Radiology. Cardiothoracic imaging最新文献

Purpose To evaluate the predictive value of myosteatosis as an opportunistic finding in coronary artery calcium (CAC) CT scans for clinically diagnosed chronic obstructive pulmonary disease (COPD) and compare it with an artificial intelligence (AI)-measured biomarker of emphysema derived from the same scans. Materials and Methods In this prospective study, baseline CAC CT scans and 20-year follow-up data were analyzed. Myosteatosis was defined as the lowest quartile of thoracic skeletal muscle mean attenuation (males < 33.5 HU, females < 27.0 HU). The emphysema-like lung biomarker was quantified as the percentage of lung voxels below -950 HU in CAC CT scans. COPD was identified using the International Classification of Diseases, Ninth Revision, Clinical Modification, and International Classification of Diseases, 10th Revision, Clinical Modification diagnostic codes from hospital discharge records. Hazard ratios (HRs) for COPD were calculated using proportional hazard regression models, comparing the bottom versus top quartiles of myosteatosis and emphysema-like lung measurements. Results Among 5535 participants in the Multi-Ethnic Study of Atherosclerosis (mean age ± SD, 62.2 years ± 10.3, 47.6% males), 396 (7.1%) were diagnosed with COPD over the 20-year follow-up period. Myosteatosis showed a stronger association with COPD than emphysema (unadjusted HRs, 5.98 [95% CI: 4.14, 8.63] and 2.12 [95% CI: 1.61, 2.78], respectively [P < .001]). After adjusting for covariates (age, sex, smoking status, body mass index, race, asthma, physical activity, inflammatory markers, and insulin resistance), the HRs were reduced to 2.74 (95% CI: 1.81, 4.16) and 1.50 (95% CI: 1.12, 2.00), respectively (P = .02). Conclusion AI-measured myosteatosis in CAC CT scans strongly predicted future diagnosed COPD independently of known risk factors. Keywords: Applications-CT, Pulmonary, Thorax, Adipose Tissue (Obesity Studies), Chronic Obstructive Pulmonary Disease, Metabolic Disorders, Myosteatosis, Coronary Artery Calcium Scan, Emphysema, AI-CVD ClinicalTrials.gov: NCT00005487 Supplemental material is available for this article. © The Author(s) 2026. Published by the Radiological Society of North America under a CC BY 4.0 license.

Purpose To compare the performance of a convolutional neural network (U-Net) with human-in-the-loop (HITL) validation against manual clinical measurements of thoracic aortic diameter at CT angiography. Materials and Methods This retrospective analysis included patients with thoracic aortic dilatation and at least two CT angiographic examinations between January 2006 and April 2023. Manual diameters were measured by technicians trained in the three-dimensional method. A multitask U-Net performed aortic segmentation, landmark localization, and automated aortic diameter measurements, followed by HITL validation. Discrepancies in diameter greater than 5 mm underwent expert remeasurement. Mid ascending aortic growth from U-Net and manual measurements were compared against a diameter-independent three-dimensional method (vascular deformation mapping). Agreement was assessed using Bland-Altman analysis and intraclass correlation coefficients. Results This study included 177 patients (101 [57%] male patients; median age, 64 years [IQR, 57-71]). Among 2028 paired measurements, 1955 (96%) passed the HITL validation. Validation reduced the 95% limits of agreement from -4.2 to 4.3 mm to -2.9 to 3.4 mm and reduced large discrepancies by 61% (from 61 to 23 measurements; P < .01). Expert remeasurements of discrepancies showed lower mean difference ± SD compared with U-Net measurements (2.2 mm ± 1.7 vs 7.0 mm ± 2.9; P < .01). U-Net measurements were more likely to yield negative growth values than manual measurements (odds ratio, 0.56; 95% CI: 0.44, 0.70; P < .01). Compared with vascular deformation mapping-derived growth, U-Net measurement showed stronger agreement than manual measurement (intraclass correlation coefficient, 0.74 [95% CI: 0.62, 0.80] vs 0.40 [95% CI: 0.18, 0.56]; P < .01). Conclusion Automated U-Net measurements of thoracic aorta diameters, when validated with an HITL approach, demonstrated stronger agreement with reference standard assessment than manual clinical measurements. Keywords: Aorta, Neural Networks, Vascular, CT Angiography, Segmentation, Thoracic Aortic Diameter Supplemental material is available for this article. © RSNA, 2025.

Purpose To assess whether elexacaftor-tezacaftor-ivacaftor (ETI) therapy improves bronchiectasis in cystic fibrosis at CT and to identify associated factors. Materials and Methods This retrospective study included consecutive patients with cystic fibrosis (CF) from two reference centers (between January 2020 and January 2025). Pulmonary function testing was performed, including forced expiratory volume in 1 second as percentage predicted (FEV₁%p), and CT was performed at three time points: 2 years before ETI (Y-2), at initiation of ETI (Y0), and 1 year after ETI (Y1). Bronchiectasis was assessed quantitatively and visually for shape and regional extent. Comparisons of paired medians were done using the Friedman test. Results A total of 106 patients were included (median age, 19 years [IQR, 12-29]; 59 male patients; median FEV₁%p, 80% [IQR, 55-99]). Of these 106 patients, 101 (95.3%) had mild-to-moderate disease severity, with FEV₁%p greater than 40%. Bronchiectasis normalized volumes increased between Y-2 (7.6 [IQR, 2-19]) and Y0 (15.3 [IQR, 5.6-32]) but decreased at Y1 (3.6 [IQR, 0.6-25]; P < .001). Bronchiectasis improved in 74 of 106 patients (69.8%), including 18 of 106 (16.9%) with complete resolution and 56 of 106 (52.9%) with partial reduction, with a median volume reduction of 64% and six resolved segments per patient. Bronchiectasis improvement was associated with younger age (P < .001), cylindric CT pattern (P < .001), fewer CT abnormalities (P < .001), and greater FEV1%p increase (P = .03). Younger age, lower Pseudomonas aeruginosa colonization, and lower CT mucus volume were independent predictors of bronchiectasis improvement (R² = 0.50; P < .001). Conclusion Bronchiectasis improvement occurred after ETI treatment in a substantial fraction of patients with predominantly mild-to-moderate CF. Improvement was linked to younger age and better disease status at ETI initiation, supporting early intervention. Keywords: CT-Quantitative, Tracheobronchial Tree, Chronic Obstructive Pulmonary Disease Supplemental material is available for this article. © RSNA, 2025.

Purpose To develop MedXChat, a large language model (LLM) capable of integrating radiology report generation, visual question answering (VQA), and text-to-image synthesis and evaluate its performance via computational metrics and expert radiologist assessments. Materials and Methods In this retrospective study, MedXChat was trained on the MIMIC Chest X-ray (MIMIC-CXR) database, comprising 270 790 chest radiograph-report pairs, 54 138 VQA samples, and 7500 text-to-image instruction pairs. Data were collected from 2011 to 2016. Computational evaluations of MedXChat performance were conducted using the F1 score, area under the receiver operating characteristic curve (AUC), and Fréchet inception distance (FID). Radiologist evaluations involved six experts-three junior, two senior, and one supervisor-who assessed 50 random MedXChat outputs for accuracy, consistency, and alignment with clinical standards. Results In the chest radiograph-to-report test set, MedXChat achieved an AUC of 0.67 (95% CI: 0.61, 0.75), higher than UniXGen (AUC, 0.54; P < .001) and LLM-CXR (AUC, 0.63; P = .02). Its F1 score was 0.44 versus 0.26 (P < .001) and 0.41 (P = .04), respectively. In chest radiograph-VQA, MedXChat showed higher accuracy for edema (73% vs 54% for LLM-CXR and 60% for LLaVA-Med) and pleural effusion (80% vs 53% for LLM-CXR and 61% for LLaVA-Med; all P ≤ .01). In text-to-image synthesis, it achieved the lowest FID (43.46 vs 73.29 and 106.17; P < .001) and the highest classification accuracy (71.5% vs 68.6% and 67.2%; P ≤ .05), producing high-quality images including lateral views. Conclusion MedXChat integrated report generation, VQA, and image synthesis within a unified framework, achieving state-of-the-art performance. MedXChat may support future professional applications and enhance radiologic workflows, education, and data augmentation. Keywords: Computer Aided Diagnosis (CAD), Applications - Decision Support, Applications - Multimodal, Outcomes Analysis, Technology Assessment, Comparative Studies Supplemental material is available for this article. © RSNA, 2025.

Coccidioidomycosis is caused by the dimorphic fungus Coccidioides, endemic to the southwestern United States. Most infected patients are asymptomatic or recover uneventfully, but a small proportion will develop chronic infection or dissemination. Diagnosis typically rests on organism demonstration in tissue or serology, but knowledge of Coccidioides infection imaging findings becomes important when tissue examination is not possible and serologic results are unclear. Acute coccidioidal pneumonia manifests at chest imaging with consolidation and lymphadenopathy, resolving completely or evolving into a nodule or cavity. Chronic coccidioidomycosis manifests with nodules or cavities that may persist. Uncommon coccidioidomycosis manifestations may simulate malignancy, including nodular pleural effusions, extrathoracic dissemination, and osseous disease. Thoracic coccidioidomycosis complications include pleural space cavity rupture causing bronchopleural fistula, cavity enlargement necessitating surgical intervention, and development of superinfection. Disseminated coccidioidomycosis affects the skin, central nervous system, and musculoskeletal system. Central nervous system coccidioidomycosis often manifests with basilar meningitis, with or without spinal leptomeningeal disease. Paraspinous fluid collections and discitis/vertebral body osteomyelitis simulating pyogenic infection occur, sometimes with relative disc space narrowing and slow disease progression. Musculoskeletal coccidioidomycosis manifests with lytic bone lesions and may produce peripherally enhancing fluid collections. Gastrointestinal coccidioidomycosis is uncommon, manifesting as peritonitis of unclear cause or peritoneal abscesses, rarely with solid organ low-attenuation lesions. Genitourinary coccidioidomycosis manifests as pyelonephritis or pelvic abscesses. Disseminated coccidioidomycosis diagnosis is facilitated through recognition of the common clinical context of immunocompromise coupled with travel to, or residence within, an endemic area. Keywords: Thorax, Lung, Mediastinum, Pleura, Tracheobronchial Tree, Fungus, Coccidioidomycosis, Spherules, Cavity, Nodule, Endemic, Dissemination Supplemental material is available for this article. © RSNA, 2025.

Cardiac herniation describes displacement of the heart from its expected location through a defect in the pericardium that is congenital, traumatic, or iatrogenic in origin. While rare, early recognition and treatment are essential to minimize morbidity and mortality. The authors present a case of cardiac herniation in a 29-year-old patient with a history of thymectomy who presented to the emergency department for chest pain. Coronary CT angiography and cardiac MRI played critical roles in the timely diagnosis of cardiac herniation by demonstrating waistlike ventricular narrowing. Intraoperative inspection confirmed a significant circumferential pericardial defect with protrusion of the left ventricle. Keywords: Cardiac, Pericardium, Complications Supplemental material is available for this article. © RSNA, 2025.

Quantitative imaging has emerged as a promising tool for the diagnosis, classification, and prognostication of interstitial lung disease (ILD). Both global and regional lung abnormalities can be objectively and reproducibly measured using quantitative imaging, which is particularly useful for early disease evaluation and assessment of subtle changes. Accurate ILD classification and identification of inconspicuous changes allow for more personalized treatment decisions and, ultimately, improved patient outcomes. Because CT is the primary imaging modality for ILD evaluation, most of the computer-aided support systems have been developed for this modality and are referred to as quantitative CT. While CT continues to advance with functional capability using dual-energy technology, new MRI techniques are being developed that offer the ability to further improve ILD evaluation. Recent advancements in the field of artificial intelligence underly the development of these new quantitative imaging tools. As quantitative imaging for ILD evaluation becomes more common, it will likely play an increasingly important role in the general clinical radiology workflow, necessitating a familiarity of its use for the general radiologist. This review summarizes current applications of quantitative CT in the evaluation of fibrotic ILDs, including idiopathic pulmonary fibrosis, hypersensitivity pneumonitis, and connective tissue disease-related ILD, and highlights emerging quantitative MRI techniques for ILD assessment. Keywords: Applications-CT, Deep Learning, Machine Learning, Radiomics, CT-Quantitative, Thorax, Lung © RSNA, 2025.