Publications by authors named "Ao-Yu Liu"
Background: To explore whether the combination of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) and nonmono-exponential (NME) model-based diffusion-weighted imaging (DWI) via deep neural network (DNN) can improve the prediction of breast cancer molecular subtypes compared to either imaging technique used alone.
Patients And Methods: This prospective study examined 480 breast cancers in 475 patients undergoing DCE-MRI and NME-DWI at 3.0 T.
Article Synopsis
- The study compares the effectiveness of two imaging techniques—Dynamic Contrast-Enhanced MRI (DCE-MRI) and Non-Mono-Exponential Model-Based Diffusion-Weighted Imaging (NME-DWI)—in predicting breast cancer biomarkers and molecular subtypes, using a group of 477 female patients with breast cancer.
- *The research involves extracting high-throughput features from tumor images and applying various machine learning models to classify the cancer types, assessing the performance through statistical tests like AUC.
- *Results indicate that there were no significant performance differences between DCE-MRI and NME-DWI for most classification tasks, suggesting both methods are similarly effective.