Breast cancer classification through multivariate radiomic time series analysis in DCE-MRI sequences

Abstract

Breast cancer is the most prevalent disease that poses a significant threat to women’s health. Despite the Dynamic Contrast-Enhanced MRI (DCE-MRI) has been widely used for breast cancer classification, its diagnostic performance is still suboptimal. In this work, the Radiomic workflow was implemented to classify the whole DCE-MRI sequence based on the distinction in contrast agent uptake between benign and malignant lesions. The radiomic features extracted from each of the seven time instants within the DCE-MRI sequence were fed into a multi-instant features selection strategy to select the discriminative features for time series classification. Several time series classification algorithms including Rocket, MultiRocket, K-Nearest Neighbor, Time Series Forest, and Supervised Time Series Forest were compared. Firstly, a univariate classification was performed to find the five most informative radiomic series, and then, a multivariate time series classification was implemented via a voting mechanism. The Multivariate Rocket model was the most accurate (Accuracy = 0.852, AUC-ROC = 0.852, Specificity = 0.823, Sensitivity = 0.882). The intelligible radiomic features enabled model findings explanations and clinical validation. In particular, the Energy and TotalEnergy were among the most important features, and the most descriptive for the change in signal intensity, which is the main effect of the contrast agent.