ArticleScientific reports2022
CT-based radiomics signature for differentiation between cardiac tumors and a thrombi: a retrospective, multicenter study.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Review
- Multimodal echocardiographic techniques in the diagnosis of cardiac tumors: applications and recent advances.Frontiers in cardiovascular medicine · 2026Review
- A Novel Data Augmentation Method for Radiomics Analysis Using Image Perturbations.Journal of imaging informatics in medicine · 2024Article
- The usefulness of contrast echocardiography in the evaluation of cardiac masses: a multicenter study.BMC cardiovascular disorders · 2024Article
- Cardiac Masses and Pseudomasses: An Overview about Diagnostic Imaging and Clinical Background.Medicina (Kaunas, Lithuania) · 2023Review
- Clinical characteristics of primary atrial tumor and their diagnostic value: A retrospective study of 10 years.Frontiers in surgery · 2023Article
- The diagnostic accuracy of contrast echocardiography in patients with suspected cardiac masses: A preliminary multicenter, cross-sectional study.Frontiers in cardiovascular medicine · 2022Article
- Left Ventricular Thrombus in Oncology Patients and Beyond: Imaging, Treatment Choices, and Duration.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/HemostasisReview
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7 authors.
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Abstract
The study aimed to develop and validate whether the computed tomography (CT) radiomics analysis is effective in differentiating cardiac tumors and thrombi. For this retrospective study, a radiomics model was developed on the basis of a training dataset of 192 patients (61.9 ± 13.3 years, 90 men) with cardiac masses detected in cardiac CT from January 2010 to September 2019. We constructed three models for discriminating between a cardiac tumor and a thrombus: a radiomics model, a clinical model, which included clinical and conventional CT variables, and a model that combined clinical and radiomics models. In the training dataset, the radiomics model and the combined model yielded significantly higher differentiation performance between cardiac tumors and cardiac thrombi than the clinical model (AUC 0.973 vs 0.870, p < 0.001 and AUC 0.983 vs 0.870, p < 0.001, respectively). In the external validation dataset with 63 patients (59.8 ± 13.2 years, 26 men), the combined model yielded a larger AUC compared to the clinical model (AUC 0.911 vs 0.802, p = 0.037). CT radiomics analysis is effective in differentiating cardiac tumors and thrombi. In conclusion, the combination of clinical, conventional CT, and radiomics features demonstrated an additional benefit in differentiating between cardiac tumor and thrombi compared to clinical data and conventional CT features alone.
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