Multimodal echocardiographic techniques in the diagnosis of cardiac tumors: applications and recent advances.
Authors
Affiliations (2)
Affiliations (2)
- Department of Ultrasound, Zigong Fourth People's Hospital, Zigong, Sichuan, China.
- Department of Radiology, Zigong First People's Hospital, Zigong, Sichuan, China.
Abstract
Cardiac tumors are exceedingly rare and exhibit marked pathological heterogeneity. Early clinical manifestations are often nonspecific and easily confused with intracardiac thrombi or inflammatory vegetations, posing substantial challenges for timely recognition and clinical management. Imaging plays a pivotal role in both diagnosis and differential diagnosis. Among the available modalities, echocardiography is widely used as the initial evaluation tool due to its noninvasiveness, real-time capability, and repeatability. However, conventional two-dimensional echocardiography is limited by acoustic windows, spatial resolution, and operator dependency, resulting in suboptimal accuracy in assessing tumor vascularity, attachment sites, and benign-malignant differentiation. Recent advances in three-dimensional echocardiography, transesophageal echocardiography (TEE), contrast-enhanced ultrasound (CEUS), and speckle-tracking echocardiography (STE) have enabled a more comprehensive assessment of structural, perfusion, and functional characteristics. These multimodal approaches have demonstrated superior diagnostic performance over traditional 2D imaging in several studies. Despite the promising outlook, current research still faces significant limitations, including nonstandardized imaging and contrast parameters, insufficient cross-vendor consistency of quantitative indices, a lack of externally validated diagnostic thresholds, and a paucity of high-quality, prospective multicenter evidence. This review systematically summarizes the progress of multimodal echocardiography in cardiac tumor diagnosis over the past decade, evaluates the strengths and limitations of each modality, explores the emerging roles of artificial intelligence (AI) and radiomics in quantitative assessment, and proposes future strategies for standardization, intelligent analysis, and cross-modality integration.