Academic Radiology最新文献

{"title":"Providing Radiology Services at Another Institution: Operational Considerations","authors":"Kevin L. Smith MD, Duane Schonlau MD, John L. Burns MS, Richard B. Gunderman MD, PhD","doi":"10.1016/j.acra.2025.03.036","DOIUrl":"10.1016/j.acra.2025.03.036","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 5","pages":"Pages 3128-3129"},"PeriodicalIF":3.8,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848135","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":"A Multicenter Observational Pilot Study Evaluating the Effect of Using an Entrustable Professional Activity Checklist on Resident Mid-Rotation Formative Feedback in Diagnostic Breast Imaging","authors":"Monica M. Sheth MD ,&nbsp;Priscilla J. Slanetz MD, MPH ,&nbsp;Petra Lewis MB.BS ,&nbsp;Ryan W. Woods MD, MPH ,&nbsp;El Berkaoui Ali ,&nbsp;Nancy R. Fefferman MD ,&nbsp;Caroline R. Paul MD","doi":"10.1016/j.acra.2025.02.029","DOIUrl":"10.1016/j.acra.2025.02.029","url":null,"abstract":"<div><h3>Rational and Objective</h3><div>Formative feedback is an important strategy to improve resident learning. The purpose of our study is to evaluate the impact on frequency, quality and perceptions of resident formative feedback after implementation of a diagnostic breast imaging specific entrustable professional activity based mid-rotation checklist.</div></div><div><h3>Material and Methods</h3><div>In this IRB-approved multicenter study, a six-step methodology was used to develop the validated EPA based checklist, participant pre- and post-implementation surveys, and analyze the collected data.</div></div><div><h3>Results</h3><div>26 out of 32 (81%) residents and 7 out of 9 (78%) teaching attendings found the structured feedback checklist helpful in evaluating residents' performance on the diagnostic breast imaging rotation. 9 of 9 (100%) attending stated it improved their ability to give specific, timely, actionable and thoughtful feedback and 6 of 9 (67%) agreed that their feedback was more structured. 27 of 32 (84%) residents found that the feedback they received allowed them to tailor their studying to areas that needed improvement during the remainder of their rotation.</div></div><div><h3>Conclusion</h3><div>Mid-rotation feedback using a structured EPA-based checklist improves the frequency and quality of resident formative feedback from both a residents’ and attendings’ perspective.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 5","pages":"Pages 3114-3127"},"PeriodicalIF":3.8,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848134","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 Impact of the Affirmative Action Ruling on Diversity in Radiology: Challenges and Opportunities","authors":"Zayani Sims ,&nbsp;Omer A. Awan MD, MPH, CIIP","doi":"10.1016/j.acra.2025.03.031","DOIUrl":"10.1016/j.acra.2025.03.031","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 5","pages":"Pages 2529-2532"},"PeriodicalIF":3.8,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143812800","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":"Revolutionizing Abdominal Aortic Aneurysm Diagnosis: The Promise of Molecular Imaging.","authors":"Pingyang Zhang, Yutong Liu","doi":"10.1016/j.acra.2025.03.023","DOIUrl":"https://doi.org/10.1016/j.acra.2025.03.023","url":null,"abstract":"<p><p>Abdominal aortic aneurysm (AAA) is a potentially fatal condition that is often asymptomatic in its early stages, with treatment strategies that remain controversial due to limited predictive accuracy for rupture risk. Current clinical approaches primarily rely on aneurysm size and growth rates for risk assessment, which are insufficient for identifying high-risk individuals. This review focuses on preclinical models and the development of molecular imaging technologies, which offer high-spatial-resolution visualization of pathological processes at the molecular level. These advancements provide a promising opportunity to characterize AAA beyond anatomical dimensions and address existing gaps in early diagnosis and targeted therapy. We will discuss the progression of pathophysiological alterations in AAA, the principles underlying contrast agents and molecular probes, and recent advancements in vascular wall molecular imaging within preclinical models.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143797039","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":"From Report to Rapport: Improving Communication in Breast Imaging","authors":"Catherine McNulty MD,&nbsp;Julia Gerras MD,&nbsp;Daniel Bradley MD,&nbsp;James Chalfant MD,&nbsp;Nazanin Yaghmai MD,&nbsp;Cheryce Fischer MD,&nbsp;Mollie Rashid MD,&nbsp;Bo Li MD,&nbsp;Jim Sayre PhD,&nbsp;Hannah Milch MD,&nbsp;Tiffany Chan MD","doi":"10.1016/j.acra.2025.03.024","DOIUrl":"10.1016/j.acra.2025.03.024","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 5","pages":"Pages 3106-3113"},"PeriodicalIF":3.8,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143788972","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":"Addressing Reader Concerns: A Thorough Response to the Meta-analysis of Ultrasound-Guided Thermal Ablation for Lymph Node Recurrence in Papillary Thyroid Carcinoma","authors":"Arun Upadhyaya ,&nbsp;Sadhana Acharya Upadhyaya ,&nbsp;Luchen Chang ,&nbsp;Yuanyuan Li ,&nbsp;Xi Wei","doi":"10.1016/j.acra.2025.03.045","DOIUrl":"10.1016/j.acra.2025.03.045","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 5","pages":"Pages 3137-3138"},"PeriodicalIF":3.8,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143797020","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":"Effects of Renal Function on the Multimodal Brain Networks Affecting Mild Cognitive Impairment Converters in End-Stage Renal Disease.","authors":"Ziyang Yu, Yinke Du, Huize Pang, Xiaolu Li, Yu Liu, Shuting Bu, Juzhou Wang, Mengwan Zhao, Zhenghong Ren, Xuedan Li, Li Yao","doi":"10.1016/j.acra.2025.01.031","DOIUrl":"https://doi.org/10.1016/j.acra.2025.01.031","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>Cognitive decline is common in End-Stage Renal Disease (ESRD) patients, yet its neural mechanisms are poorly understood. This study investigates structural and functional brain network reconfiguration in ESRD patients transitioning to Mild Cognitive Impairment (MCI) and evaluates its potential for predicting MCI risk.</p><p><strong>Methods: </strong>We enrolled 90 ESRD patients with 2-year follow-up, categorized as MCI converters (MCI_C, n=48) and non-converters (MCI_NC, n=42). Brain networks were constructed using baseline rs-fMRI and high angular resolution diffusion imaging, focusing on regional structural-functional coupling (SFC). A Support Vector Machine (SVM) model was used to identify brain regions associated with cognitive decline. Mediation analysis was conducted to explore the relationship between kidney function, brain network reconfiguration, and cognition.</p><p><strong>Results: </strong>MCI_C patients showed decreased network efficiency in the structural network and compensatory changes in the functional network. Machine learning models using multimodal network features predicted MCI with high accuracy (AUC=0.928 for training set, AUC=0.903 for test set). SHAP analysis indicated that reduced hippocampal SFC was the most significant predictor of MCI_C. Mediation analysis revealed that altered brain network topology, particularly hippocampal SFC, mediated the relationship between kidney dysfunction and cognitive decline.</p><p><strong>Conclusion: </strong>This study provides new insights into the link between kidney function and cognition, offering potential clinical applications for structural and functional MRI biomarkers.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143797032","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":"Comparing Abbreviated and Full MRI Protocols for Preoperative Local Staging of Locally Advanced Rectal Cancer.","authors":"Noha Yahia Ebaid, Shimaa Elsayed Badr, Reham Fawzy Mansour, Heba Alhussein Abo-Alella, Mostafa Mohamad Assy, Sara Kamel Said Eldemerdash, Mohamed Ashraf Sayed Ahmed Haasan, Heba Abdelmonem Elsayed Mohamed","doi":"10.1016/j.acra.2025.03.025","DOIUrl":"https://doi.org/10.1016/j.acra.2025.03.025","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>This study aimed to compare the diagnostic accuracy of the abbreviated MRI protocol (AP) with the full protocol (FP) in preoperative staging of locally advanced rectal cancer (LARC).</p><p><strong>Materials and methods: </strong>This prospective single-center study included 131 cases of LARC. All patients underwent the FP rectal MRI, including T2-weighted imaging (T2WI) and contrast-enhanced T1WI, as well as the AP MRI, which included only T2WI. Two independent readers with 10 and 15years of experience in gastrointestinal imaging evaluated all MRI images for both protocols. The interpretation time for each protocol was compared using the Wilcoxon Signed-Rank test. Diagnostic accuracy in predicting tumor stage, mesorectal fascia (MRF) involvement, and extramural venous invasion (EMVI) was assessed using histopathology as the reference standard. The inter-test agreement was evaluated using Cohen's Kappa test.</p><p><strong>Results: </strong>The AP protocol showed a sensitivity of 82.1%, specificity of 95.3%, and accuracy of 94.4%. In comparison, the FP protocol demonstrated a sensitivity of 91%, specificity of 100%, and accuracy of 97.6% for the local staging of LARC. There was strong agreement between both protocols in T staging, MRF involvement, and EMVI detection, with Cohen's kappa (K) values of 0.862, 0.710, and 0.863, respectively. The median interpretation time for the AP and FP protocols was 12 and 22 minutes, respectively. Moreover, the AP had a significantly shorter interpretation time than the FP (P<.001).</p><p><strong>Conclusion: </strong>The AP demonstrated high diagnostic performance with significantly reduced interpretation time, suggesting its potential as an alternative in certain clinical settings.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143797029","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":"AI-Driven Predictive Model Integrating Clinical Data with Tumoral and Peritumoral PET-Based Radiomics Features for Early-Stage Solid Non-small Cell Lung Cancer.","authors":"Sanaz Asadian","doi":"10.1016/j.acra.2025.03.053","DOIUrl":"https://doi.org/10.1016/j.acra.2025.03.053","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143797024","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":"Development of a Nomogram for Predicting Tuberous Sclerosis Complex Genotypes in Children Using Advanced Diffusion MRI and Clinical Data.","authors":"Hui Sun, Zhiping Yan, Junhang Gao, Yingzhi Zheng, Yueyu Zheng, Yang Song, Yongji Liu, Zhixian Lin, Wencai Shen, Jin Fang, Hong Qu, Yanzhao Diao, Hongmei Liu, Sulian Su, Guihua Jiang","doi":"10.1016/j.acra.2025.03.022","DOIUrl":"https://doi.org/10.1016/j.acra.2025.03.022","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>Tuberous sclerosis complex (TSC) is a multisystem genetic disorder. Focusing on central nervous system manifestations, this study developed an imaging-clinical model combining advanced diffusion MRI parameters with neurological clinical features to distinguish TSC1 vs. TSC2 genotypes.</p><p><strong>Materials and methods: </strong>Eighty-eight patients newly diagnosed with TSC were enrolled. All underwent a stratified genetic testing strategy comprising whole-exome sequencing, whole-genome sequencing, and tissue-specific deep sequencing. Diffusion spectrum imaging provided parameters from diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), and mean apparent propagator MRI (MAP-MRI). A combined prediction model was constructed using logistic regression and validated via bootstrap resampling.</p><p><strong>Results: </strong>A younger age of onset, autism, neuropsychiatric disorders, intracellular volume fraction, and q-space inverse variance were independently associated with TSC2 mutations. The combined model achieved an AUC of 0.879 (95% CI: 0.841-0.917) in the training set and 0.864 (95% CI: 0.803-0.926) in the validation set. By DeLong's test, it significantly outperformed the clinical model (AUC: 0.637, 95% CI: 0.552-0.723; p < 0.001), while the difference from the imaging model (AUC: 0.833, 95% CI: 0.763-0.903) was not statistically significant (p = 0.068). However, net reclassification (NRI = 0.702, p < 0.001) and integrated discrimination improvement (IDI = 0.097, p < 0.001) both supported the combined model's superior classification ability.</p><p><strong>Conclusion: </strong>Integrating advanced diffusion MRI parameters with clinical data significantly improves prediction of TSC1 vs. TSC2 genotypes. This combined approach offers valuable support for early diagnosis and personalized treatment in TSC.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143781358","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}