Academic Radiology最新文献

{"title":"Multimodal Imaging of Tau Pathology and Network Connectivity Dynamics Across the Alzheimer’s Continuum: A Pilot Study Using Second-Generation Tracer [18F]MK6240","authors":"Sirong Piao MD, Jie Wang MD, PhD, Kun He MD, Na Wang MD, Xueling Liu MD, Liqin Yang PhD, Fang Xie MD, PhD, Yuxin Li MD, PhD, Yifang Bao MD, PhD","doi":"10.1016/j.acra.2026.06.040","DOIUrl":"10.1016/j.acra.2026.06.040","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>Alzheimer’s disease (AD) progression involves distinct spatiotemporal changes in functional connectivity (FC) within anterior-temporal (AT) and posterior-medial (PM) networks. This study characterizes FC alterations between medial temporal lobe (MTL) subregions and AT/PM networks across the AD continuum and examines their associations with tau/Aβ pathology and atrophy using second-generation tau-PET.</div></div><div>Alzheimer’s disease (AD) progression involves distinct spatiotemporal changes in functional connectivity (FC) within anterior-temporal (AT) and posterior-medial (PM) networks. This study characterizes FC alterations between medial temporal lobe (MTL) subregions and AT/PM networks across the AD continuum and examines their associations with tau/Aβ pathology and atrophy using second-generation tau-PET.</div><div><h3>Materials and Methods</h3><div>Eighty-four participants, including 26 cognitively unimpaired Aβ negative(CU Aβ-), 19 cognitively unimpaired Aβ positive (CU Aβ+), 19 mild cognitive impairment Aβ positive(MCI Aβ+), 20 AD Aβ positive(AD Aβ+) underwent 3 T MRI, resting-state fMRI, [¹⁸F]florbetapir (Aβ), and [¹⁸F]MK6240 (tau) PET. MTL subregions were segmented via ASHS-T1, while AT/PM networks were defined using Harvard-Oxford Atlas. Group differences in FC, Aβ/tau standard uptake value ratio(SUVR), and gray matter volume (GMV) were assessed. Network SUVR values, GMV and intra-FC correlation analyses and correlation coefficient matrix plots were calculated.</div></div><div>Eighty-four participants, including 26 cognitively unimpaired Aβ negative(CU Aβ-), 19 cognitively unimpaired Aβ positive (CU Aβ+), 19 mild cognitive impairment Aβ positive(MCI Aβ+), 20 AD Aβ positive(AD Aβ+) underwent 3 T MRI, resting-state fMRI, [¹⁸F]florbetapir (Aβ), and [¹⁸F]MK6240 (tau) PET. MTL subregions were segmented via ASHS-T1, while AT/PM networks were defined using Harvard-Oxford Atlas. Group differences in FC, Aβ/tau standard uptake value ratio(SUVR), and gray matter volume (GMV) were assessed. Network SUVR values, GMV and intra-FC correlation analyses and correlation coefficient matrix plots were calculated.</div><div><h3>Results</h3><div>Compared to CU Aβ-, CU Aβ+ exhibited increased intra-/inter-network FC (peak in anterior MTL: p < 0.001), while MCI/AD groups showed progressive FC declines. Posterior network FC reduction was pronounced in AD (p < 0.01). Left BA35 connectivity increased in CU Aβ+, whereas AD demonstrated FC reductions in bilateral posterior cingulate/hippocampus. Network-level A/T/N-FC correlations were integrated in CU Aβ+ but modular in AD, with strong tau-GMV anticorrelation.</div></div><div>Compared to CU Aβ-, CU Aβ+ exhibited increased intra-/inter-network FC (peak in anterior MTL: p < 0.001), while MCI/AD groups showed progressive FC declines. Posterior network FC reduction was pronounced in AD (p < 0.01). Left BA35 connectivity increased in CU Aβ+, whereas AD demonstr","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"33 10","pages":"Pages 4593-4605"},"PeriodicalIF":4.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148537727","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":"Extracellular Volume Fraction Imaging for Hepatic Fibrosis: CT and MRI Implementation and Evidence","authors":"Jialin Qi MM,&nbsp;Yubo Chi MM,&nbsp;Weiling Xu MD,&nbsp;Jingyu Wang MM","doi":"10.1016/j.acra.2026.06.002","DOIUrl":"10.1016/j.acra.2026.06.002","url":null,"abstract":"<div><div>Accurate diagnosis and staging of liver fibrosis are essential for prognostic stratification and treatment planning in chronic liver disease. However, conventional approaches for diagnosing and monitoring fibrosis, such as biopsy, elastography and serum-based scores, are limited by invasiveness, sampling variability, or performance in early-stage disease. Extracellular volume fraction (ECV) has emerged as a promising non-invasive quantitative imaging biomarker for assessing extracellular space expansion associated with hepatic fibrotic remodeling. In this review, we summarize current preclinical and clinical data on computed tomography (CT-) and magnetic resonance imaging (MRI)-derived ECV in liver fibrosis and related chronic liver diseases, with a focus on calculation methods, biologic interpretation, and correlations with histopathology and selected clinical outcomes. We also discuss major sources of heterogeneity, key interpretive limitations, and the evidence gaps that must be addressed before hepatic ECV can be more broadly integrated as a complementary component of multiparametric liver imaging.</div></div><div>Accurate diagnosis and staging of liver fibrosis are essential for prognostic stratification and treatment planning in chronic liver disease. However, conventional approaches for diagnosing and monitoring fibrosis, such as biopsy, elastography and serum-based scores, are limited by invasiveness, sampling variability, or performance in early-stage disease. Extracellular volume fraction (ECV) has emerged as a promising non-invasive quantitative imaging biomarker for assessing extracellular space expansion associated with hepatic fibrotic remodeling. In this review, we summarize current preclinical and clinical data on computed tomography (CT-) and magnetic resonance imaging (MRI)-derived ECV in liver fibrosis and related chronic liver diseases, with a focus on calculation methods, biologic interpretation, and correlations with histopathology and selected clinical outcomes. We also discuss major sources of heterogeneity, key interpretive limitations, and the evidence gaps that must be addressed before hepatic ECV can be more broadly integrated as a complementary component of multiparametric liver imaging.</div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"33 10","pages":"Pages 4235-4245"},"PeriodicalIF":4.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148876529","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":"National Trends in NIH Funding and Clinician-Scientist Participation Across Diagnostic Radiology, Interventional Radiology, and Radiation Oncology (2012–2025)","authors":"Ajay Malhotra MD, MMM, FACR,&nbsp;Keervani Kandala MS,&nbsp;Paul Timothy Gomez BS,&nbsp;Raj Moily MBBS, PhD, MBA,&nbsp;Dhairya A. Lakhani MD,&nbsp;Marco Colasurdo MD,&nbsp;Dheeraj Gandhi MD, FRCR,&nbsp;Cristopher Whitlow MD, PhD, MHA,&nbsp;Max Wintermark MD, MBA,&nbsp;Seyedmehdi Payabvash MD,&nbsp;Ajay Gupta MD MS","doi":"10.1016/j.acra.2026.07.035","DOIUrl":"10.1016/j.acra.2026.07.035","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Rationale and Objectives&lt;/h3&gt;&lt;div&gt;To identify trends in National Institutes of Health (NIH) funding to clinician-scientists in radiology relative to overall funding in the field, and funding in radiology sub-specialties.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;To identify trends in National Institutes of Health (NIH) funding to clinician-scientists in radiology relative to overall funding in the field, and funding in radiology sub-specialties.&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Materials and Methods&lt;/h3&gt;&lt;div&gt;Data was obtained from the NIH Research Portfolio Online Reporting Tools Expenditure and Results (RePORTER) database for NIH grants issued from 2012 to 2025 principal investigators (PIs) under “Radiation-Diagnostic/Oncology”. This was cross-linked with data from the Centers for Medicare and Medicaid Services (CMS) to identify clinician-scientists in diagnostic and interventional radiology, and radiation oncology. Grants were attributed to the specialties of all clinician PIs, including Contact PIs and multiple PIs (MPIs). Number of physicians, sex, new grants awarded, grant mechanisms and funding amounts were analyzed for the three sub-specialties.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;Data was obtained from the NIH Research Portfolio Online Reporting Tools Expenditure and Results (RePORTER) database for NIH grants issued from 2012 to 2025 principal investigators (PIs) under “Radiation-Diagnostic/Oncology”. This was cross-linked with data from the Centers for Medicare and Medicaid Services (CMS) to identify clinician-scientists in diagnostic and interventional radiology, and radiation oncology. Grants were attributed to the specialties of all clinician PIs, including Contact PIs and multiple PIs (MPIs). Number of physicians, sex, new grants awarded, grant mechanisms and funding amounts were analyzed for the three sub-specialties.&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;Among 14,105 unique NIH-funded projects in the Radiation–Diagnostic/Oncology category, 437 of 3434 investigators (12.7%) across all PI roles were clinicians. Female representation among funded diagnostic radiologists increased from 11 of 64 (17.2%) in 2012 to 30 of 108 (27.8%) in 2025. Diagnostic radiologists comprised 64 of 96 funded clinicians (66.7%) in 2012 and 108 of 230 (47.0%) in 2025, and led radiation oncology in both investigator count and funding in every year. Diagnostic radiology funding rose from $39.9 million in 2012 to $87.9 million in 2025, while radiation oncology funding rose from $14.3 million to $67.3 million. Among 42 new K-series awards, radiation oncology received 28 (66.7%).&lt;/div&gt;&lt;/div&gt;&lt;div&gt;Among 14,105 unique NIH-funded projects in the Radiation–Diagnostic/Oncology category, 437 of 3434 investigators (12.7%) across all PI roles were clinicians. Female representation among funded diagnostic radiologists increased from 11 of 64 (17.2%) in 2012 to 30 of 108 (27.8%) in 2025. Diagnostic radiologists comprised 64 of 96 funded clinicians (66.7%) in 2012 and 108 of 230 (47.0%) in 2025, and led radiation oncology in both inves","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"33 10","pages":"Pages 4189-4197"},"PeriodicalIF":4.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148707920","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":"Automated Analysis of Carotid Plaque Components in CT Angiography: Performance and Prognosis in Symptomatic Carotid Stenosis Patients","authors":"Min Jie Zhao MD,&nbsp;Xiao Qing Cheng MD, PhD,&nbsp;Peng Peng Xu MD,&nbsp;Hao Tang MD,&nbsp;Peng Bo Jiang MD,&nbsp;Xin Zhang MD,&nbsp;Ling Ni MD,&nbsp;Lu Si Ying Xu MSc,&nbsp;Hui Min Pang BS,&nbsp;Jin Jing Tang BS,&nbsp;Qi Ji Jin BS,&nbsp;Chang Sheng Zhou MSc,&nbsp;Ya Liu MSc,&nbsp;Rui Liu MD,&nbsp;Hong Gao MD,&nbsp;Xiao Huan Cao MD,&nbsp;Bing Zhang MD,&nbsp;Di Jia Wu MD,&nbsp;Wu Sheng Zhu MD, PhD,&nbsp;Long Jiang Zhang MD, PhD","doi":"10.1016/j.acra.2026.06.066","DOIUrl":"10.1016/j.acra.2026.06.066","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Rationale and Objectives&lt;/h3&gt;&lt;div&gt;To develop and validate the performance and prognostic value of a deep-learning (DL) model for carotid plaque component quantification on CTA.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;To develop and validate the performance and prognostic value of a deep-learning (DL) model for carotid plaque component quantification on CTA.&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Materials and Methods&lt;/h3&gt;&lt;div&gt;A multicenter retrospective study was conducted in three stages: Stage 1: Model development and concordance analysis: A DL model was developed for plaque detection and segmentation using 2164 CTA scans (Cohort 1). DL-radiologist measurement agreement was assessed using ICC and Spearman's test (Cohort 2). Stage 2: Diagnostic validation: Performance was validated against 1) 118 co-registered CTA-OCT image pairs (Cohort 3) and 2) 146 patients with paired HR-MRI and CTA (Cohort 4). Stage 3: Prognosis validation: In 610 symptomatic patients (Cohort 5), multivariable Cox regression assessed the association between lipid core burden (LCB) and recurrent cerebrovascular events, and the incremental predictive value of LCB was quantified by ΔAUC and NRI.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;A multicenter retrospective study was conducted in three stages: Stage 1: Model development and concordance analysis: A DL model was developed for plaque detection and segmentation using 2164 CTA scans (Cohort 1). DL-radiologist measurement agreement was assessed using ICC and Spearman's test (Cohort 2). Stage 2: Diagnostic validation: Performance was validated against 1) 118 co-registered CTA-OCT image pairs (Cohort 3) and 2) 146 patients with paired HR-MRI and CTA (Cohort 4). Stage 3: Prognosis validation: In 610 symptomatic patients (Cohort 5), multivariable Cox regression assessed the association between lipid core burden (LCB) and recurrent cerebrovascular events, and the incremental predictive value of LCB was quantified by ΔAUC and NRI.&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;The DL model achieved a detection sensitivity of 0.85 with an average of 1.08 false positives per case. It demonstrated good-to-excellent agreement with radiologist assessments. In Stage 2, DL-driven lipid core component is associated with high-risk plaques identified by OCT and HR-MRI. In Stage 3, in a median 2-year follow-up, LCB independently predicted recurrent cerebrovascular events (HR: 1.08, 95% Cl: 1.03–1.13, &lt;em&gt;P&lt;/em&gt;&lt;0.001). The incorporation of LCB provided incremental risk stratification beyond clinical and CTA-driven anatomical factors (ΔAUC +0.09, NRI: 0.22, &lt;em&gt;P&lt;/em&gt;=0.001).&lt;/div&gt;&lt;/div&gt;&lt;div&gt;The DL model achieved a detection sensitivity of 0.85 with an average of 1.08 false positives per case. It demonstrated good-to-excellent agreement with radiologist assessments. In Stage 2, DL-driven lipid core component is associated with high-risk plaques identified by OCT and HR-MRI. In Stage 3, in a median 2-year follow-up, LCB independently predicted recurrent cerebrovascular events (HR: 1.08, 95% Cl: 1.03–1.13, &lt;em&gt;P&lt;/em&gt;&lt;0.","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"33 10","pages":"Pages 4681-4693"},"PeriodicalIF":4.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148551333","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 and Internal Validation of a Nomogram Model Discriminating Liver Metastases of Melanoma From Other Common Liver Metastases Based on the Combination of Conventional Ultrasonography and Contrast-enhanced Ultrasonography","authors":"Yuanyuan Xu MS,&nbsp;Hao Wu PhD,&nbsp;Haijing Tian Dr,&nbsp;Hewen Tang PhD,&nbsp;Yue Cong PhD,&nbsp;Shanshan Yin PhD,&nbsp;Wei Yang PhD,&nbsp;Kun Yan MS,&nbsp;Wei Wu PhD","doi":"10.1016/j.acra.2026.06.027","DOIUrl":"10.1016/j.acra.2026.06.027","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Rationale and Objectives&lt;/h3&gt;&lt;div&gt;By comparing conventional ultrasonography (US) and contrast-enhanced ultrasonography (CEUS) features of liver metastases of melanoma (LMM) with those of other common liver metastases from non-melanoma malignancies (non-LMM), a differential diagnosis nomogram was developed and internally validated to explore its clinical application value.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;By comparing conventional ultrasonography (US) and contrast-enhanced ultrasonography (CEUS) features of liver metastases of melanoma (LMM) with those of other common liver metastases from non-melanoma malignancies (non-LMM), a differential diagnosis nomogram was developed and internally validated to explore its clinical application value.&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Materials and Methods&lt;/h3&gt;&lt;div&gt;This single-center, retrospective, case-control study enrolled 108 patients with LMM (109 lesions) and 95 patients with non-LMM (109 lesions) at our institution from January 2017 to April 2025. All cases were confirmed by pathological diagnosis. Univariate analysis was performed to compare clinical characteristics and US/CEUS features between groups. Variables with &lt;em&gt;P&lt;/em&gt; &lt; 0.05 were entered into a stepwise bidirectional regression model (based on the Akaike information criterion (AIC)) to identify independent risk factors and assess collinearity (using variance inflation factor (VIF)). A nomogram was developed using logistic regression and generalized estimating equations (GEE) and evaluated by the area under the receiver operating characteristic curve (AUC-ROC), bootstrapping (1000 resamples), calibration curves, and decision curve analysis (DCA).&lt;/div&gt;&lt;/div&gt;&lt;div&gt;This single-center, retrospective, case-control study enrolled 108 patients with LMM (109 lesions) and 95 patients with non-LMM (109 lesions) at our institution from January 2017 to April 2025. All cases were confirmed by pathological diagnosis. Univariate analysis was performed to compare clinical characteristics and US/CEUS features between groups. Variables with &lt;em&gt;P&lt;/em&gt; &lt; 0.05 were entered into a stepwise bidirectional regression model (based on the Akaike information criterion (AIC)) to identify independent risk factors and assess collinearity (using variance inflation factor (VIF)). A nomogram was developed using logistic regression and generalized estimating equations (GEE) and evaluated by the area under the receiver operating characteristic curve (AUC-ROC), bootstrapping (1000 resamples), calibration curves, and decision curve analysis (DCA).&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;The independent risk factors included in the nomogram model were morphology, posterior acoustic enhancement, number, enhancement pattern and wash-out time. The model demonstrated an AUC of 0.892 (95% CI: 0.849–0.935) and a bias-corrected AUC of 0.873 (95% CI: 0.805–0.933) by bootstrapping. Both calibration curves and DCA demonstrated the nomogram’s favorable calibration and clinical utility (Hosmer–Lemeshow test, &lt;em&gt;P&lt;/em&gt; &","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"33 10","pages":"Pages 4276-4289"},"PeriodicalIF":4.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148426079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Linking Carotid Plaque Immune Subtypes to Multimodal Ultrasound Features","authors":"Yue Liu,&nbsp;Shuang Yang,&nbsp;Ying Wang,&nbsp;Yan Hu,&nbsp;Xiaoli Liu,&nbsp;Guangxiang Yang,&nbsp;Huanhuan Hao,&nbsp;Jianlin Wu","doi":"10.1016/j.acra.2026.07.046","DOIUrl":"10.1016/j.acra.2026.07.046","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Rationale and Objectives&lt;/h3&gt;&lt;div&gt;To establish a plaque immune microenvironment classification system and explore whether multimodal ultrasound may aid in the non-invasive identification of high-risk subtypes.This exploratory study investigated the association between PCA-derived immune subtypes and multimodal ultrasound features.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;To establish a plaque immune microenvironment classification system and explore whether multimodal ultrasound may aid in the non-invasive identification of high-risk subtypes.This exploratory study investigated the association between PCA-derived immune subtypes and multimodal ultrasound features.&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;This prospective study included 50 patients undergoing carotid endarterectomy (CEA). Seven immunohistochemistry (IHC) markers: interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), interferon-γ (IFN-γ), IL-2, cluster of differentiation 11b (CD11b), myeloperoxidase (MPO), and microvessel density (MVD) were assessed. Principal component analysis (PCA) and hierarchical clustering defined immune subtypes. Preoperative conventional ultrasound and contrast-enhanced ultrasound (CEUS) evaluated vulnerability score, plaque thickness, and intraplaque neovascularization (IPN) grade.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;This prospective study included 50 patients undergoing carotid endarterectomy (CEA). Seven immunohistochemistry (IHC) markers: interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), interferon-γ (IFN-γ), IL-2, cluster of differentiation 11b (CD11b), myeloperoxidase (MPO), and microvessel density (MVD) were assessed. Principal component analysis (PCA) and hierarchical clustering defined immune subtypes. Preoperative conventional ultrasound and contrast-enhanced ultrasound (CEUS) evaluated vulnerability score, plaque thickness, and intraplaque neovascularization (IPN) grade.&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;Hierarchical clustering revealed three functional modules: core pro-inflammatory (TNF-α, IL-6, IL-2, IFN-γ), angiogenesis-macrophage (CD11b, MVD), and MPO-alone. PCA classified 50 plaques into four subtypes: Type I (inflammation-dominated, n=8), Type II (angiogenesis-dominated, n=14), Type III (high inflammation and high angiogenesis, n=11), and Type IV (stable, n=17). Type III plaques had highest inflammatory factors and MVD, with 45.5% experiencing recent ischemic events. Ultrasound vulnerability score correlated with IL-6 (&lt;em&gt;r&lt;/em&gt;=0.54) and MVD (&lt;em&gt;r&lt;/em&gt;=0.52); IPN grade correlated strongly with MVD (&lt;em&gt;r&lt;/em&gt;=0.69). Type III showed a \"high-risk triad\": high vulnerability score (median 5.0), increased thickness (0.45 cm), and high IPN grade (45.5% grade 3). Type II had isolated high IPN grade (71.4% ≥ grade 2).&lt;/div&gt;&lt;/div&gt;&lt;div&gt;Hierarchical clustering revealed three functional modules: core pro-inflammatory (TNF-α, IL-6, IL-2, IFN-γ), angiogenesis-macrophage (CD11b, MVD), and MPO-alone. PCA classified 50 plaques into four subtypes: Type I (inflammation-dominated, n=8), Type II (an","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"33 10","pages":"Pages 4668-4680"},"PeriodicalIF":4.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148686536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Institutional Contributions to the Radiology Literature: A Subspecialty Assessment","authors":"Armin Tafazolimoghadam M.D.,&nbsp;Mahla Radmard M.D.,&nbsp;David M. Yousem M.D., M.B.A.,&nbsp;Rohini Nadgir M.D.","doi":"10.1016/j.acra.2026.06.065","DOIUrl":"10.1016/j.acra.2026.06.065","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Rationale and Objectives&lt;/h3&gt;&lt;div&gt;Academic radiologists concentrate scholarly activity within defined subspecialties, producing heterogeneous institutional research profiles. The relationship between institutional bibliometric performance and National Institutes of Health (NIH) radiology funding across subspecialties is incompletely characterized. We evaluated institutional scholarly output across U.S. radiology subspecialties and its association with NIH funding.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;Academic radiologists concentrate scholarly activity within defined subspecialties, producing heterogeneous institutional research profiles. The relationship between institutional bibliometric performance and National Institutes of Health (NIH) radiology funding across subspecialties is incompletely characterized. We evaluated institutional scholarly output across U.S. radiology subspecialties and its association with NIH funding.&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Materials and Methods&lt;/h3&gt;&lt;div&gt;This cross-sectional study analyzed U.S. academic radiology faculty using Scopus data (March 2026). Faculty were identified from institutional websites and categorized into eight subspecialties. Institutional publications, citations, and h-index values were aggregated and compared with 2025 NIH radiology funding rankings from the Blue Ridge Institute for Medical Research using Spearman’s rank correlation.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;This cross-sectional study analyzed U.S. academic radiology faculty using Scopus data (March 2026). Faculty were identified from institutional websites and categorized into eight subspecialties. Institutional publications, citations, and h-index values were aggregated and compared with 2025 NIH radiology funding rankings from the Blue Ridge Institute for Medical Research using Spearman’s rank correlation.&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;121 institutions encompassing 6488 faculty were included. Scholarly output was highly concentrated: the 5 most productive institutions accounted for 21% of publications and 23% of citations, and the top 25 for 61% and 67%. NIH funding was even more concentrated (top 5, 30%; top 25, 81%). Mayo Clinic–Rochester led in total publications (18,197) and citations (774,565). Rankings by total publications, total citations, and summed faculty h-index were closely aligned, whereas per-faculty metrics highlighted a distinct group of smaller programs. Subspecialty leadership varied substantially. Among 62 matched institutions, total publications, summed faculty h-index, total citations, and faculty count correlated most strongly with NIH funding rank (ρ = 0.748, 0.745, 0.719, and 0.702, respectively; all p &lt; 0.001).&lt;/div&gt;&lt;/div&gt;&lt;div&gt;121 institutions encompassing 6488 faculty were included. Scholarly output was highly concentrated: the 5 most productive institutions accounted for 21% of publications and 23% of citations, and the top 25 for 61% and 67%. NIH funding was even more concentrated (top 5, 30%; top 25, 81%). Mayo Clinic–Rochester led in total p","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"33 10","pages":"Pages 4218-4231"},"PeriodicalIF":4.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148649762","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":"Non-invasive CT-based Deep Learning for Human Papillomavirus Status Prediction in Oropharyngeal Cancer","authors":"Junhua Chen PhD,&nbsp;Mayuan Meng MD,&nbsp;Shuai Kuang PhD,&nbsp;Zikuan Chai MD,&nbsp;Chunxiao Ma PhD,&nbsp;Banghua Yang PhD","doi":"10.1016/j.acra.2026.04.016","DOIUrl":"10.1016/j.acra.2026.04.016","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Rationale and Objectives&lt;/h3&gt;&lt;div&gt;Human papillomavirus (HPV) status is a critical biomarker for treatment planning and patient management in oropharyngeal cancer (OPC). The purpose of this study was to develop a non-invasive and rapid alternative that achieves performance comparable to established clinical standards while enabling HPV classification without requiring additional examinations beyond the routine diagnostic and treatment workflow.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;Human papillomavirus (HPV) status is a critical biomarker for treatment planning and patient management in oropharyngeal cancer (OPC). The purpose of this study was to develop a non-invasive and rapid alternative that achieves performance comparable to established clinical standards while enabling HPV classification without requiring additional examinations beyond the routine diagnostic and treatment workflow.&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Materials and Methods&lt;/h3&gt;&lt;div&gt;We employed a siamese neural network framework with 3D DenseNet backbones in each branch, trained in an end-to-end manner. To further enhance model performance, tumor mask volumes and frequency-domain representations of CT images were incorporated as additional modalities. A total of 1612 valid cases from three independent datasets were used to evaluate model performance, including internal validation on a large-scale dataset and three external validation experiments to ensure robustness and generalizability.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;We employed a siamese neural network framework with 3D DenseNet backbones in each branch, trained in an end-to-end manner. To further enhance model performance, tumor mask volumes and frequency-domain representations of CT images were incorporated as additional modalities. A total of 1612 valid cases from three independent datasets were used to evaluate model performance, including internal validation on a large-scale dataset and three external validation experiments to ensure robustness and generalizability.&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;The proposed methods achieved an average AUC of 0.875 (95% confidence intervals (CI), [0.863,0.887]), an average Precision of 0.81 (95% CI, [0.8–0.82]), an average Recall of 0.89 (95% CI, [0.867,0.913]), and an average AUPRC of 0.922 (95% CI, [0.91,0.934]), proposed method achieved best result in all four metrics compared with reference methods. Pre-trained model achieved average AUC of 0.767 (95% CI, [0.735,0.781]), 0.726 (95% CI, [0.709,0.741]) and 0.839 (95% CI, [0.821,0.856]) in three external validations.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;The proposed methods achieved an average AUC of 0.875 (95% confidence intervals (CI), [0.863,0.887]), an average Precision of 0.81 (95% CI, [0.8–0.82]), an average Recall of 0.89 (95% CI, [0.867,0.913]), and an average AUPRC of 0.922 (95% CI, [0.91,0.934]), proposed method achieved best result in all four metrics compared with reference methods. Pre-trained model achieved average AUC of 0.767 (95% CI, [0.735,0.781]), 0.726 (95% CI, [0.709,0.741]) and 0.83","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"33 10","pages":"Pages 4548-4560"},"PeriodicalIF":4.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147788318","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":"Homogeneity of Liver Fat Distribution Serves as a Diagnostic Marker for Metabolic Dysfunction-Associated Steatohepatitis","authors":"Danfeng Yu MS,&nbsp;Da Fang PhD,&nbsp;Hailong Zhang MS,&nbsp;Tianwei Gu PhD,&nbsp;Wenhuan Feng PhD,&nbsp;Yan Bi PhD,&nbsp;Jun Chen PhD,&nbsp;Wenjuan Tang PhD","doi":"10.1016/j.acra.2026.06.016","DOIUrl":"10.1016/j.acra.2026.06.016","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Rationale and Objectives&lt;/h3&gt;&lt;div&gt;To characterize liver fat distribution in metabolic dysfunction-associated steatohepatitis (MASH) and propose a magnetic resonance imaging proton density fat fraction (MRI-PDFF)-based score for MASH identification in obesity.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;To characterize liver fat distribution in metabolic dysfunction-associated steatohepatitis (MASH) and propose a magnetic resonance imaging proton density fat fraction (MRI-PDFF)-based score for MASH identification in obesity.&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Materials and Methods&lt;/h3&gt;&lt;div&gt;Individuals with obesity were recruited for liver biopsy and contemporaneous MR scanning. The variability in fat distribution was evaluated by calculating the standard deviation (SD), range, and coefficient of variation of MRI-PDFF across liver lobes. A machine learning-assisted strategy was employed to establish a diagnostic model for MASH.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;Individuals with obesity were recruited for liver biopsy and contemporaneous MR scanning. The variability in fat distribution was evaluated by calculating the standard deviation (SD), range, and coefficient of variation of MRI-PDFF across liver lobes. A machine learning-assisted strategy was employed to establish a diagnostic model for MASH.&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;A total of 107 participants with biopsy-confirmed fatty liver were included, and 44 were diagnosed with MASH. The MASH group exhibited significantly higher fat content in all liver segments and more homogeneous fat distribution in the right liver lobe than those with non-MASH. A raw-MASH score, incorporating the mean value and SD of PDFF in the right lobe, alanine aminotransferase levels, and waist circumference, was then established to identify MASH with an area under the receiver operating characteristic curve (AUROC) of 0.90 (95%CI 0.84–0.95). It performed better than HAIR, ION, and acNASH (all &lt;em&gt;p&lt;/em&gt; &lt; 0.05), and exhibited higher AUROC than MR-MASH model (0.90 vs. 0.85, &lt;em&gt;p&lt;/em&gt; = 0.066). Besides, the dual threshold strategy of raw-MASH improved the diagnostic performance with high sensitivity and specificity.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;A total of 107 participants with biopsy-confirmed fatty liver were included, and 44 were diagnosed with MASH. The MASH group exhibited significantly higher fat content in all liver segments and more homogeneous fat distribution in the right liver lobe than those with non-MASH. A raw-MASH score, incorporating the mean value and SD of PDFF in the right lobe, alanine aminotransferase levels, and waist circumference, was then established to identify MASH with an area under the receiver operating characteristic curve (AUROC) of 0.90 (95%CI 0.84–0.95). It performed better than HAIR, ION, and acNASH (all &lt;em&gt;p&lt;/em&gt; &lt; 0.05), and exhibited higher AUROC than MR-MASH model (0.90 vs. 0.85, &lt;em&gt;p&lt;/em&gt; = 0.066). Besides, the dual threshold strategy of raw-MASH improved the diagnostic performance with high sensitivity and specificity.&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Conclusio","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"33 10","pages":"Pages 4246-4256"},"PeriodicalIF":4.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148382801","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":"Final Postprocedural DSA Intrahepatic Branch Patency Predicts Outcomes After Endovascular Recanalization for Acute Non-cirrhotic Portal Vein Thrombosis","authors":"Wenhao Xue MS,&nbsp;Yide Guo BS,&nbsp;Zhaohui Hua MS,&nbsp;Shirui Liu MD,&nbsp;Peng Xu MS,&nbsp;Baoning Zhou MS,&nbsp;Xu Li MS,&nbsp;Hui Cao PhD,&nbsp;Zhouyang Jiao PhD,&nbsp;Zhen Li PhD","doi":"10.1016/j.acra.2026.06.023","DOIUrl":"10.1016/j.acra.2026.06.023","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Rationale and Objectives&lt;/h3&gt;&lt;div&gt;To evaluate whether intrahepatic branch patency on postprocedural digital subtraction angiography (DSA) predicts outcomes after endovascular recanalization for non-cirrhotic portal vein thrombosis (NCPVT).&lt;/div&gt;&lt;/div&gt;&lt;div&gt;To evaluate whether intrahepatic branch patency on postprocedural digital subtraction angiography (DSA) predicts outcomes after endovascular recanalization for non-cirrhotic portal vein thrombosis (NCPVT).&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Materials and Methods&lt;/h3&gt;&lt;div&gt;This retrospective study included 122 patients with acute NCPVT who underwent endovascular recanalization. According to final postprocedural DSA findings, patients were classified into patent and occluded intrahepatic branch groups. Early postprocedural outcomes, follow-up adverse events, overall survival, and stent patency were compared. Kaplan-Meier and Cox regression analyses were used to identify factors associated with symptom recurrence.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;This retrospective study included 122 patients with acute NCPVT who underwent endovascular recanalization. According to final postprocedural DSA findings, patients were classified into patent and occluded intrahepatic branch groups. Early postprocedural outcomes, follow-up adverse events, overall survival, and stent patency were compared. Kaplan-Meier and Cox regression analyses were used to identify factors associated with symptom recurrence.&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;Postprocedural DSA demonstrated patent intrahepatic branches in 71 patients and occlusion in 51. Compared with the patent group, the occluded group had higher rates of bowel necrosis during hospitalization (17.6% vs 4.2%, &lt;em&gt;P&lt;/em&gt; = 0.014), gastrointestinal bleeding (35.7% vs 7.9%, &lt;em&gt;P&lt;/em&gt; &lt; 0.001), recurrent abdominal pain (78.6% vs 30.2%, &lt;em&gt;P&lt;/em&gt; &lt; 0.001), cavernous transformation of the portal vein (CTPV) (92.9% vs 65.1%, &lt;em&gt;P&lt;/em&gt; = 0.001), and secondary intervention (52.4% vs 22.2%, &lt;em&gt;P&lt;/em&gt; = 0.002) during follow-up. In multivariable Cox analysis, intrahepatic branch occlusion remained independently associated with symptom recurrence (hazard ratio, 3.08; 95% confidence interval, 1.57–6.03; &lt;em&gt;P&lt;/em&gt; = 0.001). Overall survival did not differ significantly between groups. Transjugular intrahepatic portosystemic shunt (TIPS) was not independently associated with overall symptom recurrence, although in patients with intrahepatic branch occlusion it was associated with less gastrointestinal bleeding during follow-up. Stent occlusion occurred in 35 of 49 patients with available follow-up data.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;Postprocedural DSA demonstrated patent intrahepatic branches in 71 patients and occlusion in 51. Compared with the patent group, the occluded group had higher rates of bowel necrosis during hospitalization (17.6% vs 4.2%, &lt;em&gt;P&lt;/em&gt; = 0.014), gastrointestinal bleeding (35.7% vs 7.9%, &lt;em&gt;P&lt;/em&gt; &lt; 0.001), recurrent abdominal pain (78.6% vs 30.2%, &lt;em&gt;P&lt;/em&gt; &lt; 0.001), cavernous transformat","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"33 10","pages":"Pages 4502-4512"},"PeriodicalIF":4.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148413562","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}