European Journal of Radiology Open最新文献

{"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":"2025-09-04","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":"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 ,&nbsp;Jun Ozeki ,&nbsp;Yumi Koyama ,&nbsp;Xiaoyan Tang ,&nbsp;Fumi Nozaki ,&nbsp;Mayumi Tani ,&nbsp;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 (&gt; 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 (&lt; 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":"2025-09-01","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}

{"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":"2025-08-29","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":"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":"2025-08-23","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":"Diagnostic performance of dual-layer spectral CT Radiomics and deep learning for differentiating osteoblastic bone metastases from bone islands","authors":"Yuchao Xiong ,&nbsp;Wei Guo ,&nbsp;Xuwen Zeng ,&nbsp;Fan Xu ,&nbsp;Li Wu ,&nbsp;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 &lt; 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":"2025-08-20","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}

{"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":"2025-08-05","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":"AI-driven assessment of over-scanning in chest CT: A systematic review and meta-analysis","authors":"Mo’men Bani-Ahmad ,&nbsp;Andrew England ,&nbsp;Laura McLaughlin ,&nbsp;Marwan Alshipli ,&nbsp;Kholoud Alzyoud ,&nbsp;Yasser H. Hadi ,&nbsp;Mark McEntee","doi":"10.1016/j.ejro.2025.100674","DOIUrl":"10.1016/j.ejro.2025.100674","url":null,"abstract":"<div><h3>Introduction</h3><div>Scan range is crucial for CT acquisitions. However, irrelevant over-scanning in CT is common and contributes to a significant radiation dose. This review explores the role of artificial intelligence (AI) in addressing manual over-scanning in chest CT imaging.</div></div><div><h3>Methods</h3><div>A systematic search of peer-reviewed publications was conducted between December 2015 and March 2025 in Embase, Scopus, Ovid, EBSCOhost, and PubMed. Two reviewers and an academic lecturer independently reviewed the articles to ensure adherence to inclusion criteria. The quality of the included studies was assessed using CLAIM and QUADAS-2 tools. Summary estimates on over-scanning at the upper and lower boundaries of the scan range in chest CT were derived using meta-analysis.</div></div><div><h3>Results</h3><div>Five studies employed AI algorithms to assess manual over-scanning in chest CT using either 2D topograms or 3D axial images at low and standard doses. These models accurately determine the extent of over-scanning, demonstrating strong agreement with radiologist evaluations. All included studies revealed significant variation in over-scanning at the superior (13.5 mm) and inferior (30.2 mm) boundaries of the scan range (p &lt; 0.001), with approximately two-thirds of the total over-scanning (43.2 mm) occurring at the inferior level (abdomen).</div></div><div><h3>Conclusions</h3><div>Integrating AI tools into the over-scanning evaluation process may optimise chest CT imaging protocols and enhance patient safety by reducing over-scanning and radiation dose through real-time monitoring and retrospective analysis.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"15 ","pages":"Article 100674"},"PeriodicalIF":2.9,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144724529","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":"Evaluation of assumed tumour volume in multiple myeloma using dual-energy spectral CT and its correlation between haematological findings","authors":"Tetsuya Kosaka ,&nbsp;Chisaki Masuda ,&nbsp;Sachiho Tatebe ,&nbsp;Risen Hirai ,&nbsp;Akira Tanimura","doi":"10.1016/j.ejro.2025.100675","DOIUrl":"10.1016/j.ejro.2025.100675","url":null,"abstract":"<div><h3>Objectives</h3><div>To measure the assumed tumour volume in the humerus of patients with multiple myeloma using dual-energy spectral computed tomography (DESCT) and to evaluate the correlation with haematological indicators.</div></div><div><h3>Methods</h3><div>We retrospectively analysed 82 DESCT examinations of 22 patients diagnosed with multiple myeloma. After extracting the bilateral humeri and removing the bone tissue, we measured the volume of the assumed tumour area using a single threshold based on Hounsfield unit values and double thresholds using material density images. We analysed the correlations between tumour volume and haematological indicators (β2-microglobulin, M-protein, free light chain, albumin, lactate dehydrogenase) and the trends after treatment intervention.</div></div><div><h3>Results</h3><div>A moderate correlation was identified between the assumed tumour volume in the initial scan and the β2-microglobulin level, with a correlation coefficient of ρ = 0.69 for the volume calculated from a single threshold value of Hounsfield unit and ρ = 0.57 for the volume calculated from a double threshold value of the bone(fat) material density image. No significant correlation was found between the assumed tumour volume and the M-protein or free light chain levels. In patients who underwent three or more follow-up evaluations after the initial examination, there was a similarity in the changes in the assumed tumour volume and β2-microglobulin levels after treatment.</div></div><div><h3>Conclusion</h3><div>Extracting assumed tumour volume using DESCT has sufficient potential as a biomarker for multiple myeloma.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"15 ","pages":"Article 100675"},"PeriodicalIF":2.9,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144721098","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 accuracy of MRI radiomics in predicting lymph node metastasis in prostate cancer: A systematic review","authors":"Alireza Teymouri ,&nbsp;Mohammad Saeid Khonji ,&nbsp;Parisa Alaghi ,&nbsp;Sina Azadnajafabad ,&nbsp;Ava Teymouri ,&nbsp;Sina Delazar","doi":"10.1016/j.ejro.2025.100673","DOIUrl":"10.1016/j.ejro.2025.100673","url":null,"abstract":"<div><h3>Purpose</h3><div>Prostate cancer (PCa) is frequently associated with pelvic lymph node metastasis (PLNM), which may be missed by conventional imaging, particularly in micrometastatic disease. MRI-based radiomics offers potential to improve detection. This review evaluates recent advancements and diagnostic accuracy of MRI radiomics for predicting PLNM in PCa patients.</div></div><div><h3>Methods</h3><div>PubMed, Embase, and Web of Science were systematically searched through January 1, 2025, using terms like “prostate cancer,” “radiomics,” and “pelvic lymph node metastasis.” Eligible studies were assessed using the Radiomics Quality Score (RQS). Study characteristics and performance metrics were narratively synthesized. Pooled area under the receiver operating characteristic curve (AUC) was calculated for PLNM prediction in studies using prostate as regions of interest (ROI), reported with 95 % confidence intervals (CI); p-value &lt; 0.05 was considered significant.</div></div><div><h3>Results</h3><div>Nine studies (2021–2024) involving 2344 PCa patients were included. Radiomics models using prostate as ROI achieved a pooled AUC of 0.78 (95 %CI: 0.72–0.84) with mild heterogeneity (I² = 19.81 %, p &lt; 0.38). Models with lymph nodes as ROI showed AUCs of 0.93–0.95. Integrating imaging reports and clinical data often improved diagnostic accuracy. Radiomics outperformed clinical nomograms in five studies, although the difference was insignificant in one study (p &gt; 0.05). Median RQS was 16/36; studies lacked prospective design and cost-effectiveness analysis.</div></div><div><h3>Conclusion</h3><div>MRI radiomics predicts PLNM with moderate accuracy, particularly when using pelvic lymph nodes as ROI. Standardized protocols, feature extraction, and clinical data integration are crucial for consistency. Prospective studies with larger cohorts are needed to validate these findings.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"15 ","pages":"Article 100673"},"PeriodicalIF":2.9,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144721165","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":"Combination of imaging features on pancreatic CT for predicting early recurrence after upfront pancreatoduodenectomy of pancreatic ductal adenocarcinoma","authors":"Shuanglin Zhang ,&nbsp;Yi-Xuan Guo ,&nbsp;Gui-Xue Dai ,&nbsp;Xiumin Qi ,&nbsp;Hao Wang ,&nbsp;Yongping Zhou ,&nbsp;Kai Zhang ,&nbsp;Fang-Ming Chen","doi":"10.1016/j.ejro.2025.100672","DOIUrl":"10.1016/j.ejro.2025.100672","url":null,"abstract":"<div><h3>Purpose</h3><div>This study aimed to identify preoperative computed tomography (CT) imaging features for predicting early recurrence after upfront pancreatoduodenectomy of pancreatic ductal adenocarcinoma (PDAC), and to assess the diagnostic performance and prognostic relevance of their combination.</div></div><div><h3>Methods</h3><div>This study retrospectively included PDAC patients who underwent pancreatoduodenectomy and preoperative pancreatic CT between January 2016 and December 2023. Early recurrence is defined based on imaging evidence or pathology within 12 months after surgery. Significant imaging features for early recurrence were identified using univariate and multivariate analyses. Disease-free survival (DFS) and overall survival (OS) were analyzed in relation to these significant imaging features.</div></div><div><h3>Results</h3><div>A total of 149 patients were evaluated (median age: 67 years; interquartile range: 41–89 years; 82 men), among whom 70 (47.0 %) experienced early recurrence. Rim enhancement, tumor necrosis, peripancreatic tumor infiltration, and suspicious metastatic lymph nodes, were independently associated with early recurrence. When any two or more of these four significant imaging features were combined, the specificity was 86.1 % (68/79) and the sensitivity was 88.6 % (60/70). DFS and OS were significantly worse in PDAC patients with two or more of these features compared to those with none or only one (all log-rank <em>P</em> &lt; 0.001).</div></div><div><h3>Conclusion</h3><div>A combination of two or more imaging features such as rim enhancement, tumor necrosis, peripancreatic tumor infiltration, and suspicious metastatic lymph nodes, could be used as a prognostic imaging marker for early recurrence, demonstrating effective diagnostic performance and an association with DFS and OS after pancreatoduodenectomy of PDAC.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"15 ","pages":"Article 100672"},"PeriodicalIF":1.8,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711448","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}