  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.
