Evaluation demonstrates stable myocardial radiomic features improve detection of repaired tetralogy of Fallot, indicating motion stability enhances diagnostic machine learning models.
Myocardial motion influences the stability of radiomic features of cardiac magnetic resonance (CMR) cine images. We aimed to evaluate the impact of the stability of myocardial radiomic features on the classification performance of differentiation of repaired tetralogy of Fallot (rTOF) patients from normal volunteers. The stability of each radiomic feature during cardiac cycle was assessed by coefficient of variation (CV) of the feature value. 50 of 107 radiomic features (46.7%) in normal volunteers were stable features. The classification model established only with stable features presented the best classification performance (AUC=0.94) which was able to be improved by chi-square feature selection.
No takes yet. Share an insight, caveat, or question.
Chiu et al. (2024) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: