Academic Radiology最新文献

{"title":"Referee Letters for Academic Radiology Promotion Dossiers: Benchmarking Effort.","authors":"Mark E Mullins, Tara M Catanzano, Joshua P Nickerson, Eric B England, Jessica B Robbins","doi":"10.1016/j.acra.2026.08.081","DOIUrl":"https://doi.org/10.1016/j.acra.2026.08.081","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>As part of promotion consideration, letters of evaluation are requested from external (to the candidate's institution) reviewers (referees). In some instances, internal letters are also requested. The process of responding to, reviewing the provided materials, writing the letter, and submitting it takes time. To our knowledge, there is no benchmark for the number of reference letters requested from senior faculty to or the time required to provide these letters. Our aim was to quantify this effort.</p><p><strong>Materials and methods: </strong>5 Professors at different institutions, in different parts of the United States, representing a range of subspecialties, were anonymously surveyed. The number and type of referee letters written, estimates of the time spent with each portion of letter writing, and the number of years writing letters were tabulated.</p><p><strong>Results: </strong>In total, this group has written 218 letters over 49 total years of service (Table 1). This consisted of 179 external (average 4.7/year) and 39 internal (0.8/year). There were 28 instances of repeat letter requests (including consideration for different faculty ranks and changing institutions). We estimate it takes between 2.5 and 8 h to complete the necessary review of institutional guidelines and individual qualifications to complete one of these letters (Table 2).</p><p><strong>Conclusion: </strong>This work provides benchmarking information for the academic radiology community, including appointment and promotions committees, department chairs, and faculty members. The time, effort, energy, and attention that is required is significant but essential to academic career advancement. We recommend that departments and institutions recognize and assign value to this work.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148898302","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":"Abnormal Structural Covariance Networks and Impaired Executive Function in Adolescents with Mild Traumatic Brain Injury.","authors":"Zusheng Cheng, Cheng Xu, Jiahao Li, Junjie Zhao, Guanjinghui Xu, Hui Xu","doi":"10.1016/j.acra.2026.08.060","DOIUrl":"https://doi.org/10.1016/j.acra.2026.08.060","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>Overwhelming evidence found that adolescents with mild traumatic brain injury (mTBI) exhibit structural brain alterations. The influence of mTBI on the adolescents' structural covariance networks (SCNs) remains unexplored.</p><p><strong>Materials and methods: </strong>In this study, data from the Adolescent Brain Cognitive Development Study were adopted, comprising 450 adolescents with mTBI and 450 matched controls for clinical behavior analysis. After structural magnetic resonance imaging quality control, 415 adolescents with mTBI and 404 matched controls were finally included in the SCNs analysis. The SCNs were established using cortical thickness (CT) and cortical surface area (CSA), with group differences assessed via nonparametric permutation tests.</p><p><strong>Results: </strong>The results demonstrated that adolescents with mTBI suffered from impaired cognitive function, including worse working memory, processing speed, and sleep quality. Furthermore, adolescents with mTBI demonstrated both reduced CSA and CT in the executive control network (ECN), including the superior parietal lobule, anterior cingulate cortex, superior frontal gyrus, and insula. In addition, network nodal metrics in these regions also showed abnormalities.</p><p><strong>Conclusion: </strong>These findings might suggest that impaired executive function is associated with aberrant SCNs of hubs in ECN in adolescents with mTBI.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148898278","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":"Spectral CT-derived Adrenal Extracellular Volume for Functional Lateralization in Primary Aldosteronism.","authors":"Yang Peng, Xinyi Wang, Shaodong Lu, Guanglei Tang, Weijian Yun, Shuang Yu, Xiaomin Liu, Weiwei Deng, Junxing Chen, Jian Guan","doi":"10.1016/j.acra.2026.08.097","DOIUrl":"https://doi.org/10.1016/j.acra.2026.08.097","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>There is currently a lack of validated noninvasive method for subtyping patients with primary aldosteronism (PA). This study aimed to investigate the feasibility of using spectral CT-derived adrenal extracellular volume (ECV) map for determining functional lateralization in PA.</p><p><strong>Methods: </strong>This study retrospectively included 105 patients with PA [44 with a single aldosterone-producing nodule (APN) as model group, and remaining 61 cases as validation group] and 21 non-PA controls. According to the results of adrenal venous sampling, patients in validation group were further divided into LD (left-sided dominance), RD (right-sided dominance), and ND (no dominant side) groups. Based on histogram analysis of ECV maps, the average ECV distribution histograms of APN and normal adrenal gland (NAG) were plotted, and the probability-weighted volume index of aldosterone-producing lesion (V_p) was calculated for bilateral adrenal glands (V_pL &V_pR).</p><p><strong>Results: </strong>The average ECV distribution histogram of APNs was significantly lower than NAGs (p<0.001). For (V_pL-V_pR), there were significant differences among LD, RD, ND group, and controls (740.91±505.26 mm³ vs. -615.18 ± 708.82 mm³ vs. 80.88 ± 304.96 mm³ vs. 66.76±264.47 mm³, p<.001). The ROC analysis showed AUCs of (V_pL-V_pR) for differentiating LD with ND or RD with ND were 0.885 and 0.845. The optimal diagnostic accuracy was 77.0% (47/61) for determination of LD, RD, or ND in patients in validation group.</p><p><strong>Conclusion: </strong>Histogram analysis of adrenal ECV map and its derived parameters can assess the overall aldosterone-producing function of a unilateral adrenal gland, and may assist in determining the functional lateralization.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148898290","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":"Hands-on Artificial Intelligence Education for Radiology Residents: A Three-year Feasibility and Curriculum Implementation Study.","authors":"Jie Zhang, Gary Ge","doi":"10.1016/j.acra.2026.08.095","DOIUrl":"https://doi.org/10.1016/j.acra.2026.08.095","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Rationale and objectives: &lt;/strong&gt;Artificial intelligence (AI) has rapidly transformed radiology practice, yet structured and practical AI education remains inconsistently integrated into radiology residency training. We developed and implemented a hands-on AI curriculum designed to integrate core computational principles with clinically relevant imaging applications. This study describes the curriculum design and evaluates its feasibility, reproducibility, and preliminary educational outcomes over three consecutive years of implementation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Materials and methods: &lt;/strong&gt;An 8-hour interactive AI rotation was incorporated into the diagnostic radiology residency curriculum at a single academic institution from 2023 through 2025. The curriculum combined brief didactic instruction with structured laboratory modules covering convolution, radiomics, machine learning, deep learning, model evaluation, bias, and case-based applications. Each year, a new cohort completed the curriculum. Cohort sizes were 9 residents in 2023, 8 in 2024, and 10 in 2025 (total N=27). As an educational innovation initiative, evaluation focused on post-rotation knowledge demonstration and qualitative feedback rather than pre-post comparison. Post-rotation knowledge was assessed with written quizzes covering core curriculum domains, and learner perceptions were subsequently evaluated using a 9-item, 5-point Likert-scale survey (1 = strongly disagree, 5 = strongly agree) administered to residents who had completed the curriculum, supplemented by free-text feedback.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Across three consecutive cohorts (N=27), all residents completed the rotation and post-rotation assessment, and curricular structure and duration remained consistent across cohorts, supporting feasibility and reproducibility. Estimated aggregate post-training assessment performance was approximately 80-90% correct responses, though individual scores were not centrally archived and this percentage should be interpreted as an approximate, retrospective estimate rather than validated outcome data. A survey administered to residents who had completed the curriculum (16 of 27 eligible residents responded) showed mixed to favorable perceptions across nine domains. Ratings were most positive for overall value (62% agree/strongly agree versus 19% disagree/strongly disagree) and awareness of AI limitations (62% versus 12%), but split closer to evenly for several other domains, including appropriateness of technical complexity, where 50% of respondents disagreed that complexity matched their training level. Free-text feedback consistently recommended a more introductory primer for learners without prior AI/coding exposure and greater emphasis on clinical application.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;A structured, hands-on AI curriculum integrated into residency training is feasible and sustainable across independent cohorts, with post-training assessment performance an","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148898323","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":"Dual-Approach AI for Pediatric Supracondylar Fractures: Multiclass Radiograph Classification with Explainable AI and Diagnostic Meta-analysis of AI-Based Computational Approaches.","authors":"Che-Wei Lin, Febryan Setiawan, Syaifullah Hikmahtiyar, Pei-Chun Lai, Fa-Chuan Kuan, Kai-Lan Hsu, Wei-Chen Lin, Pin-Yu Chen, Ming-Tung Huang, Chii-Jeng Lin, Su-Ying Lee, Po-Ting Wu, Kai-Cheng Lin, Chien-An Shih","doi":"10.1016/j.acra.2026.08.048","DOIUrl":"https://doi.org/10.1016/j.acra.2026.08.048","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Rationale and objectives: &lt;/strong&gt;Pediatric supracondylar fractures (SCFs) are the most common elbow injury in children, yet radiographic diagnosis remains challenging due to complex developmental anatomy, with initially missed fracture rates of 17-77%. Prior artificial intelligence (AI) studies have been limited to binary classification frameworks without Gartland subtype differentiation, and no diagnostic test accuracy meta-analysis specific to supracondylar fractures exists. This study aimed to develop the first multiclass deep learning model for SCF detection and Gartland classification and to systematically compare diagnostic performance across AI-based computational diagnostic approaches, including deep learning architectures and radiomics-based machine learning, through meta-analysis.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Materials and methods: &lt;/strong&gt;A retrospective cohort of 1082 pediatric elbow radiographs (2004-2018) was analyzed using YOLOv11 Nano for multiclass classification (No-SCF, Gartland Type I-III; class distribution: No-SCF n=576, Type I n=21, Type II n=125, Type III n=360). Data augmentation mitigated class imbalance, though the limited Type I sample may constrain generalizability for non-displaced fractures. Three patient-level validation strategies were employed: random split (80:10:10), stratified 5-fold cross-validation, and leave-one-out cross-validation. NormEnsemble-HiResCAM explainable AI (XAI) provided visualization of decision-making patterns. A Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA)-compliant meta-analysis synthesized data from four studies (2232 images) encompassing convolutional neural network (CNN)-based and radiomics-based approaches using random-effects modeling.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Multiclass accuracy without bone segmentation was 90.98%, 93.12%, and 90.81% across random split, stratified k-fold, and leave-one-out cross-validation, respectively; binary accuracy was 88.89%, 92.83%, and 88.46%. Bone segmentation integration consistently improved accuracy across all strategies (multiclass +3.9 throughout; binary +6.2 to +10.2% points). Meta-analysis revealed pooled sensitivity of 92.0% (95% confidence interval [CI]: 87.0-96.0%) and specificity of 85.0% (95% CI: 77.0-92.0%; I²=92.9%). CNN-based algorithms showed numerically higher specificity than the single included radiomics study, though this comparison warrants caution. YOLOv11 achieved the highest specificity (98.0%) and diagnostic odds ratio (365.00). XAI visualizations demonstrated anatomically stratified attention across fracture grades: physeal-centered activation for normal/Type I, expanded physeal activation for Type II, and fragment-boundary foci for Type III.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;YOLOv11 Nano provided the first multiclass deep learning framework for pediatric SCF Gartland subtype classification with strong diagnostic performance. Meta-analysis demonstrated ","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148898269","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 Translational Gap in AI for Oropharyngeal Squamous Cell Carcinoma: A TRIPOD+AI Scoping Review of Methodological Barriers to Treatment Deintensification.","authors":"Giuseppe Salvaggio, Rosalia Gargano, Giuseppe Cutaia, Francesco Bencivinni, Ludovico La Grutta, Federico Sireci, Albert Comelli, Neeraj Lalwani","doi":"10.1016/j.acra.2026.08.075","DOIUrl":"https://doi.org/10.1016/j.acra.2026.08.075","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>To map artificial intelligence (AI) and radiomics applications in computed tomography (CT), magnetic resonance imaging (MRI), and fluorodeoxyglucose positron emission tomography/CT (FDG-PET/CT) for oropharyngeal squamous cell carcinoma (OPSCC) in the context of human papillomavirus (HPV) status and treatment deintensification, evaluate reporting quality using TRIPOD+AI (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis + Artificial Intelligence), and identify barriers to clinical translation.</p><p><strong>Materials and methods: </strong>Following PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews), we searched PubMed/MEDLINE and Scopus (January 2015-December 2025) for studies applying AI/radiomics to CT, MRI, and FDG-PET/CT in histologically confirmed OPSCC, focusing on HPV prediction and treatment deintensification. TRIPOD+AI (27 items, 0-2 scoring) assessed reporting quality.</p><p><strong>Results: </strong>Sixty-one studies were included; 34 addressed HPV prediction, 29 survival or prognosis, and 3 treatment response, with several studies addressing more than one objective. CT was the most frequent modality (49.2%), followed by MRI (29.5%) and FDG-PET/CT (21.3%, all 18F-FDG), with manual segmentation (85.2%) and handcrafted radiomics with machine learning (57.4%) being the most common. HPV models reported areas under the curve (AUCs) of 0.65-0.95, but external validation remained limited (22/61, 36.1%), often with performance decline. TRIPOD+AI revealed critical gaps: missing data handling (fully reported 3.3%, absent 54.1%), calibration (6.6% fully, 91.8% absent), interpretable risk groups (27.9% fully, 68.9% absent), data availability (19.7%), and code availability (11.5%). Most studies (78.7%) restricted analysis to the primary tumor; nodal disease was incorporated in 11/61 (18.0%) and was the sole target in 2/61 (3.3%).</p><p><strong>Conclusion: </strong>CT-, MRI-, and FDG-PET/CT-based AI/radiomics models for OPSCC show promising discrimination, but persistent reporting gaps in calibration, transparency, and risk stratification, plus limited external validation and insufficient nodal coverage, preclude safe clinical implementation. Findings apply specifically to HPV-oriented objectives across CT, MRI, and FDG-PET/CT. Standardized protocols, rigorous validation, and structured reporting are needed before these tools can guide treatment deintensification.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148898308","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":"\"Reports in Medical Illustration (REMIL) in Musculoskeletal Radiology: An Evaluation of Evolving AI Models\".","authors":"V Umamaheswara Reddy, Nsl Susmitha, Rajesh Botchu","doi":"10.1016/j.acra.2026.08.082","DOIUrl":"https://doi.org/10.1016/j.acra.2026.08.082","url":null,"abstract":"<p><strong>Objective: </strong>Radiology reports remain predominantly text-based, requiring clinicians and patients to mentally reconstruct imaging findings. Reports in Medical Illustration (REMIL) represent an emerging approach in which artificial intelligence (AI) generates simplified visual summaries directly from report text. This study aimed to evaluate the feasibility, anatomical accuracy, and clinical utility of AI-generated REMIL in musculoskeletal (MSK) radiology.</p><p><strong>Methods: </strong>Twenty-five MSK imaging cases were selected. Identical report text and standardized prompts were provided to three premium multimodal AI systems-ChatGPT (GPT-4 with DALL-E 3), Perplexity AI, and Google Gemini 3.0 Pro. Each model generated a representative illustration based solely on the report description. Two fellowship-trained musculoskeletal radiologists independently assessed each illustration for anatomical accuracy and clinical usefulness. Errors were categorized as minor or major, and image-generation time was recorded.</p><p><strong>Results: </strong>Google Gemini 3.0 Pro demonstrated the most consistent performance, producing anatomically accurate illustrations in approximately 40-42% of cases and clinically useful images in 60-65% of cases, whereas ChatGPT and Perplexity AI frequently generated visually plausible images with substantial anatomical inaccuracies. Major errors were observed across all models, particularly in complex cases involving multiple anatomical structures or imaging planes. Simpler cases with a single dominant abnormality were illustrated more accurately by all models.</p><p><strong>Conclusion: </strong>AI-generated REMIL holds promise as an adjunctive tool for enhancing communication in MSK radiology by providing rapid visual summaries of imaging findings. However, current AI models exhibit inconsistent anatomical accuracy and are not yet reliable for unsupervised clinical use. REMIL should therefore be implemented only with radiologist validation.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148892801","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":"Canadian Medical Students Interested in Radiology Report Greater Perceived Importance of Procedural Roles and Greater Career Sustainability Amid Artificial Intelligence.","authors":"Colin Connors, Sean Coady, Joshua Critch, Natasha Vatcher, Connie Hapgood","doi":"10.1016/j.acra.2026.08.086","DOIUrl":"https://doi.org/10.1016/j.acra.2026.08.086","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>Artificial intelligence (AI) integration and perceptions of radiology's procedural scope may influence medical students' interest in radiology. We aimed to determine whether perceptions of radiology's scope and AI integration differed according to student interest in radiology.</p><p><strong>Methods: </strong>An anonymous cross-sectional survey containing binary, Likert-scale, and open-ended questions was distributed to medical students at a Canadian institution (N=73; 19.7% effective response rate). Responses were stratified by interest in radiology. Group comparisons used Fisher's exact and Mann-Whitney U tests; correlations were assessed using Spearman's coefficient. Open-ended responses were grouped into recurring categories.</p><p><strong>Results: </strong>Interested students rated greater perceived importance of procedural-role items, including therapeutic procedures (median 7 vs 5; p=0.0248), with significant positive correlations between radiology interest and perceived importance of both therapeutic (r=0.3710, p=0.0020) and diagnostic procedures (r=0.2615, p=0.0325). Non-interested students reported a higher perceived impact of AI (median 8 vs 6; p=0.0146), whereas interested students reported greater perceived career sustainability (median 7 vs 5; p=0.0022). Among career values, only patient contact differed, rated lower by interested students (median 6 vs 8, p=0.023). Only 44% of students reported adequate radiology exposure; qualitative responses favored experiential learning, while current exposure was predominantly didactic. Interested students more often participated in shadowing (37.5% vs 12.5%; p=0.0282).</p><p><strong>Conclusions: </strong>Interested students reported greater perceived importance of assessed procedural roles and greater confidence in radiology's sustainability despite AI, with minimal career-value differences. Procedural-scope and AI perceptions may be associated with radiology interest, suggesting that experiential education addressing these domains may support student engagement with radiology.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148892810","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":"Workload and Diagnostic Safety in Radiology Residents: A Recovery-Strain Mediation Analysis.","authors":"Weiwei Zhuang, Linbo Xie, Tongzhou Xu, Xin Lin, Yafei Wang, Ying Tang, Xu Steven Xu, Jiming Zhu, Min Yuan","doi":"10.1016/j.acra.2026.08.084","DOIUrl":"https://doi.org/10.1016/j.acra.2026.08.084","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>Diagnostic safety in radiology may be influenced by occupational conditions beyond the point of image interpretation. This study examined whether workload was associated with diagnostic safety-related events directly or through sequential effects on recovery impairment and psychological strain. MATERIALS AND METHODS: In a nationwide cross-sectional survey of 3729 radiology residents from 407 training institutions in mainland China, we evaluated structural pathways linking workload, recovery impairment, psychological strain, and recent self-reported diagnostic safety-related events. Workload, recovery impairment, and psychological strain were operationalized as standardized composite scores. Generalized structural equation modeling with a weighted least squares mean- and variance-adjusted estimator was used to estimate direct and indirect associations.</p><p><strong>Results: </strong>Workload was positively associated with recovery impairment (β = 0.45, P < .001), and recovery impairment was strongly associated with psychological strain (β = 1.63, P < .001). Psychological strain was associated with 1-month diagnostic safety-related events (β = 0.39, P = .03), whereas direct paths from workload and recovery impairment to events were not statistically significant. The serial indirect pathway from workload through recovery impairment and psychological strain to safety-related events was statistically significant (P = .03). In the 3-month sensitivity analysis, estimates followed the same direction as the primary model but were smaller in magnitude and did not reach statistical significance. Findings were consistent across individual workload indicators and training stages.</p><p><strong>Conclusion: </strong>Among radiology residents in China, workload was associated with recent self-reported diagnostic safety-related events primarily through impaired recovery and psychological strain rather than through a direct workload effect. These findings support a recovery-strain framework for understanding diagnostic vulnerability during training and suggest that protecting restorative capacity may be relevant to diagnostic safety interventions.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148892765","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":"Logarithmic and Conventional MRI Biomarkers of the Tumor-Edema Relationship in Meningioma Derived From Reproducible Volumetric Segmentation.","authors":"Antonio Navarro-Ballester, Rocío Fleta-Gascón","doi":"10.1016/j.acra.2026.08.074","DOIUrl":"https://doi.org/10.1016/j.acra.2026.08.074","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>Peritumoral edema is a common imaging finding in intracranial meningiomas and has been associated with histologic grade and tumor aggressiveness. However, quantitative magnetic resonance imaging (MRI) biomarkers of the tumor-edema relationship remain insufficiently explored. This study evaluated volumetric tumor-edema metrics as predictors of meningioma grade.</p><p><strong>Materials and methods: </strong>In this retrospective study, 187 consecutive patients (median age, 61 years; 118 women) with histopathologically confirmed intracranial meningioma who underwent preoperative MRI between 2003 and 2025 were included. Tumor and peritumoral edema volumes were obtained using manual volumetric segmentation, with interobserver reproducibility evaluated in a randomly selected subset of 20 cases independently segmented by both readers. Six quantitative tumor-edema indices were calculated. Diagnostic performance for differentiating grade I from grade II-III meningiomas was assessed using receiver operating characteristic analysis and logistic regression.</p><p><strong>Results: </strong>Histologically, 145 of 187 tumors (77.5%) were grade I and 42 (22.5%) were grade II-III. Most edema-derived metrics showed modest but consistent discriminatory performance (area under the receiver operating characteristic curve [AUC] range, 0.61-0.64). The infiltration efficiency index showed the highest discriminatory performance (AUC, 0.64; P = .006), whereas the allometric index yielded an AUC of 0.62 (P = .014). Edema fraction was significantly associated with tumor grade at logistic regression (odds ratio, 3.15; P = .042). Absence of measurable peritumoral edema occurred in 51 tumors (27.3%), including 46 grade I tumors.</p><p><strong>Conclusion: </strong>Volumetric MRI biomarkers describing the tumor-edema relationship provide modest but consistent discrimination between grade I and grade II-III meningiomas. Scaling-based metrics showed slightly higher diagnostic performance than conventional edema-tumor indices, although overall discriminatory performance remained limited.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148892763","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}