European Journal of Radiology Open最新文献

{"title":"Evaluation of an artificial intelligence generated Brock score to determine malignancy risk of screen-detected pulmonary nodules","authors":"Kathryn J. Long ,&nbsp;Michael Balassone ,&nbsp;Saigopal Sathyamurthy ,&nbsp;Vikash Challa ,&nbsp;Deepak Adarsh ,&nbsp;Gerard A. Silvestri","doi":"10.1016/j.ejro.2026.100765","DOIUrl":"10.1016/j.ejro.2026.100765","url":null,"abstract":"<div><h3>Background</h3><div>Estimation of malignancy risk is a crucial step in the evaluation of indeterminate pulmonary nodules (IPN) on computed tomography (CT) scans. Artificial intelligence (AI) tools can improve the risk prediction of IPN. The purpose of this study was to compare the performance of an AI generated Brock score with standard Brock score to estimate malignancy risk.</div></div><div><h3>Methods</h3><div>The AI tool qCT (Qure.ai Technologies, Mumbai, India) was tested on a case-control series of patients with screen-detected nodules from the National Lung Cancer Screening Trial (NLST). Standard Brock score and AI Brock score were calculated for each nodule using clinical data from the NLST dataset and nodule characteristics derived from the dataset and AI interpretation of CT images, respectively. Performance of each model was assessed using the area under the receiver operating curve (AUC). The sensitivity and specificity of each model was calculated at fixed thresholds of ≥ 2, 5, and 10% cancer risk.</div></div><div><h3>Results</h3><div>A total of 478 patients (239 cases, 239 controls) were included. The AUC of the AI Brock score and standard Brock score were 0.72 (95% CI 0.67–0.76) and 0.76 (95% CI 0.72–0.81), respectively (p = 0.053). At a 5% threshold, the sensitivity and specificity were 69% and 65% for AI and 57% and 82% for the standard.</div></div><div><h3>Conclusion</h3><div>The accuracy of the AI tool was lower than the standard Brock score, though not reaching statistical significance. Across all risk thresholds, the AI tool had higher sensitivity, but lower specificity than the standard Brock score, suggesting use might complement clinician assessments of nodule risk.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"16 ","pages":"Article 100765"},"PeriodicalIF":2.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148178111","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":"Automatic measurement of Caton index on knee X-ray images using a key point detection model","authors":"Ting Li ,&nbsp;Nadeer M. Gharaibeh ,&nbsp;Gang Wu","doi":"10.1016/j.ejro.2025.100687","DOIUrl":"10.1016/j.ejro.2025.100687","url":null,"abstract":"<div><h3>Purpose</h3><div>To explore the feasibility of the You Only Look Once (YOLO) algorithm in the measurement of Carton index.</div></div><div><h3>Methods</h3><div>1156 knee X-ray images were collected from two centers (960 and 196). Five key points at patella and tibia on knee X-ray were labeled using the software of Labelme. YOLO11 pose models (including YOLO11n, YOLO11m and YOLO11x) were refined by labeled images from center A, and was then used to detect keypoints on images from center B. A line was the line between anterior edge of the tibial plateau and the lower pole of patellar articular surface, and B line was patellar articular surface. Carton index (A/B ratio) of 196 cases was obtained by senior radiologist, junior radiologist and YOLO respectively. The Bland Altman plot, Pearson Correlation test, Mean Absolute Error (MAE) and Intra-class correlation coefficient (ICC) were used to evaluate the agreement in measurement.</div></div><div><h3>Results</h3><div>Carton index of 196 images were automatically obtained with YOLO11n-pose, YOLO11m-pose and YOLO11x-pose. The ICC between senior and junior radiologists was 0.89. Pearson correlation coefficients were 0.23, 0.43 and 0.73 respectively for YOLO11n, YOLO11m and YOLO11x. ICC were 0.23, 0.42 and 0.72 respectively for YOLO11n, YOLO11m and YOLO11x. MAE were 0.20, 0.17 and 0.10 respectively for YOLO11n, YOLO11m and YOLO11x.</div></div><div><h3>Conclusions</h3><div>YOLO11x-pose model shows promise in the automatic measurement of Carton index on the knee X-ray image.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"15 ","pages":"Article 100687"},"PeriodicalIF":2.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095125","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":"Non-invasive diagnostic value of pericoronary fat attenuation index for identifying culprit lesions in acute coronary syndrome","authors":"Fengfeng Yang ,&nbsp;Zhengyang Li ,&nbsp;Haoran Cai,&nbsp;Jing Zhu,&nbsp;Huijia Liu,&nbsp;Yang Zhao","doi":"10.1016/j.ejro.2025.100682","DOIUrl":"10.1016/j.ejro.2025.100682","url":null,"abstract":"<div><h3>Objectives</h3><div>This study aimed to determine the efficacy of fat attenuation index (FAI) as a non-invasive diagnostic tool in the precise identification of culprit lesions in individuals diagnosed with acute coronary syndrome (ACS).</div></div><div><h3>Methods</h3><div>A retrospective analysis of 230 patients with non-ST-segment elevation ACS. PCAT attenuation (FAI_standard) was measured in the proximal 40-mm segment of each major coronary artery. Furthermore, the average PCAT attenuation of the identified lesions was designated as FAI_lesion. The average PCAT attenuation across the complete length of coronary artery, referred to as FAI_average, was computed. Plaque characteristics (volume, composition) were analyzed via coronary computed tomography angiography. Multivariable logistic regression identified predictors of culprit lesions, and diagnostic performance was assessed using area under the curve (AUC) and decision curve analysis.</div></div><div><h3>Results</h3><div>Culprit lesions exhibited significantly elevated levels of PCAT attenuation across the parameters of FAI_standard, FAI_average, and FAI_lesion. FAI_lesion demonstrated superior diagnostic accuracy versus FAI_standard and FAI_average, and also emerged as the strongest independent predictor (Odds ratio = 2.598, P &lt; 0.001). In training and test sets, a composite model integrating FAI_lesion with additional indices demonstrated enhanced diagnostic efficacy for the detection of culprit lesions in patients with ACS (AUC = 0.960, 0.803). Low-attenuation plaque volume (&lt;30 HU) was independently associated with culprit lesions (OR = 3.12, P = 0.002).</div></div><div><h3>Conclusion</h3><div>FAI_lesion, a superior non-invasive biomarker for high-risk ACS lesions compared to traditional FAI, enables earlier precise risk stratification through clinical integration.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"15 ","pages":"Article 100682"},"PeriodicalIF":2.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144996333","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":"Comparative evaluation of ADC and ADC ratio in differentiating Gleason Score 7 in prostate cancer imaging","authors":"D. Samaras ,&nbsp;G. Agrotis ,&nbsp;D. Tsivaka ,&nbsp;M. Vakalopoulou ,&nbsp;K. Vassiou ,&nbsp;V. Tzortzis ,&nbsp;M. Vlychou ,&nbsp;I. Tsougos","doi":"10.1016/j.ejro.2025.100710","DOIUrl":"10.1016/j.ejro.2025.100710","url":null,"abstract":"<div><h3>Background</h3><div>Multiparametric Magnetic Resonance Imaging (mpMRI), including diffusion-weighted imaging (DWI), plays a key role in detecting and stratifying prostate cancer (PCa). The Apparent Diffusion Coefficient (ADC) aids in tissue characterization and may assist in distinguishing between Gleason score 7 subgroups (GS 3 +4 and GS 4 +3). This study aimed to evaluate mean ADC and ADC_ratio (ratio of tumor-to-normal ADC) in differentiating these subgroups and to assess the influence of magnetic field strength (1.5 T vs. 3.0 T) on diagnostic performance.</div></div><div><h3>Methods</h3><div>This retrospective study included 48 patients with histologically confirmed GS 7 PCa who underwent mpMRI at 1.5 T or 3.0 T. Two experienced radiologists independently and blindly delineated tumor and reference ROIs. Inter-observer agreement was evaluated using the intraclass correlation coefficient (ICC). Correlation, t-tests, and receiver operating characteristic (ROC) analyses were used to assess relationships, group differences, and optimal cut-off values.</div></div><div><h3>Results</h3><div>Excellent inter-observer agreement was found for both mean ADC and ADC_ratio (ICC<span><math><mo>&gt;</mo></math></span>0.9). At 3.0 T, mean ADC (10.9 ×10⁻⁴ mm²/s vs 8.55 ×10⁻⁴ mm²/s, p = 0.011) and ADC_ratio (0.68 vs 0.54, p = 0.003) significantly distinguished GS 3 + 4 from GS 4 + 3, while no differences were observed at 1.5 T. Spearman’s ρ confirmed stronger correlations at 3.0 T (ADCratio ρ=−0.510; p = 0.004). For the combined (3.0 T + 1.5 T) dataset, ADC_ratio achieved an AUC of 0.748 and mean ADC an AUC of 0.718 (p &gt; 0.05).</div></div><div><h3>Conclusion</h3><div>ADC_ratio demonstrated high reproducibility and stable diagnostic performance across scanners, supporting its potential as a reliable imaging biomarker for prostate cancer grading.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"15 ","pages":"Article 100710"},"PeriodicalIF":2.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145568665","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":"Enhancing accuracy of detecting left atrial dilatation on CT pulmonary angiography","authors":"Louis Tapper ,&nbsp;Samer Alabed ,&nbsp;Ahmed Maiter ,&nbsp;Andrzej Lejawka ,&nbsp;Mahan Salehi ,&nbsp;Krit Dwivedi ,&nbsp;Pankaj Garg ,&nbsp;David G. Kiely ,&nbsp;Peter Metherall ,&nbsp;Rob J. van der Geest ,&nbsp;Kavita Karunasaagarar ,&nbsp;Michael Sharkey ,&nbsp;Andrew J. Swift","doi":"10.1016/j.ejro.2025.100696","DOIUrl":"10.1016/j.ejro.2025.100696","url":null,"abstract":"<div><h3>Introduction</h3><div>Left atrial (LA) dilatation predicts several cardiovascular disorders. Identifying LA dilatation on computed tomography pulmonary angiography (CTPA) could aid diagnosis of cardiovascular disease. This study assessed an artificial intelligence (AI) segmentation model’s performance at detecting LA dilatation on CTPA.</div></div><div><h3>Methods</h3><div>Patients with suspected pulmonary hypertension (PH) who underwent CTPA and cardiac MRI (CMR) were retrospectively identified from a single centre registry. The LA was segmented by an AI tool for CTPA and a validated AI tool for CMR. LA volume measurements were categorised for LA dilatation based on existing threshold values. The expert radiologist's reports of the CTPA studies were also categorised for LA dilatation. Automated CTPA LA volumes and corresponding radiologist reports were compared against the reference standard of CMR.</div></div><div><h3>Results</h3><div>451 patients were included (mean age 64 ± 13 years, 62.5 % female, 85.8 % white). Automated LA volume measurements on CTPA showed strong positive correlation with those on CMR (ρ = 0.92, p &lt; 0.001) with minimal bias on Bland-Altman analysis (-4 mL, 95 %CI −39 to +31 mL). Automated LA measurements on CTPA showed higher agreement with those on CMR (κ = 0.80) than the radiologist reports (κ = 0.62). Automated LA measurements on CTPA showed higher accuracy metrics (sensitivity 81.0 %, specificity 96.8 %, positive predictive value (PPV) 88.5 %, negative predictive value (NPV) 94.4 %) than the radiologist reports (sensitivity 66.7 %, specificity 93.1 %, PPV 74.5 %, NPV 90.2 %).</div></div><div><h3>Conclusion</h3><div>Deep learning increases the accuracy of LA volume measurements on non-ECG gated CTPA, improving radiologist performance in detecting LA dilatation.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"15 ","pages":"Article 100696"},"PeriodicalIF":2.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145319582","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-based deep learning radiomics analysis for preoperative Lauren classification in gastric cancer and explore the tumor microenvironment","authors":"Ming Cheng ,&nbsp;Yimin Guo ,&nbsp;Huiping Zhao ,&nbsp;Hanyue Zhang ,&nbsp;Pan Liang ,&nbsp;Jianbo Gao","doi":"10.1016/j.ejro.2025.100667","DOIUrl":"10.1016/j.ejro.2025.100667","url":null,"abstract":"<div><h3>Purpose</h3><div>This study aimed to investigate the usefulness of CT-based deep learning radiomics analysis (DLRA) for preoperatively differentiating Lauren classification in gastric cancer (GC) patients and explore the tumor microenvironment.</div></div><div><h3>Methods</h3><div>578 patients were recruited from January 2015 to June 2024, and divided into the training cohort (n = 311), the internal validation cohort (n = 132), and the external validation cohort (n = 135). Clinical characteristics were collected. Radiomics features were extracted from CT images at arterial phase (AP) and venous phase (VP). A radiomics nomogram incorporating radiomics signature and clinical information was built for distinguishing Lauren classification, and its discrimination, calibration, and clinical usefulness were evaluated. RNA sequencing data from The Cancer Imaging Archive database were used to perform transcriptomics analysis.</div></div><div><h3>Results</h3><div>The nomogram incorporating the two radiomics signatures and clinical characteristics exhibited good discrimination of Lauren classification on all cohorts [overall C-indexes 0.815 (95 % CI: 0.739–0.869) in the training cohort, 0.785 (95 % CI: 0.702–0.834) in the internal validation cohort, 0.756 (95 % CI: 0.685–0.816) in the external validation cohort]. It outperformed the clinical model in predictive ability. The calibration and decision curve substantiated the model's excellent fitness and clinical applicability. Further, transcriptomics analysis showed that the differentially expressed genes of different Lauren types were mainly enriched in pathways related to cell contraction and migration, and the infiltration degree of various immune cells was also significantly different.</div></div><div><h3>Conclusions</h3><div>DLRA effectively differentiated Lauren classification in GC, and our analysis of transcriptomic data across different Lauren subtypes revealed the heterogeneity within the GC microenvironment.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"15 ","pages":"Article 100667"},"PeriodicalIF":1.8,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330263","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":"Glymphatic dysfunction as an imaging biomarker for cognitive impairment in patients with β-thalassemia major: A multimodal MRI study","authors":"Xingye Yang ,&nbsp;Meiru Bu ,&nbsp;Xi Deng ,&nbsp;Haifeng Zheng ,&nbsp;Chengli Wu ,&nbsp;Wei Cui ,&nbsp;Muliang Jiang ,&nbsp;Bihong T. Chen","doi":"10.1016/j.ejro.2025.100692","DOIUrl":"10.1016/j.ejro.2025.100692","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Cerebral lymphatic abnormalities have been associated with neurodegenerative processes. However, brain glymphatic system and its role in cognitive impairment in β-thalassemia major (β-TM) remain unknown. This study assessed brain glymphatic alterations in β-TM using advanced neuroimaging and their correlation with cognitive deficits.</div></div><div><h3>Method</h3><div>Thirty-five β-TM patients and forty matched healthy controls (HC) underwent standardized cognitive assessment and diffusion tensor imaging (DTI). Automated segmentation derived glymphatic parameters: Analysis Along the Perivascular Space index (DTI-ALPS index), choroid plexus volume fraction (CPVF), perivascular space volume fraction (PVSVF), and fractional volume of free water in white matter (FW-WM) and were compared between groups. Correlations with cognitive scores were analyzed.</div></div><div><h3>Result</h3><div>Compared with the HC, β-TM patients showed significantly lower DTI-ALPS index values in the left hemisphere (1.4527vs1.5275,P = 0.003), the right hemisphere (1.3648vs1.4492,P = 0.004) and bilateral hemispheres (1.4087vs1.4884,P = 0.001), alongside higher CPVF values in the left hemisphere (0.5426vs0.4655,P = 0.034), the right hemisphere (0.5296vs0.4439,P = 0.018) and bilateral hemispheres (1.0731vs0.9095,P = 0.01). The patient group also had higher PVSVF values in the white matter (0.2848vs0.2488,P = 0.841), the subcortical regions (0.1718vs0.1470,P = 0.349) and whole brain (0.4566vs0.3958,P = 0.678), and had higher FW-WM value (0.1580vs0.1557,P = 0.384). Lower DTI-ALPS index and higher FW-WM value was associated with poorer cognitive performance in the β-TM patients (P &lt; 0.05).</div></div><div><h3>Conclusions</h3><div>The DTI-ALPS index is a potential neuroimaging biomarker for glymphatic dysfunction and cognitive impairment in β-TM. Increased CP volume implicated underlying glymphatic alterations, supporting integration of glymphatic MRI metrics into clinical cognitive assessment for this population.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"15 ","pages":"Article 100692"},"PeriodicalIF":2.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145264885","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":"Analysis of a clot-based combined radiomics model for predicting embolic etiology in acute ischemic stroke patients","authors":"Ru Ding ,&nbsp;Kun An ,&nbsp;Fen Wang ,&nbsp;Zirui Cao ,&nbsp;Yan Zeng ,&nbsp;Lili Guo","doi":"10.1016/j.ejro.2025.100709","DOIUrl":"10.1016/j.ejro.2025.100709","url":null,"abstract":"<div><h3>Background and objectives</h3><div>Acute ischemic stroke (AIS) remains the primary cause of mortality and disability among adults in China<strong>.</strong> Different etiologies of acute ischemic stroke (AIS) are considered important factors affecting neurological function. The accurate etiological classification of AIS prior to surgery is crucial. To investigate and explore the predictive value of a clot-based combined radiomics model for identifying the etiological subtypes of acute ischemic stroke.</div></div><div><h3>Materials and methods</h3><div>A total of 263 patients with acute ischemic stroke caused by anterior circulation large artery occlusion were retrospectively enrolled. These were grouped into training (180), testing (45), and external validation cohorts (38). NCCT and CTA scans were adopted to segment region of interest (ROI) of clots. Feature selection was conducted to establish Clinical model, radiomics models (NCCT, CTA, and NCCT&amp;CTA), and combined model.</div></div><div><h3>Results</h3><div>The AUCs of the clinical model and radiomics models (NCCT, CTA and NCCT&amp;CTA) in the testing cohort were 0.8288(95 % CI: 0.7174–0.9403), 0.8133(95 % CI:0.6853–0.9414), 0.8075(95 % CI:0.6844–0.9307) and 0.8535(95 % CI:0.6774–1), respectively. The combined model achieved a greater AUC than the other four models in the testing cohort (0.9077 [95 % CI: 0.821–0.9944]). Clinical decision curve analysis (DCA) demonstrated that the radiomics (NCCT&amp;CTA) model and combined model show better net benefits within a relatively wide range of threshold probabilities.</div></div><div><h3>Conclusion</h3><div>The combined radiomics model achieved good predictive efficacy for distinguishing the etiological subtypes of acute ischemic stroke and can provide valuable information for the precise selection of recanalization strategies in clinical practice.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"15 ","pages":"Article 100709"},"PeriodicalIF":2.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145568669","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":"Comparative evaluation of four reconstruction techniques for prostate T2-weighted MRI: Sensitivity encoding, compressed sensing, deep learning, and super-resolution","authors":"Noriko Nishioka ,&nbsp;Noriyuki Fujima ,&nbsp;Satonori Tsuneta ,&nbsp;Daisuke Kato ,&nbsp;Takashi Kamiishi ,&nbsp;Masato Yoshikawa ,&nbsp;Rina Kimura ,&nbsp;Keita Sakamoto ,&nbsp;Ryuji Matsumoto ,&nbsp;Takashige Abe ,&nbsp;Jihun Kwon ,&nbsp;Masami Yoneyama ,&nbsp;Kohsuke Kudo","doi":"10.1016/j.ejro.2025.100671","DOIUrl":"10.1016/j.ejro.2025.100671","url":null,"abstract":"<div><h3>Purpose</h3><div>To evaluate and compare the image quality and lesion conspicuity of prostate T2-weighted imaging (T2WI) using four reconstruction methods: conventional Sensitivity Encoding (SENSE), compressed sensing (CS), model-based deep learning reconstruction (DL), and deep learning super-resolution reconstruction (SR).</div></div><div><h3>Methods</h3><div>This retrospective study included 49 patients who underwent multiparametric MRI (mpMRI) or biparametric MRI (bpMRI) for suspected prostate cancer. Axial T2WI was acquired using two protocols: conventional SENSE and CS-based acquisition. From the CS-based data, three reconstruction methods (CS, DL, and SR) were applied to generate additional images. Two board-certified radiologists independently assessed overall image quality and sharpness using a 4-point Likert scale (1 = poor, 4 = excellent). Quantitative analysis included signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and sharpness index. PI-RADS T2WI scoring and lesion conspicuity were preliminarily evaluated in 18 individuals with pathologically confirmed prostate cancer. Statistical comparisons were conducted using the Wilcoxon signed-rank test.</div></div><div><h3>Results</h3><div>SR consistently achieved the highest scores in both qualitative (overall image quality, image sharpness) and quantitative (SNR, CNR, sharpness index) assessments, compared with SENSE, CS, and DL (all pairwise comparisons, Bonferroni-corrected p &lt; 0.0001). In lesion-based analysis, SR showed a trend toward improved lesion conspicuity, although PI-RADS T2WI scores were similar across reconstruction.</div></div><div><h3>Conclusion</h3><div>SR reconstruction demonstrated superior image quality in both qualitative and quantitative assessments and showed potential benefits for lesion visualization. These findings, although based on a small sample, suggest that SR may be a promising approach for prostate MRI and warrants further investigation in larger populations.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"15 ","pages":"Article 100671"},"PeriodicalIF":1.8,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144679963","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":"2025-12-01","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}