Journal of Computer Assisted Tomography最新文献

{"title":"Pilot Study Examining the Use of DCE MRI With Pharmacokinetic Analysis to Evaluate Hypoxia in Prostate Cancer.","authors":"Eduardo Miguel Febronio, André de Freitas Secaf, Fernando Chahud, Jorge Elias, Rodolfo B Reis, Valdair F Muglia","doi":"10.1097/RCT.0000000000001707","DOIUrl":"10.1097/RCT.0000000000001707","url":null,"abstract":"<p><strong>Purpose: </strong>This study aimed to investigate the association between tumor hypoxia, assessed through anti-HIF (hypoxia-inducible factor) staining, and aggressiveness in prostate cancer using a pharmacokinetic model, particularly those derived from the Tofts model, in predicting tumor aggressiveness.</p><p><strong>Material and methods: </strong>From January 2019 to April 2021, we conducted a retrospective search of patients with confirmed prostate cancer and a previous magnetic resonance imaging (MRI) examination. After exclusions, a total of 57 consecutive patients were enrolled. Patient data, including demographic, laboratory, and pathologic variables, were collected. MRI acquisition followed PI-RADS guidelines, encompassing T2-weighted, diffusion-weighted imaging, and dynamic contrast-enhanced imaging. An experienced abdominal radiologist conducted both morphologic and quantitative MRI analyses, evaluating parameters such as lesion size, apparent diffusion coefficient values, and the Tofts pharmacokinetics (TF) model. The histopathologic analysis included the International Society of Uropathology (ISUP) score and hypoxia marker immunohistochemistry.</p><p><strong>Results: </strong>There were no significant demographic and imaging differences between hypoxic and nonhypoxic tumors, except for elevated prostate-specific antigen levels in the latter and decreased normalized apparent diffusion coefficient in the former. Morphologic assessments revealed larger lesions in the hypoxia group. While Ktrans showed a positive association with hypoxia, it did not independently predict high-risk lesions.</p><p><strong>Conclusions: </strong>Our results suggest that pharmacokinetic analysis by the Tofts model was associated with tumors with hypoxia. However, this parameter was not an independent predictor of more aggressive tumors. Further studies, with a larger number of patients, multi-institutional and prospective, are needed to verify this possible association.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":"571-576"},"PeriodicalIF":1.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143995910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Resident Education in the Age of AI.","authors":"Erin Gomez, Cheng Ting Lin","doi":"10.1097/RCT.0000000000001697","DOIUrl":"10.1097/RCT.0000000000001697","url":null,"abstract":"<p><strong>Abstract: </strong>Artificial intelligence (AI) is a rapidly expanding field of interest to radiologists for its utility as an adjunct in detecting and reporting disease and its potential influence on the role of radiologists and their practices. As radiology educators, we are responsible for developing and providing access to curricular elements that will prepare residents to be good stewards of artificial intelligence resources while also acquiring fundamental knowledge and skills that are essential to daily practice. Residency programs should consider collaborative approaches as well as solicit support from national societies in the development and curation of their AI curricula.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":"556-558"},"PeriodicalIF":1.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142780226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Validation of a Deep Learning Tool for Detection of Incidental Vertebral Compression Fractures.","authors":"Michelle Dai, Bryan-Clement Tiu, Jacob Schlossman, Angela Ayobi, Charlotte Castineira, Julie Kiewsky, Christophe Avare, Yasmina Chaibi, Peter Chang, Daniel Chow, Jennifer E Soun","doi":"10.1097/RCT.0000000000001726","DOIUrl":"10.1097/RCT.0000000000001726","url":null,"abstract":"<p><strong>Objective: </strong>This study evaluated the performance of a deep learning-based vertebral compression fracture (VCF) detection tool in patients with incidental VCF. The purpose of this study was to validate this tool across multiple sites and multiple vendors.</p><p><strong>Methods: </strong>This was a retrospective, multicenter, multinational blinded study using anonymized chest and abdominal CT scans performed for indications other than VCF in patients ≥50 years old. Images were obtained from 2 teleradiology companies in France and United States and were processed by CINA-VCF v1.0, a deep learning algorithm designed for VCF detection. Ground truth was established by majority consensus across 3 board-certified radiologists. Overall performance of CINA-VCF was evaluated, as well as subset analyses based on imaging acquisition parameters, baseline patient characteristics, and VCF severity. A subgroup was also analyzed and compared with available clinical radiology reports.</p><p><strong>Results: </strong>Four hundred seventy-four CT scans were included in this study, comprising 166 (35.0%) positive and 308 (65.0%) negative VCF cases. CINA-VCF demonstrated an area under the curve (AUC) of 0.97 (95% CI: 0.96-0.99), accuracy of 93.7% (95% CI: 91.1%-95.7%), sensitivity of 95.2% (95% CI: 90.7%-97.9%), and specificity of 92.9% (95% CI: 89.4%-96.5%). Subset analysis based on VCF severity resulted in a specificity of 94.2% (95% CI: 90.9%-96.6%) for grade 0 negative cases and a specificity of 64.3% (95% CI: 35.1%-87.2%) for grade 1 negative cases. For grades 2 and 3 positive cases, sensitivity was 89.7% (95% CI: 79.9%-95.8%) and 99.0% (95% CI: 94.4%-100.0%), respectively.</p><p><strong>Conclusions: </strong>CINA-VCF successfully detected incidental VCF and even outperformed clinical reports. The performance was consistent among all subgroups analyzed. Limitations of the tool included various confounding pathologies such as Schmorl's nodes and borderline cases. Despite these limitations, this study validates the applicability and generalizability of the tool in the clinical setting.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":"669-674"},"PeriodicalIF":1.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143059146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging Virtual Containers for High-Powered, Collaborative AI Research in Radiology.","authors":"Lucas Aronson, John Garrett, Andrew L Wentland","doi":"10.1097/RCT.0000000000001687","DOIUrl":"10.1097/RCT.0000000000001687","url":null,"abstract":"<p><strong>Abstract: </strong>Numerous obstacles confront radiologists interested in the use of artificial intelligence (AI) models within the field of radiology. For example, discrepancies between the radiologist's and an AI developer's hardware and software specifications pose a substantial hindrance to using AI models. Additionally, accessing and using GPU computers can lead to compatibility issues and add to these challenges. Finally, the dissemination of AI models and the ability to download preexisting AI models are not simple tasks due to the size and complexity of most programs. Virtual containers offer a solution to such compatibility issues and provide a simplified way for radiologists to use AI models. Virtual containers are software tools that bundle code, required programs, and necessary software packages to ensure that a program runs identically for all users, regardless of their computing environment. This article outlines the features of virtual containers (compatibility, versatility, and portability) and highlights an applied use case for virtual containers in the development of an AI model.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":"49 4","pages":"559-562"},"PeriodicalIF":1.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144608475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Commentary: Foreword From the Guest Editors: Section on Artificial Intelligence.","authors":"Angela Tong, Linda C Chu","doi":"10.1097/RCT.0000000000001778","DOIUrl":"https://doi.org/10.1097/RCT.0000000000001778","url":null,"abstract":"","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":"49 4","pages":"520"},"PeriodicalIF":1.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144612170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Image Findings and Background Characteristics of Extrapleural Air Collection: A Pneumothorax Mimicker.","authors":"Hiroki Nishinaka, Mami Oi, Takashi Hiroi, Sho Ishikawa, Hitomi Kawamura, Koji Tokunaga, Shigeaki Umeoka","doi":"10.1097/RCT.0000000000001747","DOIUrl":"10.1097/RCT.0000000000001747","url":null,"abstract":"<p><strong>Objective: </strong>The extrapleural space (EPS) is a potential space external to the thoracic cavity. Extrapleural air collection (EAC) refers to the accumulation of air within the EPS. Differentiating EAC from pneumothorax using imaging is challenging but crucial for accurate diagnosis and effective management. This retrospective study aimed to identify imaging and clinical findings that aid in distinguishing EAC from pneumothorax.</p><p><strong>Materials and methods: </strong>Two radiologists reviewed 2771 cases of pneumothorax identified in computed tomography reports, focusing on web-like linear septa within air collection around the lung (described as a web appearance). Twenty-two patients met the inclusion criteria. Additional imaging findings, including pneumomediastinum, dependent distribution (air localized dorsally relative to the ventral margin of the descending aorta), and perivascular distribution (air surrounding the internal thoracic artery), were evaluated. Patient background information was analyzed using medical records. We also investigated CT images of a control group to determine whether these imaging characteristics could be observed in age/sex-matched patients with typical pneumothorax.</p><p><strong>Results: </strong>Pneumomediastinum was observed in all cases. Dependent distribution and perivascular distribution were present in 59% (13/22) and 50% (11/22) of cases, respectively.Concurrent interstitial lung disease and steroid use were identified in 77% (17/22) and 73% (16/22) of patients, respectively. In contrast, pneumomediastinum, dependent distribution and perivascular distribution were present in 4.5% (1/22), 55% (12/22), and 0% (0/22) of cases in the control group. In particular, pneumomediastinum ( P =3.71×10 -10 ) and perivascular distribution ( P =1.53×10 -4 ) were statistically more frequent in EAC patients than in the control group.</p><p><strong>Conclusions: </strong>Pneumomediastinum is a critical diagnostic feature of EAC. Dependent distribution and perivascular distribution are valuable imaging findings for diagnosing EAC. Careful interpretation is warranted when pneumomediastinum is observed in patients with interstitial lung disease or those on steroid therapy.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144505832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic Multiclass Tissue Segmentation Using Deep Learning in Brain MR Images of Tumor Patients.","authors":"Ankit Kandpal, Puneet Kumar, Rakesh Kumar Gupta, Anup Singh","doi":"10.1097/RCT.0000000000001750","DOIUrl":"10.1097/RCT.0000000000001750","url":null,"abstract":"<p><strong>Objective: </strong>Precise delineation of brain tissues, including lesions, in MR images is crucial for data analysis and objectively assessing conditions like neurological disorders and brain tumors. Existing methods for tissue segmentation often fall short in addressing patients with lesions, particularly those with brain tumors. This study aimed to develop and evaluate a robust pipeline utilizing convolutional neural networks for rapid and automatic segmentation of whole brain tissues, including tumor lesions.</p><p><strong>Materials and methods: </strong>The proposed pipeline was developed using BraTS'21 data (1251 patients) and tested on local hospital data (100 patients). Ground truth masks for lesions as well as brain tissues were generated. Two convolutional neural networks based on deep residual U-Net framework were trained for segmenting brain tissues and tumor lesions. The performance of the pipeline was evaluated on independent test data using dice similarity coefficient (DSC) and volume similarity (VS).</p><p><strong>Results: </strong>The proposed pipeline achieved a mean DSC of 0.84 and a mean VS of 0.93 on the BraTS'21 test data set. On the local hospital test data set, it attained a mean DSC of 0.78 and a mean VS of 0.91. The proposed pipeline also generated satisfactory masks in cases where the SPM12 software performed inadequately.</p><p><strong>Conclusions: </strong>The proposed pipeline offers a reliable and automatic solution for segmenting brain tissues and tumor lesions in MR images. Its adaptability makes it a valuable tool for both research and clinical applications, potentially streamlining workflows and enhancing the precision of analyses in neurological and oncological studies.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144505831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Superior Performance of Synchronized Percutaneous Microwave Ablation and Immediate Percutaneous Biopsy for Highly Suspected Malignant Pulmonary Ground-Glass Nodules.","authors":"Yining Liang, Jiawei Du, Bing Wang, Dongpo Wang, Chenghai Li, Wei Kang, Dailun Hou","doi":"10.1097/RCT.0000000000001772","DOIUrl":"https://doi.org/10.1097/RCT.0000000000001772","url":null,"abstract":"<p><strong>Objective: </strong>In recent years, experience has been accumulated in microwave ablation (MWA) of ground-glass nodules (GGNs). The goal of this retrospective study was to explore the feasibility, safety, and efficacy of synchronized computed tomography (CT)-guided MWA combined with intraoperative percutaneous biopsy (PB) for the treatment of highly suspected malignant GGNs.</p><p><strong>Methods: </strong>From January 2021 to February 2025, 93 patients with highly suspected malignant GGNs underwent MWA and PB. Forty-one patients in group A were treated with sequential low power MWA-PB-radical MWA in one session. Fifty-two patients in group B were treated with staged procedures. The pathologic diagnostic results and pathology positive diagnosis rate were evaluated. The technical success, complete ablation rate, and complications were analyzed. The total operative time, irradiation dose, hospitalization time, and hospitalization expenses were compared between the 2 groups.</p><p><strong>Results: </strong>The technical success rate of both groups was 100%. The complete ablation rates of group A and group B were 100% and 98.1%, respectively (P>0.05). The positive pathologic diagnosis rate of group A was 90.2% (37/41). The incidence of pneumothorax and intrapulmonary hemorrhage was lower in group A than in group B (29.3% vs. 50.0%, P=0.04; 17.1% vs. 61.5%, P<0.001). The total operative time, irradiation dose, hospitalization time, and hospitalization expenses were lower in group A than in group B (all P<0.001).</p><p><strong>Conclusion: </strong>Synchronized MWA and intraoperative PB is a safe and effective strategy with satisfactory technical success, complete ablation rates, and acceptable rates of positive pathologic diagnosis, which is an alternative treatment for GGNs with high suspicion of malignancy.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144496831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Diagnostic Value of 18F-FDG PET in Parkinson Disease Based on Voxel Analysis.","authors":"Bing Han, Jifeng Zhang, Dongxue Wang, Lili Liu, Yong Wan, Wei Yuan, Yipeng Li, Yuhang Zhang, Ping Li","doi":"10.1097/RCT.0000000000001763","DOIUrl":"https://doi.org/10.1097/RCT.0000000000001763","url":null,"abstract":"<p><strong>Purpose: </strong>To evaluate the accuracy of statistical parametric mapping (SPM) and Scenium in the differential diagnosis of Parkinson disease (PD) and atypical Parkinsonian syndromes based on 18F-fluoro-deoxy-glucose (18F-FDG) imaging, and to explore the application of these 2 software programs in analyzing patients with Parkinson disease of varying severity, as well as to construct and evaluate the metabolic profiles of PD patients using Scenium.</p><p><strong>Methods: </strong>A total of 64 patients with Parkinsonian syndrome who met the diagnostic criteria were included in this study. PET images were used for disease diagnosis with SPM and Scenium based on diagnostic charts, and the diagnostic accuracy of both software programs was assessed through consistency analysis. Meanwhile, an in-depth analysis was performed to compare the sensitivity, specificity, positive predictive value, and negative predictive value of the 2 software programs. In addition, Scenium was used to construct a diagnostic model for PD.</p><p><strong>Results: </strong>SPM demonstrated greater accuracy in distinguishing between PD and APS, with a significantly higher Kappa value (K_spm=0.704) compared with Scenium (K_scenium=0.440). The sensitivity and specificity of SPM were 82.5% and 91.7%, respectively. Further, a PD diagnostic model was constructed by incorporating PET parameters from the contralateral central region and basal ganglia, achieving a diagnostic accuracy of 82.9%.</p><p><strong>Conclusions: </strong>SPM can more accurately differentiate the diagnosis of Parkinson disease from atypical Parkinson syndrome compared with Scenium.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144496832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hepatobiliary Tumor Board: A Multidisciplinary Review of Challenging Cases.","authors":"Amar Shah, Ajaykumar C Morani, Ching-Wei D Tzeng, Mohamad Bassam Sonbol, Hyun Kim, Koushik Das, Deyali Chatterjee, Motoyo Yano","doi":"10.1097/RCT.0000000000001733","DOIUrl":"https://doi.org/10.1097/RCT.0000000000001733","url":null,"abstract":"<p><p>Multidisciplinary case conferences have been shown to improve patient outcomes. However, such case conferences may be unavailable in some settings. This multidisciplinary discussion of 4 challenging hepatobiliary cases was presented at the 2023 Society of Advanced Body Imaging Annual Meeting in Dallas, TX. The cases include an \"imaging occult\" pancreatic tumor, a large pancreatic mass, an intraductal biliary mass, and a polypoid intraluminal gallbladder mass.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144496811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}