Abbara, Suhny

Articles published in Radiology: Cardiothoracic Imaging in 2025 highlight advances in cardiothoracic imaging through technical innovation, quantitative biomarkers, and artificial intelligence applications that support improved diagnosis, risk stratification, and patient-centered management across cardiac, vascular, and thoracic disease.

Abstract Background Cardiomegaly is a clinically significant incidental finding on chest computed tomography (CT) associated with heart failure, arrhythmias, and sudden cardiac death. Qualitative radiologist assessment is variable, and automated AI tools may enable objective opportunistic cardiac volumetry. Purpose To evaluate whether AI-enabled total cardiac volume (TCVAI) derived from non-ECG-gated, non-contrast chest CT can identify cardiomegaly as defined by echocardiography. Materials and Methods This retrospective study included 307 consecutive patients (median age, 67 years; 56% male) who underwent non-contrast chest CT at a single center on 7 scanner types (4 vendors) and clinically indicated echocardiography within 31 days. A commercially available AI tool (AI-Rad Companion, Siemens Healthineers) automatically quantified TCVAI, indexed to body surface area (TCVAI/BSA). Echocardiography reports were reviewed for chamber dilation and left ventricular hypertrophy (LVH), collectively defined as cardiomegaly. Associations between TCVAI/BSA and echocardiographic findings were assessed using correlation, ordinal regression, and receiver operating characteristic (ROC). Interscan repeatability was evaluated in 248 patients with 544 repeat CT examinations. Prespecified sex-specific thresholds were tested in a temporally independent validation cohort of 50 patients. Results Median TCVAI was higher in patients with cardiomegaly than those without (1061.9 vs 798.4 mL; P < .001). TCVAI/BSA was associated with chamber dilation and LVH severity on univariate analysis and remained associated in multivariable ordinal models, except for right ventricular dilation. Discriminatory performance was fair to good, with area under the curve (AUC) 0.81 (95% CI, 0.75-0.87) in men and 0.77 (95% CI, 0.69-0.85) in women. Interscan repeatability was excellent (intraclass correlation coefficient [ICC]: 0.93). In independent validation, performance ranged from sensitivity 89.3%/specificity 27.3% at a high-sensitivity threshold to sensitivity 28.6%/specificity 100% at a high-specificity threshold. Conclusion AI-derived cardiac volume from routine chest CT shows fair to good performance for identifying echocardiography-defined cardiomegaly with high measurement repeatability, supporting a potential role for automated cardiac volumetry as an objective, opportunistic biomarker.

Prosthetic heart valve (PHV) dysfunction is increasingly seen due to the increase in the number of PHV that are being implanted worldwide. Cardiac CT imaging has emerged as a valuable tool to assess PHVs and determine the cause of dysfunction. This consensus document first summarizes the available techniques for PHV assessment. Then the use of CT in PHV (dys)function assessment is discussed in detail including consensus statements for correct indications and patient selection for CT assessment of PHVs, image acquisition, reconstruction and measurement protocols and how to interpret and report the CT findings for specific types of PHV dysfunction. This article was published in Journal of Cardiovascular Computed Tomography, Budde RPJ et al, Cardiac computed tomography for prosthetic heart valve assessment: An expert consensus document of the Society of Cardiovascular Computed Tomography (SCCT), the American College of Cardiology (ACC), the European Society of Cardiovascular Radiology (ESCR), the North American Society for Cardiovascular Imaging (NASCI), the Radiological Society of North America (RSNA), the Society for Cardiovascular Angiography & Interventions (SCAI), and the Society of Thoracic Surgeons (STS). The articles are identical except for minor stylistic and spelling differences in keeping with each journal’s style. Either citation may be used when referencing this document. © 2025 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. on behalf of the Society of Cardiovascular Computed Tomography. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ). Keywords: Computed Tomography, Valve, Cardiac, Imaging, Dysfunction, Prosthetic

The field of cardiothoracic imaging is expanding rapidly with new techniques that enable greater diagnostic accuracy and more personalized patient management, ultimately improving outcomes and enhancing clinical decision-making.

Objectives: To evaluate if dual-energy CT (DECT) pulmonary angiography (CTPA) can detect anemia with the aid of machine learning. Methods: Inclusion of 100 patients (mean age ± SD, 51.3 ± 14.8 years; male-to-female ratio, 42/58) who underwent DECT CTPA and hemoglobin (Hb) analysis within 24 h, including 50 cases with Hb below and 50 controls with Hb ≥ 12 g/dL. Blood pool attenuation was assessed on virtual noncontrast (VNC) images at eight locations. A classification model using extreme gradient-boosted trees was developed on a training set (n = 76) for differentiating cases from controls. The best model was evaluated in a separate test set (n = 24). Results: Blood pool attenuation was significantly lower in cases than controls (p-values < 0.01), except in the right atrium (p = 0.06). The machine learning model had sensitivity, specificity, and accuracy of 83%, 92%, and 88%, respectively. Measurements at the descending aorta had the highest relative importance among all features; a threshold of 43 HU yielded sensitivity, specificity, and accuracy of 68%, 76%, and 72%, respectively. Conclusion: VNC imaging and machine learning shows good diagnostic performance for detecting anemia on DECT CTPA.

Abstract This International Consensus Classification and Nomenclature for the congenital bicuspid aortic valve condition recognizes 3 types of bicuspid valves: 1. The fused type (right-left cusp fusion, right-non-coronary cusp fusion and left-non-coronary cusp fusion phenotypes); 2. The 2-sinus type (latero-lateral and antero-posterior phenotypes); and 3. The partial-fusion (forme fruste) type. The presence of raphe and the symmetry of the fused type phenotypes are critical aspects to describe. The International Consensus also recognizes 3 types of bicuspid valve-associated aortopathy: 1. The ascending phenotype; 2. The root phenotype; and 3. Extended phenotypes.