Combination of DCE-MRI and NME-DWI via Deep Neural Network for Predicting Breast Cancer Molecular Subtypes

Motivation: Breast cancer exhibits diverse molecular subtypes with varying responses to treatment. Goal(s): This study aims to explore the potential enhancement of breast cancer molecular subtype prediction by combining DCE and NME-DWI through DNNs. Approach: 475 patients with 480 breast cancers were recruited and classified into molecular subtypes using IHC staining and FISH examination. Manual lesion segmentation and analysis using IVIM, diffusion kurtosis, and stretched exponential models. DNN models for molecular subtype prediction, based on single DCE-MRI or NME-DWI datasets were constructed and compared. Results: DNN classification accuracy significantly varied among the three imaging datasets (P < 0.05), with MP-MRI outperforming DCE-MRI and NME-DWI. Impact: This study's integration of DCE-MRI and NME-DWI through DNNs for breast cancer subtype prediction advances non-invasive genotyping, potentially transforming personalized treatment strategies and improving outcomes in breast cancer patients.