those with close or positive margin (< 0.05 for all). Receiver operating characteristics revealed that threshold of 40 mm for NMEs provided high specificity of 91.7 %.</div></div><div><h3>Conclusion</h3><div>The MRI-PMS led to a low rate of positive margins during BCS in patients with breast cancer with NMEs. Large NMCs and NMEs are associated with positive or close margin.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"15 ","pages":"Article 100681"},"PeriodicalIF":2.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144922064","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

Weight-bearing MRI of the cervical spine: A scoping review of clinical utility and emerging applications 颈椎负重MRI：临床应用和新兴应用的范围综述

IF 2.9 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Open

Pub Date : 2025-12-01 Epub Date: 2025-10-08 DOI: 10.1016/j.ejro.2025.100694

Jonathan Verderame , Muhammad Shakib Arslan , Farhan Mukhtar , Zaheer Abbas

Objective

Weight-bearing magnetic resonance imaging enables assessment of the cervical spine and craniocervical junction under physiological load, potentially revealing pathology that is occult on conventional supine imaging. This scoping review synthesizes current evidence, maps clinical and emerging applications, and identifies key gaps requiring further investigation.

Methods

A structured search was conducted in PubMed, Scopus, Web of Science, Google Scholar, and Semantic Scholar (July 2025). Eligible studies were reviewed for diagnostic utility, technical considerations, clinical indications, and outcomes. Methodological quality was appraised descriptively in line with Joanna Briggs Institute guidance.

Results

Nine studies, published between 2008 and 2025, met inclusion criteria. Upright and dynamic MRI detected posture-dependent changes including spinal canal narrowing, cord compression, foraminal stenosis, ligamentous buckling, cerebellar tonsillar descent, altered sagittal alignment, and CSF flow differences. Findings were more pronounced in flexion extension and upright postures compared with supine imaging. Normative studies established reference metrics for CCJ motion and prevertebral soft tissue width. Preliminary evidence also highlights applications in connective tissue disorders, Chiari malformation, and upper cervical chiropractic practice, although most studies were feasibility reports with small sample sizes and heterogeneous protocols.

Conclusion

Emerging evidence suggests that WBMRI provides added diagnostic value in selected cervical spine and CCJ conditions by revealing dynamic or load-sensitive pathology not captured on standard supine imaging. While current evidence remains preliminary, standardized protocols, higher-field technologies, and large multicenter outcome-based studies are essential to validate diagnostic thresholds, improve reproducibility, and define the role of WBMRI in routine clinical care.

目的负重磁共振成像能够评估生理负荷下的颈椎和颅颈交界处，潜在地揭示传统仰卧位成像所隐藏的病理。这一范围审查综合了目前的证据，绘制了临床和新兴应用地图，并确定了需要进一步调查的关键差距。方法在PubMed、Scopus、Web of Science、b谷歌Scholar、Semantic Scholar（2025年7月）中进行结构化检索。对符合条件的研究进行了诊断效用、技术考虑、临床适应症和结果的审查。方法质量按照乔安娜布里格斯研究所的指导进行描述性评价。结果2008年至2025年间发表的9项研究符合纳入标准。直立和动态MRI检测到姿势依赖性变化，包括椎管狭窄、脊髓压迫、椎间孔狭窄、韧带屈曲、小脑扁桃体下降、矢状面排列改变和脑脊液流量差异。与仰卧位相比，屈伸位和直立位的影像学表现更为明显。规范研究建立了CCJ运动和椎前软组织宽度的参考指标。初步证据也强调了结缔组织疾病、Chiari畸形和上颈椎捏脊术的应用，尽管大多数研究都是小样本量和异质方案的可行性报告。结论：越来越多的证据表明，WBMRI通过揭示标准仰卧位成像未捕获的动态或负荷敏感病理，为选定的颈椎和CCJ疾病提供了额外的诊断价值。虽然目前的证据仍然是初步的，但标准化的方案、更高领域的技术和基于结果的大型多中心研究对于验证诊断阈值、提高可重复性和确定WBMRI在常规临床护理中的作用至关重要。

{"title":"Weight-bearing MRI of the cervical spine: A scoping review of clinical utility and emerging applications","authors":"Jonathan Verderame , Muhammad Shakib Arslan , Farhan Mukhtar , Zaheer Abbas","doi":"10.1016/j.ejro.2025.100694","DOIUrl":"10.1016/j.ejro.2025.100694","url":null,"abstract":"<div><h3>Objective</h3><div>Weight-bearing magnetic resonance imaging enables assessment of the cervical spine and craniocervical junction under physiological load, potentially revealing pathology that is occult on conventional supine imaging. This scoping review synthesizes current evidence, maps clinical and emerging applications, and identifies key gaps requiring further investigation.</div></div><div><h3>Methods</h3><div>A structured search was conducted in PubMed, Scopus, Web of Science, Google Scholar, and Semantic Scholar (July 2025). Eligible studies were reviewed for diagnostic utility, technical considerations, clinical indications, and outcomes. Methodological quality was appraised descriptively in line with Joanna Briggs Institute guidance.</div></div><div><h3>Results</h3><div>Nine studies, published between 2008 and 2025, met inclusion criteria. Upright and dynamic MRI detected posture-dependent changes including spinal canal narrowing, cord compression, foraminal stenosis, ligamentous buckling, cerebellar tonsillar descent, altered sagittal alignment, and CSF flow differences. Findings were more pronounced in flexion extension and upright postures compared with supine imaging. Normative studies established reference metrics for CCJ motion and prevertebral soft tissue width. Preliminary evidence also highlights applications in connective tissue disorders, Chiari malformation, and upper cervical chiropractic practice, although most studies were feasibility reports with small sample sizes and heterogeneous protocols.</div></div><div><h3>Conclusion</h3><div>Emerging evidence suggests that WBMRI provides added diagnostic value in selected cervical spine and CCJ conditions by revealing dynamic or load-sensitive pathology not captured on standard supine imaging. While current evidence remains preliminary, standardized protocols, higher-field technologies, and large multicenter outcome-based studies are essential to validate diagnostic thresholds, improve reproducibility, and define the role of WBMRI in routine clinical care.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"15 ","pages":"Article 100694"},"PeriodicalIF":2.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145264883","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

Diagnostic performance of dual-layer spectral CT Radiomics and deep learning for differentiating osteoblastic bone metastases from bone islands 双层光谱CT放射组学和深度学习鉴别成骨细胞骨转移和骨岛的诊断价值

IF 2.9 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Open

Pub Date : 2025-12-01 Epub Date: 2025-08-20 DOI: 10.1016/j.ejro.2025.100679

Yuchao Xiong , Wei Guo , Xuwen Zeng , Fan Xu , Li Wu , Jiahui Ou

Background

This study aimed to compare the diagnostic performance of radiomic features derived from dual-layer spectral detector computed tomography (DLSCT) and a deep learning (DL) model applied to conventional CT images in the differentiation of osteoblastic bone metastases (OBM) from bone islands (BI).

Methods

This retrospective study included patients with osteogenic lesions who underwent DLSCT examinations between March 2023 and September 2023. We extracted first-order radiomic features (e.g., mean, maximum, entropy) from both conventional and spectral images. A previously validated DL model was applied to the conventional CT images. We evaluated diagnostic performance using ROC curve analysis, comparing AUC, sensitivity, and specificity.

Results

The study included 216 lesions from 94 patients (66 ± 12 years; 48 males, 46 females): 125 BI and 91 OBM lesions. Significant differences were observed between OBM and BI groups for the mean, maximum, entropy, and uniformity of first-order radiomic features (all P < 0.05). DLSCT (entropy from VMI40keV) and the DL model had comparable AUCs (0.93 vs. 0.96; P = 0.274). However, DLSCT showed superior sensitivity (92 % vs. 62 %; P = 0.002) but comparable specificity (88 % vs. 96 %; P = 0.07) for diagnosing OBM compared to the DL model.

Conclusion

Radiomic features from DLSCT differentiate between BI and OBM with diagnostic performance comparable to that of a DL model. Furthermore, VMI40keV image-derived entropy demonstrated superior sensitivity in diagnosing OBM compared to the DL model.

本研究旨在比较双层光谱检测器计算机断层扫描（DLSCT）和应用于传统CT图像的深度学习（DL）模型的放射学特征在区分成骨细胞骨转移（OBM）和骨岛（BI）中的诊断性能。方法本回顾性研究纳入了2023年3月至2023年9月期间接受DLSCT检查的成骨病变患者。我们从常规图像和光谱图像中提取一阶放射特征（例如，平均值，最大值，熵）。将先前验证的DL模型应用于常规CT图像。我们使用ROC曲线分析评估诊断效果，比较AUC、敏感性和特异性。结果共纳入94例患者的216个病变（66例 ± ，12岁，男48例，女46例）：BI 125个，OBM 91个。在一阶放射学特征的平均值、最大值、熵和均匀性方面，OBM组和BI组之间存在显著差异（P均为 <； 0.05）。DLSCT（来自VMI40keV的熵）和DL模型具有可比的auc （0.93 vs. 0.96; P = 0.274）。然而，与DL模型相比，DLSCT在诊断OBM方面表现出更高的灵敏度（92 %对62 %；P = 0.002）和相当的特异性（88 %对96 %；P = 0.07）。结论DLSCT的放射学特征可以区分BI和OBM，其诊断性能与DL模型相当。此外，与DL模型相比，VMI40keV图像衍生熵在诊断OBM方面表现出更高的灵敏度。

{"title":"Diagnostic performance of dual-layer spectral CT Radiomics and deep learning for differentiating osteoblastic bone metastases from bone islands","authors":"Yuchao Xiong , Wei Guo , Xuwen Zeng , Fan Xu , Li Wu , Jiahui Ou","doi":"10.1016/j.ejro.2025.100679","DOIUrl":"10.1016/j.ejro.2025.100679","url":null,"abstract":"<div><h3>Background</h3><div>This study aimed to compare the diagnostic performance of radiomic features derived from dual-layer spectral detector computed tomography (DLSCT) and a deep learning (DL) model applied to conventional CT images in the differentiation of osteoblastic bone metastases (OBM) from bone islands (BI).</div></div><div><h3>Methods</h3><div>This retrospective study included patients with osteogenic lesions who underwent DLSCT examinations between March 2023 and September 2023. We extracted first-order radiomic features (e.g., mean, maximum, entropy) from both conventional and spectral images. A previously validated DL model was applied to the conventional CT images. We evaluated diagnostic performance using ROC curve analysis, comparing AUC, sensitivity, and specificity.</div></div><div><h3>Results</h3><div>The study included 216 lesions from 94 patients (66 ± 12 years; 48 males, 46 females): 125 BI and 91 OBM lesions. Significant differences were observed between OBM and BI groups for the mean, maximum, entropy, and uniformity of first-order radiomic features (all P < 0.05). DLSCT (entropy from VMI40keV) and the DL model had comparable AUCs (0.93 vs. 0.96; P = 0.274). However, DLSCT showed superior sensitivity (92 % vs. 62 %; P = 0.002) but comparable specificity (88 % vs. 96 %; P = 0.07) for diagnosing OBM compared to the DL model.</div></div><div><h3>Conclusion</h3><div>Radiomic features from DLSCT differentiate between BI and OBM with diagnostic performance comparable to that of a DL model. Furthermore, VMI40keV image-derived entropy demonstrated superior sensitivity in diagnosing OBM compared to the DL model.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"15 ","pages":"Article 100679"},"PeriodicalIF":2.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144879230","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

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 : 2025-12-01 Epub 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。

{"title":"Development of a hybrid 2.5D deep learning model for glioma survival prediction using T1-weighted MRI from the CGGA database","authors":"Kai Jin, Caixing Sun, Liang Xia","doi":"10.1016/j.ejro.2025.100697","DOIUrl":"10.1016/j.ejro.2025.100697","url":null,"abstract":"<div><h3>Background</h3><div>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.</div></div><div><h3>Methods</h3><div>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.</div></div><div><h3>Results</h3><div>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).</div></div><div><h3>Conclusions</h3><div>The 2.5D multi-source framework provides a clinically feasible, non-invasive tool for preoperative survival prediction, enabling personalized therapeutic strategies for glioma patients.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"15 ","pages":"Article 100697"},"PeriodicalIF":2.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145415984","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

Differences in myocardial involvement between new onset and longstanding systemic lupus erythematosus patients assessed by cardiovascular magnetic resonance 心血管磁共振评估新发和长期系统性红斑狼疮患者心肌受累的差异。

IF 1.8 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Open

Pub Date : 2025-06-01 Epub Date: 2024-12-20 DOI: 10.1016/j.ejro.2024.100623

Zhen Wang , Xing Tang , Chaohui Hang , Hui Gao , Jinxiu Yang , Yuchi Han , Yongqiang Yu , Zongwen Shuai , Ren Zhao , Xiaohu Li

Objectives

Subclinical myocardial involvement is common in systemic lupus erythematosus (SLE), but differences between new onset and longstanding SLE are not fully elucidated. This study compared myocardial involvement in new onset versus longstanding SLE using cardiovascular magnetic resonance (CMR).

Materials and methods

We prospectively enrolled 24 drug-naïve new onset SLE patients, 27 longstanding SLE patients, and 20 healthy controls. All participants underwent clinical evaluation and CMR examination. We analyzed left ventricular (LV) morphological, functional parameters, and tissue characterization parameters: native T1, T2, extracellular volume fraction (ECV), and late gadolinium enhancement (LGE).

Results

Both new onset and longstanding SLE groups showed elevated native T1, T2, and ECV values compared to the control group (all P < 0.05). Additionally, the new onset SLE group exhibited higher T2 values compared to the longstanding SLE group [55.3 vs. 52.8 ms, P < 0.05]. The new onset group also demonstrated higher left ventricular (LV) end-diastolic volume index (LVEDVi), LV end-systolic volume index (LVSVi), and LV mass index (LVMi) than controls (all P < 0.05), with LVEDVi significantly higher than in the longstanding group (P < 0.05).

Conclusion

CMR tissue characterization imaging can detect early myocardial involvement in patients with new onset and longstanding SLE. Patients with new onset SLE exhibit more pronounced myocardial edema than those with longstanding SLE. This suggests that SLE patients are at risk of myocardial damage at various stages of the disease, underscoring the need for early monitoring and long-term management to prevent the progression of myocardial remodeling.

目的：亚临床心肌受累在系统性红斑狼疮（SLE）中很常见，但新发和长期SLE之间的差异尚未完全阐明。这项研究使用心血管磁共振（CMR）比较了新发和长期SLE的心肌受累情况。材料和方法：我们前瞻性地招募了24例drug-naïve新发SLE患者，27例长期SLE患者和20例健康对照。所有参与者均进行了临床评估和CMR检查。我们分析了左心室（LV）形态学、功能参数和组织表征参数：原生T1、T2、细胞外体积分数（ECV）和晚期钆增强（LGE）。结果：与对照组相比，新发和长期SLE组的T1、T2和ECV值均升高（P均为 ）。结论：CMR组织表征成像可以检测新发和长期SLE患者的早期心肌受累。新发SLE患者比长期SLE患者表现出更明显的心肌水肿。这表明SLE患者在疾病的各个阶段都有心肌损伤的风险，强调早期监测和长期管理的必要性，以防止心肌重构的进展。

{"title":"Differences in myocardial involvement between new onset and longstanding systemic lupus erythematosus patients assessed by cardiovascular magnetic resonance","authors":"Zhen Wang , Xing Tang , Chaohui Hang , Hui Gao , Jinxiu Yang , Yuchi Han , Yongqiang Yu , Zongwen Shuai , Ren Zhao , Xiaohu Li","doi":"10.1016/j.ejro.2024.100623","DOIUrl":"10.1016/j.ejro.2024.100623","url":null,"abstract":"<div><h3>Objectives</h3><div>Subclinical myocardial involvement is common in systemic lupus erythematosus (SLE), but differences between new onset and longstanding SLE are not fully elucidated. This study compared myocardial involvement in new onset versus longstanding SLE using cardiovascular magnetic resonance (CMR).</div></div><div><h3>Materials and methods</h3><div>We prospectively enrolled 24 drug-naïve new onset SLE patients, 27 longstanding SLE patients, and 20 healthy controls. All participants underwent clinical evaluation and CMR examination. We analyzed left ventricular (LV) morphological, functional parameters, and tissue characterization parameters: native T1, T2, extracellular volume fraction (ECV), and late gadolinium enhancement (LGE).</div></div><div><h3>Results</h3><div>Both new onset and longstanding SLE groups showed elevated native T1, T2, and ECV values compared to the control group (all P < 0.05). Additionally, the new onset SLE group exhibited higher T2 values compared to the longstanding SLE group [55.3 vs. 52.8 ms, P < 0.05]. The new onset group also demonstrated higher left ventricular (LV) end-diastolic volume index (LVEDVi), LV end-systolic volume index (LVSVi), and LV mass index (LVMi) than controls (all P < 0.05), with LVEDVi significantly higher than in the longstanding group (P < 0.05).</div></div><div><h3>Conclusion</h3><div>CMR tissue characterization imaging can detect early myocardial involvement in patients with new onset and longstanding SLE. Patients with new onset SLE exhibit more pronounced myocardial edema than those with longstanding SLE. This suggests that SLE patients are at risk of myocardial damage at various stages of the disease, underscoring the need for early monitoring and long-term management to prevent the progression of myocardial remodeling.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"14 ","pages":"Article 100623"},"PeriodicalIF":1.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11731982/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142985000","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

Evaluation of large language models in generating pulmonary nodule follow-up recommendations 大语言模型在生成肺结节随访建议中的评价

IF 1.8 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Open

Pub Date : 2025-06-01 Epub Date: 2025-04-30 DOI: 10.1016/j.ejro.2025.100655

Junzhe Wen , Wanyue Huang , Huzheng Yan , Jie Sun , Mengshi Dong , Chao Li , Jie Qin

Rationale and objectives

To evaluate the performance of large language models (LLMs) in generating clinically follow-up recommendations for pulmonary nodules by leveraging radiological report findings and management guidelines.

Materials and methods

This retrospective study included CT follow-up reports of pulmonary nodules documented by senior radiologists from September 1st, 2023, to April 30th, 2024. Sixty reports were collected for prompting engineering additionally, based on few-shot learning and the Chain of Thought methodology. Radiological findings of pulmonary nodules, along with finally prompt, were input into GPT-4o-mini or ERNIE-4.0-Turbo-8K to generate follow-up recommendations. The AI-generated recommendations were evaluated against radiologist-defined guideline-based standards through binary classification, assessing nodule risk classifications, follow-up intervals, and harmfulness. Performance metrics included sensitivity, specificity, positive/negative predictive values, and F1 score.

Results

On 1009 reports from 996 patients (median age, 50.0 years, IQR, 39.0–60.0 years; 511 male patients), ERNIE-4.0-Turbo-8K and GPT-4o-mini demonstrated comparable performance in both accuracy of follow-up recommendations (94.6 % vs 92.8 %, P = 0.07) and harmfulness rates (2.9 % vs 3.5 %, P = 0.48). In nodules classification, ERNIE-4.0-Turbo-8K and GPT-4o-mini performed similarly with accuracy rates of 99.8 % vs 99.9 % sensitivity of 96.9 % vs 100.0 %, specificity of 99.9 % vs 99.9 %, positive predictive value of 96.9 % vs 96.9 %, negative predictive value of 100.0 % vs 99.9 %, f1-score of 96.9 % vs 98.4 %, respectively.

Conclusion

LLMs show promise in providing guideline-based follow-up recommendations for pulmonary nodules, but require rigorous validation and supervision to mitigate potential clinical risks. This study offers insights into their potential role in automated radiological decision support.

依据放射学报告结果和管理指南，评估大语言模型（LLMs）在生成肺结节临床随访建议方面的表现。材料与方法本回顾性研究纳入2023年9月1日至2024年4月30日资深放射科医师记录的肺结节CT随访报告。采用少弹学习和思维链方法，收集了60份报告，并进行了额外的工程提示。肺结节的影像学表现，以及最终提示，输入gpt - 40 -mini或ERNIE-4.0-Turbo-8K，以产生随访建议。通过二元分类、评估结节风险分类、随访间隔和危害，根据放射科医生定义的基于指南的标准对人工智能生成的建议进行评估。性能指标包括敏感性、特异性、阳性/阴性预测值和F1评分。结果996例患者报告1009份(中位年龄50.0岁，IQR 39.0 ~ 60.0岁；511例男性患者)、erie -4.0- turbo - 8k和gpt - 40 -mini在随访建议的准确性（94.6 % vs 92.8 %,P = 0.07）和有害率（2.9 % vs 3.5 %,P = 0.48）方面表现相当。结节的分类、厄尼- 4.0 -涡轮- 8 - k和GPT-4o-mini执行同样的准确率为99.8 vs 99.9  % % 96.9 vs 100.0  % %的敏感性,特异性99.9 vs 99.9  % %,阳性预测值96.9 vs 96.9  % %,负面预测值100.0 vs 99.9  % %,f1-score 96.9 vs 98.4  % %,分别。结论llm有望为肺结节提供基于指南的随访建议，但需要严格的验证和监督以降低潜在的临床风险。这项研究为它们在自动化放射决策支持中的潜在作用提供了见解。

{"title":"Evaluation of large language models in generating pulmonary nodule follow-up recommendations","authors":"Junzhe Wen , Wanyue Huang , Huzheng Yan , Jie Sun , Mengshi Dong , Chao Li , Jie Qin","doi":"10.1016/j.ejro.2025.100655","DOIUrl":"10.1016/j.ejro.2025.100655","url":null,"abstract":"<div><h3>Rationale and objectives</h3><div>To evaluate the performance of large language models (LLMs) in generating clinically follow-up recommendations for pulmonary nodules by leveraging radiological report findings and management guidelines.</div></div><div><h3>Materials and methods</h3><div>This retrospective study included CT follow-up reports of pulmonary nodules documented by senior radiologists from September 1st, 2023, to April 30th, 2024. Sixty reports were collected for prompting engineering additionally, based on few-shot learning and the Chain of Thought methodology. Radiological findings of pulmonary nodules, along with finally prompt, were input into GPT-4o-mini or ERNIE-4.0-Turbo-8K to generate follow-up recommendations. The AI-generated recommendations were evaluated against radiologist-defined guideline-based standards through binary classification, assessing nodule risk classifications, follow-up intervals, and harmfulness. Performance metrics included sensitivity, specificity, positive/negative predictive values, and F1 score.</div></div><div><h3>Results</h3><div>On 1009 reports from 996 patients (median age, 50.0 years, IQR, 39.0–60.0 years; 511 male patients), ERNIE-4.0-Turbo-8K and GPT-4o-mini demonstrated comparable performance in both accuracy of follow-up recommendations (94.6 % vs 92.8 %, P = 0.07) and harmfulness rates (2.9 % vs 3.5 %, P = 0.48). In nodules classification, ERNIE-4.0-Turbo-8K and GPT-4o-mini performed similarly with accuracy rates of 99.8 % vs 99.9 % sensitivity of 96.9 % vs 100.0 %, specificity of 99.9 % vs 99.9 %, positive predictive value of 96.9 % vs 96.9 %, negative predictive value of 100.0 % vs 99.9 %, f1-score of 96.9 % vs 98.4 %, respectively.</div></div><div><h3>Conclusion</h3><div>LLMs show promise in providing guideline-based follow-up recommendations for pulmonary nodules, but require rigorous validation and supervision to mitigate potential clinical risks. This study offers insights into their potential role in automated radiological decision support.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"14 ","pages":"Article 100655"},"PeriodicalIF":1.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143886442","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

CT-based intratumoral and peritumoral radiomics nomogram to predict spread through air spaces in lung adenocarcinoma with diameter ≤ 3 cm: A multicenter study 基于ct的瘤内和瘤周放射组学图预测直径≤ 3 cm的肺腺癌通过空气间隙扩散：一项多中心研究。

IF 1.8 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Open

Pub Date : 2025-06-01 Epub Date: 2025-01-02 DOI: 10.1016/j.ejro.2024.100630

Yangfan Su , Junli Tao , Xiaosong Lan , Changyu Liang , Xuemei Huang , Jiuquan Zhang , Kai Li , Lihua Chen

Purpose

The aim of this study was to explore and develop a preoperative and noninvasive model for predicting spread through air spaces (STAS) status in lung adenocarcinoma (LUAD) with diameter ≤ 3 cm.

Methods

This multicenter retrospective study included 640 LUAD patients. Center I included 525 patients (368 in the training cohort and 157 in the validation cohort); center II included 115 patients (the test cohort). We extracted radiomics features from the intratumor, extended tumor and peritumor regions. Multivariate logistic regression and boruta algorithm were used to select clinical independent risk factors and radiomics features, respectively. We developed a clinical model and four radiomics models (the intratumor model, extended tumor model, peritumor model and fusion model). A nomogram based on prediction probability value of the optimal radiomics model and clinical independent risk factors was developed to predict STAS status.

Results

Maximum diameter and nodule type were clinical independent risk factors. The extended tumor model achieved satisfactory STAS status discrimination performance with the AUC of 0.74, 0.71 and 0.80 in the three cohorts, respectively, performed better than other radiomics models. The integrated discrimination improvement value revealed that the nomogram outperformed compared to the clinical model with the value of 12 %. Patients with high nomogram score (≥ 77.31) will be identified as STAS-positive.

Conclusions

Peritumoral information is significant to predict STAS status. The nomogram based on the extended tumor model and clinical independent risk factors provided good preoperative prediction of STAS status in LUAD with diameter ≤ 3 cm, aiding surgical decision-making.

目的：本研究的目的是探索和建立一种预测直径≤ 3 cm的肺腺癌（LUAD）通过空气间隙扩散（STAS）状态的术前无创模型。方法：对640例LUAD患者进行多中心回顾性研究。中心1纳入525例患者（368例在培训队列，157例在验证队列）；第二中心纳入115例患者（试验队列）。我们从肿瘤内、肿瘤扩展和肿瘤周围区域提取放射组学特征。采用多变量logistic回归和boruta算法分别选择临床独立危险因素和放射组学特征。我们建立了一个临床模型和四个放射组学模型（肿瘤内模型、扩展肿瘤模型、肿瘤周围模型和融合模型）。建立基于最佳放射组学模型预测概率值和临床独立危险因素的nomogram预测STAS状态。结果：最大直径和结节类型是临床独立的危险因素。扩展肿瘤模型获得了令人满意的STAS状态识别性能，三个队列的AUC分别为0.74、0.71和0.80，优于其他放射组学模型。综合判别改进值显示nomogram优于临床模型，其值为12 %。nomogram评分高（≥77.31）的患者为stas阳性。结论：肿瘤周围信息对预测STAS状态有重要意义。基于扩展肿瘤模型和临床独立危险因素的nomogram术前预测直径≤ 3 cm的LUAD的STAS状态，有助于手术决策。

{"title":"CT-based intratumoral and peritumoral radiomics nomogram to predict spread through air spaces in lung adenocarcinoma with diameter ≤ 3 cm: A multicenter study","authors":"Yangfan Su , Junli Tao , Xiaosong Lan , Changyu Liang , Xuemei Huang , Jiuquan Zhang , Kai Li , Lihua Chen","doi":"10.1016/j.ejro.2024.100630","DOIUrl":"10.1016/j.ejro.2024.100630","url":null,"abstract":"<div><h3>Purpose</h3><div>The aim of this study was to explore and develop a preoperative and noninvasive model for predicting spread through air spaces (STAS) status in lung adenocarcinoma (LUAD) with diameter ≤ 3 cm.</div></div><div><h3>Methods</h3><div>This multicenter retrospective study included 640 LUAD patients. Center I included 525 patients (368 in the training cohort and 157 in the validation cohort); center II included 115 patients (the test cohort). We extracted radiomics features from the intratumor, extended tumor and peritumor regions. Multivariate logistic regression and boruta algorithm were used to select clinical independent risk factors and radiomics features, respectively. We developed a clinical model and four radiomics models (the intratumor model, extended tumor model, peritumor model and fusion model). A nomogram based on prediction probability value of the optimal radiomics model and clinical independent risk factors was developed to predict STAS status.</div></div><div><h3>Results</h3><div>Maximum diameter and nodule type were clinical independent risk factors. The extended tumor model achieved satisfactory STAS status discrimination performance with the AUC of 0.74, 0.71 and 0.80 in the three cohorts, respectively, performed better than other radiomics models. The integrated discrimination improvement value revealed that the nomogram outperformed compared to the clinical model with the value of 12 %. Patients with high nomogram score (≥ 77.31) will be identified as STAS-positive.</div></div><div><h3>Conclusions</h3><div>Peritumoral information is significant to predict STAS status. The nomogram based on the extended tumor model and clinical independent risk factors provided good preoperative prediction of STAS status in LUAD with diameter ≤ 3 cm, aiding surgical decision-making.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"14 ","pages":"Article 100630"},"PeriodicalIF":1.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11754163/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143029858","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

Low KV-low contrast medium dose one-stop dual source CT high pitch integrated coronary-carotid-cerebral-aortic CTA improves image quality and reduces both radiation and contrast medium doses 低kv -低造影剂剂量一站式双源CT高音高一体化冠-颈-脑-主动脉CTA提高图像质量，降低辐射和造影剂剂量

IF 1.8 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Open

Pub Date : 2025-06-01 Epub Date: 2025-01-29 DOI: 10.1016/j.ejro.2025.100637

Meng Wang, Chao Zheng, Lin Yang, Juan Su, Bo Wang, JieXin Sheng

Rationale and objectives

To evaluate the impact of low dose-low contrast medium injection protocol on radiation dose and image quality of one-stop dual source high pitch integrated coronary -carotid -cerebral- aortic CTA.

Methods

A total of 211 non-obese patients, consisting of 44 females with an average age of 59 years, were split into two groups: low dose group (LD group n = 136) and routine dose group (RD group n = 75). Noise, attenuation, signal to noise ratio (SNR), and contrast to noise ratio (CNR) were compared between the groups by setting region of interest (ROI) in target vessels to evaluate objective image quality. Two radiologists assessed subjective image quality using the 5-point scale. volumetric CT dose index (CTDIvol), dose length production (DLP), and effective radiation dose (ED) were compared, while contrast medium (CM) dose was assessed by CM volume and iodine uptake (IU).

Results

Both radiation and CM dose were significantly reduced in the LD group compared with the RD group, DLP and ED were reduced by 51 %, and CM volume and IU were reduced by 13 % (all p < 0.05). Attenuation and noise were higher, while SNR and CNR were close to or slightly higher in the LD group compared with the RD group. The LD group had higher subjective image quality scores while all scores in the two groups satisfied the diagnostic requirements.

Conclusion

Low-kV, low-CM one-stop dual-source high-pitch integrated coronary-carotid-cerebral-aortic CTA can ensure image quality while significantly reducing the doses of contrast medium and radiation

目的探讨低剂量-低造影剂注射方案对一站式双源高音高综合冠状动脉-颈动脉-脑主动脉CTA放射剂量和图像质量的影响。方法211例非肥胖患者，女性44例，平均年龄59岁，随机分为低剂量组（LD组n = 136）和常规剂量组（RD组n = 75）。通过设定目标血管的感兴趣区域（ROI），比较各组间的噪声、衰减、信噪比（SNR）和噪比（CNR），评价客观图像质量。两名放射科医生使用5分制评估主观图像质量。对比体积CT剂量指数（CTDIvol）、剂量长度产生（DLP）和有效辐射剂量（ED），对比造影剂（CM）剂量通过CM体积和碘摄取（IU）评估。结果与RD组相比，LD组放疗和CM剂量均显著降低，DLP和ED减少51 %，CM体积和IU减少13 % （p均为 <； 0.05）。与RD组相比，LD组的衰减和噪声更高，而信噪比和CNR接近或略高。LD组主观影像质量评分较高，两组均满足诊断要求。结论低kv、低cm一站式双源高音高一体化冠-颈-脑-主动脉CTA在保证图像质量的同时，可显著降低造影剂剂量和辐射剂量

{"title":"Low KV-low contrast medium dose one-stop dual source CT high pitch integrated coronary-carotid-cerebral-aortic CTA improves image quality and reduces both radiation and contrast medium doses","authors":"Meng Wang, Chao Zheng, Lin Yang, Juan Su, Bo Wang, JieXin Sheng","doi":"10.1016/j.ejro.2025.100637","DOIUrl":"10.1016/j.ejro.2025.100637","url":null,"abstract":"<div><h3>Rationale and objectives</h3><div>To evaluate the impact of low dose-low contrast medium injection protocol on radiation dose and image quality of one-stop dual source high pitch integrated coronary -carotid -cerebral- aortic CTA.</div></div><div><h3>Methods</h3><div>A total of 211 non-obese patients, consisting of 44 females with an average age of 59 years, were split into two groups: low dose group (LD group n = 136) and routine dose group (RD group n = 75). Noise, attenuation, signal to noise ratio (SNR), and contrast to noise ratio (CNR) were compared between the groups by setting region of interest (ROI) in target vessels to evaluate objective image quality. Two radiologists assessed subjective image quality using the 5-point scale. volumetric CT dose index (CTDIvol), dose length production (DLP), and effective radiation dose (ED) were compared, while contrast medium (CM) dose was assessed by CM volume and iodine uptake (IU).</div></div><div><h3>Results</h3><div>Both radiation and CM dose were significantly reduced in the LD group compared with the RD group, DLP and ED were reduced by 51 %, and CM volume and IU were reduced by 13 % (all p < 0.05). Attenuation and noise were higher, while SNR and CNR were close to or slightly higher in the LD group compared with the RD group. The LD group had higher subjective image quality scores while all scores in the two groups satisfied the diagnostic requirements.</div></div><div><h3>Conclusion</h3><div>Low-kV, low-CM one-stop dual-source high-pitch integrated coronary-carotid-cerebral-aortic CTA can ensure image quality while significantly reducing the doses of contrast medium and radiation</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"14 ","pages":"Article 100637"},"PeriodicalIF":1.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143101133","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

Prediction of lymph node metastasis in papillary thyroid carcinoma using non-contrast CT-based radiomics and deep learning with thyroid lobe segmentation: A dual-center study 基于非对比ct的放射组学和甲状腺叶分割的深度学习预测甲状腺乳头状癌淋巴结转移：一项双中心研究

IF 1.8 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Open

Pub Date : 2025-06-01 Epub Date: 2025-02-24 DOI: 10.1016/j.ejro.2025.100639

Hao Wang , Xuan Wang , Yusheng Du , You Wang , Zhuojie Bai , Di Wu , Wuliang Tang , Hanling Zeng , Jing Tao , Jian He

Objectives

This study aimed to develop a predictive model for lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) patients by deep learning radiomic (DLRad) and clinical features.

Methods

This study included 271 thyroid lobes from 228 PTC patients who underwent preoperative neck non-contrast CT at Center 1 (May 2021–April 2024). LNM status was confirmed via postoperative pathology, with each thyroid lobe labeled accordingly. The cohort was divided into training (n = 189) and validation (n = 82) cohorts, with additional temporal (n = 59 lobes, Center 1, May–August 2024) and external (n = 66 lobes, Center 2) test cohorts. Thyroid lobes were manually segmented from the isthmus midline, ensuring interobserver consistency (ICC ≥ 0.8). Deep learning and radiomics features were selected using LASSO algorithms to compute DLRad scores. Logistic regression identified independent predictors, forming DLRad, clinical, and combined models. Model performance was evaluated using AUC, calibration, decision curves, and the DeLong test, compared against radiologists' assessments.

Results

Independent predictors of LNM included age, gender, multiple nodules, tumor size group, and DLRad. The combined model demonstrated superior diagnostic performance with AUCs of 0.830 (training), 0.799 (validation), 0.819 (temporal test), and 0.756 (external test), outperforming the DLRad model (AUCs: 0.786, 0.730, 0.753, 0.642), clinical model (AUCs: 0.723, 0.745, 0.671, 0.660), and radiologist evaluations (AUCs: 0.529, 0.606, 0.620, 0.503). It also achieved the lowest Brier scores (0.167, 0.184, 0.175, 0.201) and the highest net benefit in decision-curve analysis at threshold probabilities > 20 %.

Conclusions

The combined model integrating DLRad and clinical features exhibits good performance in predicting LNM in PTC patients.

目的通过深度学习放射学（DLRad）和临床特征，建立甲状腺乳头状癌（PTC）患者淋巴结转移（LNM）的预测模型。方法本研究纳入228例PTC患者的271个甲状腺叶，这些患者于2021年5月至2024年4月在中心1行术前颈部非对比CT检查。术后病理证实LNM状态，并相应标记每个甲状腺叶。该队列分为训练队列（n = 189）和验证队列（n = 82），另外还有时间队列（n = 59个叶，中心1,2024年5 - 8月）和外部测试队列（n = 66个叶，中心2）。手动从峡中线分割甲状腺叶，确保观察者之间的一致性（ICC≥0.8）。使用LASSO算法选择深度学习和放射组学特征来计算DLRad分数。逻辑回归确定了独立的预测因子，形成了DLRad、临床和联合模型。使用AUC、校准、决策曲线和DeLong测试对模型性能进行评估，并与放射科医生的评估进行比较。结果LNM的独立预测因素包括年龄、性别、多发结节、肿瘤大小组和DLRad。该组合模型的auc值分别为0.830（训练）、0.799（验证）、0.819（时间检验）和0.756（外部检验），优于DLRad模型（auc值分别为0.786、0.730、0.753、0.642）、临床模型（auc值分别为0.723、0.745、0.671、0.660）和放射科医师评价模型（auc值分别为0.529、0.606、0.620、0.503）。在阈值概率>； 20 %的决策曲线分析中，它也获得了最低的Brier分数（0.167,0.184,0.175,0.201）和最高的净效益。结论将DLRad与临床特征相结合的联合模型对PTC患者的LNM有较好的预测效果。

{"title":"Prediction of lymph node metastasis in papillary thyroid carcinoma using non-contrast CT-based radiomics and deep learning with thyroid lobe segmentation: A dual-center study","authors":"Hao Wang , Xuan Wang , Yusheng Du , You Wang , Zhuojie Bai , Di Wu , Wuliang Tang , Hanling Zeng , Jing Tao , Jian He","doi":"10.1016/j.ejro.2025.100639","DOIUrl":"10.1016/j.ejro.2025.100639","url":null,"abstract":"<div><h3>Objectives</h3><div>This study aimed to develop a predictive model for lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) patients by deep learning radiomic (DLRad) and clinical features.</div></div><div><h3>Methods</h3><div>This study included 271 thyroid lobes from 228 PTC patients who underwent preoperative neck non-contrast CT at Center 1 (May 2021–April 2024). LNM status was confirmed via postoperative pathology, with each thyroid lobe labeled accordingly. The cohort was divided into training (n = 189) and validation (n = 82) cohorts, with additional temporal (n = 59 lobes, Center 1, May–August 2024) and external (n = 66 lobes, Center 2) test cohorts. Thyroid lobes were manually segmented from the isthmus midline, ensuring interobserver consistency (ICC ≥ 0.8). Deep learning and radiomics features were selected using LASSO algorithms to compute DLRad scores. Logistic regression identified independent predictors, forming DLRad, clinical, and combined models. Model performance was evaluated using AUC, calibration, decision curves, and the DeLong test, compared against radiologists' assessments.</div></div><div><h3>Results</h3><div>Independent predictors of LNM included age, gender, multiple nodules, tumor size group, and DLRad. The combined model demonstrated superior diagnostic performance with AUCs of 0.830 (training), 0.799 (validation), 0.819 (temporal test), and 0.756 (external test), outperforming the DLRad model (AUCs: 0.786, 0.730, 0.753, 0.642), clinical model (AUCs: 0.723, 0.745, 0.671, 0.660), and radiologist evaluations (AUCs: 0.529, 0.606, 0.620, 0.503). It also achieved the lowest Brier scores (0.167, 0.184, 0.175, 0.201) and the highest net benefit in decision-curve analysis at threshold probabilities > 20 %.</div></div><div><h3>Conclusions</h3><div>The combined model integrating DLRad and clinical features exhibits good performance in predicting LNM in PTC patients.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"14 ","pages":"Article 100639"},"PeriodicalIF":1.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143474694","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

Role of region-of-interest magnetic resonance imaging fusion biopsy in mitigating overtreatment of localized prostate cancer – A retrospective cohort study 感兴趣区域磁共振成像融合活检在减轻局限性前列腺癌过度治疗中的作用-一项回顾性队列研究

IF 1.8 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Open

Pub Date : 2025-06-01 Epub Date: 2025-03-07 DOI: 10.1016/j.ejro.2025.100642

Carrie Wang , Purvish Trivedi , Esther Katende , Varun Awasthi , Riley Smith , Ryan Putney , Yahya Bondokji , Jong Y. Park , Jasreman Dhillon , Kosj Yamoah

Background

Traditional ultrasonography-based prostate biopsy uses a transrectal approach for systematic sampling of 12 cores. The magnetic resonance imaging (MRI) fusion biopsy uses a targeted approach, first identifying regions of interest (ROI) clinically suspicious for prostate cancer (PCa) through MRI, before performing a prostate biopsy aided by ultrasonography.

Methods

The single-center institutional retrospective cohort study used 442 men who were recommended for localized PCa management. Cohort A (n = 346) comprised patients who underwent MRI-guided TRUS biopsies, which included both standard 12-core TRUS biopsies and MRI-targeted biopsies performed simultaneously. Cohort B (n = 96) comprised patients who received only standard TRUS biopsy. The primary endpoint was Gleason reclassification, defined as the change in Gleason scores between standard TRUS and targeted region-of-interest (ROI) biopsies among cohort A. Secondary endpoint assessed the role of ROI biopsies in mitigating overtreatment by analyzing the probability of undergoing treatment and the duration of active surveillance (AS).

Results

Among men classified as no tumor on standard biopsy, 16.9 % showed Gleason disease on subsequent ROI biopsy. Additionally, ROI group also had a longer time to receive primary treatment (P = .017), as they were more likely to opt for AS (54 %). Lastly, median time spent on AS was longer for the ROI group compared with the non-ROI cohort (P = .002).

Conclusion

Adding multiparametric MRI (mpMRI) biopsy to standard TRUS biopsy may increase the detection of PCa. Additionally, mpMRI may allow patients to remain safely on AS, thereby reducing the need of prostate biopsies and improving cost-effectiveness.

传统的基于超声的前列腺活检采用经直肠方法对12个核进行系统采样。磁共振成像（MRI）融合活检采用一种有针对性的方法，首先通过MRI识别临床怀疑前列腺癌（PCa）的感兴趣区域（ROI），然后在超声辅助下进行前列腺活检。方法采用单中心机构回顾性队列研究，纳入442名推荐行局部前列腺癌治疗的男性。队列A （n = 346）包括接受mri引导下TRUS活检的患者，其中包括标准的12核TRUS活检和同时进行的mri靶向活检。队列B （n = 96）包括仅接受标准TRUS活检的患者。主要终点是Gleason再分类，定义为队列a中标准TRUS和目标感兴趣区域（ROI）活检之间Gleason评分的变化。次要终点通过分析接受治疗的概率和主动监测（as）的持续时间来评估ROI活检在减轻过度治疗方面的作用。结果在标准活检无肿瘤的男性中，16.9 %在随后的ROI活检中显示Gleason病。此外，ROI组也有更长的时间接受初级治疗（P = .017），因为他们更有可能选择as（54 %）。最后，与非ROI队列相比，ROI组在AS上花费的中位数时间更长（P = .002）。结论在标准TRUS活检基础上增加多参数MRI （mpMRI）活检可提高前列腺癌的检出率。此外，mpMRI可以使患者安全地接受AS治疗，从而减少前列腺活检的需要，提高成本效益。

{"title":"Role of region-of-interest magnetic resonance imaging fusion biopsy in mitigating overtreatment of localized prostate cancer – A retrospective cohort study","authors":"Carrie Wang , Purvish Trivedi , Esther Katende , Varun Awasthi , Riley Smith , Ryan Putney , Yahya Bondokji , Jong Y. Park , Jasreman Dhillon , Kosj Yamoah","doi":"10.1016/j.ejro.2025.100642","DOIUrl":"10.1016/j.ejro.2025.100642","url":null,"abstract":"<div><h3>Background</h3><div>Traditional ultrasonography-based prostate biopsy uses a transrectal approach for systematic sampling of 12 cores. The magnetic resonance imaging (MRI) fusion biopsy uses a targeted approach, first identifying regions of interest (ROI) clinically suspicious for prostate cancer (PCa) through MRI, before performing a prostate biopsy aided by ultrasonography.</div></div><div><h3>Methods</h3><div>The single-center institutional retrospective cohort study used 442 men who were recommended for localized PCa management. Cohort A (n = 346) comprised patients who underwent MRI-guided TRUS biopsies, which included both standard 12-core TRUS biopsies and MRI-targeted biopsies performed simultaneously. Cohort B (n = 96) comprised patients who received only standard TRUS biopsy. The primary endpoint was Gleason reclassification, defined as the change in Gleason scores between standard TRUS and targeted region-of-interest (ROI) biopsies among cohort A. Secondary endpoint assessed the role of ROI biopsies in mitigating overtreatment by analyzing the probability of undergoing treatment and the duration of active surveillance (AS).</div></div><div><h3>Results</h3><div>Among men classified as no tumor on standard biopsy, 16.9 % showed Gleason disease on subsequent ROI biopsy. Additionally, ROI group also had a longer time to receive primary treatment (<em>P</em> = .017), as they were more likely to opt for AS (54 %). Lastly, median time spent on AS was longer for the ROI group compared with the non-ROI cohort (<em>P</em> = .002).</div></div><div><h3>Conclusion</h3><div>Adding multiparametric MRI (mpMRI) biopsy to standard TRUS biopsy may increase the detection of PCa. Additionally, mpMRI may allow patients to remain safely on AS, thereby reducing the need of prostate biopsies and improving cost-effectiveness.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"14 ","pages":"Article 100642"},"PeriodicalIF":1.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563569","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