Radiomics Combined with Transcriptomics Improves Prediction of Breast Cancer Recurrence, Molecular Subtype and Grade

Background/Objectives: Breast cancer (BrCA) is among the deadliest cancers for women in the world. The disease has four distinct molecular subtypes which can be determined by gene expression profiling. Understanding these subtypes has enabled the development of targeted therapeutics. Additionally, following initial successful treatment, some patients experience disease recurrence events. Methods: In this study, we used radiomics coupled with machine learning techniques to predict molecular subtypes and disease recurrence events from a dataset of MRI features deriving from a single-institutional, retrospective collection of 922 biopsy-confirmed invasive BrCA patients. The feature-rich and comprehensive dataset consists of radiomic as well as demographic, clinical, and molecular subtype information. We focused our analyses on Black and White patients who were 50 years or younger at diagnosis (n = 346) to identify racial disparities that exist between molecular subtypes and disease recurrence events. Random Forest and AdaBoostM1 were applied to over 500 radiomics features. Results: Radiomics alone or combined with gene expression data can accurately predict molecular subtype and disease recurrence events for both racial groups. In total, we found over 40 radiomics features that have significant associations with race. The radiomic features that are most predictive in the Breast and Fibroglandular Tissue Volume imaging category for Black patients was breast volume (Breast_Vol) and for White patients was post contrast tissue volume (TissueVol_PostCon). Conclusions: These results suggest that radiomics can be used to predict differences in BrCA recurrence and molecular subtype between racial groups and can have an impact on clinical outcomes.