Academic Radiology最新文献

{"title":"Comparative Efficacy and Safety of Ultrasound-Guided Thermal Ablation for Benign Thyroid Nodules versus Low-Risk Follicular Neoplasms: A Single-Center Retrospective Study.","authors":"Yu-Tong Liu, Ying Wei, Zhen-Long Zhao, Jie Wu, Shi-Liang Cao, Na Yu, Yan Li, Li-Li Peng, Ming-An Yu","doi":"10.1016/j.acra.2025.07.004","DOIUrl":"https://doi.org/10.1016/j.acra.2025.07.004","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>To compare the efficacy and safety of thermal ablation (TA) in the treatment of benign thyroid nodules (BTN) and follicular thyroid neoplasms (FTN).</p><p><strong>Materials and methods: </strong>This retrospective study included 1900 patients with BTN or FTN who underwent TA between January 2016 and August 2024. Patients were categorized according to the Bethesda category, and propensity score matching (PSM) was employed to control for confounding factors. Kaplan-Meier curves were used to analyze disease progression and tumor disappearance.</p><p><strong>Results: </strong>After PSM (1:1), 106 patients (median age 45 years [IQR 36-56]; 90 women) were included in the BTN group, and 106 patients (median age 46 years [IQR 34-58]; 87 women) were included in the FTN group. The median follow-up durations were 36 months (IQR, 12-60) for the BTN group and 35 months (IQR, 18-49) for the FTN group. Technical success rates were 100% in both groups. The median volume reduction rates (VRR) at 12 months were 86.7% (IQR: 62.5%-95.1%) in the BTN group and 91.5% (IQR: 64.3%-100%) in the FTN group. No significant differences were observed between the BTN and FTN groups in disease progression (4.7% vs. 5.7%, P > 0.99), progression-free survival rates (1-year:95.8% vs. 99.1%, P = 0.449; 3-year: 96.2% vs. 84.5%, P = 0.883; 5-year: 93.1% vs. 96.2%, P = 0.594; overall: 84.5% vs. 93.1%, P = 0.7), complications (2.8% vs. 3.8%, P > 0.99), or tumor disappearance (50.1% vs. 32.5%, P = 0.089). Transient hoarseness was the only major complication.</p><p><strong>Conclusion: </strong>TA could achieve comparable safety and efficacy outcomes for both BTN and FTN.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144692330","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":"Educational Competencies for Artificial Intelligence in Radiology: A Scoping Review.","authors":"Sunam Jassar, Zili Zhou, Sierra Leonard, Alaa Youssef, Linda Probyn, Kulamakan Kulasegaram, Scott J Adams","doi":"10.1016/j.acra.2025.06.044","DOIUrl":"https://doi.org/10.1016/j.acra.2025.06.044","url":null,"abstract":"<p><strong>Objective: </strong>The integration of artificial intelligence (AI) in radiology may necessitate refinement of the competencies expected of radiologists. There is currently a lack of understanding on what competencies radiology residency programs should ensure their graduates attain related to AI. This study aimed to identify what knowledge, skills, and attitudes are important for radiologists to use AI safely and effectively in clinical practice.</p><p><strong>Methods: </strong>Following Arksey and O'Malley's methodology, a scoping review was conducted by searching electronic databases (PubMed, Embase, Scopus, and ERIC) for articles published between 2010 and 2024. Two reviewers independently screened articles based on the title and abstract and subsequently by full-text review. Data were extracted using a standardized form to identify the knowledge, skills, and attitudes surrounding AI that may be important for its safe and effective use.</p><p><strong>Results: </strong>Of 5920 articles screened, 49 articles met inclusion criteria. Core competencies were related to AI model development, evaluation, clinical implementation, algorithm bias and handling discrepancies, regulation, ethics, medicolegal issues, and economics of AI. While some papers proposed competencies for radiologists focused on technical development of AI algorithms, other papers centered competencies around clinical implementation and use of AI.</p><p><strong>Conclusion: </strong>Current AI educational programming in radiology demonstrates substantial heterogeneity with a lack of consensus on the knowledge, skills, and attitudes for the safe and effective use of AI in radiology. Further research is needed to develop consensus on the core competencies for radiologists to safely and effectively use AI to support the integration of AI training and assessment into residency programs.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144692331","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":"Emerging Diagnostic Role of Sonographic Glandular Tissue Components in Breast Ultrasound Risk Stratification.","authors":"Yuan Zhong, Yin-Ting Chen, Yi-de Qiu, Yi-Sheng Xiao, Xiao-Dan Chen, Lu-Yi Wang, Geng-Xi Cai, Yan-Yan Xiao, Jie-Yi Ye, Wei-Jun Huang","doi":"10.1016/j.acra.2025.07.026","DOIUrl":"https://doi.org/10.1016/j.acra.2025.07.026","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144692333","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":"Deep Learning-Driven Multimodal Fusion Model for Prediction of Middle Cerebral Artery Aneurysm Rupture Risk.","authors":"Xiufen Jia, Yongchun Chen, Kuikui Zheng, Chao Chen, Jinjin Liu","doi":"10.1016/j.acra.2025.07.001","DOIUrl":"https://doi.org/10.1016/j.acra.2025.07.001","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>The decision to treat unruptured intracranial aneurysms remains a clinical dilemma. Middle cerebral artery (MCA) aneurysms represent a prevalent subtype of intracranial aneurysms. This study aims to develop a multimodal fusion deep learning model for stratifying rupture risk in MCA aneurysms.</p><p><strong>Materials and methods: </strong>We retrospectively enrolled internal cohort and two external validation datasets with 578 and 51 MCA aneurysms, respectively. Multivariate logistic regression analysis was performed to identify independent predictors of rupture. Aneurysm morphological parameters were quantified using reconstructed CT angiography (CTA) images. Radiomics features of aneurysms were extracted through computational analysis. We developed MCANet - a multimodal data-driven classification model integrating raw CTA images, radiomics features, clinical parameters, and morphological characteristics - to establish an aneurysm rupture risk assessment framework. External validation was conducted using datasets from two independent medical centers to evaluate model generalizability and small-sample robustness. Four key metrics, including accuracy, F1-score, precision, and recall, were employed to assess model performance.</p><p><strong>Results: </strong>In the internal cohort, 369 aneurysms were ruptured. Independent predictors of rupture included: the presence of multiple aneurysms, aneurysm location, aneurysm angle, presence of daughter-sac aneurysm, and height-width ratio. MCANet demonstrated satisfactory predictive performance with 91.38% accuracy, 96.33% sensitivity, 90.52% precision, and 93.33% F1-score. External validation maintained good discriminative ability across both independent cohorts.</p><p><strong>Conclusion: </strong>The MCANet model effectively integrates multimodal heterogeneous data for MCA aneurysm rupture risk prediction, demonstrating clinical applicability even in data-constrained scenarios. This model shows potential to optimize therapeutic decision-making and mitigate patient anxiety through individualized risk assessment.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144683555","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":"e-Learning in Radiological Image Interpretation for Medical Students: A Systematic Review.","authors":"Kristal Lee, Kriscia Tapia, Patrick C Brennan, Mo'ayyad E Suleiman, Catherine M Jones, Ernest Ekpo","doi":"10.1016/j.acra.2025.06.055","DOIUrl":"https://doi.org/10.1016/j.acra.2025.06.055","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>Evidence for the effectiveness of radiological image interpretation (RII) e-learning approaches for medical students (MS) is unclear. Therefore, this review evaluated the effectiveness of e-learning interventions for MS RII education. Specifically, it evaluated the impact of instructional design and e-learning delivery on skill acquisition and explored the association between these outcomes and the recommended published curricula.</p><p><strong>Materials and methods: </strong>A systematic search of databases (EmBase [OVID, MEDLINE]), PubMed, Science Direct, Scopus, Web of Science) was conducted to February 2024. Inclusion criteria were MS participating in RII education using e-learning. Outcomes assessed were knowledge and diagnostic-skill capabilities. Quality was appraised using the Modified Medical Education Research Study Quality Instrument. Evidence synthesis was performed via thematic analysis using a deductive and iterative process.</p><p><strong>Results: </strong>30 moderate quality studies were reviewed. Online learning platforms (n=15) were the most common form of e-learning delivery. 21 studies incorporated interactive learning using various instructional designs. Multiple topics covered in the published curricula were studied, with high-urgency pathologies minimally represented. The findings indicate that experiential learning is important for RII diagnostic-skill development; learning outcomes for pathologies of varying complexity are not equivalent and are impacted by the selection of delivery and instructional design; large case volumes have a strong positive association with RII outcomes.</p><p><strong>Conclusion: </strong>Interactivity and experiential learning support diagnostic-skill development in RII but are not necessary for radiology knowledge acquisition. There is a paucity of data on the role of case volume on higher-order tasks and educational interventions for high-urgency pathology detection.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144692332","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":"Predictive Value of Machine Learning and Nomogram Models Based on Brain Amyloid SUVR in Alzheimer's Disease.","authors":"Weijing Meng, Aimin Wang, Xianzhu Cong, Lin Rui Xu, Fenglin Wang, Fuyan Shi","doi":"10.1016/j.acra.2025.07.003","DOIUrl":"https://doi.org/10.1016/j.acra.2025.07.003","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>This study aimed to develop machine learning and nomogram models based on brain amyloid standardized uptake value ratio (SUVR) for the prediction of Alzheimer's disease (AD). Least absolute shrinkage and selection operator (LASSO) regression was employed to identify key brain regions associated with amyloid SUVR, which were then integrated into a composite SUVR_Score. A nomogram model was subsequently constructed to support clinical decision-making.</p><p><strong>Methods: </strong>A total of 751 subjects diagnosed with either cognitively normal (CN; n=550) or Alzheimer's disease (AD; n=201) were selected from the Alzheimer's Disease Neuroimaging Initiative database (2007-2023). Clinical data and amyloid SUVR values from multiple brain regions were collected. Subjects were randomly assigned to training (n=525) and validation (n=226) cohorts at a 7:3 ratio. LASSO regression was used to identify region-specific SUVR values significantly associated with AD, which were combined into a composite SUVR_Score. Seven machine learning algorithms, comprising decision tree (DT), random forest (RF), eXtreme Gradient Boosting, support vector machine, k-Nearest Neighbors, LightGBM, and Naive Bayes, were trained using seven variables. The model with the best performance was selected, and a nomogram incorporating key predictors was developed to estimate AD risk. Model performance was assessed using receiver operating characteristic curves, calibration plots, and decision curve analysis (DCA).</p><p><strong>Results: </strong>LASSO regression identified nine brain regions with amyloid SUVR values significantly associated with AD, which were combined into a composite SUVR_Score. Among the machine learning models, RF demonstrated the best performance in the validation cohort (AUC=0.974; accuracy=0.911; sensitivity=0.857; specificity=0.965). A nomogram was then constructed using four predictors: sex, marital status, education, and SUVR_Score, achieving an AUC of 0.965. Calibration curves showed high agreement between predicted and observed outcomes, while DCA confirmed favorable clinical utility.</p><p><strong>Conclusion: </strong>Machine learning and nomogram models based on brain amyloid SUVR effectively distinguish AD from CN individuals and offer valuable support for clinical diagnosis and risk prediction.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144692334","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":"2.5D Deep Learning-Based Prediction of Pathological Grading of Clear Cell Renal Cell Carcinoma Using Contrast-Enhanced CT: A Multicenter Study.","authors":"Zi Yang, Haitao Jiang, Shuai Shan, Xu Wang, Quanming Kou, Chao Wang, Pengfei Jin, Yuyun Xu, Xiaohui Liu, Yudong Zhang, Yuqing Zhang","doi":"10.1016/j.acra.2025.06.056","DOIUrl":"https://doi.org/10.1016/j.acra.2025.06.056","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>To develop and validate a deep learning model based on arterial phase-enhanced CT for predicting the pathological grading of clear cell renal cell carcinoma (ccRCC).</p><p><strong>Materials and methods: </strong>Data from 564 patients diagnosed with ccRCC from five distinct hospitals were retrospectively analyzed. Patients from centers 1 and 2 were randomly divided into a training set (n=283) and an internal test set (n=122). Patients from centers 3, 4, and 5 served as external validation sets 1 (n=60), 2 (n=38), and 3 (n=61), respectively. A 2D model, a 2.5D model (three-slice input), and a radiomics-based multi-layer perceptron (MLP) model were developed. Model performance was evaluated using the area under the curve (AUC), accuracy, and sensitivity.</p><p><strong>Results: </strong>The 2.5D model outperformed the 2D and MLP models. Its AUCs were 0.959 (95% CI: 0.9438-0.9738) for the training set, 0.879 (95% CI: 0.8401-0.9180) for the internal test set, and 0.870 (95% CI: 0.8076-0.9334), 0.862 (95% CI: 0.7581-0.9658), and 0.849 (95% CI: 0.7766-0.9216) for the three external validation sets, respectively. The corresponding accuracy values were 0.895, 0.836, 0.827, 0.825, and 0.839. Compared to the MLP model, the 2.5D model achieved significantly higher AUCs (increases of 0.150 [p<0.05], 0.112 [p<0.05], and 0.088 [p<0.05]) and accuracies (increases of 0.077 [p<0.05], 0.075 [p<0.05], and 0.101 [p<0.05]) in the external validation sets.</p><p><strong>Conclusion: </strong>The 2.5D model based on 2.5D CT image input demonstrated improved predictive performance for the WHO/ISUP grading of ccRCC.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144668915","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":"Enhanced Image Quality and Comparable Diagnostic Performance of Prostate Fast Bi-MRI with Deep Learning Reconstruction.","authors":"Liting Shen, Ying Yuan, Jin Liu, Yue Cheng, Qian Liao, Rongchao Shi, Tianyu Xiong, Hui Xu, Liang Wang, Zhenghan Yang","doi":"10.1016/j.acra.2025.06.059","DOIUrl":"https://doi.org/10.1016/j.acra.2025.06.059","url":null,"abstract":"<p><strong>Rational and objectives: </strong>To evaluate image quality and diagnostic performance of prostate biparametric MRI (bi-MRI) with deep learning reconstruction (DLR).</p><p><strong>Materials and methods: </strong>This prospective study included 61 adult male urological patients undergoing prostate MRI with standard-of-care (SOC) and fast protocols. Sequences included T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) maps. DLR images were generated from FAST datasets. Three groups (SOC, FAST, DLR) were compared using: (1) five-point Likert scale, (2) signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), (3) lesion slope profiles, (4) dorsal capsule edge rise distance (ERD). PI-RADS scores were assigned to dominant lesions. ADC values were measured in histopathologically confirmed cases. Diagnostic performance was analyzed via receiver operating characteristic (ROC) curves (accuracy/sensitivity/specificity). Statistical tests included Friedman test, one-way ANOVA with post hoc analyses, and DeLong test for ROC comparisons (P<0.05).</p><p><strong>Results: </strong>FAST scanning protocols reduced acquisition time by nearly half compared to the SOC scanning protocol. When compared to T2WI_FAST, DLR significantly improved SNR, CNR, slope profile, and ERD (P < 0.05). Similarly, DLR significantly enhanced SNR, CNR, and image sharpness when compared to DWI_FAST (P < 0.05). No significant differences were observed in PI-RADS scores and ADC values between groups (P > 0.05). The areas under the ROC curves, sensitivity, and specificity of ADC values for distinguishing benign and malignant lesions remained consistent (P > 0.05).</p><p><strong>Conclusion: </strong>DLR enhances image quality in fast prostate bi-MRI while preserving PI-RADS classification accuracy and ADC diagnostic performance.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144668917","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":"Enhancing Microwave Ablation for Lung Lesions with Cone-Beam Computed Tomography Guidance and Intrapulmonary Fine Adjustment in a Hybrid Operating Room.","authors":"Ling-Kai Chang, Po-Keng Su, Shwetambara Malwade, Wen-Yuan Chung, Pak-Si Chan, Shu-Chun Chen, Lun-Che Chen, Shun-Mao Yang","doi":"10.1016/j.acra.2025.06.058","DOIUrl":"https://doi.org/10.1016/j.acra.2025.06.058","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>To review outcomes of the first 150 consecutive microwave ablation (MWA) cases at our institution to assess the safety and diagnostic performance of a new workflow for cone-beam computed tomography (CBCT)-guided percutaneous MWA performed under general anesthesia in a hybrid operating room (HOR).</p><p><strong>Materials and methods: </strong>This retrospective study included 150 consecutive patients who underwent MWA in the CBCT-equipped HOR between July 2020 and January 2024. The procedural workflow involved general anesthesia with patient fixation, CBCT scanning, iGuide needle pathway planning, needle placement with a laser beam and augmented fluoroscopy guidance, and post-procedure ablation-zone assessment. Technical advancements included the use of a coaxial needle for synchronous biopsy and ablation and a fine adjustment tool.</p><p><strong>Results: </strong>In total, 145 lesions in 127 patients (82 women and 45 men; mean age, 59.8±13.1years [standard deviation]; single nodule, 113 patients; multiple nodules, 14 patients) were analyzed. The median global operating-room time, procedure time, total dose area product, and postoperative stay were 110 min, 45 min, 19,701 μGym², and 2 days, respectively. CBCT-guided MWA improved diagnostic yield for subcentimeter lung lesions to 50.0% with intra-parenchymal fine adjustment, compared to 10.1% without. Pneumothorax rates decreased significantly to 6% with coaxial needles for biopsy and ablation, compared to 12.1% with prior methods. Post-procedure complications were mostly tolerable, with two fatal complications occurring in the early cohort.</p><p><strong>Conclusion: </strong>The MWA technique is safe and feasible, with various technical strategies enhancing its efficacy. The intra-parenchymal fine adjustment method significantly improves small-nodule biopsy yield.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144668918","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":"TIPS-Impact of Experience on Procedural Efficiency and Safety.","authors":"Thomas Maximilian Helmberger, Marco Berning, Sophia Freya Ulrike Blum, Marie-Luise Kromrey, Patricia Hahlbohm, Stefan Sulk, Christoph Georg Radosa, Steffen Löck, Jens-Peter Kühn, Ralf-Thorsten Hoffmann, Felix Schön","doi":"10.1016/j.acra.2025.06.041","DOIUrl":"https://doi.org/10.1016/j.acra.2025.06.041","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>To investigate the impact of operator experience and the overall expertise of the medical center on technical success and procedural parameters of transjugular intrahepatic portosystemic shunt (TIPS) in a longitudinal setting.</p><p><strong>Materials and methods: </strong>TIPS procedures conducted at a German tertiary hospital from 2017 to 2023 were enrolled retrospectively. The impact of the center's expertise on technical success and procedural parameters was assessed by logistic and linear regressions stratified by year. Comparative analyses between more and less experienced operators were performed using Mann-Whitney U, Chi-square, and Fisher's exact tests.</p><p><strong>Results: </strong>A total of 245 TIPS procedures (161 men, mean age 59.8±10.9years) were performed, with a technical success rate of 95.5% (234/245). Technical success remained stable over time (2017: 91.7% vs. 2023: 94.0%, p=0.532). Over time, significant reductions were observed in total procedure time (2017: 63.4±22.1 min vs. 2023: 44.4± 29.1 min, p<0.001), fluoroscopy time (2017: 18.8±10.6 min vs. 2023: 12.8±8.6 min, p=0.002), radiation dose (2017: 114.5±113.7 Gy*cm² vs. 2023: 51.3±33.3 Gy*cm²; p<0.001), and contrast agent usage (2017: 72.0±34.6 mL vs. 2023: 48.1±24.7 mL, p<0.001). More experienced operators had significantly shorter fluoroscopy times than less experienced operators (14.0±9.9 min vs. 16.4±9.1 min, p=0.016).</p><p><strong>Conclusion: </strong>TIPS is a highly technically successful procedure. Increased experience at the performing center is associated with reduced procedure times, fluoroscopy times, and radiation doses, while technical success rates remain stable. Additionally, procedures performed by more experienced operators were associated with shorter fluoroscopy times.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144668922","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}