Clinical radiology最新文献

{"title":"Integrated clinical and imaging predictors of 60-day mortality in acute aortic dissection: emphasizing innominate and carotid artery involvement","authors":"L. Zhou ,&nbsp;C. Chen ,&nbsp;W. Gong ,&nbsp;L. Shang ,&nbsp;L. Liu ,&nbsp;W. Duan ,&nbsp;M. Zheng","doi":"10.1016/j.crad.2025.107078","DOIUrl":"10.1016/j.crad.2025.107078","url":null,"abstract":"<div><h3>Objectives</h3><div>To develop and validate a risk stratification model for 60-day mortality in acute aortic dissection by integrating computed tomography angiography and echocardiographic findings, with particular emphasis on aortic branch vessel involvement and surgical timing outcomes.</div></div><div><h3>Methods</h3><div>This retrospective cohort study analyzed 1,464 acute aortic dissection patients (development cohort) and two validation cohorts (temporal: n=340; geographical: n=421) between 2007-2019. Risk factors were assessed through CT angiography and echocardiography. Multivariable logistic regression and LASSO analyses identified independent predictors of 60-day mortality. Model performance was evaluated using AUC-ROC analysis and calibration plots.</div></div><div><h3>Results</h3><div>The 60-day mortality rate was 14.3% (210/1,464 patients). Independent predictors included age (adjusted OR 1.28, 95% CI 1.06–1.54), Stanford Type A (adjusted OR 1.85, 95% CI 1.42–2.41), innominate artery involvement (adjusted OR 1.45, 95% CI 1.15–1.83), left atrial enlargement (adjusted OR 1.32, 95% CI 1.04–1.67), and elevated FDP (adjusted OR 1.35, 95% CI 1.01–1.80). Combined risk factors showed the highest mortality, particularly pericardial effusion plus aortic regurgitation (42.3% in Type A). Mortality increased with delayed intervention from 15.2% (≤6 hours) to 35.8% (&gt;24 hours) in Type A dissections. The model demonstrated robust performance in the temporal (AUC 0.77) and geographical (AUC 0.78) validation cohorts.</div></div><div><h3>Conclusions</h3><div>Integration of imaging parameters with clinical factors provides reliable mortality prediction in acute aortic dissection. Upper aortic branch involvement and cardiac imaging markers are significant predictors of mortality. Early intervention, particularly within 6 hours, is associated with improved survival. This risk stratification model may guide clinical decision-making regarding intervention timing and treatment strategy selection.</div></div>","PeriodicalId":10695,"journal":{"name":"Clinical radiology","volume":"90 ","pages":"Article 107078"},"PeriodicalIF":1.9,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145205941","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":"Clinical feasibility of fully automated three-dimensional liver segmentation in low-dose chest computed tomography (CT) for assessing hepatic steatosis: an alternative to abdominal CT","authors":"S. Han ,&nbsp;I. Joo ,&nbsp;J. Park ,&nbsp;S.K. Jeon ,&nbsp;S.H. Yoon","doi":"10.1016/j.crad.2025.107079","DOIUrl":"10.1016/j.crad.2025.107079","url":null,"abstract":"<div><h3>Aim</h3><div>The aim of this study was to evaluate the clinical feasibility of fully automated three-dimensional (3D) liver segmentation using low-dose chest computed tomography (LDCT) as an alternative to nonenhanced abdominal computed tomography (AbCT) for the assessment of hepatic steatosis (HS).</div></div><div><h3>Materials and Methods</h3><div>We retrospectively analysed 642 adults with both LDCT and nonenhanced AbCT at 120 kVp. Using a fully automated 3D segmentation algorithm; the mean volumetric computed tomography (CT) attenuation of the liver was measured using both LDCT (for the included liver volume) and AbCT. The LDCT- and AbCT-measured liver Hounsfield unit (HU) values were compared using the intraclass correlation coefficient (ICC) and linear regression analysis. The correlation between LDCT- and AbCT-estimated HS grades using predefined cut-offs of 57 HU for mild HS and 40 HU for moderate HS was assessed using kappa statistics. Diagnostic performances of LDCT-measured HU were assessed using receiver operating characteristic (ROC) curve analysis with AbCT-based HS grades as a reference.</div></div><div><h3>Results</h3><div>LDCT-measured liver HU showed excellent absolute agreement (ICC = 0.961, <em>P</em>&lt;0.001) and a significant correlation (adjusted <em>R</em>^<em>2</em> = 0.857) with AbCT-measured values. Subjects were categorised as normal/mild/moderate-to-severe HS in 322/283/37 by LDCT and 321/283/38 by AbCT, respectively, with significant correlations found for detecting HS (unweighted-κ = 0.741) and for grading HS (weighted-κ = 0.755). When using AbCT as a reference, the LDCT-measured liver HU resulted in an area under the ROC curve of 0.925 for mild HS and 0.986 for moderate HS.</div></div><div><h3>Conclusion</h3><div>The LDCT-derived volumetric liver HU and HS grading based on these measurements strongly aligned with AbCT-based evaluation, indicating the potential of LDCT as an AbCT alternative in HS screening.</div></div>","PeriodicalId":10695,"journal":{"name":"Clinical radiology","volume":"90 ","pages":"Article 107079"},"PeriodicalIF":1.9,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145217714","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 CT radiomics-based machine learning model for predicting high-grade pathological components in stage IA lung adenocarcinoma: a two-center study","authors":"Z.-S. Pu ,&nbsp;Y.-F. He ,&nbsp;S.-L. Qiu ,&nbsp;Y.-R. Yang ,&nbsp;Z.-H. Feng ,&nbsp;Y.-H. Yang ,&nbsp;Z.-H. Li ,&nbsp;D.-P. Gao ,&nbsp;D.-F. Zhang","doi":"10.1016/j.crad.2025.107077","DOIUrl":"10.1016/j.crad.2025.107077","url":null,"abstract":"<div><h3>Aim</h3><div>To explore the application value of a machine learning model based on CT radiomics in predicting high-grade components in clinical stage IA lung adenocarcinoma.</div></div><div><h3>Materials and methods</h3><div>A retrospective dataset of 405 patients with pathologically confirmed stage IA lung adenocarcinoma who underwent surgical resection at two hospitals was collected (156 cases in the HGC group and 249 cases in the non-HGC group). Radiomic features were extracted from each patient's gross tumor volume (GTV) and peritumoral volume (PTV). Lasso-SVM was employed to develop radiomics, clinical, and combined models in the training dataset. The models' performance and clinical utility were evaluated in both internal and external validation datasets using receiver operating characteristic (ROC) curves and decision curve analysis (DCA).</div></div><div><h3>Results</h3><div>A total of 12 GTV features, 9 PTV features, and 14 GPTV features were selected for building the model. The combined model incorporating radiomic features and the clinical feature achieved an area under the curve (AUC) value of 0.860 (95% CI: 0.809–0.912) in the training set, 0.849 (95% CI: 0.764–0.933) in the internal validation set, and 0.822 (95% CI: 0.730–0.914) in the external validation set.</div></div><div><h3>Conclusion</h3><div>Machine learning model based on CT radiomics are helpful in preoperatively identifying high-grade components in clinical stage IA lung adenocarcinoma.</div></div>","PeriodicalId":10695,"journal":{"name":"Clinical radiology","volume":"90 ","pages":"Article 107077"},"PeriodicalIF":1.9,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145205980","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":"Deep learning model trained using multi-energy computed tomography (CT) data shows better metal artifact reduction for lumbar CT imaging","authors":"K. Ye ,&nbsp;B. Pan ,&nbsp;J. Li ,&nbsp;Z. Pan ,&nbsp;H. Yuan ,&nbsp;N. Gong","doi":"10.1016/j.crad.2025.107076","DOIUrl":"10.1016/j.crad.2025.107076","url":null,"abstract":"<div><h3>AIM:</h3><div>To develop different deep learning–based metal artifact reduction (MAR) models (deep-MAR) based on virtual monochromatic images (VMIs) at both multiple energy levels and single-energy level, and compare their performance under wider energy levels.</div></div><div><h3>MATERIALS AND METHODS</h3><div>We enrolled 93 patients with lumbar implants who underwent multi-energy CT scans and then reconstructed into VMIs at energy levels of 70 and 100 keV (10 randomly selected cases at levels ranging from 40 to 140 keV). Original images processed by modelMAR were served as the established reference. Deep-MAR models were trained using diverse datasets at energy levels of 70 KeV, 100 KeV, and two levels (model70, model100, and modelmix). Afterwards, original images were processed using three deep-MAR models, and peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) were calculated. Objective and subjective image qualities of all processed images were also compared.</div></div><div><h3>RESULTS</h3><div>From 40 to 140 keV, PSNR and SSIM of modelmix were comparable to or higher than those of model70 and model100. For attenuation correction, modelmix performed better than model100 at 70 keV level and model70 at 100 keV level (<em>P</em>&lt;0.010) but comparably to modelMAR at both levels (<em>P</em>&gt;0.050). Meanwhile, image noise in the spinal canal of three deep-MAR models at 100 keV level were lower than these of modelMAR (<em>P</em>&lt;0.010). The scores of subjective image quality for modelmix were comparable to or higher than those of model70 and model100.</div></div><div><h3>CONCLUSION</h3><div>With better image quality across broader energy levels, multiple energy CT data are recommended to be comprised in the training of deep-MAR model for postoperative lumbar CT scanning.</div></div>","PeriodicalId":10695,"journal":{"name":"Clinical radiology","volume":"90 ","pages":"Article 107076"},"PeriodicalIF":1.9,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145184752","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":"Comparison of the consistency between interns in accurate quantification of split kidney glomerular filtration rate","authors":"L. Xie ,&nbsp;J. Qin ,&nbsp;Fei Li ,&nbsp;H. Ren ,&nbsp;R. Wu ,&nbsp;H. Chen ,&nbsp;Y. Jin ,&nbsp;P. Fang ,&nbsp;X. Pang","doi":"10.1016/j.crad.2025.107071","DOIUrl":"10.1016/j.crad.2025.107071","url":null,"abstract":"<div><h3>AIM</h3><div>The aim of this study was to evaluate the consistency of an innovative method that combines ⁹⁹Tc^m-diethylene triamine pentaacetic acid (DTPA) renal dynamic imaging with ⁹⁹Tc^m-DTPA dual plasma clearance to precisely quantify the split kidney glomerular filtration rate (sk-GFR).</div></div><div><h3>MATERIALS AND METHODS</h3><div>154 patients were prospectively included in this study and underwent renal dynamic imaging using ⁹⁹Tc^m-DTPA (Gate’s method, g-GFR) and the dual plasma clearance method (true glomerular filtration rate [t-GFR]). Our team developed an innovative method to precisely determine sk-GFR based on the ⁹⁹Tc^m-DTPA dual plasma clearance method combined with the simplified Gate’s method (g'-GFR). The precise sk-GFR was calculated based on the following formula: precise glomerular filtration rate (p-GFR_left) = g'-GFR_left/(g'-GFR_left + g'-GFR_right) × t-GFR. The variability and consistency of sk-GFR, calculated by four interns, were assessed using paired t-test, Pearson’s correlation, and Kendall’s W test. Furthermore, the consistency among the four interns with two methods was assessed across different stages of renal dysfunction.</div></div><div><h3>RESULTS</h3><div>All four interns used both methods to measure sk-GFR. Although three interns achieved consistent results, one intern reported significantly lower sk-GFR values than the others. Four interns used Gate’s method with a g-GFR_left Kendall’s W coefficient of 0.846 and g-GFR_right Kendall’s W coefficient of 0.776. Additionally, they used an innovative method, which resulted in a Kendall’s W coefficient of 0.967 for both p-GFR_left and p-GFR_right. The consistency of sk-GFR using the innovative method was higher than that using the Gate’s method at different stages of renal dysfunction.</div></div><div><h3>CONCLUSION</h3><div>The innovative method provided a standardised procedure for estimating sk-GFR, thereby offering more reliable estimates by less experienced interns.</div></div>","PeriodicalId":10695,"journal":{"name":"Clinical radiology","volume":"90 ","pages":"Article 107071"},"PeriodicalIF":1.9,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145198544","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":"The ‘split fat sign’ revisited","authors":"S. Crompton,&nbsp;N. Kotnis","doi":"10.1016/j.crad.2025.107072","DOIUrl":"10.1016/j.crad.2025.107072","url":null,"abstract":"<div><div>This review article revisits the ‘split fat sign’, reviewing its aetiology and prevalence in benign and malignant soft tissue tumours in both the intermuscular and intramuscular locations. The term ‘split fat sign’ was first used in 1999, referring to the presence of a rim of fat surrounding neurogenic neoplasms in the intermuscular space on magnetic resonance imaging (MRI). The presence of fat in the intermuscular plane is a normal finding and therefore the ‘split fat sign’ can be seen in both benign and malignant intermuscular lesions. The term ‘split fat sign’ has also been used to refer to the presence of perilesional fat on MRI surrounding intramuscular tumours. Other terms have also been used to describe the finding in association with intramuscular tumours, including ‘fatty rind’ and ‘peritumoral fat’. Although typically associated with benign tumours, the presence of perilesional fat has also been described in multiple cases of malignant intramuscular lesions. The radiologist must be aware that the presence of perilesional fat surrounding a deep-seated soft tissue tumour is not diagnostic of a benign lesion. Whenever there is diagnostic uncertainty, cases should be reviewed at a soft tissue sarcoma centre to ensure appropriate ongoing management.</div></div>","PeriodicalId":10695,"journal":{"name":"Clinical radiology","volume":"90 ","pages":"Article 107072"},"PeriodicalIF":1.9,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145205652","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":"Letter to the editor regarding Junker et al.: safety of computed tomography (CT)-guided percutaneous cryoablation in patients treated for clinical T1 renal cell carcinoma with the need for preprocedural ureteral stenting: an international cohort study","authors":"T. Ito-Ihara ,&nbsp;S. Teramukai ,&nbsp;O. Ukimura","doi":"10.1016/j.crad.2025.107074","DOIUrl":"10.1016/j.crad.2025.107074","url":null,"abstract":"","PeriodicalId":10695,"journal":{"name":"Clinical radiology","volume":"91 ","pages":"Article 107074"},"PeriodicalIF":1.9,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145291489","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":"AI bone lesion classifier with sensitivity-driven optimization for radiographs","authors":"B. Rinott ,&nbsp;C.Z. Dekel ,&nbsp;A. Ilivitzki ,&nbsp;D. Militianu ,&nbsp;E. Bercovich","doi":"10.1016/j.crad.2025.107075","DOIUrl":"10.1016/j.crad.2025.107075","url":null,"abstract":"<div><h3>Aim</h3><div>This study aims to develop a deep learning classifier for detecting primary bone lesions on radiographs, emphasizing high sensitivity while maintaining practical clinical usability.</div></div><div><h3>Material and Methods</h3><div>Radiographs of the upper and lower extremities were reviewed by board-certified radiologists and categorized into two groups: “Normal” (without bone lesions) and “Abnormal” (with bone lesions). The final dataset comprised 1,177 radiographs from 310 patients, including 547 abnormal and 630 normal cases.</div><div>The MobileNetV2 architecture was trained with a sensitivity-driven approach designed to minimize false negatives. Model performance was evaluated on a hold-out test set, and attention maps were generated to enhance interpretability and visualize regions contributing to the model's decisions.</div></div><div><h3>Results</h3><div>The model was tested on a naïve hold-out test set. The results received on the test set: sensitivity of 96.6%, specificity of 82.2%, accuracy of 87.9%, area under the curve (AUC) of 0.94, and 95% confidence interval of [0.901, 0.981].</div></div><div><h3>Conclusion</h3><div>The study demonstrates the feasibility of deploying AI-based tools for radiographic detection of bone tumors with a sensitivity-focused optimization. These tools have the potential to enhance diagnostic accuracy, reduce diagnostic delays, and support population health initiatives.</div></div>","PeriodicalId":10695,"journal":{"name":"Clinical radiology","volume":"90 ","pages":"Article 107075"},"PeriodicalIF":1.9,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145184779","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":"Preventing unnecessary referrals for incidental breast lesions detected on cross-sectional imaging: a retrospective study of the local referral pathway","authors":"R. Fatima,&nbsp;L.S. Babu,&nbsp;S. Sharma","doi":"10.1016/j.crad.2025.107073","DOIUrl":"10.1016/j.crad.2025.107073","url":null,"abstract":"<div><h3>AIM</h3><div>Increased use of cross-sectional imaging has resulted in greater detection of incidental breast lesions. There are no guidelines for how these findings should be followed up. Prior to the implementation of our pathway, local practice was for general radiologists to refer incidental breast findings either to a symptomatic breast clinic or to breast Multi disciplinary team meeting (MDT). This referral pathway allows general radiologists to refer patients with incidental breast findings to a breast radiologist (by adding a JBREAS code in the report) who can then review the imaging. The breast radiologist can then decide whether symptomatic clinic referral is appropriate and communicate this via an addendum to the original report. The <strong>aim</strong> of this study was to review scans with a JBREAS code between 2015 and 2024 to assess whether the system is effective in avoiding unnecessary referrals.</div></div><div><h3>MATERIALS AND METHODS</h3><div>retrospective review of scans with JBREAS code between 27/11/2015 and 22/06/2024 was conducted. An analysis of patient data using Computerised Radiology Information System (CRIS) and Integrated Clinical Environment (ICE) for patient and scan details and histology results was performed.</div></div><div><h3>RESULTS</h3><div>Out of a total of 736 scans, 344 patients were recommended for breast clinic referral. Seventy-three of those referred to clinic had malignant histology.</div></div><div><h3>CONCLUSION</h3><div>The data show that our pathway reduced referrals by over 50%. This significantly reduced the burden on oversubscribed breast clinics. Advantages to patients include saving unnecessary anxiety and trips to the hospital, thereby reducing travel costs and environmental impact. Furthermore, the pathway enabled rapid assessment of incidental breast lesions that were subsequently found to be malignant in nature (10% of total JBREAS referrals).</div></div>","PeriodicalId":10695,"journal":{"name":"Clinical radiology","volume":"91 ","pages":"Article 107073"},"PeriodicalIF":1.9,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145312549","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":"Methodological comments on shear wave elastography of thenar muscles and the median nerve in carpal tunnel syndrome (CTS)","authors":"Enes Gurun,&nbsp;Mesut Ozturk,&nbsp;Mustafa Basaran","doi":"10.1016/j.crad.2025.107068","DOIUrl":"10.1016/j.crad.2025.107068","url":null,"abstract":"","PeriodicalId":10695,"journal":{"name":"Clinical radiology","volume":"90 ","pages":"Article 107068"},"PeriodicalIF":1.9,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145119747","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}