European Journal of Radiology Open最新文献

{"title":"Deep-learning-based 3D content-based image retrieval system on chest HRCT: Performance assessment for interstitial lung diseases and usual interstitial pneumonia","authors":"Akira Oosawa ,&nbsp;Atsuko Kurosaki ,&nbsp;Atsushi Miyamoto ,&nbsp;Shigeo Hanada ,&nbsp;Yuichiro Nei ,&nbsp;Hiroshi Nakahama ,&nbsp;Yui Takahashi ,&nbsp;Takahiro Mitsumura ,&nbsp;Hisashi Takaya ,&nbsp;Tomohisa Baba ,&nbsp;Tae Iwasawa ,&nbsp;Masatoshi Hori ,&nbsp;Shoji Kido ,&nbsp;Takashi Ogura ,&nbsp;Noriyuki Tomiyama ,&nbsp;Kazuma Kishi ,&nbsp;Meiyo Tamaoka","doi":"10.1016/j.ejro.2025.100670","DOIUrl":"10.1016/j.ejro.2025.100670","url":null,"abstract":"<div><h3>Background</h3><div>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.</div></div><div><h3>Methods</h3><div>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.</div></div><div><h3>Results</h3><div>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.</div></div><div><h3>Conclusions</h3><div>Our CBIR system showed high accuracy for identifying cases with/without UIP, suggesting its potential to support UIP differentiation in clinical practice.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"15 ","pages":"Article 100670"},"PeriodicalIF":1.8,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144694930","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}

{"title":"Diaphragmatic curvature analysis using dynamic digital radiography","authors":"Takuya Hino ,&nbsp;Akinori Tsunomori ,&nbsp;Noriaki Wada ,&nbsp;Akinori Hata ,&nbsp;Taiki Fukuda ,&nbsp;Yusei Nakamura ,&nbsp;Yoshitake Yamada ,&nbsp;Tomoyuki Hida ,&nbsp;Mizuki Nishino ,&nbsp;Masako Ueyama ,&nbsp;Atsuko Kurosaki ,&nbsp;Takeshi Kubo ,&nbsp;Shoji Kudoh ,&nbsp;Kousei Ishigami ,&nbsp;Hiroto Hatabu","doi":"10.1016/j.ejro.2025.100676","DOIUrl":"10.1016/j.ejro.2025.100676","url":null,"abstract":"<div><h3>Purpose</h3><div>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.</div></div><div><h3>Methods</h3><div>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.</div></div><div><h3>Results</h3><div>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 &lt; 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 &lt; 0.001; left, normal, 254.9 ± 131.5 mm², mild COPD, −12.5 ± 136.5 mm², severe COPD, −100.7 ± 134.1 mm², p &lt; 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).</div></div><div><h3>Conclusion</h3><div>The value of ΔAUD was smaller according to the severity of COPD. ΔAUD can be helpful in distinguishing healthy subjects from COPD patients.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"15 ","pages":"Article 100676"},"PeriodicalIF":2.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144773008","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}

{"title":"Performance of an artificial intelligence tool for multi-step acute stroke imaging: A multicenter diagnostic study","authors":"Thibault Agripnidis ,&nbsp;Angela Ayobi ,&nbsp;Sarah Quenet ,&nbsp;Yasmina Chaibi ,&nbsp;Christophe Avare ,&nbsp;Alexis Jacquier ,&nbsp;Nadine Girard ,&nbsp;Jean-François Hak ,&nbsp;Anthony Reyre ,&nbsp;Gilles Brun ,&nbsp;Ahmed-Ali El Ahmadi","doi":"10.1016/j.ejro.2025.100678","DOIUrl":"10.1016/j.ejro.2025.100678","url":null,"abstract":"<div><h3>Objective</h3><div>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).</div></div><div><h3>Materials &amp; methods</h3><div>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.</div></div><div><h3>Results</h3><div>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.</div></div><div><h3>Conclusion</h3><div>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.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"15 ","pages":"Article 100678"},"PeriodicalIF":2.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144917052","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}

{"title":"High performance of low/ultralow-dose photon-counting CT for pulmonary metastasis in young musculoskeletal malignancy patients","authors":"Shanshui Zhou ,&nbsp;Qiyuan Bao ,&nbsp;Haipeng Dong ,&nbsp;Jiqiang Li ,&nbsp;Zhihan Xu ,&nbsp;Lianjun Du ,&nbsp;Wenjie Yang ,&nbsp;Yong Lu ,&nbsp;Weibin Zhang ,&nbsp;Fuhua Yan ,&nbsp;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> &lt; 0.001) and 92.58 % (3.92 [2.96; 4.95] vs. 0.27 [0.22; 0.34] mSv; <em>p</em> &lt; 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 &lt;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}

{"title":"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","authors":"Patrick Ghibes ,&nbsp;Florian Hagen ,&nbsp;Petros Martirosian ,&nbsp;Stephan Ursprung ,&nbsp;Konstantin Nikolaou ,&nbsp;Daniel Raskin ,&nbsp;Abraham Levitin ,&nbsp;Levester Kirksey ,&nbsp;Sasan Partovi","doi":"10.1016/j.ejro.2025.100706","DOIUrl":"10.1016/j.ejro.2025.100706","url":null,"abstract":"<div><h3>Purpose</h3><div>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).</div></div><div><h3>Methods</h3><div>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.</div></div><div><h3>Results</h3><div>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 &lt; 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.</div></div><div><h3>Conclusion</h3><div>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.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"15 ","pages":"Article 100706"},"PeriodicalIF":2.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145464875","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}

{"title":"Combined predictive model for prostate cancer screening: Development and validation study","authors":"Yu Li ,&nbsp;Fang Yang ,&nbsp;Xuebin Liu ,&nbsp;Jiping Luo ,&nbsp;Siyu Dan ,&nbsp;Xiuli He ,&nbsp;Guihao Hu ,&nbsp;Ling He ,&nbsp;Xiachuan Qin ,&nbsp;Tao Wu ,&nbsp;Wensheng Yue","doi":"10.1016/j.ejro.2025.100683","DOIUrl":"10.1016/j.ejro.2025.100683","url":null,"abstract":"<div><h3>Background</h3><div>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.</div></div><div><h3>Methods</h3><div>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.</div></div><div><h3>Results</h3><div>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, <em>P</em> = 0.015), abnormal DRE (OR=3.06, 95 % CI: 1.09–8.60, <em>P</em> = 0.034), TRUS-defined ill-defined zone boundaries (OR=9.61, 95 % CI: 3.37–39.02, <em>P</em> = 0.002), and hyper-enhancement (OR=7.07, 95 % CI: 2.69–21.89, <em>P</em> &lt; 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).</div></div><div><h3>Conclusion</h3><div>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.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"15 ","pages":"Article 100683"},"PeriodicalIF":2.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144996474","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}

{"title":"MRI-derived extracellular volume fraction as a prognostic biomarker for early recurrence after R0 resection of pancreatic ductal adenocarcinoma","authors":"Jing-Yi Liu ,&nbsp;Qi Wang ,&nbsp;Yi-Tong Lu ,&nbsp;Yue-Luan Jiang ,&nbsp;Dominik Nichel ,&nbsp;Robert Grimm ,&nbsp;Liang Zhu ,&nbsp;Meng-Hua Dai","doi":"10.1016/j.ejro.2025.100711","DOIUrl":"10.1016/j.ejro.2025.100711","url":null,"abstract":"<div><h3>Objectives</h3><div>Pancreatic ductal adenocarcinoma (PDAC) has a high recurrence risk after R0 resection. This study aimed to evaluate the prognostic value of preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) pharmacokinetic parameters, including volume transfer constant (K^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.</div></div><div><h3>Methods</h3><div>In this retrospective cohort study, 61 patients (mean age 60.7 ± 9.7 years; 35 males) with histologically confirmed PDAC underwent preoperative 3 T MRI between January 2017 and May 2024, including DCE-MRI and dual-time-point T1 mapping. Two radiologists independently measured K^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.</div></div><div><h3>Results</h3><div>ECV demonstrated excellent reproducibility (ICC = 0.99) and independently predicted shorter RFS (HR = 1.020; 95 % CI: 1.003–1.038; p = 0.022). An optimal ECV cutoff of 31.99 % effectively stratified patients into high- and low-risk groups with significantly different median RFS (10.9 vs. 17.4 months, p = 0.012). However, other DCE-MRI parameters (K^trans, k_ep, v_e) showed poor reproducibility and lacked independent prognostic value for RFS or OS.</div></div><div><h3>Conclusion</h3><div>MRI-derived tumor ECV is a robust, reproducible biomarker for predicting early recurrence after R0 resection in PDAC patients, potentially assisting in preoperative risk stratification.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"15 ","pages":"Article 100711"},"PeriodicalIF":2.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145614350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Photon counting CT improves coronary stent imaging and fat attenuation index assessment across reconstruction modes","authors":"Yujie Gao ,&nbsp;Yaru Yang ,&nbsp;Qiuju Hu ,&nbsp;Yane Zhao ,&nbsp;Yong Yuan ,&nbsp;Jiliang Chen ,&nbsp;Bangjun Guo ,&nbsp;Dongsheng Jin ,&nbsp;song luo ,&nbsp;Guangming Lu","doi":"10.1016/j.ejro.2025.100695","DOIUrl":"10.1016/j.ejro.2025.100695","url":null,"abstract":"<div><h3>Objectives</h3><div>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).</div></div><div><h3>Methods</h3><div>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.</div></div><div><h3>Results</h3><div>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 &lt; 0.001). Subjective image quality scores for 0.2 mm UHR images were superior to those for simulated EID-CT (all p &lt; 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 &lt; 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) &lt; 50 % in diameter from stents without ISR, with cut-off value of −98HU and −99.5HU, respectively.</div></div><div><h3>Conclusions</h3><div>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.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"15 ","pages":"Article 100695"},"PeriodicalIF":2.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145319584","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}

{"title":"CT Radiomics-based machine learning approach for the invasiveness of pulmonary ground-glass nodules prediction","authors":"Rui Chen ,&nbsp;Hu Zhang ,&nbsp;Xingwen Huang ,&nbsp;Haitao Han ,&nbsp;Jinbo Jian","doi":"10.1016/j.ejro.2025.100680","DOIUrl":"10.1016/j.ejro.2025.100680","url":null,"abstract":"<div><h3>Objective</h3><div>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).</div></div><div><h3>Methods</h3><div>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.</div></div><div><h3>Results</h3><div>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.</div></div><div><h3>Conclusion</h3><div>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.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"15 ","pages":"Article 100680"},"PeriodicalIF":2.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144889510","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}

{"title":"Dual-metric Bayesian optimization of B-spline mesh size for 4DCT lung registration","authors":"Liang Tan,&nbsp;Liyuan Chen,&nbsp;Huanli Luo,&nbsp;Xin Yang,&nbsp;Bin Feng,&nbsp;Fu Jin","doi":"10.1016/j.ejro.2025.100698","DOIUrl":"10.1016/j.ejro.2025.100698","url":null,"abstract":"<div><h3>Objectives</h3><div>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.</div></div><div><h3>Methods</h3><div>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.</div></div><div><h3>Results</h3><div>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 &gt; 0.94, HD₉₅&lt; 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).</div></div><div><h3>Conclusions</h3><div>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.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"15 ","pages":"Article 100698"},"PeriodicalIF":2.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416105","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}