European Journal of Radiology Open最新文献_第5页

Deep-learning-based 3D content-based image retrieval system on chest HRCT: Performance assessment for interstitial lung diseases and usual interstitial pneumonia 基于深度学习的胸部HRCT三维内容图像检索系统：对间质性肺疾病和常见性间质性肺炎的性能评价

IF 1.8 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Open

Pub Date : 2025-12-01 Epub Date: 2025-07-23 DOI: 10.1016/j.ejro.2025.100670

Akira Oosawa , Atsuko Kurosaki , Atsushi Miyamoto , Shigeo Hanada , Yuichiro Nei , Hiroshi Nakahama , Yui Takahashi , Takahiro Mitsumura , Hisashi Takaya , Tomohisa Baba , Tae Iwasawa , Masatoshi Hori , Shoji Kido , Takashi Ogura , Noriyuki Tomiyama , Kazuma Kishi , Meiyo Tamaoka

Background

Diffuse parenchymal lung diseases have various conditions and CT imaging findings. Differentiating interstitial lung diseases (ILDs) and determining the presence or absence of usual interstitial pneumonia (UIP), can be challenging, even for experienced radiologists. To address this challenge, we developed a 3D-content-based image retrieval system (CBIR) and investigated its clinical usefulness.

Methods

Using deep learning technology, we developed a prototype system that analyzes thin-slice whole lung HRCT images, automatically registers them in a database, and retrieves similar images. To evaluate search performance, we used a database of 2058 cases and assessed image similarity between query and retrieved cases using a 5-point visual score (5: Similar, 4: Somewhat similar, 3: Neither, 2: Somewhat dissimilar, 1: Dissimilar). To assess clinical usefulness, we evaluated the concordance of labels (ILD/non-ILD, with/without UIP) between query and retrieved cases, using a database of 301 cases across 57 diseases.

Results

For search performance, the mean score of visual similarity between 70 queries and their top 5 retrieved cases was 4.37 ± 0.83. For clinical usefulness, label concordance between 25 queries and their top 5 retrieved cases was assessed across 4 labels. For ILD, the mean concordance of labels was 0.94 ± 0.15, while for non-ILD, it was 0.64 ± 0.31. For cases with UIP, the mean concordance of labels was 0.86 ± 0.17, while for cases without UIP, it was 0.83 ± 0.24.

Conclusions

Our CBIR system showed high accuracy for identifying cases with/without UIP, suggesting its potential to support UIP differentiation in clinical practice.

背景弥漫性肺实质疾病有多种症状和CT影像表现。鉴别间质性肺疾病（ild）和确定是否存在通常的间质性肺炎（UIP），可能是具有挑战性的，即使是经验丰富的放射科医生。为了解决这一挑战，我们开发了一个基于3d内容的图像检索系统（CBIR）并研究了其临床用途。方法利用深度学习技术，开发了一个原型系统，对全肺HRCT薄层图像进行分析，自动注册到数据库中，并检索相似图像。为了评估搜索性能，我们使用了一个包含2058个案例的数据库，并使用5分视觉评分来评估查询和检索案例之间的图像相似性（5分相似，4分有点相似，3分都不相似，2分有点不相似，1分不相似）。为了评估临床有用性，我们使用涵盖57种疾病的301例数据库，评估了查询和检索病例之间标签（ILD/非ILD，有/没有UIP）的一致性。结果在搜索性能方面，70个查询与前5个检索案例的视觉相似度平均得分为4.37 ± 0.83。对于临床有用性，在4个标签上评估25个查询和前5个检索病例之间的标签一致性。对于ILD，标签的平均一致性为0.94 ± 0.15，而对于非ILD，其平均一致性为0.64 ± 0.31。对于有UIP的病例，标签的平均一致性为0.86 ± 0.17，而对于没有UIP的病例，标签的平均一致性为0.83 ± 0.24。结论我们的CBIR系统对UIP有较高的识别准确率，可用于临床UIP的鉴别。

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引用次数: 0

Diaphragmatic curvature analysis using dynamic digital radiography 动态数字射线照相法分析横膈膜曲率

IF 2.9 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Open

Pub Date : 2025-12-01 Epub Date: 2025-08-05 DOI: 10.1016/j.ejro.2025.100676

Takuya Hino , Akinori Tsunomori , Noriaki Wada , Akinori Hata , Taiki Fukuda , Yusei Nakamura , Yoshitake Yamada , Tomoyuki Hida , Mizuki Nishino , Masako Ueyama , Atsuko Kurosaki , Takeshi Kubo , Shoji Kudoh , Kousei Ishigami , Hiroto Hatabu

Purpose

To investigate area under diaphragm (AUD) obtained by dynamic digital radiography (DDR) for the differentiation between normal subjects and chronic obstructive pulmonary disease (COPD) patients.

Methods

This retrospective study included healthy volunteers and COPD patients recruited from 2009 to 2014 at Fukujuji Hospital, who received DDR and pulmonary functional test. AUD was defined as an area under a hemidiaphragm and above the line connecting the ipsilateral costophrenic angle to the top of the hemidiaphragm on DDR image. AUD in full inspiration minus AUD in full expiration (ΔAUD) was also calculated. The diaphragmatic surface was demarcated manually on DDR image to calculate AUD. Three-group comparison of AUD and ΔAUD among normal, mild COPD, and severe COPD subjects was tested with one-way analysis of variance, followed by multiple comparison with Tukey-Kramer method. The diagnostic accuracy of COPD by ΔAUD was assessed using receiver-operating-characteristics (ROC) curve.

Results

Sixty-eight participants (36 men, 29 COPD patients) were enrolled. AUD in full inspiration was larger in healthy volunteers than in COPD patients (right, p < 0.001; left, p = 0.02). ΔAUD were different in the three-group comparison (right, normal, 208.7 ± 184.6 mm², mild COPD, −18.1 ± 117.5 mm², severe COPD −97.5 ± 150.0 mm², p < 0.001; left, normal, 254.9 ± 131.5 mm², mild COPD, −12.5 ± 136.5 mm², severe COPD, −100.7 ± 134.1 mm², p < 0.001). ROC curve showed high diagnostic performance of COPD by unilateral ΔAUD (right, area-under curve 0.942; left, area-under-curve 0.965).

Conclusion

The value of ΔAUD was smaller according to the severity of COPD. ΔAUD can be helpful in distinguishing healthy subjects from COPD patients.

目的探讨动态数字x线摄影（DDR）获得的膈下面积（AUD）在鉴别慢性阻塞性肺疾病（COPD）患者中的价值。方法回顾性研究纳入2009 - 2014年在福大学医院招募的健康志愿者和COPD患者，接受DDR和肺功能检查。AUD定义为DDR图像上半膈下、同侧肋膈角与半膈顶部连线以上的区域。同时计算充分吸气时的澳元减去完全呼气时的澳元（ΔAUD）。在DDR图像上手动标定膈面，计算AUD。三组比较正常、轻度和重度COPD受试者的AUD和ΔAUD，采用单因素方差分析，然后采用Tukey-Kramer法进行多重比较。采用受试者-工作特征（ROC）曲线评价ΔAUD对COPD的诊断准确性。结果共纳入68名参与者（36名男性，29名COPD患者）。健康志愿者完全吸气时的AUD大于COPD患者(右，p <； 0.001；离开时,p = 0.02)。Δ澳大利亚是不同的三组比较(正常, 208.7±184.6  平方毫米,轻微的慢性阻塞性肺病, −18.1±117.5  平方毫米,严重的慢性阻塞性肺病 −97.5±150.0  平方毫米,p & lt; 0.001;离开,正常,254.9 ±131.5  平方毫米,轻微的慢性阻塞性肺病, −12.5±136.5  平方毫米,严重的慢性阻塞性肺病, −100.7±134.1  平方毫米,p & lt; 0.001)。ROC曲线显示单侧ΔAUD对COPD有较高的诊断价值(右，曲线下面积0.942；左侧，曲线下面积0.965)。结论ΔAUD值随COPD的严重程度而变小。ΔAUD可以帮助区分健康受试者和COPD患者。

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引用次数: 0

Performance of an artificial intelligence tool for multi-step acute stroke imaging: A multicenter diagnostic study 多步急性脑卒中成像人工智能工具的性能：一项多中心诊断研究

IF 2.9 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Open

Pub Date : 2025-12-01 Epub Date: 2025-08-29 DOI: 10.1016/j.ejro.2025.100678

Thibault Agripnidis , Angela Ayobi , Sarah Quenet , Yasmina Chaibi , Christophe Avare , Alexis Jacquier , Nadine Girard , Jean-François Hak , Anthony Reyre , Gilles Brun , Ahmed-Ali El Ahmadi

Objective

Several artificial intelligence (AI) tools have been developed to assist in the stroke imaging workflow, which remains a major disease of the 21st century. This study evaluated the combined performance of an FDA-cleared and CE-marked AI-based device with three modules designed to detect intracerebral hemorrhage (ICH), identify large vessel occlusion (LVO), and calculate Alberta Stroke Program Early CT Scores (ASPECTS).

Materials & methods

Non-contrast CT (NCCT) and/or computed tomography angiography (CTA) for suspicion of stroke acquired at La Timone and Nord University hospitals (Marseille, France) between March 2019 and March 2020 were retrospectively collected. The AI tool, CINA-HEAD (Avicenna.AI), processed the data to flag ICH, LVO, and calculate ASPECTS. The results were compared to ground truth evaluations by four expert neuroradiologists to compute diagnostic performances.

Results

A total of 373 NCCT and 331 CTA from 405 patients (mean age 64.9 ± 18.9 SD, 52.6 % female) were included. The AI tool achieved an accuracy of 94.6 % [95 % CI: 91.8 %-96.7 %] for ICH detection on NCCT and of 86.4 % [95 % CI: 82.2 %-89.9 %] for LVO identification on CTA. The region-based ASPECTS analysis yielded an accuracy of 88.6 % [95 % CI: 87.8 %-89.3 %] and the dichotomized ASPECTS classification (ASPECTS ≥ 6) achieved 80.4 % accuracy.

Conclusion

This study demonstrates the reliable, stepwise performance of an AI-based stroke imaging tool across the diagnostic cascade of ICH and LVO detection and ASPECTS scoring. Such robust multi-stage evaluation supports its potential for streamlining acute stroke triage and decision-making.

目的：脑卒中仍是21世纪的主要疾病，目前已开发了多种人工智能（AI）工具来辅助脑卒中成像工作流程。本研究评估了fda批准和ce标记的人工智能设备的综合性能，该设备具有三个模块，用于检测脑出血（ICH）、识别大血管闭塞（LVO）和计算阿尔伯塔卒中计划早期CT评分（ASPECTS）。材料和方法回顾性收集2019年3月至2020年3月在La Timone和Nord University医院（法国马赛）获得的疑似卒中的非对比CT （NCCT）和/或计算机断层扫描血管造影（CTA）。人工智能工具china - head（阿维森纳）AI)，处理数据标记ICH， LVO，并计算ASPECTS。结果与四位神经放射专家的真实评估进行比较，以计算诊断性能。结果405例患者（平均年龄64.9 ± 18.9 SD，女性52.6% %）共纳入373例NCCT和331例CTA。人工智能工具在NCCT上检测ICH的准确率为94.6 %[95 % CI: 91.8 %-96.7 %]，在CTA上识别LVO的准确率为86.4 %[95 % CI: 82.2 %-89.9 %]。基于区域的ASPECTS分析的准确率为88.6% %[95 % CI: 87.8 %- 89.3% %]，二分类的ASPECTS分类（ASPECTS≥6）的准确率为80.4 %。本研究证明了基于人工智能的脑卒中成像工具在ICH和LVO检测的诊断级联以及ASPECTS评分方面具有可靠的、逐步的性能。这种强大的多阶段评估支持其简化急性卒中分诊和决策的潜力。

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引用次数: 0

High performance of low/ultralow-dose photon-counting CT for pulmonary metastasis in young musculoskeletal malignancy patients 低/超低剂量光子计数CT对年轻肌肉骨骼恶性肿瘤肺转移的高效诊断

IF 2.9 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Open

Pub Date : 2025-12-01 Epub Date: 2025-09-27 DOI: 10.1016/j.ejro.2025.100689

Shanshui Zhou , Qiyuan Bao , Haipeng Dong , Jiqiang Li , Zhihan Xu , Lianjun Du , Wenjie Yang , Yong Lu , Weibin Zhang , Fuhua Yan , Le Qin

Purpose

To investigate the performance of chest photon-counting CT (PCCT) at low-dose (LD) and ultralow-dose (ULD) in young musculoskeletal malignancy patients with pulmonary metastasis and compare with prior standard-dose energy-integrating CT (EICT).

Materials and methods

From August to November 2023, this prospective study recruited consecutive participants with prior EICT images and grouped them into LD and ULD groups to receive PCCT examination. Two observers independently and blindly evaluated the image quality using a five-point Likert scale. Intraindividual differences between PCCT and EICT were compared using the Wilcoxon signed-rank test or paired samples t-test.

Results

The LD and ULD groups included 50 (19 [16; 21] years; 33 males) and 50 participants (19 [14; 21] years; 30 males), respectively. The interval between EICT and PCCT examinations was 116 (88.5; 194) days. Compared with EICT, PCCT obtained median effective dose reduction rates of 87.62 % (3.78 [3.15; 5.18] vs. 0.43 [0.39; 0.58] mSv; p < 0.001) and 92.58 % (3.92 [2.96; 4.95] vs. 0.27 [0.22; 0.34] mSv; p < 0.001) in the LD and ULD groups, respectively. For subjective assessments, PCCT has superior overall image quality (5 [5; 5] vs. 5 [4; 5]) and lung nodule visualization (5 [5; 5] vs. 5 [4; 5]) to EICT (all p-values <0.001).

Conclusion

PCCT provided higher image quality and lung nodule visualization with significant dose reduction compared to EICT in these young musculoskeletal malignancy patients.

目的探讨低剂量（LD）和超低剂量（ULD）胸部光子计数CT （PCCT）在年轻肌肉骨骼恶性肿瘤合并肺转移患者中的表现，并与既往标准剂量能量积分CT （EICT）进行比较。材料与方法本前瞻性研究于2023年8月至11月连续招募有EICT影像的受试者，将其分为LD组和ULD组接受PCCT检查。两名观察员独立和盲目评估图像质量使用五点李克特量表。PCCT和EICT的个体差异采用Wilcoxon符号秩检验或配对样本t检验进行比较。结果LD组50例（19[16；21]岁，男性33例），ULD组50例（19[14；21]岁，男性30例）。EICT与PCCT检查间隔116（88.5;194）天。与EICT相比，PCCT在LD组和ULD组的中位有效剂量减少率分别为87.62 %（3.78[3.15；5.18]比0.43 [0.39;0.58]mSv; p <； 0.001）和92.58 %（3.92[2.96；4.95]比0.27 [0.22;0.34]mSv; p <； 0.001）。在主观评价方面，PCCT具有优于EICT的整体图像质量（5 [5;5]vs. 5[4; 5]）和肺结节显示（5 [5;5]vs. 5[4; 5]）（所有p值<；0.001）。结论与EICT相比，pcct在年轻肌肉骨骼恶性肿瘤患者中具有更高的图像质量和更清晰的肺结节显示，且剂量明显降低。

{"title":"High performance of low/ultralow-dose photon-counting CT for pulmonary metastasis in young musculoskeletal malignancy patients","authors":"Shanshui Zhou , Qiyuan Bao , Haipeng Dong , Jiqiang Li , Zhihan Xu , Lianjun Du , Wenjie Yang , Yong Lu , Weibin Zhang , Fuhua Yan , Le Qin","doi":"10.1016/j.ejro.2025.100689","DOIUrl":"10.1016/j.ejro.2025.100689","url":null,"abstract":"<div><h3>Purpose</h3><div>To investigate the performance of chest photon-counting CT (PCCT) at low-dose (LD) and ultralow-dose (ULD) in young musculoskeletal malignancy patients with pulmonary metastasis and compare with prior standard-dose energy-integrating CT (EICT).</div></div><div><h3>Materials and methods</h3><div>From August to November 2023, this prospective study recruited consecutive participants with prior EICT images and grouped them into LD and ULD groups to receive PCCT examination. Two observers independently and blindly evaluated the image quality using a five-point Likert scale. Intraindividual differences between PCCT and EICT were compared using the Wilcoxon signed-rank test or paired samples <em>t</em>-test.</div></div><div><h3>Results</h3><div>The LD and ULD groups included 50 (19 [16; 21] years; 33 males) and 50 participants (19 [14; 21] years; 30 males), respectively. The interval between EICT and PCCT examinations was 116 (88.5; 194) days. Compared with EICT, PCCT obtained median effective dose reduction rates of 87.62 % (3.78 [3.15; 5.18] vs. 0.43 [0.39; 0.58] mSv; <em>p</em> < 0.001) and 92.58 % (3.92 [2.96; 4.95] vs. 0.27 [0.22; 0.34] mSv; <em>p</em> < 0.001) in the LD and ULD groups, respectively. For subjective assessments, PCCT has superior overall image quality (5 [5; 5] vs. 5 [4; 5]) and lung nodule visualization (5 [5; 5] vs. 5 [4; 5]) to EICT (all <em>p</em>-values <0.001).</div></div><div><h3>Conclusion</h3><div>PCCT provided higher image quality and lung nodule visualization with significant dose reduction compared to EICT in these young musculoskeletal malignancy patients.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"15 ","pages":"Article 100689"},"PeriodicalIF":2.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145157320","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 non-contrast quiescent-interval slice-selective (QISS) magnetic resonance angiography for evaluation of the renal arterial vasculature compared to computed tomography angiography (CTA) as reference standard 非对比静止间隔切片选择（QISS）磁共振血管造影对肾动脉血管的诊断性能与计算机断层血管造影（CTA）作为参考标准的比较

IF 2.9 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Open

Pub Date : 2025-12-01 Epub Date: 2025-11-08 DOI: 10.1016/j.ejro.2025.100706

Patrick Ghibes , Florian Hagen , Petros Martirosian , Stephan Ursprung , Konstantin Nikolaou , Daniel Raskin , Abraham Levitin , Levester Kirksey , Sasan Partovi

Purpose

To evaluate the diagnostic quality and detection of anatomical variants in branching patterns of the renal arteries for non-contrast quiescent-interval slice-selective (QISS) MR Angiography (MRA) compared to CT Angiography (CTA).

Methods

Patients who underwent a QISS MRA of the renal arteries as well as CTA as reference standard were included in this retrospective study. Signal-to-noise ratio (SNR), contrast-to-noise ratio (SNR), and vessel diameter were determined in the left and right renal arterial systems. Image quality and diagnostic confidence were assessed with a standardized five-point Likert scale. Sensitivity, specificity and accuracy for the detection of anatomical variants in branching patterns (accessory renal artery, aberrant renal artery and early branching) of the renal arterial system were determined compared to CTA as reference standard.

Results

30 patients (59 renal arteries) were included in this retrospective study. CTA reached significantly higher median SNR compared to QISS MRA (10.96, inter-quartile range (IQR) 6.70–16.11 vs. 5.65, IQR 4.38–8.76, respectively, p < 0.001). Median CNR was significantly higher in QISS MRA (16.75, IQR 13.09–20.96) compared to CTA (13.22, IQR 7.49–18.57), p = 0.006. Diameters of the renal arteries were similar between QISS MRA and CTA (5.8 mm, IQR 4.90–6.60 versus 5.8 mm, IQR 4.75, 6.70, p = 0.893). Diagnostic confidence was rated excellent for both, though significantly higher for CTA (5, IQR 5–5,) compared to QISS MRA (5, IQR 4–5), p = 0.003). 19 of 20 variants in branching pattern could be detected successfully by QISS.

Conclusion

QISS MRA offers similar diagnostic confidence and image quality to CTA as reference standard. Further, QISS MRA demonstrates excellent diagnostic accuracy in detecting anatomical variants of branching patterns of the renal arterial vasculature.

目的评价非对比静止间隔切片选择（QISS） MR血管造影（MRA）与CT血管造影（CTA）对肾动脉分支形态解剖变异的诊断质量和检测效果。方法回顾性研究采用肾动脉QISS MRA和CTA作为参考标准的患者。测定左、右肾动脉系统的信噪比（SNR）、信噪比（SNR）和血管直径。用标准化的李克特五点量表评估图像质量和诊断可信度。对比CTA作为参考标准，检测肾动脉系统分支形态（副肾动脉、异常肾动脉和早期分支）解剖变异的敏感性、特异性和准确性。结果30例患者（59条肾动脉）纳入回顾性研究。与QISS MRA相比，CTA的中位信噪比明显更高（10.96，四分位间距（IQR） 6.70-16.11 vs. 5.65, IQR 4.38-8.76, p <； 0.001）。QISS MRA的中位CNR （16.75, IQR 13.09-20.96）显著高于CTA (13.22, IQR 7.49-18.57), p = 0.006。肾动脉直径在QISS MRA和CTA之间相似（5.8 mm, IQR 4.90-6.60 vs 5.8 mm, IQR 4.75, 6.70, p = 0.893）。两者的诊断可信度都被评为优秀，尽管CTA （5, IQR 5 - 5,）与QISS MRA （5, IQR 4-5）相比显着更高，p = 0.003)。20个分支型变异中有19个可以通过QISS检测到。结论作为参考标准，qiss MRA的诊断置信度和图像质量与CTA相当。此外，QISS MRA在检测肾动脉血管分支模式的解剖变异方面表现出出色的诊断准确性。

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引用次数: 0

Combined predictive model for prostate cancer screening: Development and validation study 前列腺癌筛查的联合预测模型：开发和验证研究

IF 2.9 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Open

Pub Date : 2025-12-01 Epub Date: 2025-09-04 DOI: 10.1016/j.ejro.2025.100683

Yu Li , Fang Yang , Xuebin Liu , Jiping Luo , Siyu Dan , Xiuli He , Guihao Hu , Ling He , Xiachuan Qin , Tao Wu , Wensheng Yue

Background

Early detection of prostate cancer (PCa) remains challenging, as prostate-specific antigen (PSA) testing and digital rectal examination (DRE) offer limited specificity. Transrectal ultrasound (TRUS) is routinely used for biopsy guidance, but its diagnostic potential for PCa screening is underexplored. We aimed to evaluate TRUS-derived morphological features and develop a nomogram that integrates clinical and TRUS characteristics to improve PCa risk stratification.

Methods

Consecutive patients with suspected PCa were enrolled from two tertiary centers (training cohort: n = 154, October 2021–January 2023; validation cohort: n = 51, December 2021–June 2022). Demographic data, laboratory-derived PSA indices (including PSA density), and TRUS parameters (independently assessed by two blinded sonographers) were collected and analyzed. A predictive nomogram was constructed using multivariate logistic regression and externally validated.

Results

In the training cohort (mean age 70.9 ± 8.0 years; 72 PCa, 82 benign), independent predictors of PCa included elevated PSA density (OR=3.86, 95 % CI: 1.30–11.40, P = 0.015), abnormal DRE (OR=3.06, 95 % CI: 1.09–8.60, P = 0.034), TRUS-defined ill-defined zone boundaries (OR=9.61, 95 % CI: 3.37–39.02, P = 0.002), and hyper-enhancement (OR=7.07, 95 % CI: 2.69–21.89, P < 0.001). The nomogram achieved strong discrimination (training C-index=0.933, 95 % CI: 0.881–0.986; validation C-index=0.907, 95 % CI: 0.792–0.970) with 84.7 % sensitivity, 87.8 % specificity, and 86.4 % accuracy. Pathological concordance was high (kappa=0.726).

Conclusion

TRUS-derived features (ill-defined zones, hyper-enhancement) significantly enhance PCa detection when combined with clinical parameters. Our nomogram provides a practical, visual tool to guide biopsy decisions and demonstrates robust performance across cohorts.

背景前列腺癌（PCa）的检测仍然具有挑战性，因为前列腺特异性抗原（PSA）检测和直肠指检（DRE）的特异性有限。经直肠超声（TRUS）通常用于活检指导，但其在前列腺癌筛查中的诊断潜力尚未得到充分探索。我们的目的是评估TRUS衍生的形态学特征，并开发一个整合临床和TRUS特征的nomogram，以改善PCa的风险分层。方法从两个三级中心连续招募疑似PCa患者（培训队列：n = 154,2021年10月- 2023年1月；验证队列：n = 51,2021年12月- 2022年6月）。收集和分析人口统计数据、实验室衍生的PSA指数（包括PSA密度）和TRUS参数（由两名盲法超声医师独立评估）。采用多元逻辑回归构建预测模态图，并进行外部验证。ResultsIn训练队列(平均年龄70.9 ± 8.0年;72 PCa, 82良性),PCa的独立预测因子包括高PSA密度(或= 3.86,95 % CI: 1.30 - -11.40, P = 0.015),异常DRE(或= 3.06,95 % CI: 1.09 - -8.60, P = 0.034),TRUS-defined模糊区边界(或= 9.61,95 % CI: 3.37 - -39.02, P = 0.002),和hyper-enhancement(或= 7.07,95 % CI: 2.69 - -21.89, P & lt; 0.001)。nomogram具有较强的判别性（training C-index=0.933, 95 % CI: 0.881-0.986; validation C-index=0.907, 95 % CI: 0.792-0.970）， sensitivity为84.7 %，specificity为87.8 %，accuracy为86.4 %。病理一致性高（kappa=0.726）。结论trus衍生特征（区域不清、超增强）结合临床参数可显著提高前列腺癌的检出率。我们的图提供了一个实用的、可视化的工具来指导活检的决定，并在队列中展示了强大的性能。

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引用次数: 0

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

IF 2.9 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Open

Pub Date : 2025-12-01 Epub Date: 2025-11-26 DOI: 10.1016/j.ejro.2025.100711

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

Objectives

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

Methods

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

Results

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

Conclusion

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

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

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引用次数: 0

Photon counting CT improves coronary stent imaging and fat attenuation index assessment across reconstruction modes 光子计数CT改善冠状动脉支架成像和脂肪衰减指数评估跨重建模式

IF 2.9 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Open

Pub Date : 2025-12-01 Epub Date: 2025-10-13 DOI: 10.1016/j.ejro.2025.100695

Yujie Gao , Yaru Yang , Qiuju Hu , Yane Zhao , Yong Yuan , Jiliang Chen , Bangjun Guo , Dongsheng Jin , song luo , Guangming Lu

Objectives

To investigate the effect of photon counting CT (PCCT) ultra–high resolution (UHR) mode on coronary stent visualization and fat attenuation index (FAI) assessment in individuals with percutaneous coronary intervention (PCI).

Methods

Patients who underwent PCI with stent placement and following coronary CT angiography (CCTA) by using a PCCT system were enrolled during January to August 2024. Simulated energy integrating detector CT (EID-CT) images (0.8 mm) were reconstructed with kernel Bv48, while UHR images (0.2 mm) were reconstructed using kernel Bv60 and Bv72. Objective and subjective image quality, stent-specific FAI and peri-stent FAI was evaluated.

Results

A total of 41 patients with 51 isolated stents (69.76 ± 8.75 years; 34 men) were included. Compared to simulated EID-CT, 0.2 mm Bv72 images showed larger in-stent diameters and reduced blooming artifacts (all p < 0.001). Subjective image quality scores for 0.2 mm UHR images were superior to those for simulated EID-CT (all p < 0.01). The stent-specific FAI and peri-stent FAI of 51 isolated stents was lower in the 0.2 mm UHR images than in simulated EID-CT (all p < 0.05). The reconstruction mode of 0.2 mm Bv72 showed the ability of stent-specific FAI and peri-stent FAI to distinguish stents with in-stent restenosis (ISR) < 50 % in diameter from stents without ISR, with cut-off value of −98HU and −99.5HU, respectively.

Conclusions

PCCT UHR mode improved the image quality of coronary stents, reduced the FAI values and provided cut-off values based on stent-specific FAI and peri-stent FAI.

目的探讨光子计数CT （PCCT）超高分辨率（UHR）模式对经皮冠状动脉介入治疗（PCI）患者冠脉支架显像及脂肪衰减指数（FAI）评估的影响。方法于2024年1月至8月，采用PCCT系统行PCI支架置入术及冠状动脉CT血管造影（CCTA）的患者入组。模拟能量积分检测器CT （EID-CT）图像（0.8 mm）用内核Bv48重建，UHR图像（0.2 mm）用内核Bv60和Bv72重建。评估客观和主观图像质量、支架特异性FAI和支架周围FAI。结果共纳入51例孤立支架患者41例（69.76 ± 8.75岁，男性34例）。与模拟EID-CT相比，0.2 mm Bv72图像显示支架内直径更大，假影减少（p均 <； 0.001）。0.2 mm UHR图像的主观图像质量评分优于模拟EID-CT （p均 <； 0.01）。51个离体支架在0.2 mm UHR图像上的支架特异性FAI和支架周围FAI均低于模拟reid - ct （p均 <； 0.05）。重建模式为0.2 mm Bv72，表明支架特异性FAI和支架周围FAI能够区分支架内再狭窄（ISR） <； 50 %直径的支架与无ISR的支架，截断值分别为- 98HU和- 99.5HU。结论spcct UHR模式提高了冠状动脉支架的图像质量，降低了FAI值，并提供了基于支架特异性FAI和支架周围FAI的截断值。

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引用次数: 0

CT Radiomics-based machine learning approach for the invasiveness of pulmonary ground-glass nodules prediction 基于CT放射组学的肺磨玻璃结节侵袭性预测的机器学习方法

IF 2.9 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Open

Pub Date : 2025-12-01 Epub Date: 2025-08-23 DOI: 10.1016/j.ejro.2025.100680

Rui Chen , Hu Zhang , Xingwen Huang , Haitao Han , Jinbo Jian

Objective

To develop and validate a machine learning model based on CT radiomics to improve the ability to differentiate pathological subtypes of pulmonary ground-glass nodules (GGN).

Methods

A retrospective analysis was conducted on clinical data and radiological images from 392 patients with lung adenocarcinoma at Binzhou Medical University Hospital between January 1, 2020 to May 31, 2023. All patients underwent preoperative thin-section chest CT scans and surgical resection. A total of 400 GGNs were included. Regions of interest (ROI) were delineated on the slice showing the largest diameter of the lesions. Based on pathological confirmation, the nodules were divided into two groups: Group 1 (adenocarcinoma in situ, AIS or minimally invasive adenocarcinoma, MIA, 209 nodules) and Group 2 (invasive adenocarcinoma, IAC, 191nodules). The dataset was randomly split into a training set (280 nodules, 70 %) and a validation set (120 nodules, 30 %) at a 7:3 ratio. In the training set, feature dimensionality reduction was performed using minimum redundancy maximum relevance (mRMR) as well as least absolute shrinkage and selection operator (LASSO) to screen out discriminative radiomics features. Then seven machine learning models—logistic regression (LR), support vector machine (SVM), random forest (RF), extra trees, XGBoost, GradientBoosting, and AdaBoost—were constructed. Model performance and prediction efficacy were evaluated based on indicators such as area under the curve (AUC), accuracy, specificity, and sensitivity using receiver operating characteristic (ROC) curves.

Results

Eight radiomics features were ultimately identified. Among the seven models, the GradientBoosting model exhibited the best performance, achieving an AUC of 0.929 (95 % CI: 0.9004–0.9584), accuracy of 0.85, sensitivity of 0.851, and specificity of 0.849 in the training set.

Conclusion

The GradientBoosting model based on CT radiomics features demonstrates superior performance in predicting pathological subtypes of ground glass nodular lung adenocarcinoma, providing a reliable auxiliary tool for clinical diagnosis.

目的建立并验证基于CT放射组学的机器学习模型，以提高肺磨玻璃结节（GGN）病理亚型的鉴别能力。方法回顾性分析滨州医科大学附属医院2020年1月1日至2023年5月31日392例肺腺癌患者的临床资料和影像学资料。所有患者术前均行胸部薄层CT扫描和手术切除。共纳入400个ggn。感兴趣区域（ROI）在显示病变最大直径的切片上勾画。根据病理证实，将结节分为两组：1组（原位腺癌，AIS或微创腺癌，MIA， 209个结节）和2组（侵袭性腺癌，IAC， 191个结节）。数据集以7:3的比例随机分为训练集（280个结节，70 %）和验证集（120个结节，30 %）。在训练集中，使用最小冗余最大相关性（mRMR）以及最小绝对收缩和选择算子（LASSO）进行特征降维，以筛选出判别性放射组学特征。然后构建了逻辑回归（LR）、支持向量机（SVM）、随机森林（RF）、额外树（extra trees）、XGBoost、GradientBoosting和adaboost等7个机器学习模型。采用受试者工作特征（ROC）曲线，根据曲线下面积（AUC）、准确度、特异性和敏感性等指标评价模型的性能和预测效果。结果最终确定了八个放射组学特征。7个模型中，GradientBoosting模型表现最好，AUC为0.929(95 % CI: 0.9004-0.9584)，准确率为0.85，灵敏度为0.851，特异性为0.849。结论基于CT放射组学特征的GradientBoosting模型在预测磨玻璃结节性肺腺癌病理亚型方面具有较好的效果，为临床诊断提供了可靠的辅助工具。

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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 : 2025-12-01 Epub 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