Abstract / Summary
Objectives To address the limitations of current imaging modalities, the present study aimed to develop and validate a novel intracavitary contrast-enhanced ultrasound (CEUS) approach for the quantitative assessment of pleural effusion (PE) volume and differentiation of exudative PE from transudative PE. Methods This cross-sectional diagnostic accuracy study prospectively enrolled 100 patients with PE undergoing US-guided thoracentesis. Intracavitary CEUS was performed using SonoVue microbubbles, and time-intensity curve parameters (A, B, and A+B) were analyzed. PE volume was measured based on the drainage volume, and effusions were classified as exudates or transudates according to Light’s criteria. Diagnostic performance was assessed by receiver operating characteristic (ROC) analysis, and a linear regression model was developed to predict PE volume. Results Exudative PE showed significantly higher baseline intensity parameter B (−35.18 ± 5.17 vs. −41.41 ± 8.64, p < 0.01) and lower peak intensity increment parameter A (1.20 ± 3.27 vs. 6.61 ± 6.10, p < 0.01) compared to transudative PE. The combined parameter A+B (steady-state microbubble concentration index) showed a strong correlation with PE volume ( r = 0.912, R 2 = 0.831), yielding the predictive model: V(mL) = −33.345 × (A+B) − 453.071 (intraclass correlation coefficient = 0.950). The ROC analysis revealed moderate diagnostic accuracy for the A value (area under the curve (AUC) = 0.779) and B value (AUC = 0.719) in differentiating exudative PE from transudative PE. Conclusion Intracavitary CEUS could serve as a reliable, minimally invasive, feasible radiation-free adjunctive technique for quantifying PE volume and differentiating exudative versus transudative PE in patients undergoing thoracentesis. The A+B-based regression model provides high predictive accuracy, while A and B values facilitate effusion classification. This quantitative CEUS workflow may facilitate clinical decision-making, and its utility for serial monitoring requires further verification in large-scale real-world cohorts.