A feature fusion method based on radiomic features and revised deep features for improving tumor prediction in ultrasound images

Abstract

Background

Radiomic features and deep features are both vitally helpful for the accurate prediction of tumor information in breast ultrasound. However, whether integrating radiomic features and deep features can improve the prediction performance of tumor information is unclear.

Methods

A feature fusion method based on radiomic features and revised deep features was proposed to predict tumor information. Radiomic features were extracted from the tumor region on ultrasound images, and the optimal radiomic features were subsequently selected based on Gini score. Revised deep features, which were extracted using the revised CNN models integrating prior information, were combined with radiomic features to build a logistic regression classifier for tumor prediction. The performance was evaluated using area under the receiver operating characteristic (ROC) curve (AUC).

Results

The results showed that the proposed feature fusion method (AUC = 0.9845) obtained better prediction performance than that based on radiomic features (AUC = 0.9796) or deep features (AUC = 0.9342).

Conclusions

Our results demonstrate that the proposed feature fusion framework integrating the radiomic features and revised deep features is an efficient method to improve the prediction performance of tumor information.