Exploring Kinetic Curves Features for the Classification of Benign and Malignant Breast Lesions in DCE-MRI

TL;DR

This study introduces a fully automated method that combines kinetic curve features and radiomic features from DCE-MRI to improve the accuracy of classifying benign and malignant breast lesions, achieving an AUC of 0.94.

Contribution

It presents a novel approach that leverages both dynamic kinetic curves and radiomic features for enhanced breast lesion classification in DCE-MRI.

Findings

Achieved an AUC of 0.94 on in-house dataset

Combining kinetic and radiomic features improves classification accuracy

Method is fully automated and publicly available

Abstract

Breast cancer is the most common malignant tumor among women and the second cause of cancer-related death. Early diagnosis in clinical practice is crucial for timely treatment and prognosis. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has revealed great usability in the preoperative diagnosis and assessing therapy effects thanks to its capability to reflect the morphology and dynamic characteristics of breast lesions. However, most existing computer-assisted diagnosis algorithms only consider conventional radiomic features when classifying benign and malignant lesions in DCE-MRI. In this study, we propose to fully leverage the dynamic characteristics from the kinetic curves as well as the radiomic features to boost the classification accuracy of benign and malignant breast lesions. The proposed method is a fully automated solution by directly analyzing the 3D features from the DCE-MRI. The proposed method is evaluated on an in-house dataset including 200 DCE-MRI scans with 298 breast tumors (172 benign and 126 malignant tumors), achieving favorable classification accuracy with an area under curve (AUC) of 0.94. By simultaneously considering the dynamic and radiomic features, it is beneficial to effectively distinguish between benign and malignant breast lesions. The algorithm is publicly available at https://github.com/ryandok/JPA.