Improvement in breast lesion classification utilizing deep learning and treatment response assessment maps (TRAMs)

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has been widely used for breast lesion diagnosis. However, standard DCE-MRI-based diagnosis has low specificity, leading to unnecessary biopsies and other invasive procedures. A treatment response assessment map (TRAM) involves subtracting the T1-weighted DCE-MRI approximately five minutes after the injection of the contrast agent from a delayed-phase T1-MRI. TRAM characterizes the spatial distribution of contrast accumulation and clearance, potentially aiding in differentiating between benign and malignant lesions. Meanwhile, deep learning-based modeling has shown promising results in many medical imaging diagnostic tasks. In this project, we developed a deep learning model dedicated to breast lesion classification based on TRAM. We used a 3D convolutional residual network (ResNet18) to learn image representations from TRAM. The ResNet18-extracted features were then fed to a fully connected classifier for lesion classification. The TRAM-based model was compared with a model trained on standard multi-phase DCE-MRI. The model trained on TRAM achieved a higher area under the receiver operating characteristic curve (AUROC) (0.870 vs. 0.835), higher sensitivity (0.848 vs. 0.818), and higher specificity (0.823 vs. 0.759) than the model trained on standard DCE-MRI. The presented TRAM-based analyses may be able to aid in the clinical decision-making process during diagnosis and treatment.