CT-based radiomics signature for differentiation between cardiac tumors and a thrombi: a retrospective, multicenter study

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.

Figure 1

Patient selection flowchart.

Figure 2

Flowchart showing the process of radiomics analysis. LASSO least absolute shrinkage and selection operator; ROC receiver operating characteristic.

Figure 3

ROC curve of the radiomics model, clinical model, and combined model with radiomics and clinical model to predict cardiac tumor in different datasets. (a) The training dataset. (b) The external validation dataset.

Figure 4

Calibration curves of the different models in the training and validation datasets. (a) The radiomics model. (b) The clinical model. (c) The combined model.

Figure 5

A 67-year-old man with cardiac thrombus in the left ventricle. A lobulated mass (25 mm) in the left ventricle. The predictive probability of tumor based on radiomics features of this mass was 0.237.

Figure 6

A 53-year-old man with cardiac myxoma in the left atrium. A lobulated mass (43 mm) in the left atrium. The predictive probability of tumor based on radiomics features of this mass was 0.821.