Radiology. Cardiothoracic imaging最新文献

Purpose To evaluate the prognostic value of the periaortic fat attenuation index (FAI), which noninvasively captures vascular inflammation on noncontrast CT images, in managing uncomplicated type B aortic dissection (TBAD). Materials and Methods In this retrospective multicenter study (January 2011-December 2022), an automated machine learning algorithm measured periaortic FAI of the descending thoracic aorta at noncontrast CT. Patients who underwent CT for chest or back pain were included to compare FAI between those with acute aortic dissection, including both type A and B, and those without. Further, prognostic evaluation of patients with uncomplicated TBAD was conducted using multivariable Cox proportional hazards regression. Results A total of 688 patients (median age, 69 years [IQR, 56-79]; 400 male patients), including 380 with acute aortic dissection and 308 without, were analyzed for the diagnostic characteristics of FAI, and 135 patients with uncomplicated TBAD (median age, 70 years [IQR, 59-79]; 90 male patients) were followed up for prognosis. FAI values at initial presentation were higher in patients with acute aortic dissection than in those without (median, -74.5 vs -78.7 HU; P < .001). In patients with uncomplicated TBAD, FAI values peaked on the 6th day after onset. These patients were divided into two groups according to the median peak FAI value of -64 HU. During a median follow up of 529 days, those with higher peak FAI values (≥-64 HU) had higher rates of all-cause death and aortic events (log-rank P < .001). High peak FAI independently predicted these events (adjusted hazard ratio, 4.54 [95% CI: 1.69, 12.21]; P = .003). Conclusion A higher peak FAI value was an independent risk factor for adverse events in patients with uncomplicated TBAD. Keywords: Acute Aortic Dissection, Fat Attenuation Index, Noncontrast CT, Perivascular Adipose Tissue, Prognosis UMIN Clinical Trials Registry (AIDFAI study no. UMIN000053435). Supplemental material is available for this article. © The Author(s) 2025. Published by the Radiological Society of North America under a CC BY 4.0 license.

Purpose To compare left ventricular (LV) peak early diastolic strain rate (PEDSR) and peak late diastolic strain rate (PLDSR) using cardiac MRI feature tracking (FT) across a spectrum of diastolic dysfunction and determine the association between diastolic strain rates and cardiac remodeling. Materials and Methods Between October 2008 and December 2022, cardiac MRI and echocardiography were performed in prospectively recruited cohorts with type 2 diabetes mellitus, heart failure with preserved ejection fraction, and severe aortic stenosis, as well as asymptomatic participants without diabetes. Diastolic dysfunction was classified using established echocardiography guidelines. Global circumferential and longitudinal PEDSR and PLDSR were measured at cardiac MRI. Linear regression was performed to identify independent associations between LV diastolic strain rates and remodeling. Results A total of 600 participants (mean age, 65.2 years ± 8.4 [SD]; 361 of 600 male participants [60%]) were included. Proportions of participants with normal diastolic function and those with grade 1, indeterminate, and grade 2 or 3 diastolic dysfunction were 92 of 600 (15%), 401 of 600 (67%), 85 of 600 (14%), and 22 of 600 (4%), respectively. Compared with participants who had normal function, PEDSR decreased in those with grade 1 dysfunction (circumferential PEDSR, 0.99 sec^-1 ± 0.22 vs 0.81 sec^-1 ± 0.24 [P < .001]; longitudinal PEDSR, 0.79 sec^-1 ± 0.19 vs 0.60 sec^-1 ± 0.19 [P < .001]) and remained low throughout worsening stages of diastolic dysfunction. In contrast, compared with participants who had normal diastolic function, PLDSR increased in those with grade 1 dysfunction (circumferential PLDSR, 0.70 sec^-1 ± 0.17 vs 0.82 sec^-1 ± 0.23 [P < .001]; longitudinal PLDSR, 0.73 sec^-1 ± 0.18 vs 0.80 sec^-1 ± 0.27 [P < .001]) and declined progressively with worsening diastolic dysfunction. After multivariable adjustment for risk factors, inverse associations persisted between PEDSR and PLDSR with cardiac remodeling. Conclusion A distinctive pattern of cardiac MRI FT early and late diastolic strain rates was observed across the range of diastolic dysfunction. Keywords: Diastolic Dysfunction, Peak Early Diastolic Strain Rate, Peak Late Diastolic Strain Rate, Feature Tracking Supplemental material is available for this article. © The Author(s) 2025. Published by the Radiological Society of North America under a CC BY 4.0 license.

Purpose To define criteria for evaluating the diagnostic adequacy of expiratory CT and use these criteria to evaluate how expiratory quality affects the performance of quantitative air trapping in predicting the presence and progression of chronic lung allograft dysfunction (CLAD). Materials and Methods Consecutive post-lung transplantation inspiratory-expiratory chest CT scans acquired at the authors' institution between March 2020 and November 2023 were retrospectively evaluated for diagnostic adequacy by grading the tracheal morphology on expiratory CT scans and comparing CT lung volume measurements with spirometry data. Lung volumes and voxelwise air trapping were measured using a deep learning algorithm. Air trapping was compared against changes in spirometry data at baseline and follow-up using Pearson correlation and receiver operating characteristic curve analysis. Results A total of 603 inspiratory-expiratory chest CT scans in 192 patients who underwent lung transplantation (mean age, 57.2 years ± 13.3 [SD]); 121 male patients) were evaluated. Tracheal morphology was identified as predominantly convex on 29% (175 of 613) of the CT scans, resulting in an overestimation of the expiratory volume in these studies. The correlation of the lung volume measurements between CT and spirometry improved with tracheal concavity. A baseline air trapping level of 50% had 82.6% specificity and 34.0% sensitivity for diagnosing CLAD on studies with predominantly concave or flat morphology and a volume change of 1.0 L or greater. A 20% increase in air trapping resulted in 92.1% specificity and 20.0% sensitivity for a concurrent 10% decline in the forced expiratory volume in 1 second (FEV₁). Conclusion Tracheal morphology was used to assess the diagnostic adequacy of expiratory phase CT. Increased air trapping was highly specific, but not very sensitive, for predicting an FEV₁ decline and helped in the diagnosis and monitoring of CLAD progression. Keywords: CT, CT-Quantitative, Pulmonary, Lung, Physiological Studies, QA/QC, Transplantation, Technology Assessment, Quality Assurance, Artificial Intelligence, Air Trapping, Bronchiolitis Obliterans, Lung Transplant Supplemental material is available for this article. © RSNA, 2025.

Purpose To perform a systematic review and meta-analysis to evaluate longitudinal trends in the incidence of device-related thrombosis (DRT) following left atrial appendage occlusion (LAAO), with the aim of informing future postprocedural surveillance schedules. Materials and Methods PubMed, Embase, and Cochrane Library databases were retrospectively searched up to May 2023 for clinical trials and observational studies with at least 20 adults who underwent LAAO and had a DRT detected with CT or transesophageal echocardiography. DRT events were stratified by time since procedure, and cumulative incidence rates were calculated using random-effects and mixed-effects models. Quality assessments were completed using the Risk Of Bias In Non-randomized Studies-of Interventions (ROBINS-I) and Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. Results A review of 2181 records resulted in the inclusion of 96 articles, encompassing 18 024 patients (mean ages ranged from 63.5 years ± 7.3 [SD] to 92.9 years ± 2.8; 61.7% male [11 121 of 18 024]). The total DRT incidence was 2.65% (478 of 18 024; range, 0%-17.5%). The mean cumulative DRT incidence rates using the random-effects model were 0.83% (n = 996; 95% CI: 0.23, 2.98; I² = 0.0%) at 30 days, 1.24% (n = 10 909; 95% CI: 0.90, 1.70; I² = 37.3%) at 45 days, 2.14% (n = 4559; 95% CI: 1.41, 3.25; I² = 36.9%) at 90 days, 2.06% (n = 8088; 95% CI: 1.45, 2.92; I² = 62.5%) at 180 days, and 2.9% (n = 7346; 95% CI: 2.07, 4.06; I² = 70.6%) beyond 180 days. Similar results were achieved with the mixed-effects model. Conclusion DRT incidence changed over time following LAAO. The greatest rise in the incidence occurred during the first 90 days after the procedure, although the cumulative rates continued to increase beyond 180 days. Keywords: Device-related Thrombosis, Left Atrial Appendage Occlusion, Meta-analysis, Transesophageal Echocardiography, CT Supplemental material is available for this article. © RSNA, 2025.

Glycogen storage disease type III (GSDIII) is a metabolic disorder characterized by glycogen accumulation in tissues, such as the liver, skeletal muscle, and myocardium. While cardiac involvement is rare, it can result in serious complications including cardiomyopathy and arrhythmias. The cardiac manifestations of GSDIII have been scarcely reported. This case demonstrates the role of cardiac MRI in evaluating myocardial involvement in a patient with GSDIII, highlights the progression of myocardial changes over a 2-year follow-up period, and finds two novel heterozygous AGL mutations, expanding the spectrum of known AGL mutation in Chinese patients with GSDIII. Keywords: Cardiac, MR-Imaging, Myocardium Supplemental material is available for this article. © RSNA, 2025.

Purpose To evaluate the impact of a fully automated, multitask deep learning (DL) algorithm on interreader agreement of coronary artery disease (CAD) detection and stenosis classification using coronary CT angiography (CCTA). Materials and Methods This retrospective study included CCTA examinations (n = 623 patients) performed for clinical indications on CT systems from multiple vendors between January 2010 and December 2019. An expert reader (reader 1) analyzed all CCTA scans manually and with artificial intelligence (AI)-assisted reading at the lesion, coronary segment, and patient levels using the CAD Reporting and Data System (CAD-RADS). The AI algorithm detected, quantified, and classified coronary lesions. Interreader agreement was evaluated using a second expert reader (reader 2), who analyzed a randomly selected subset of 274 patients. CAD-RADS scores from radiologist reports (reader 3) were available for 362 patients. In a subgroup of 30 patients with disagreements, R2 also interpreted the cases using AI assistance. Agreement between readings, with and without AI, was assessed using Spearman correlation, and logistic regression and mixed models evaluated the impact of AI-assisted reading on CAD-RADS classification. Results The final study sample included 11 214 coronary segments analyzed from 623 patients (mean age ± SD, 54.8 years ± 15.7; 341 male). Of these patients, 295 (47.9%) had no CAD (CAD-RADS 0), 213 (33.6%) had low risk of coronary obstruction (CAD-RADS < 3), and 115 (18.5%) had high risk of obstructive disease (CAD-RADS ≥ 3). With AI assistance, reader 1 demonstrated improved agreement with reader 2 (ρ = 0.899-0.949; P < .001) and reader 3 (ρ = 0.889-0.938; P < .001). In the subgroup with reader 1-AI disagreement, agreement between reader 1 and reader 2 was low with manual readings (ρ = 0.688) but increased substantially when both readers used AI-assisted reading (ρ = 0.975; P < .001). Conclusion AI-assisted reading using a DL algorithm significantly improved interreader agreement for CAD-RADS classification at CCTA. Keywords: Applications - CT, CT-Coronary Angiography, Deep Learning Supplemental material is available for this article. © RSNA, 2025.

Accurate cancer staging is essential for guiding treatment decisions and predicting outcomes. The TNM classification is based on three principal elements: size and extent of the primary tumor (T), the degree of spread to regional lymph nodes (N), and the presence of distant metastases (M). In thymic epithelial tumors, clinical TNM staging relies predominantly on cross-sectional imaging, particularly CT and MRI, placing radiologists at the forefront of staging accuracy. Their assessments substantially impact both clinical decision-making and the quality of data in staging registries. A major update in this field is the ninth edition of the TNM classification for thymic epithelial tumors, effective January 2025, which refines TNM category definitions to improve staging accuracy and clinical applicability. This review article outlines these updates, illustrating their application through case examples, and emphasizes the radiologist's crucial role in cancer staging, including selection of appropriate imaging techniques, interpretation of key radiologic features, and effective communication of findings to multidisciplinary teams. These insights aim to enhance staging precision and improve patient outcomes in thymic malignancies. Keywords: Thymus, Staging © RSNA, 2025.

Coronary artery calcium (CAC) is a specific marker of subclinical coronary atherosclerosis and is strongly associated with short- and long-term atherosclerotic cardiovascular disease (ASCVD) risk. Although noncontrast electrocardiographically gated cardiac CT is the reference standard for quantification of CAC (approximately 1 mSv), studies have shown that CAC can also be qualitatively interpreted and quantified on noncardiac chest CT scans with similar prognostic value. While use of dedicated CAC scans is increasing, measurement of incidental CAC represents a major untapped opportunity for ASCVD prevention, given that nearly 20 times more chest CT examinations are performed annually in the United States than dedicated CAC scans. Incidental measurement of CAC at chest CT incurs no additional cost or radiation for patients and can identify those with significant CAC burden who may be inadequately treated with ASCVD risk reduction therapies. This review outlines the fundamentals of CAC scoring, with a focus on detection and quantification of incidental CAC. It details the technical approaches and challenges of incidental CAC assessment and provides recommendations for routine reporting, clinical advisories, and subsequent patient management. The review also presents first-hand experiences from a large academic medical center's initiative to standardize incidental CAC reporting. Future directions include the use of artificial intelligence to automate both basic and advanced CAC interpretation.