To evaluate the impact of target and whole vessel computational fluid dynamics (CFD) modeling strategies on CT-derived fractional flow reserve (CT-FFR). The study enrolled patients suffering from moderate to severe intracranial atherosclerotic stenosis (ICAS) who underwent invasive FFR mearsurement and head CT angiography (CTA). CTA were used to reconstruct target and whole vessel CT-FFR models, and the calculation time of both models was recorded. Receiver operating characteristic (ROC) analysis assessed the diagnostic performance of two CT-FFR models with FFR ≤ 0.80 or 0.75 defining ischemia-specific stenosis. 17 eligible patients (mean age, 58.5 ± 8.8 years, 13 males) were finally evaluated. The area under curve (AUC) of CT-FFR ≤ 0.80 measured based on target and whole vessel CT-FFR ≤ 0.80 was 1.000 (95

Rationale and Objectives To develop and validate the performance and prognostic value of a deep-learning (DL) model for carotid plaque component quantification on CTA. Materials and Methods 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. Results 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, P<0.001). The incorporation of LCB provided incremental risk stratification beyond clinical and CTA-driven anatomical factors (ΔAUC +0.09, NRI: 0.22, P=0.001). Conclusion The DL model accurately quantifies carotid plaque components on CTA, with LCB adding prognostic value for recurrent cerebrovascular events in patients with symptomatic carotid stenosis.

AIMS:Cardiovascular disease (CVD) risk assessment and risk-guided lipid management are cornerstones of primary prevention of atherosclerotic coronary artery disease (ACAD). However, the 10-year CVD risk is often limited by individual-level inaccuracy and poor adherence. This trial aimed to determine whether a coronary computed tomography angiography (CCTA)-guided strategy improves lipid management in asymptomatic populations. METHODS AND RESULTS:This pragmatic, open-label, assessor-blinded randomized trial was conducted in Nanjing, China. Asymptomatic community-dwelling adults aged 40-69 years with no history of CVD or prior use of lipid-lowering medication (LLM) were recruited. Participants were randomized to receive individualized LLM recommendations based either on CCTA findings (CCTA group) or on 10-year CVD risk (usual-care group). The primary outcome was the proportion of participants with regular LLM use (≥24 of preceding 30 days) at both the 6-month and 12-month follow-up visits. Of 3503 randomized participants, 3491 (1748 CCTA, 1743 usual-care) were included in the primary analysis (median [IQR] age, 55 [48-61] years; 60.2% women). Among 1516 participants undergoing CCTA, ACAD was detected in 565 (37.3%). The median follow-up was 12.5 months. Regular LLM use was higher in the CCTA group than in the usual-care group (12.5% [218/1748] vs. 8.1% [141/1743]; adjusted risk ratio, 1.57 [95% confidence interval, 1.29-1.91]; P < 0.001). During follow-up, invasive coronary procedures were more frequent in the CCTA group than in the usual-care group (29 procedures vs. 1). CONCLUSION:In asymptomatic populations, a CCTA-guided strategy modestly improved lipid management, though it increased invasive coronary procedures. Longer follow-up is needed to determine its effect on clinical events. Study Registration  http://www.clinicaltrials.com; Identifier: NCT05725096.

OBJECTIVES:To evaluate the feasibility and accuracy of low-dose carotid CT angiography (CTA) for quantitative assessment of carotid plaque characteristics. METHODS:In this prospective study, patients with carotid atherosclerosis underwent both low- and conventional-dose carotid CTA on the same day. Each patient received one of three low-dose protocols-80 kVp/40 mL (subgroup A), 60 kVp/40 mL (B), or 60 kVp/20 mL (C)-along with the conventional protocol (100 kVp/40 mL) as the reference standard. Quantitative plaque analysis was performed using automated software, including total plaque volume, volume of each plaque component (calcification, fiber, fibrolipid, and lipid), and stenosis-related measurements. Radiation dose metrics, including CTDIvol, dose-length product (DLP), and effective dose (ED), were recorded and compared. Intra-individual comparisons were conducted between low-dose and conventional-dose scans. RESULTS:A total of 154 plaques (from 67 patients) were analyzed. Compared with conventional-dose CTA, low-dose protocols significantly reduced radiation exposure (ED: 0.33 ± 0.15 mSv vs. 0.98 ± 0.10 mSv, p < 0.001), corresponding to a reduction of approximately 66 %. No significant differences were observed between low-dose and conventional-dose CTA in total plaque volume, plaque component volumes, diameter stenosis, or maximal area stenosis (all p > 0.05). Subgroup analysis demonstrated that quantitative plaque measurements remained stable across subgroups A, B, and C compared with the conventional-dose group (all p > 0.05). Across different plaque types (calcified, mixed, and non-calcified), quantitative assessment of plaque components remained consistent between low-dose and conventional-dose CTA. CONCLUSION:Low-dose CTA using 60 kVp enables substantial radiation dose reduction while preserving reliable quantitative assessment of carotid plaque characteristics.

Background: Cardiovascular disease (CVD) remains the leading cause of global mortality, necessitating a largescale bioimaging database to advance personalized precision medicine strategies. Objectives: The Dongzong Cardiovascular Bio-imaging Registry (DAILY) study was designed to establish a largescale Chinese cardiovascular bioimaging database (NCT06894095). It aims to address ethnic disparities in genetics and imaging, and to elucidate crucial driving mechanisms and effects of genetic biology and exposure factors on imaging-derived intermediate phenotypes and CVD. Methods: This prospective, multicenter study plans to enroll 50,000 adults from six medical centers across China. It employs a comprehensive data framework, systematically collecting information on exposure factors, highthroughput multiomics (blood and saliva samples), and cardiopulmonary computed tomography (CT) imaging (chest CT, cardiac CT) for cardiac, coronary and pulmonary phenotypes assessment. The primary outcome is a composite major adverse cardiovascular event, including all-cause death, nonfatal myocardial infarction, and nonfatal stroke. Results: To September 10, 2025, the study has recruited 8699 participants in Nanjing, China. Standardized biospecimen collection was performed, obtaining blood samples from 8632 participants and saliva specimens from 8621 individuals, and cardiopulmonary CT scans have been completed in 8699 participants. The cohort maintains a 1-year follow-up rate of 98.9%. Conclusions: The DAILY study will deepen our understanding of ethnic disparities in CVD, help elucidate the effects of genetic and exposure factors on cardiopulmonary intermediate phenotypes, and decipher the pathophysiological mechanisms underlying CVD.

Tumor fibrosis plays a critical role in driving therapeutic heterogeneity and drug resistance. However, relevant research in non-small cell lung cancer (NSCLC) remains limited. This study aimed to determine the prognostic value of tumor fibrosis and develop a novel radiomics fibrosis stratification tool (RaFiST) for non-invasive patient stratification. In this multicenter retrospective study of 532 patients with resected NSCLC, tumor fibrosis was histopathologically quantified via collagen fraction. RaFiST was developed using pre-treatment contrast-enhanced CT scans from training and external test cohorts. Prognostic performance was subsequently compared among clinical, direct radiomics, and combined models. Transcriptomic analysis investigated the model’s underlying molecular mechanisms. Multivariable Cox regression revealed that the fibrosis score was an independent risk factor for disease-free survival (DFS) and overall survival (OS) at both centers. An optimal cutoff of 11.12

Coronary computed tomography angiography (CCTA)-derived fractional flow reserve (CT-FFR) has emerged as a critical tool for assessing functional ischemia in coronary artery disease (CAD). This expert consensus, developed by an international working group, provides a comprehensive overview of the clinical applications, diagnostic performance, and future perspectives of CT-FFR. The consensus highlights the use of CT-FFR as an indicator for abnormal coronary physiology, demonstrating superior diagnostic performance over anatomical CCTA alone, with pooled accuracy of 71

PURPOSE:Although anatomical Coronary Artery Disease Reporting and Data System (CAD-RADS) is widely used for coronary CT angiography reporting, it has limited specificity for predicting lesion-specific ischemia. This study evaluates whether the novel functional CAD-RADS, integrating CT-derived fractional flow reserve, provides superior 5-year prognostic value in patients with stable coronary artery disease (CAD). METHODS:A single-center prospective cohort study enrolled 1,096 participants aged ≥18 years with CAD referred for CT angiography with stenosis degrees of 25% to 80%. Primary end points were major adverse cardiac events (MACEs). The appropriateness of management decisions relative to anatomical or functional CAD-RADS recommendations was explored. Statistical analyses included Kaplan-Meier estimates, Cox proportional hazards models, measures of integrated discrimination improvement, and net reclassification improvement. RESULTS:After a median follow-up of 64 months, 158 MACEs occurred. Both functional and anatomical CAD-RADS categories predicted MACE (P < .001). Functional CAD-RADS showed a higher C-index (0.780; 95% confidence interval [CI]: 0.764-0.796) compared with anatomical CAD-RADS (0.723; 95% CI: 0.703-0.743) for predicting MACE (P = .035), with improved discrimination (integrated discrimination improvement: 0.053 [95% CI: 0.016-0.110]; P = .008). The proportion of inappropriate management decisions was lower for functional CAD-RADS (9.9%) than for anatomical CAD-RADS (11.0%, P < .001). The hazard ratios for MACE when comparing dichotomous appropriate and inappropriate management decisions relative to functional CAD-RADS were 9.544 (95% CI: 5.794-15.720; P < .001), and 2.475 (95% CI: 1.369-4.474); P < .001) for anatomical CAD-RADS recommendations, corresponding to the number needed to treat of 1.912 (95% CI: 1.605-2.364), and 5.076 (95% CI: 3.546-10.204), respectively. CONCLUSION:Functional CAD-RADS may offer improved predictive power for moderate-term outcomes compared with anatomical CAD-RADS, suggesting potential clinical utility in guiding patient management and informing treatment algorithms for CAD, particularly in patients with intermediate stenosis.

This study developed a deep learning model for automated choroid plexus (ChP) segmentation and examined its relationship with systemic inflammation and processing speed and attention deficits (PSAD) in SLE patients without major neuropsychiatric syndromes. In this multicenter retrospective study, 137 SLE patients without major neuropsychiatric syndromes and 159 healthy controls (HCs) were enrolled. The Swin-UNETR model was trained for ChP segmentation on 3D T1-weighted MR images. SLE patients were classified as with processing speed and attention deficits (SLE-PSAD, n = 43) or intact processing speed and attention (SLE-IPSA, n = 94). Clinical, laboratory, and imaging data were compared among groups. Correlation, mediation, and LASSO regression analyses were performed. Swin-UNETR achieved high segmentation accuracy (median DSC = 0.89 internal, 0.82 external, P < 0.001). ChP volume was significantly greater in SLE-PSAD patients than in SLE-IPSA patients and HCs (P < 0.001) and positively correlated with systemic inflammation index (SII, r = 0.34, P < 0.001). Bayesian logistic regression identified increased ChP volume (aOR = 2.57), elevated SII (aOR = 2.47), and low complement component 3 (C3, aOR = 0.47) as independent PSAD risk factors. ChP volume mediated 39.2

Genes impacting the bioaccumulation of perfluoroalkyl and polyfluoroalkyl substances (PFASs)and their neurotoxic effects on the brain and behavior remain unclear. Here,we examined genome-wide associations with serum accumulation of 13 PFASs in 6,823 Chinese adults. We revealed that perfluoroheptanoic acid (PFHpA) accumulation was associated with genetic variants at two loci (3q29: P = 5.20 ×10-19; 6p22.2: P = 3.69 ×10-23), mapping to 56 genes.Blood expression of 27 of these genes was associated with PFHpA accumulation in 573 subsamples. Eight genes showed potential causal effects on PFHpA accumulation,functionally linked to innate immunity (TRIM38, ZDHHC19, MUC20)and organic solute transport (SLC51A and SLC17A3). We assessed the impact of PFASs on cortical thickness and surface area, white matter fractional anisotropy and mean diffusivity,along with 25 behavioral phenotypes. We identified that seven PFASs were correlated with reduced cortical morphology, primarily in the prefrontal cortex. We also found a statistical causal effect of PFHpA accumulation on the surface area in the right frontomarginal cortex, which mediated the effect of PFHpA on anxiety. These findings indicate that serum PFHpA accumulation may be regulated by genes related to innate immunity and solute transport, heightening anxiety by impairing the prefrontal cortex.

High-risk coronary plaque underlies major adverse cardiovascular events, shifting coronary imaging beyond luminal stenosis toward plaque characterization. CCTA identifies vulnerable features including low-attenuation plaque, positive remodeling, spotty calcifications, and napkin-ring sign, while enabling quantification of plaque burden and composition. Dual-energy and photon-counting CT improve tissue characterization, whereas AI and radiomics support automated analysis. Integration with FFR-CT and CT or MR perfusion imaging links plaque vulnerability with functional ischemia. Beyond diagnosis, CCTA enables longitudinal monitoring of plaque remodeling and stabilization under therapy. This review summarizes the role of CCTA in risk stratification, therapeutic monitoring, and personalized coronary artery disease management.

Background Deep learning (DL) models for quantifying plaques at coronary CT angiography (CCTA) are rarely used in routine clinical care. Purpose To develop a fully automated DL model for coronary plaque quantification and to evaluate its prognostic value. Materials and Methods Patients who underwent CCTA were retrospectively enrolled from 17 Chinese hospitals between June 2009 and May 2024. The imaging data of these patients were randomly split into training and validation sets at a 7:3 ratio to develop a fully automated DL model for quantifying plaque volume (PV), PlaqueSegNet, which was subsequently externally tested with four independent datasets: a paired CCTA and intravascular US (IVUS) dataset, a subset of the China CT-derived fractional flow reserve (CT-FFR) study 3 dataset collected with different CT scanners, a serial CCTA dataset within a 3-month interval, and a photon-counting CT dataset. The prognostic value of PlaqueSegNet was evaluated using the Harrell C-index in three cohorts: China CT-FFR study 2, China CT-FFR study 1.1, and a serial CCTA cohort. Results The training dataset included 1409 patients (mean age, 63 years ± 10 [SD]; 795 male), and the internal validation dataset included 604 patients (mean age, 63 years ± 10; 329 male). PlaqueSegNet demonstrated excellent agreement and reproducibility for quantifying PV against IVUS and expert readers across the four external datasets (all intraclass correlation coefficients, >0.90), albeit with wide limits of agreement in Bland-Altman analysis. The C-index of PlaqueSegNet for predicting major adverse cardiac events (MACEs) was 0.64 (95% CI: 0.62, 0.67) in the China CT-FFR study 2 (median follow-up, 2.3 years), 0.65 (95% CI: 0.60, 0.69) in the China CT-FFR study 1.1 (median follow-up, 5.3 years), and 0.74 (95% CI: 0.66, 0.84) in the serial CCTA cohort (median follow-up, 3.6 years). Conclusion PlaqueSegNet provided fully automated measurements of PV from CCTA that closely agreed with expert readers and IVUS and carried prognostic value for future MACEs. Clinical trial registration no. NCT06025305 © RSNA, 2026 Supplemental material is available for this article. See also the editorial by Williams in this issue.

The Reporting and Data Systems (RADS) framework has become a key driver of structured reporting and standardization in radiology. This review summarizes the development and local adaptation of major RADS systems in China. Established RADS frameworks (such as those for breast, liver, and lung imaging) are already widely implemented in clinical practice, and more recently introduced frameworks (including thyroid, prostate, and coronary artery disease RADS) are gaining broader adoption, whereas emerging frameworks (such as ovarian-adnexal, bone, colon, neck and lymph node RADS) remain at an early exploratory stage. Despite ongoing challenges related to the local adaptation of RADS frameworks, data sharing, and quality control, China's large imaging volume and rapid integration of artificial intelligence provide unique opportunities. Through continued efforts in the local adaptation and optimization of frameworks, multicenter validation, and intelligent automation, Chinese radiology practice is expected to contribute significantly to global radiology standardization.

Heart-brain comorbidities are common and devastating, yet their genetic mechanisms remain unclear. Here, we explored the genetic mechanisms underlying comorbidities between five heart diseases and ten brain disorders. We observed varying degrees of polygenic overlap (dice coefficient: 0.04-0.60) among heart-brain disease pairs, along with 12 positive genetic correlations, 25 colocalizations, and 392 shared loci with consistent effects. Genes shared across different disease pairs were enriched for distinct biological processes; for example, genes shared by coronary artery disease with stroke, Alzheimer's disease, depression, and multiple sclerosis showed enrichment for heart development, lipid metabolism, synapse development, and immune cell differentiation, respectively. We conducted genome-wide association studies for the first time on ten heart-brain comorbidities and identified 51 associations, including 12 attributable to genetic sharing between diseases and six unique to comorbidity. This study improves our understanding of genetic mechanisms underlying heart-brain comorbidities and highlight the value of genome-wide association studies of comorbidity.

Angiographic enhancement of non-contrast CT (NCCT) using AI techniques is essential for diagnosing patients unable to use contrast agents. However, AI angiography remains a challenging task because of the feature fragility, structural complexity, and spatial continuity. In this paper, we propose an angiographic framework based on a conditional multi-view diffusion model called AEGIS with three innovations: multi-view hybrid learning (MHL), conditional angiographic diffusion estimation (CADE), and multi-view map fusion (MMF). 1) MHL targets Contrast Map (CM), the difference between NCCT and CT angiography, from multiple views to perceive 3D features in 2D space, enhancing the stability of feature representation. 2) CADE is a conditional diffusion model using NCCT as spatial guidance, providing crucial information for CM generation. 3) MMF adopts a lightweight AutoEncoder for filtering and fusing multi-view CMs, maintaining coherence between adjacent slices while modifying slight bias in low-dimensional representations, thus optimizing data quality and accuracy. Experiments demonstrate our superior performance, which achieve state-of-the-art image quality (PSNR+ 6.69, SSIM+ 3.17, MSE-46.38), segmentation evaluation (CADIR x 10.49 , HSDIR x 5.57 ) and feature distance (FID-64.27). Visualizations and positive evaluation scores from clinicians further reveals that AEGIS has significant potential in clinical applications.

BACKGROUND:After myocardial infarction (MI), macrophage-mediated clearance of dead cells, a process known as efferocytosis, represents a pivotal role in tissue remodeling. Efficient efferocytosis contributes to rescuing neighboring viable cardiomyocytes, drives the phenotypic transition of reparative macrophages, and facilitates the resolution of inflammation. In this study, we explored the roles of CD40 and the signals transduced by its 2 downstream adaptor-protein binding sites (TRAF2/3/5 and TRAF6) in the cardiac macrophage efferocytosis after MI. METHODS:Systemic, myeloid- and macrophage-specific CD40-deficient mice were used to determine the functional significance of CD40 during post-MI repair. The effects of CD40 on macrophages functional states were evaluated with single-cell RNA sequencing (scRNA-seq). Flow cytometry, immunofluorescence staining, Western blot, and ELISA were used to assess the efferocytosis and inflammatory status of macrophages after MI. CD40-TRAF2/3/5-/- and CD40-TRAF6-/- mice were used to explore the roles of CD40 downstream signaling intermediates in MI and macrophage efferocytosis. RESULTS:The expression level of CD40 was increased remarkably from 3 to 7 days after MI. Myeloid-derived macrophages emerged as the dominant population expressing CD40. CD40 deficiency resulted in an augmented infarct size and compromised cardiac function after MI. Further investigations demonstrated that CD40 deficiency led to a notable decline in macrophage efferocytosis, which is associated with a reduced abundance of cluster 0 cells, identified by scRNA-seq, representing the precursor of reparative macrophages. Moreover, scRNA-seq indicated that CD40+ macrophages could be classified primarily into 2 distinct cell subsets: 1 subset was associated mainly with efferocytosis functions, and the other was involved predominantly in immune-inflammatory responses. Direct activation of CD40 failed to upregulate macrophage efferocytosis but instead induced a proinflammatory state. These implied differential effects of the signals transduced by the 2 TRAF binding sites (TRAF2/3/5 and TRAF6) downstream of CD40 on efferocytosis. Findings from CD40-TRAF2/3/5-/- and CD40-TRAF6-/- mice confirmed that the CD40-TRAF2/3/5 signaling served as a crucial determinant in mediating CD40-related efferocytosis. STAT6 was identified as a key downstream factor in this process. Adenovirus-mediated gene transfer to overexpress a CD40 variant retaining TRAF2/3/5 binding site but lacking the TRAF6 in cardiac macrophages led to improvements in cardiac function and macrophage efferocytosis after MI. CONCLUSIONS:Our study established a pivotal positive role of macrophage CD40 in post-MI repair by facilitating macrophage efferocytosis. Specifically, TRAF2/3/5 rather than TRAF6 serves as the crucial signaling pathway that mediates CD40-associated efferocytosis.