Wiessman, Maya

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.

Coronary CT angiography (CCTA) offers excellent negative predictive value for ruling out obstructive coronary artery disease (CAD); however, several interpretative pitfalls can lead to diagnostic errors including false-negative, false-positive results or inaccurate estimation of degree of stenosis. These errors may influence patient management, prompt unnecessary or missed downstream testing, and ultimately affect clinical outcomes. This two-part article reviews the most common sources of diagnostic inaccuracy in CCTA through case-based examples. Part I highlights factors that contribute to missed or overdiagnosed coronary lesions while Part II focuses on pitfalls that lead to underestimation or overestimation of stenosis severity and outlines practical strategies to mitigate these errors. Recognizing the inherent limitations of CCTA and employing a systematic, structured interpretive approach are essential for preserving its high diagnostic accuracy and ensuring optimal patient care.

Accurate quantification of coronary stenosis on coronary CT angiography (CCTA) is essential for appropriate risk stratification and clinical decision-making, yet several technical and interpretative pitfalls can compromise diagnostic accuracy. This second part of a two-part review examines common factors that lead to overestimation or underestimation of stenosis severity, with attention to how these errors influence CAD-RADS categorization, downstream testing, and patient management. Using case-based examples, we describe key contributors to misinterpretation and present practical strategies to mitigate these pitfalls. By recognizing these nuances and employing a structured, standardized interpretive approach, readers can minimize errors in stenosis assessment and enhance the reliability of CCTA as a noninvasive tool for evaluating coronary artery disease.