Academic Radiology最新文献

{"title":"CT-Defined Pectoralis Muscle Density Predicts 30-Day Mortality in Hospitalized Patients with COVID-19: A Nationwide Multicenter Study","authors":"Andreas Michael Bucher ,&nbsp;Julius Behrend ,&nbsp;Constantin Ehrengut ,&nbsp;Lukas Müller ,&nbsp;Tilman Emrich ,&nbsp;Dominik Schramm ,&nbsp;Alena Akinina ,&nbsp;Roman Kloeckner ,&nbsp;Malte Sieren ,&nbsp;Lennart Berkel ,&nbsp;Christiane Kuhl ,&nbsp;Marwin-Jonathan Sähn ,&nbsp;Matthias A. Fink ,&nbsp;Dorottya Móré ,&nbsp;Bohdan Melekh ,&nbsp;Hakan Kardas ,&nbsp;Felix G. Meinel ,&nbsp;Hanna Schön ,&nbsp;Norman Kornemann ,&nbsp;Diane Miriam Renz ,&nbsp;Alexey Surov","doi":"10.1016/j.acra.2024.11.054","DOIUrl":"10.1016/j.acra.2024.11.054","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>The prognostic role of computed tomography (CT)-defined skeletal muscle features in COVID-19 is still under investigation. The aim of the present study was to evaluate the prognostic role of CT-defined skeletal muscle area and density in patients with COVID-19 in a multicenter setting.</div></div><div><h3>Materials and Methods</h3><div>This retrospective study is a part of the German multicenter project RACOON (Radiological Cooperative Network of the COVID-19 pandemic). The acquired sample included 1379 patients, 389 (28.2%) women and 990 (71.8%) men. In each case, chest CT was analyzed and pectoralis muscle area and density were calculated. Data were analyzed by means of descriptive statistics. Group differences were calculated using the Mann–Whitney-U test and Fisher’s exact test. Univariable and multivariable logistic regression analyses were performed.</div></div><div><h3>Results</h3><div>The 30-day mortality was 17.9%. Using median values as thresholds, low pectoralis muscle density (LPMD) was a strong and independent predictor of 30-day mortality, HR<!--> <!-->=<!--> <!-->2.97, 95%-CI: 1.52–5.80, p<!--> <!-->=<!--> <!-->0.001. Also in male patients, LPMD predicted independently 30-day mortality, HR<!--> <!-->=<!--> <!-->2.96, 95%-CI: 1.42–6.18, p<!--> <!-->=<!--> <!-->0.004. In female patients, the analyzed pectoralis muscle parameters did not predict 30-day mortality.</div><div>For patients under 60 years of age, LPMD was strongly associated with 30-day mortality, HR<!--> <!-->=<!--> <!-->2.72, 95%-CI: 1.17;6.30, p<!--> <!-->=<!--> <!-->0.019. For patients over 60 years of age, pectoralis muscle parameters could not predict 30-day mortality.</div></div><div><h3>Conclusion</h3><div>In male patients with COVID-19, low pectoralis muscle density is strongly associated with 30-day mortality and can be used for risk stratification. In female patients with COVID-19, pectoralis muscle parameters cannot predict 30-day mortality.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 4","pages":"Pages 2133-2140"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142830787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generative Adversarial Network Based Contrast Enhancement: Synthetic Contrast Brain Magnetic Resonance Imaging","authors":"Merve Solak ,&nbsp;Murat Tören ,&nbsp;Berkutay Asan ,&nbsp;Esat Kaba ,&nbsp;Mehmet Beyazal ,&nbsp;Fatma Beyazal Çeliker","doi":"10.1016/j.acra.2024.11.021","DOIUrl":"10.1016/j.acra.2024.11.021","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>Magnetic resonance imaging (MRI) is a vital tool for diagnosing neurological disorders, frequently utilising gadolinium-based contrast agents (GBCAs) to enhance resolution and specificity. However, GBCAs present certain risks, including side effects, increased costs, and repeated exposure. This study proposes an innovative approach using generative adversarial networks (GANs) for virtual contrast enhancement in brain MRI, with the aim of reducing or eliminating GBCAs, minimising associated risks, and enhancing imaging efficiency while preserving diagnostic quality.</div></div><div><h3>Material and Methods</h3><div>In this study, 10,235 images were acquired in a 3.0 Tesla MRI scanner from 81 participants (54 females, 27 males; mean age 35 years, range 19–68 years). T1-weighted and contrast-enhanced images were obtained following the administration of a standard dose of a GBCA. In order to generate \"synthetic\" images for contrast-enhanced T1-weighted, a CycleGAN model, a sub-model of the GAN structure, was trained to process pre- and post-contrast images. The dataset was divided into three subsets: 80% for training, 10% for validation, and 10% for testing. TensorBoard was employed to prevent image deterioration throughout the training phase, and the image processing and training procedures were optimised. The radiologists were presented with a non-contrast input image and asked to choose between a real contrast-enhanced image and synthetic MR images generated by CycleGAN corresponding to this non-contrast MR image (Turing test).</div></div><div><h3>Results</h3><div>The performance of the CycleGAN model was evaluated using a combination of quantitative and qualitative analyses. For the entire dataset, in the test set, the mean square error (MSE) was 0.0038, while the structural similarity index (SSIM) was 0.58. Among the submodels, the most successful model achieved an MSE of 0.0053, while the SSIM was 0.8. The qualitative evaluation was validated through a visual Turing test conducted by four radiologists with varying levels of clinical experience.</div></div><div><h3>Conclusion</h3><div>The findings of this study support the efficacy of the CycleGAN model in generating synthetic contrast-enhanced T1-weighted brain MR images. Both quantitative and qualitative evaluations demonstrated excellent performance, confirming the model’s ability to produce realistic synthetic images. This method shows promise in potentially eliminating the need for intravenous contrast agents, thereby minimising the associated risks of their use.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 4","pages":"Pages 2220-2232"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142856162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neural Network and Logistic Regression Models Based on Ultrasound Radiomics and Clinical-Pathological Features to Predict Occult Level II Lymph Node Metastasis in Papillary Thyroid Carcinoma","authors":"Jia-Wei Feng ,&nbsp;Shui-Qing Liu ,&nbsp;Yu-Xin Yang ,&nbsp;Gao-Feng Qi ,&nbsp;Xin Ye ,&nbsp;Jing Ye ,&nbsp;Yong Jiang ,&nbsp;Hui Lin","doi":"10.1016/j.acra.2024.12.037","DOIUrl":"10.1016/j.acra.2024.12.037","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>Papillary thyroid carcinoma (PTC) often metastasizes to lateral cervical lymph nodes, especially in level II. This study aims to develop predictive models to identify level II lymph node metastasis (LNM), guiding selective neck dissection (SND) to minimize unnecessary surgery and morbidity in low-risk patients.</div></div><div><h3>Methods</h3><div>A retrospective cohort of 313 PTC patients who underwent modified radical neck dissection (MRND) between October 2020 and January 2023 was analyzed. The patients were randomly assigned to a training cohort (70%) and a validation cohort (30%). Five predictive models were developed using neural networks (NNET) and logistic regression (LR) based on ultrasound radiomic features, clinical-pathological data, or a combination of both. Each model’s performance was evaluated based on accuracy, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity in predicting occult level II LNM. SHapley Additive exPlanations and nomogram were used to interpret the most important features in the models.</div></div><div><h3>Results</h3><div>The occurrence rate of level II LNM was 28% in the cohort. Among the five predictive models developed, the LR-radiomics signature model demonstrated the highest performance, achieving an accuracy of 96.8% and an AUC of 0.989 in the validation set. In comparison, the NNET-radiomic + clinical feature model achieved an AUC of 0.935, while other models exhibited moderate to low accuracy and AUCs ranging from 0.699 to 0.785. The decision curve analysis demonstrated that the LR-radiomics signature model provided the greatest clinical utility, offering the highest net benefit across a range of decision thresholds for identifying occult level II LNM.</div></div><div><h3>Conclusion</h3><div>Our study developed predictive models using ultrasound-derived radiomic features and clinical-pathological data to assess the risk of occult level II LNM in PTC. The LR-radiomics signature model demonstrated high accuracy, making it a valuable tool for guiding personalized treatment decisions, by informing MRND for high-risk patients and supporting SND for low-risk patients to minimize unnecessary surgical interventions and optimize clinical outcomes.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 4","pages":"Pages 1918-1933"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142933447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On-Call Radiology Resident Preliminary Report Major Discrepancies: A Meta-analysis","authors":"Kathryn A. Szymanski BS ,&nbsp;An T. Hoang BS ,&nbsp;Dane Van Tassel MD ,&nbsp;Paul Kang MS, MPH ,&nbsp;Cory M. Pfeifer MD, MBA, MPH, MS","doi":"10.1016/j.acra.2025.01.015","DOIUrl":"10.1016/j.acra.2025.01.015","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>Throughout their training, radiology residents frequently perform independent overnight call, with attendings overreading reports the following morning. Call shifts are a fundamental part of radiology resident training, offering independence that can improve decision-making skills and professional confidence. However, because errors have the potential to affect patient care, analysis of clinically significant errors is critical. This meta-analysis was performed to organize existing data on on-call resident preliminary report discrepancy rates and to compare rates across modalities and resident years.</div></div><div><h3>Materials and Methods</h3><div>A PubMed search was performed in August 2024 using (\"resident report discrepancy\" OR (\"resident\" AND \"error\") OR \"preliminary\") AND \"radiology\" AND \"call”. Articles were included if they met the criteria, and relevant information was collected. Statistical analysis was performed.</div></div><div><h3>Results</h3><div>The PubMed search resulted in 107 articles, of which 20 met inclusion criteria. These studies included 616,918 preliminary reports. Pooled preliminary report major discrepancy rate (%) by modality was 0.64 for radiographs, 0.38 for US, 1.35 for CT, and 1.86 for MRI and by resident year was 1.27 for R1s, 1.05 for R2s, 0.88 for R3s, and 0.67 for R4s. The highest discrepancy rate was seen for R1s reading MRI (8.70%). The majority of papers included describe residents taking independent call, with only three having fellow or attending supervised call part of the time.</div></div><div><h3>Conclusion</h3><div>Radiology residents are valuable members of the healthcare team and demonstrate high accuracy during independent call shifts. Fellow or attending real-time supervision can shorten the time to final report, and whether a hospital implements this should be decided by analyzing its individual system. This analysis is limited by variability in the classification of major discrepancies and inability to further classify data by body region scanned. In light of this, we encourage standardization in future reporting.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 4","pages":"Pages 2342-2356"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143790808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantification of Intratumoral Heterogeneity Based on Habitat Analysis for Preoperative Assessment of Lymphovascular Invasion in Colorectal Cancer.","authors":"Yexin Su, Hongyue Zhao, Zhehao Lyu, Peng Xu, Ziyue Zhang, Huiting Zhang, Mengjiao Wang, Lin Tian, Peng Fu","doi":"10.1016/j.acra.2025.03.014","DOIUrl":"https://doi.org/10.1016/j.acra.2025.03.014","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>Preoperative knowledge of the status of lymphovascular invasion (LVI) status in colorectal cancer (CRC) patients can provide valuable information for choosing appropriate treatment strategies. This study aimed to explore the value of heterogeneity features derived from the habitat analysis of ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography (PET) images in predicting LVI.</p><p><strong>Materials and methods: </strong>Pretreatment ¹⁸F-FDG PET/computed tomography (CT) images from 177 patients diagnosed with CRC were retrospectively obtained (training cohort, n=106; validation cohort, n=71). Conventional radiomics features and habitat-derived tumor heterogeneity features were extracted from ¹⁸F-FDG PET scans. The output probabilities of the imaging-based random forest model were used to generate a radiomics score (Radscore) and intratumoral heterogeneity score (ITHscore). Multivariate logistic regression analysis was used to determine the independent risk factors for LVI. On this basis, four LVI status classification models were developed using (a) clinical variables (Clinical model), (b) tumor heterogeneity features (ITHscore model), (c) radiomics features (Radscore model), and (d) clinical variables, tumor heterogeneity features, and radiomics features (Combined model). The area under the curve (AUC) and decision curve analysis were used to evaluate model performance.</p><p><strong>Results: </strong>Among all of the variables, the PET/CT-reported lymph node status, ITHscore, and Radscore were retained as predictors related to the risk of LVI in CRC patients (P<0.05). The predictive effect of the ITHscore model (AUC: 0.712) was better than that of the Radscore model (AUC: 0.650) and Clinical model (AUC: 0.652) in the validation cohort. The Combined model achieved better classification effects and clinical usefulness, and the AUCs of the training and validation cohorts were 0.857 and 0.798, respectively. A nomogram of the Combined model was established, and the calibration plot was well fitted (P>0.05). In addition, the results of Spearman's rank correlation tests showed that there was no significant correlation between the ITHscore and Radscore (R=0.044, P=0.655 in the training cohort; R=0.067, P=0.580 in the validation cohort).</p><p><strong>Conclusion: </strong>Our results showed that the ITHscore is a novel and stable quantitative indicator of LVI and is helpful for effectively facilitating the risk stratification of LVI in CRC patients after integrating clinical variables and radiomics features.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143774787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Progress and Impact of a Radiology Residency Research Track over 12 Years","authors":"Joshua D. Brown MD, PhD,&nbsp;Babajide Owosela,&nbsp;Elizabeth A. Krupinski PhD,&nbsp;Brent D. Weinberg MD, PhD,&nbsp;Mark E. Mullins MD, PhD,&nbsp;Patricia Balthazar MD, MPH","doi":"10.1016/j.acra.2024.09.025","DOIUrl":"10.1016/j.acra.2024.09.025","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>Radiology is a dynamic and ever-evolving field, necessitating research and innovation. However, the conventional medical training model falls short in fostering research skills, crucial for cultivating the upcoming cohort of physician-scientists. Our radiology residency research track (RT) was instituted to offer a dedicated research pathway, to foster the next generation of research-focused academic radiologists. The track provides an integrated 4-year longitudinal curriculum and academic time. This study assessed the impact and progress of our RT over 12 years.</div></div><div><h3>Materials and Materials</h3><div>Using publicly available online data from Doximity, PubMed, and Scopus we collected information on all graduates from our Diagnostic and Interventional Radiology residency program graduation classes between 2010 and 2022, including most recent job position, position type (academic vs. private), and publications. We compared RT and non-research track (NRT) residents.</div></div><div><h3>Results</h3><div>Out of 185 graduates, 179 profiles (97%) were retrievable, including all 13 RT residents. The average number of publications per resident during residency was 1.1 (186 total) for NRT graduates and 7.2 (93 total) for RT graduates (p &lt; 0.001). Throughout their entire careers to date, NRT graduates averaged 7.3 publications per resident (1249 total), while RT graduates averaged 31.7 publications per resident (412 total) (p &lt; 0.001). The average number of citations per graduate was 123 (21212 total) for NRT and 552 (7175 total) for RT (p &lt; 0.001). Additionally, 36% of NRT graduates and 92% of RT graduates (p = 0.005) held academic job positions.</div></div><div><h3>Conclusion</h3><div>Residents from the radiology residency research track were more likely to assume academic positions and had a higher number of publications and citations per resident compared to their non-research track counterparts, suggesting the track serves as an effective pipeline for cultivating academic radiologists.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 4","pages":"Pages 2334-2341"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143790842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Updating the American Association of Physicists in Medicine (AAPM) Diagnostic Radiology Resident Physics Curriculum: Strategies, Content, and Dissemination","authors":"Jie Zhang PhD ,&nbsp;Christina L. Brunnquell PhD ,&nbsp;Trevor J. Andrews PhD ,&nbsp;Richard H. Behrman PhD ,&nbsp;Karen L. Brown MHP ,&nbsp;Bennett S. Greenspan MD, MS ,&nbsp;Ping Hou PhD ,&nbsp;Kalpana M. Kanal PhD ,&nbsp;Hamid Reza Khosravi PhD ,&nbsp;Yun Liang PhD ,&nbsp;Megan E. Lipford PhD ,&nbsp;Benjamin C. Musall PhD ,&nbsp;Adel A. Mustafa PhD ,&nbsp;Ashley E. Rubinstein PhD ,&nbsp;Brandon J. Russell MS ,&nbsp;Adrian A. Sanchez PhD, MD ,&nbsp;Sameer Tipnis PhD ,&nbsp;William F. Sensakovic PhD","doi":"10.1016/j.acra.2025.02.035","DOIUrl":"10.1016/j.acra.2025.02.035","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>The Diagnostic Radiology Resident Physics Curriculum (DRRPC), initiated in 2007 by the American Association of Physicists in Medicine (AAPM) and last updated in 2018, is an essential educational resource for those teaching physics to radiology residents. Regular updates are crucial to ensure the curriculum aligns with evolving technologies and clinical practices, maintaining its relevance and effectiveness in educating the next generation of radiologists. The paper aims to describe the update strategies of the DRRPC, focusing on the current iteration, its structure, and the newest updates.</div></div><div><h3>Materials and Methods</h3><div>The update process, led by the Diagnostic Radiology Resident Physics Curriculum Working Group, commenced with a comprehensive survey targeting AAPM members who contribute to radiology physics teaching. The survey was conducted to assess the curriculum’s current applicability and gather feedback for improvements. Subsequent updates were based on extensive stakeholder consultations and detailed analysis of survey data.</div></div><div><h3>Results</h3><div>The revision process has led to significant enhancements in the curriculum, emphasizing practical clinical applications and the integration of cutting-edge technology. New modules on advanced image processing, artificial intelligence, informatics, and radiopharmaceutical therapy were developed, responding to the evolving needs of radiological education and practice.</div></div><div><h3>Conclusion</h3><div>The updated DRRPC supports educators in providing a dynamic and relevant training experience.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 4","pages":"Pages 2364-2370"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143588049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Harnessing Multi-Omics: Integrating Radiomics and Pathomics for Predicting Microsatellite Instability in Rectal Cancer","authors":"Abdul Sammad MBBS ,&nbsp;Zhongxiang Ding MD, PhD","doi":"10.1016/j.acra.2025.02.015","DOIUrl":"10.1016/j.acra.2025.02.015","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 4","pages":"Pages 1946-1948"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143426637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated Kidney Stone Composition Analysis with Photon-Counting Detector CT, a Performance Study—A Phantom Study","authors":"Daniel Dillinger ,&nbsp;Stephan Waldeck ,&nbsp;Daniel Overhoff ,&nbsp;Sebastian Faby ,&nbsp;Markus Jürgens ,&nbsp;Bernhard Schmidt ,&nbsp;Albrecht Hesse ,&nbsp;Justine Schoch ,&nbsp;Hans Schmelz ,&nbsp;Rico Stoll ,&nbsp;Tim Nestler","doi":"10.1016/j.acra.2024.10.045","DOIUrl":"10.1016/j.acra.2024.10.045","url":null,"abstract":"<div><h3>Background</h3><div>For treatment of urolithiasis, the stone composition is of particular interest, as uric acid (UA) stones can be treated by chemolitholysis. In this ex vivo study, we employed an advanced composition analysis approach for urolithiasis utilizing spectral data obtained from a photon-counting detector CT (PCDCT) to differentiate UA and non-UA stones. Our primary objective was to assess the accuracy of this analysis method.</div></div><div><h3>Methods</h3><div>A total of 148 urinary stones with a known composition that was measured by the standard reference method infrared spectroscopy (reference) were placed in an abdomen phantom and scanned in the PCDCT. Our objectives were to assess the stone detection rates of PCDCT and the accuracy of the prediction of the stone composition in UA vs non-UA compared to the reference.</div></div><div><h3>Results</h3><div>Automated detection recognized 86.5% of all stones, with best detection rate for stones larger &gt; 5 mm in diameter (95.4%, 88.8% for stones larger than 3 mm, 94.7% for stones larger than 4 mm). Depending on the volume, we found a recognition rate of 92.8% for stones larger than 20 mm³ and 94.0% for stones with more than 30 mm³. Prediction of UA composition showed an overall sensitivity and a positive predictive value of 66.7% and a specificity and negative predictive value of 94.5%. Best diagnostic values volume wise were found by only including stones with a larger volume than 30 mm³, there we found a sensitivity of 91.7%, and a specificity of 92.4%. Sensitivity in dependance of the largest diameter was best for stones larger than 5 mm (85.7%), but specificity decreased with increasing diameter (to 91.3%).</div></div><div><h3>Conclusion</h3><div>Automated urinary stone composition analysis with PCDCT showed a good automated detection rate of 86.5% up to 95.4% depending on stone diameter. The differentiation between non-UA and UA stones is performed with an NPV of 94.5% and a PPV of 66.7%. The prediction probability of non-UA stones was very good. This means the automatic detection and differentiation algorithm can identify the patients which will not profit from chemolitholysis.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 4","pages":"Pages 2005-2012"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of White Matter Integrity by Using Diffusion Tensor Imaging in Patients with Presbycusis","authors":"Yagmur Basak Polat MD ,&nbsp;Bahar Atasoy MD ,&nbsp;Huseyin Ozdemir MD ,&nbsp;Orhan Ozturan MD ,&nbsp;Emre Polat MD ,&nbsp;Ummuhan Ebru Karabulut MD ,&nbsp;Serdar Balsak MD ,&nbsp;Alpay Alkan MD","doi":"10.1016/j.acra.2024.11.013","DOIUrl":"10.1016/j.acra.2024.11.013","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>This study aims to evaluate white matter microstructure integrity in patients diagnosed with presbycusis (age-related hearing loss) using diffusion tensor imaging (DTI) and to investigate the relationship between DTI parameters and hearing loss severity.</div></div><div><h3>Materials and Methods</h3><div>Patients aged 50 and above with presbycusis (pure-tone average [PTA] ≥<!--> <!-->20<!--> <!-->dB) were categorized as mild (PTA 20–34<!--> <!-->dB), moderate (PTA 35–49<!--> <!-->dB), or severe (PTA ≥<!--> <!-->50<!--> <!-->dB). Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were measured in 16 white matter regions. The relationship between DTI parameters and speech discrimination scores was assessed using multiple linear regression, adjusting for age, sex, and vascular risk profile.</div></div><div><h3>Results</h3><div>The study included 148 patients (mild<!--> <!-->=<!--> <!-->32, moderate<!--> <!-->=<!--> <!-->84, severe<!--> <!-->=<!--> <!-->32). DTI analysis showed significantly lower FA in the left cingulum (p = 0.001) and right IFOF (p = 0.003) in the severe group compared to the mild and moderate groups, while RD in the left cingulum was higher in the severe group (p = 0.006). The mild group exhibited significantly lower left IFOF RD (p &lt; 0.001) compared to the moderate and severe groups, and significantly lower left cingulum body MD (p = 0.004) compared to the severe group. Significant associations were found between speech discrimination scores and DTI parameters, including right hippocampal cingulum MD (p = 0.030), left IFOF RD (p = 0.033), right Heschl’s gyrus MD (p = 0.018), and AD (p = 0.008).</div></div><div><h3>Conclusion</h3><div>This study demonstrated significant alterations in white matter microstructure across different severities of presbycusis. Further research is needed to fully understand the cognitive and central auditory dysfunctions associated with presbycusis.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 4","pages":"Pages 2163-2170"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142741272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}