Key result
Combined clinical, CT, and radiomics model improves cardiac tumor versus thrombus differentiation to AUC ~0.91.
Why the study?
The study aimed to develop and validate whether computed tomography radiomics analysis is effective in differentiating cardiac tumors and thrombi.
Does a combined CT-based radiomics and clinical model improve the differentiation between cardiac tumors and thrombi compared to a clinical model alone in patients with cardiac masses?
Observational (n=255)
Yes
Does a combined CT-based radiomics and clinical model improve the differentiation between cardiac tumors and thrombi compared to a clinical model alone in patients with cardiac masses?
Absolute Event Rate: 0.911% vs 0.802%
p-value: p=0.037
A combined model incorporating CT radiomics, clinical data, and conventional CT features significantly improves the diagnostic differentiation between cardiac tumors and thrombi compared to clinical and conventional CT features alone.
May aid noninvasive differentiation of cardiac masses on CT; leaves open prospective validation before clinical use.
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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Lee et al. (2022) conducted an observational in Cardiac masses (tumors and thrombi) (n=255). Combined model (clinical, conventional CT, and radiomics features) vs. Clinical model (clinical and conventional CT features) was evaluated on Differentiation between cardiac tumors and thrombi (AUC) (95% CI 0.839-0.982, p=0.037). A combined model of clinical, conventional CT, and radiomics features improved differentiation between cardiac tumors and thrombi compared to clinical features alone (AUC 0.911 vs 0.802, p=0.037).
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