Clinical radiology最新文献

{"title":"Response to ‘Multimodality imaging findings in secondary dysmenorrhea’","authors":"T. Lewis, J. Collins","doi":"10.1016/j.crad.2025.107069","DOIUrl":"10.1016/j.crad.2025.107069","url":null,"abstract":"","PeriodicalId":10695,"journal":{"name":"Clinical radiology","volume":"90 ","pages":"Article 107069"},"PeriodicalIF":1.9,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145109332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating the role of multiparametric and biparametric MRI based on PI-RADS v2.1 and machine learning models in prostate cancer diagnosis","authors":"D. Zhuang ,&nbsp;S. Zhong ,&nbsp;S. Chen ,&nbsp;J. Zou ,&nbsp;H. Jin ,&nbsp;X. Xu ,&nbsp;H. Zhang ,&nbsp;G. Hu","doi":"10.1016/j.crad.2025.107070","DOIUrl":"10.1016/j.crad.2025.107070","url":null,"abstract":"<div><h3>Purpose</h3><div>We compared the diagnostic performance of multiparametric MRI (mpMRI) and biparametric MRI (bpMRI) in detecting clinically significant prostate cancer (csPCa) using the Prostate Imaging Reporting and Data System (PI-RADS) version 2.1. Additionally, we constructed multiple machine learning (ML) models for detecting csPCa using PI-RADS scores and clinical parameters.</div></div><div><h3>Materials and Methods</h3><div>We enrolled 583 patients with 594 lesions who underwent mpMRI before MRI/transrectal ultrasound (MRI-TRUS) fusion-targeted biopsy and systematic biopsy. The diagnostic performance of bpMRI and mpMRI was analyzed by the area under the curve (AUC). We built multiple ML models for detecting csPCa. The input parameters were: PI-RADS scores in bpMRI or mpMRI, age, prostate-specific antigen (PSA), MRI-defined PSA density (PSAD), and prostate volume (PV). Training and test cohorts included 475 and 119 lesions, respectively.</div></div><div><h3>Results</h3><div>The AUCs of bpMRI and mpMRI for the diagnosis of csPCa in total lesions were 0.88 and 0.90, respectively (p&lt;0.05). mpMRI had higher sensitivity (93.1%) but lower specificity (77.3%) compared to bpMRI (sensitivity: 79.2%; specificity: 86.2%). All the ML models exhibited the high AUC in detecting csPCa (0.93–0.96 based on mpMRI models and 0.91–0.94 based on bpMRI models). There were no statistically significant differences in the AUC values between the two groups of ML models in test sets.</div></div><div><h3>Conclusions</h3><div>Compared to bpMRI, the AUC of mpMRI based on PI-RADS v2.1 to detect csPCa was higher. The diagnostic performance of ML models for detecting csPCa using PI-RADS scores and clinical parameters was excellent and comparable between mpMRI and bpMRI.</div></div>","PeriodicalId":10695,"journal":{"name":"Clinical radiology","volume":"90 ","pages":"Article 107070"},"PeriodicalIF":1.9,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145184757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Imaging features of non-nasopharyngeal lymphoepithelial carcinoma of the head and neck","authors":"K.Y. Li ,&nbsp;H.M. Kwok ,&nbsp;H.S. Leung ,&nbsp;K.F. Johnny Ma ,&nbsp;K.T. Wong ,&nbsp;A.D. King","doi":"10.1016/j.crad.2025.107066","DOIUrl":"10.1016/j.crad.2025.107066","url":null,"abstract":"<div><h3>AIM</h3><div>Lymphoepithelial carcinoma (LEC) of the head and neck is rare outside the nasopharynx. Radiological literature describing its appearances is limited. Our study aims to summarise the imaging features and clinical characteristics.</div></div><div><h3>MATERIALS AND METHODS</h3><div>This is a retrospective cohort study on pathologically proven cases of LEC from two local major hospital clusters. Multimodality pretreatment imaging features were studied. Demographics and clinical information, including treatment, recurrence-free survival (RFS) and overall survival (OS) were also evaluated.</div></div><div><h3>RESULTS</h3><div>Thirty LECs were identified in thirty patients, comprising 19 in the major salivary glands and 11 outside the major salivary glands. Tumours showed T2 intermediate signal, restricted diffusion (mean apparent diffusion coefficient [ADC] 0.701x10⁻³ mm²/s), moderate enhancement, and high 18F-fluorodeoxyglucose (FDG) avidity. Ill-defined margins (90%) and invasion of adjacent structures (43.3%) were common, notably 73.7% of major salivary gland tumours showed extensive glandular invasion. Nodal metastases were common, in 21/30 (70%), and 38.1% showed necrosis.</div><div>RFS was worse in the presence of nodal metastasis (<em>P</em> = 0.038) but not in OS, with a mean follow-up time of 53.5 months. There were no significant differences in RFS or OS between LEC patients treated with or without surgery, or between major salivary gland and nonmajor salivary gland LECs.</div></div><div><h3>CONCLUSION</h3><div>Head and neck LEC shows intermediate T2W signal, moderate enhancement, diffusion restriction, high FDG avidity and a propensity for nodal metastasis with necrosis, similar to those described in the literature for NPC. Recurrence-free-survival was worse in the presence of nodal metastases but was similar when treated by surgery or chemo-radiotherapy.</div></div>","PeriodicalId":10695,"journal":{"name":"Clinical radiology","volume":"90 ","pages":"Article 107066"},"PeriodicalIF":1.9,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145109333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual-layer spectral computed tomography (CT) parameters for identifying severe aortic regurgitation in aortic valve disease patients","authors":"Z.X. Chen ,&nbsp;J. Xu ,&nbsp;W. Tian ,&nbsp;N. Yang ,&nbsp;X.D. Lu ,&nbsp;J.Z. Xu ,&nbsp;Y.X. Li ,&nbsp;L. Xu ,&nbsp;Z.L. Zhang ,&nbsp;G. Wang","doi":"10.1016/j.crad.2025.107067","DOIUrl":"10.1016/j.crad.2025.107067","url":null,"abstract":"<div><h3>AIM</h3><div>To investigate the diagnostic potential of dual-layer computed tomography (DLCT) in detecting the severe aortic regurgitation (sAR) among patients with aortic valve disease (AVD).</div></div><div><h3>MATERIALS AND METHODS</h3><div>This retrospective study included 53 AVD patients who underwent both transthoracic echocardiography (TTE) and DLCT within one week. Patients were categorised into sAR (n = 16) and nonsevere AR (non-sAR, n = 37) groups based on TTE findings. DLCT parameters, including aortic annulus dimensions (max/min diameter, circumference, area), sinus of Valsalva (SOV) diameter, ascending aorta diameter (AoD), and myocardial extracellular volume (ECV) fraction, were analysed. Logistic regression analysis was employed to identify risk factors for sAR in patients with AVD, and the diagnostic performance of DLCT parameters was evaluated using receiver operating characteristic (ROC) curves.</div></div><div><h3>RESULTS</h3><div>Patients with sAR were significantly younger and had larger aortic valve parameters compared to the non-sAR group. Computed tomography-ECV (CT-ECV) was notably higher in the sAR group (33.19 ± 3.86% vs 29.05 ± 4.58%, <em>P</em>&lt; 0.05). Logistic regression analysis identified CT-ECV and SOV diameter as independent predictors of sAR (CT-ECV: OR = 1.531, 95% CI: 1.133–2.070, <em>P</em>= 0.006; SOV diameter: OR= 1.198, 95% CI: 1.056–1.359, <em>P</em>= 0.005). Both parameters effectively distinguished sAR from non-sAR patients. Their combined model enhanced diagnostic performance (AUC = 0.867) and maintained excellent accuracy (AUC = 0.819) in the mild-moderate AR (mAR) subgroup.</div></div><div><h3>CONCLUSION</h3><div>DLCT-derived CT-ECV and aortic valve parameters effectively identify sAR in AVD patients. The combination of CT-ECV and SOV diameter offers the highest diagnostic accuracy, potentially improving AVD management.</div></div>","PeriodicalId":10695,"journal":{"name":"Clinical radiology","volume":"91 ","pages":"Article 107067"},"PeriodicalIF":1.9,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145312596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Yin-Yang artefact in contrast enhanced mammography","authors":"A. Sen","doi":"10.1016/j.crad.2025.107065","DOIUrl":"10.1016/j.crad.2025.107065","url":null,"abstract":"","PeriodicalId":10695,"journal":{"name":"Clinical radiology","volume":"90 ","pages":"Article 107065"},"PeriodicalIF":1.9,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145109336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Radiomics for bone tumour diagnosis and management","authors":"V. Molinari ,&nbsp;S. Gitto ,&nbsp;F. Serpi ,&nbsp;S. Fusco ,&nbsp;D. Albano ,&nbsp;C. Messina ,&nbsp;M. Del Fabbro ,&nbsp;G.M. Peretti ,&nbsp;L.M. Sconfienza","doi":"10.1016/j.crad.2025.107062","DOIUrl":"10.1016/j.crad.2025.107062","url":null,"abstract":"<div><div>Radiomics is an advanced imaging technique that extracts quantitative features from medical images. It can be useful to support diagnosis and management, particularly in bone oncology. By analysing complex patterns within imaging data, radiomics can provide detailed insights into tumour characteristics that are not apparent through visual inspection alone. This approach leverages machine learning algorithms to identify and quantify features like texture, shape, and intensity of tumours, which can help in discriminating between benign and malignant lesions and predicting treatment response and outcome in malignant tumours. In bone tumour diagnosis, radiomics can improve accuracy in differentiating benign lesions from skeletal metastases and primary malignant tumours, identifying different bone-tumour histotypes, and predicting tumour grade. Regarding management, it aids in treatment planning by predicting response to neoadjuvant therapy in chemosensitive tumours, such as osteosarcoma, thus personalising treatment strategies. Additionally, radiomic features can be used to monitor disease progression and recurrence of malignant bone tumours more effectively than traditional imaging methods. Recent studies have demonstrated that integrating radiomics with clinical data enhances predictive models for patient outcomes. However, challenges such as standardising imaging protocols and ensuring reproducibility of radiomic features remain.</div></div>","PeriodicalId":10695,"journal":{"name":"Clinical radiology","volume":"91 ","pages":"Article 107062"},"PeriodicalIF":1.9,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145265164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Commentary on “Thoracic dorsal root ganglia: Visualisation of normal anatomy and pathological findings by T2 fast field echo (T2FFE) compared with balanced fast field echo (BFFE) and nerve-sheath signal increased with inked rest-tissue rapid acquisition of relaxation enhancement imaging (SHINKEI).”","authors":"P. Aphale ,&nbsp;H. Shekhar ,&nbsp;S. Dokania","doi":"10.1016/j.crad.2025.107061","DOIUrl":"10.1016/j.crad.2025.107061","url":null,"abstract":"","PeriodicalId":10695,"journal":{"name":"Clinical radiology","volume":"90 ","pages":"Article 107061"},"PeriodicalIF":1.9,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145107972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel calibration of vertebral bone quality score for the evaluation of osteoporosis in the lumbar spine","authors":"F. Hu ,&nbsp;F. Jing ,&nbsp;D. Zhao ,&nbsp;H. Bian ,&nbsp;P. Li ,&nbsp;C. Chen ,&nbsp;X. Ma ,&nbsp;S. Lu ,&nbsp;Z. Wang ,&nbsp;Q. Yang","doi":"10.1016/j.crad.2025.107063","DOIUrl":"10.1016/j.crad.2025.107063","url":null,"abstract":"<div><h3>Aim</h3><div>The magnetic resonance imaging (MRI)–based vertebral bone quality (VBQ) score can be used as a screening tool for identifying patients who may have osteoporosis; however, a significant influence of the magnetic fields of 1.5 T and 3.0 T on the score has been reported. The purpose of this study was to introduce a novel calibration to overcome the problem.</div></div><div><h3>Materials and Methods</h3><div>We standardised fat infiltration measurement by calibrating the VBQ score to subcutaneous fat, terming it the calibrated-VBQ score. We retrospectively collected patients who underwent both the 1.5-T and 3.0-T MRI examinations. The self-paired VBQ score, a modified-VBQ score, and the calibrated-VBQ score were compared between different magnetic fields. Correlation analysis and receiver operating characteristic (ROC) curve analysis were performed.</div></div><div><h3>Results</h3><div>The study included 83 patients (mean age: 67.7 ± 8.3 years). The analysis of self-paired patients revealed that the 1.5-T and 3.0-T magnetic fields had no influence on the calibrated-VBQ score, whereas a significant influence on the VBQ scores and the modified-VBQ score was found. By correlation analysis and ROC curve analysis, a better diagnostic effect of the calibrated-VBQ score for osteoporosis could be found than with the VBQ score and the modified-VBQ score.</div></div><div><h3>Conclusion</h3><div>The calibrated-VBQ score could overcome the inconsistency between the magnetic fields of 1.5 T and 3.0 T, and it could be considered as a better screening tool for osteoporosis compared with the traditional VBQ scores.</div></div>","PeriodicalId":10695,"journal":{"name":"Clinical radiology","volume":"90 ","pages":"Article 107063"},"PeriodicalIF":1.9,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145102881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lymphovascular invasion (LVI) assessment in breast cancer with magnetic resonance imaging (MRI)-based artificial intelligence (AI) approaches","authors":"D.E. Tekcan Sanli ,&nbsp;A.N. Sanli","doi":"10.1016/j.crad.2025.107060","DOIUrl":"10.1016/j.crad.2025.107060","url":null,"abstract":"","PeriodicalId":10695,"journal":{"name":"Clinical radiology","volume":"90 ","pages":"Article 107060"},"PeriodicalIF":1.9,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145109335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advanced multicompartment diffusion model for noninvasive grading of endometrial cancer: comparative analysis with apparent diffusion coefficient (ADC) histogram parameters","authors":"T. Yang ,&nbsp;Z. Zhuang ,&nbsp;W. Hu ,&nbsp;D. Wu ,&nbsp;H. Liu ,&nbsp;G. Shi ,&nbsp;Y. Zhou ,&nbsp;Y. Dai","doi":"10.1016/j.crad.2025.107064","DOIUrl":"10.1016/j.crad.2025.107064","url":null,"abstract":"<div><h3>AIM</h3><div>To evaluate the predictive ability of restricted spectrum imaging (RSI) model-derived parameters for grading endometrial cancer (EC) and compare their performance with the histogram parameters of the mono-exponential model.</div></div><div><h3>MATERIALS AND METHODS</h3><div>A total of 63 patients with EC were enrolled. Regions of interest were manually delineated, and voxel-wise fitting was performed using both a mono-exponential model and 2–4-compartments RSI models. The optimal model was determined based on the Bayesian Information Criterion. Histogram parameters of the apparent diffusion coefficient (ADC) (mean, variance, 10/25/50/75/90th percentile, minimum, maximum, kurtosis, skewness) were extracted. One-way analysis of variance (ANOVA) or Kruskal-Wallis tests were employed to analyse differences in magnetic resonance imaging (MRI) parameters across histological grades. Receiver operating characteristic curve analysis was used to assess their diagnostic performance.</div></div><div><h3>RESULTS</h3><div>The four-compartment RSI model was identified as the optimal model for characterising EC lesions. RSI4-F1/F3 exhibited significant differences between G1/G2 or G1+G2/G3 lesions, and their combination achieved the highest diagnostic performance (AUC = 0.872, 0.759), outperforming all ADC histogram parameters. Significant differences in RSI4-F1/F2/F3 were observed between G1/G3 lesions, with their combination yielding an AUC of 0.922, comparable to ADCmin (AUC = 0.918). Only RSI4-F1/F3 effectively differentiated between G2/G3 or lesions, with their combination yielding the highest AUC (0.729). Incorporating tumour size further enhanced diagnostic performance across all grades (AUC = 0.913, 0.946, 0.757, 0.791 for G1/G2, G1/G3, G2/G3, G1+G2/G3, respectively).</div></div><div><h3>CONCLUSION</h3><div>The four-compartment RSI model provides valuable insights into the component weights of tumour microenvironment across EC grades. This approach enhances the noninvasive grading of EC lesions.</div></div>","PeriodicalId":10695,"journal":{"name":"Clinical radiology","volume":"90 ","pages":"Article 107064"},"PeriodicalIF":1.9,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145109334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}