Academic Radiology最新文献

{"title":"Post-COVID Pandemic Radiology Resident Readout Best Practices: An Institutional Needs Assessment and Initial Guidelines","authors":"Sophia R. O’Brien MD, MSEd,&nbsp;Christine E. Edmonds MD,&nbsp;Jillian W. Lazor MD,&nbsp;Susan Shamimi-Noori MD, MSEd,&nbsp;Mary Scanlon MD,&nbsp;Austin R. Pantel MD, MSTR,&nbsp;Scott Simpson DO, MSEd","doi":"10.1016/j.acra.2024.10.057","DOIUrl":"10.1016/j.acra.2024.10.057","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>Radiology resident readout practices were adapted during the COVID pandemic, with several institutions transitioning to virtual and asynchronous readouts. Some pandemic-era practices persist today, with unclear effects on resident education. We developed institutional Readout Best Practices and assessed implementation.</div></div><div><h3>Materials and Methods</h3><div>A voluntary, anonymous needs assessment survey was emailed to radiology residents in June 2023 and an informal faculty needs assessment was performed. Results informed development of a Readout Best Practices guide, shared with trainees and faculty in July 2023. A voluntary, anonymous, faculty pre-implementation survey was sent in July 2023, followed by resident and faculty post-implementation surveys in November 2023.</div></div><div><h3>Results</h3><div>Our institutional needs assessment revealed that residents and faculty preferred a combination of live in-person readouts and asynchronous readouts based on resident year and specific rotation. Notably, residents desired more in-person readouts than they were receiving. Following implementation of Readout Best Practices, there was no change in readout methods reported by residents or faculty; however, 56% of faculty reported making changes to their educational practices and 50% of residents who had read the Best Practices guide adopted changes on service to improve their learning. Faculty reported implementation was limited by high volumes, staffing, and workflow.</div></div><div><h3>Conclusion</h3><div>Despite no significant changes in reported readout style, faculty and residents reported practice changes following implementation of Readout Best Practices, suggesting that effects on teaching and learning at the individual level were not captured by our survey. This study identified challenges in balancing volumes, education, and wellness, and the data support discussion of both educational and clinical workflow optimization. Future studies are warranted to determine the status of post-pandemic radiology readouts beyond our institution.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 3","pages":"Pages 1743-1751"},"PeriodicalIF":3.8,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142752237","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":"Hypoechoic Halo Thickness in Thyroid Nodules: A Retrospective Analysis with Editorial Reflection.","authors":"Weizhen Shi, Ming Zhang, Weiyi Tang, Kui Tang","doi":"10.1016/j.acra.2025.02.018","DOIUrl":"https://doi.org/10.1016/j.acra.2025.02.018","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143537515","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-Assisted Diagnosis of Malignant Cerebral Edema Following Endovascular Thrombectomy.","authors":"Yuting Song, Jiayi Hong, Feifan Liu, Junyu Liu, Yuting Chen, Zhaosheng Li, Jun Su, Sheng Hu, Jingjing Fu","doi":"10.1016/j.acra.2025.02.021","DOIUrl":"https://doi.org/10.1016/j.acra.2025.02.021","url":null,"abstract":"<p><strong>Background: </strong>Malignant cerebral edema (MCE) is a significant complication following endovascular thrombectomy (EVT) in the treatment of acute ischemic stroke. This study aimed to develop and validate a deep learning-assisted diagnosis model based on the hyperattenuated imaging marker (HIM), characterized by hyperattenuation on head non-contrast computed tomography immediately after thrombectomy, to facilitate radiologists in predicting MCE in patients receiving EVT.</p><p><strong>Methods: </strong>This study included 271 patients, with 168 in the training cohort, 43 in the validation cohort, and 60 in the prospective internal test cohort. Deep learning models including ResNet 50, ResNet 101, ResNeXt50_32×4d, ResNeXt101_32×8d, and DenseNet 121 were constructed. The performance of senior and junior radiologists with and without optimal model assistance was compared.</p><p><strong>Results: </strong>ResNeXt101_32×8d had the best predictive performance, the analysis of the receiver operating characteristic curve indicated an area under the curve (AUC) of 0.897 for the prediction of MCE in the validation group and an AUC of 0.889 in the test group. Moreover, with the assistance of the model, radiologists exhibited a significant improvement in diagnostic performance, the AUC increased by 0.137 for the junior radiologist and 0.096 for the junior radiologist respectively.</p><p><strong>Conclusion: </strong>Our study utilized the ResNeXt-101 neural network, combined with HIM, to validate a deep learning model for predicting MCE post-EVT. The developed deep learning model demonstrated high discriminative ability, and can serve as a valuable adjunct to radiologists in clinical practice.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143538086","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":"Denoising Diffusion Probabilistic Model to Simulate Contrast-enhanced spinal MRI of Spinal Tumors: A Multi-Center Study.","authors":"Chenxi Wang, Senpeng Zhang, Jun Xu, Honghao Wang, Qizheng Wang, Yupeng Zhu, Xiaoying Xing, Dapeng Hao, Ning Lang","doi":"10.1016/j.acra.2025.02.024","DOIUrl":"https://doi.org/10.1016/j.acra.2025.02.024","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>To generate virtual T1 contrast-enhanced (T1CE) sequences from plain spinal MRI sequences using the denoising diffusion probabilistic model (DDPM) and to compare its performance against one baseline model pix2pix and three advanced models.</p><p><strong>Methods: </strong>A total of 1195 consecutive spinal tumor patients who underwent contrast-enhanced MRI at two hospitals were divided into a training set (n = 809, 49 ± 17 years, 437 men), an internal test set (n = 203, 50 ± 16 years, 105 men), and an external test set (n = 183, 52 ± 16 years, 94 men). Input sequences were T1- and T2-weighted images, and T2 fat-saturation images. The output was T1CE images. In the test set, one radiologist read the virtual images and marked all visible enhancing lesions. Results were evaluated using sensitivity (SE) and false discovery rate (FDR). We compared differences in lesion size and enhancement degree between reference and virtual images, and calculated signal-to-noise (SNR) and contrast-to-noise ratios (CNR) for image quality assessment.</p><p><strong>Results: </strong>In the external test set, the mean squared error was 0.0038±0.0065, and structural similarity index 0.78±0.10. Upon evaluation by the reader, the overall SE of the generated T1CE images was 94% with FDR 2%. There was no difference in lesion size or signal intensity ratio between the reference and generated images. The CNR was higher in the generated images than the reference images (9.241 vs. 4.021; P＜0.001).</p><p><strong>Conclusion: </strong>The proposed DDPM demonstrates potential as an alternative to gadolinium contrast in spinal MRI examinations of oncologic patients.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143536740","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":"Multi-parametric MRI Habitat Radiomics Based on Interpretable Machine Learning for Preoperative Assessment of Microsatellite Instability in Rectal Cancer.","authors":"Yueyan Wang, Bo Xie, Kai Wang, Wentao Zou, Aie Liu, Zhong Xue, Mengxiao Liu, Yichuan Ma","doi":"10.1016/j.acra.2025.02.009","DOIUrl":"https://doi.org/10.1016/j.acra.2025.02.009","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>This study constructed an interpretable machine learning model based on multi-parameter MRI sub-region habitat radiomics and clinicopathological features, aiming to preoperatively evaluate the microsatellite instability (MSI) status of rectal cancer (RC) patients.</p><p><strong>Materials and methods: </strong>This retrospective study recruited 291 rectal cancer patients with pathologically confirmed MSI status and randomly divided them into a training cohort and a testing cohort at a ratio of 8:2. First, the K-means method was used for cluster analysis of tumor voxels, and sub-region radiomics features and classical radiomics features were respectively extracted from multi-parameter MRI sequences. Then, the synthetic minority over-sampling technique method was used to balance the sample size, and finally, the features were screened. Prediction models were established using logistic regression based on clinicopathological variables, classical radiomics features, and MSI-related sub-region radiomics features, and the contribution of each feature to the model decision was quantified by the Shapley-Additive-Explanations (SHAP) algorithm.</p><p><strong>Results: </strong>The area under the curve (AUC) of the sub-region radiomics model in the training and testing groups was 0.848 and 0.8, respectively, both better than that of the classical radiomics and clinical models. The combined model performed the best, with AUCs of 0.908 and 0.863 in the training and testing groups, respectively.</p><p><strong>Conclusion: </strong>We developed and validated a robust combined model that integrates clinical variables, classical radiomics features, and sub-region radiomics features to accurately determine the MSI status of RC patients. We visualized the prediction process using SHAP, enabling more effective personalized treatment plans and ultimately improving RC patient survival rates.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143524233","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":"Long-Term Outcomes of Transarterial Chemoembolization plus Ablation versus Surgical Resection in Patients with Large BCLC Stage A/B HCC.","authors":"Ying-Wen Hou, Tian-Qi Zhang, Li-Di Ma, Yi-Quan Jiang, Xue Han, Tian Di, Lu Tang, Rong-Ping Guo, Min-Shan Chen, Jin-Xin Zhang, Zhi-Mei Huang, Jin-Hua Huang","doi":"10.1016/j.acra.2025.02.012","DOIUrl":"https://doi.org/10.1016/j.acra.2025.02.012","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>Large hepatocellular carcinoma (HCC) exhibits heterogeneous morphologies and varied responses to treatment. We evaluated outcomes of patients with different large HCC classifications receiving surgical resection (SR) or transarterial chemoembolization plus ablation (TA).</p><p><strong>Materials and methods: </strong>Patients with HCC ≥ 5 cm receiving SR or TA between May 2016 and December 2020 at one center were analyzed retrospectively and with propensity score matching (PSM). Overall survival (OS) and progression-free survival (PFS) of the 2 treatment groups were compared. Tumors were classified according to imaging morphology and gross pathology: Type I, simple nodular; Type II, simple nodular with extranodular growth or confluent multinodular; Type III, infiltrative.</p><p><strong>Results: </strong>Of 644 patients, 374 met the inclusion criteria (300 received SR and 74 received TA). Before PSM, median follow-up was 51.2 (IQR 29.6-65.3) months, and the SR group had longer OS (HR 2.13, 95% CI 1.44-3.15, p<0.001) and PFS (HR 2.31, 95% CI 1.66-3.20, p<0.001) than the TA group; after PSM these differences were not significant (all p>0.05). Infiltrative HCC (Type III) was an independent negative prognostic factor for OS and PFS. Within both treatment groups, patients with infiltrative HCC had shorter OS and PFS than patients with non-infiltrative HCC (Types I and II) (all p<0.001).</p><p><strong>Conclusion: </strong>For patients with HCC ≥ 5 cm, tumor classification is an important prognostic factor. In patients with non-infiltrative HCC, TA and SR had comparable OS after PSM. For patients with infiltrative HCC, TA and SR had limited efficacy.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143525060","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":"The Scourge of Workplace Bullying.","authors":"Richard B Gunderman","doi":"10.1016/j.acra.2025.02.023","DOIUrl":"https://doi.org/10.1016/j.acra.2025.02.023","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143517198","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":"Predicting Microvascular Invasion and Early Recurrence in Hepatocellular Carcinoma Using DeepLab V3+ Segmentation of Multiregional MR Habitat Images.","authors":"Zhenhuan Huang, Yifan Pan, Wanrong Huang, Feng Pan, Huifang Wang, Chuan Yan, Rongping Ye, Shuping Weng, Jingyi Cai, Yueming Li","doi":"10.1016/j.acra.2025.02.006","DOIUrl":"https://doi.org/10.1016/j.acra.2025.02.006","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>Accurate identification of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is crucial for treatment and prognosis. Single-modality and feature fusion models using manual segmentation fail to provide insights into MVI. This study aims to develop a DeepLab V3+ model for automated segmentation of HCC magnetic resonance (MR) images and a decision fusion model to predict MVI and early recurrence (ER).</p><p><strong>Materials and methods: </strong>This retrospective study included 209 HCC patients (146 in the training and 63 in the test cohorts). The performance of DeepLab V3+ for HCC MR image segmentation was evaluated using Dice Loss and F1 score. Intraclass correlation coefficients (ICCs) assessed feature extraction reliability. Spearman's correlation analyzed the relationship between tumor volumes from automated and manual segmentation, with agreement evaluated using Bland-Altman plots. Model performance was assessed using the area under the receiver operating characteristic curve (ROC AUC), calibration curves, and decision curve analysis. A nomogram predicted ER of HCC after surgery, with Kaplan-Meier analysis for 2-year recurrence-free survival (RFS).</p><p><strong>Results: </strong>The DeepLab V3+ model demonstrated high segmentation accuracy, with strong agreement in feature extraction (ICC: 0.802-0.999). The decision fusion model achieved AUCs of 0.968 and 0.878 for MVI prediction, and the nomogram for predicting ER yielded AUCs of 0.782 and 0.690 in the training and test cohorts, respectively, with significant RFS differences between the risk groups.</p><p><strong>Conclusion: </strong>The DeepLab V3+ model accurately segmented HCC. The decision fusion model significantly improved MVI prediction, and the nomogram offered valuable insights into recurrence risk for clinical decision-making.</p><p><strong>Availability of data and materials: </strong>The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143517188","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":"Teaching the Technology: Evaluating a Video-Based PACS Curriculum for Radiology Residents.","authors":"David Gullotti, Moustafa Abou Areda, Wen-Chi Hsu, Victoria Shi, Tae Kyung Kim, Erin Gomez, Cheng Ting Lin","doi":"10.1016/j.acra.2025.02.010","DOIUrl":"https://doi.org/10.1016/j.acra.2025.02.010","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>Picture archiving and communication system (PACS) is integral to radiology, yet effective training to master PACS viewer tools remains challenging. This study aimed to evaluate the impact of a video-based curriculum on radiology residents' PACS confidence and technical proficiency.</p><p><strong>Materials and methods: </strong>A prospective cohort study was conducted at a tertiary academic institution during 2022-2023, enrolling first-year (R1) and second-year (R2) radiology residents. The Intervention Group received a 30-minute instructional video on PACS tools 3 months after their orientation. The Reference Group did not receive additional training. Comfort levels and PACS task performance were assessed before and after the intervention.</p><p><strong>Results: </strong>A total of 24 residents participated, with 12 in each group. The Intervention Group showed a significant increase in self-reported confidence in PACS skills following the video intervention (median [interquartile range]=-1.01 [-1.30, -0.43] vs. 2.07 [1.37, 2.38], p<0.01). The Intervention Group maintained a higher confidence level at the R2 level compared to the Reference Group, though the difference was not statistically significant. Additionally, the Intervention Group completed their assigned PACS tasks in significantly less time (total time: 220.5 s [179.5, 259.5] vs. 315.0 s [211.8, 397.0], p=0.04).</p><p><strong>Conclusion: </strong>The video-based PACS curriculum effectively improved radiology residents' technical skills and confidence. These results highlight the importance of supplementing on-the-job learning with formal technical training to reinforce proficiency with the PACS viewer.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143517196","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-Enhanced Ultra-high-resolution CT Imaging for Superior Temporal Bone Visualization.","authors":"Lavinia Brockstedt, Nils F Grauhan, Andrea Kronfeld, Mario Alberto Abello Mercado, Julia Döge, Antoine Sanner, Marc A Brockmann, Ahmed E Othman","doi":"10.1016/j.acra.2025.02.002","DOIUrl":"https://doi.org/10.1016/j.acra.2025.02.002","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>This study assesses the image quality of temporal bone ultra-high-resolution (UHR) Computed tomography (CT) scans in adults and children using hybrid iterative reconstruction (HIR) and a novel, vendor-specific deep learning-based reconstruction (DLR) algorithm called AiCE Inner Ear.</p><p><strong>Material and methods: </strong>In a retrospective, single-center study (February 1-July 30, 2023), UHR-CT scans of 57 temporal bones of 35 patients (5 children, 23 male) with at least one anatomical unremarkable temporal bone were included. There is an adult computed tomography dose index volume (CTDIvol 25.6 mGy) and a pediatric protocol (15.3 mGy). Images were reconstructed using HIR at normal resolution (0.5-mm slice thickness, 512² matrix) and UHR (0.25-mm, 1024² and 2048² matrix) as well as with a vendor-specific DLR advanced intelligent clear-IQ engine inner ear (AiCE Inner Ear) at UHR (0.25-mm, 1024² matrix). Three radiologists evaluated 18 anatomic structures using a 5-point Likert scale. Signal-to-noise (SNR) and contrast-to-noise ratio (CNR) were measured automatically.</p><p><strong>Results: </strong>In the adult protocol subgroup (n=30; median age: 51 [11-89]; 19 men) and the pediatric protocol subgroup (n=5; median age: 2 [1-3]; 4 men), UHR-CT with DLR significantly improved subjective image quality (p<0.024), reduced noise (p<0.001), and increased CNR and SNR (p<0.001). DLR also enhanced visualization of key structures, including the tendon of the stapedius muscle (p<0.001), tympanic membrane (p<0.009), and basal aspect of the osseous spiral lamina (p<0.018).</p><p><strong>Conclusion: </strong>Vendor-specific DLR-enhanced UHR-CT significantly improves temporal bone image quality and diagnostic performance.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143505866","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}