Using ensemble learning and genetic algorithm on magnetic resonance imaging radiomics to classify molecular subtypes of breast cancer

Abstract

Breast cancer (BRCA) is one of the most frequent malignant tumors with the highest incidence of cancer and the second most common oncologic cause of death in women. BRCA can be classified into different molecular subtypes, such as basal‐like, represented by triple‐negative BRCA (estrogen receptor [ER] negative, progesterone receptor [PR] negative, and human epidermal growth factor receptor 2 [HER‐2] negative). This study aims to determine whether radiomics features extracted from magnetic resonance imaging (MRI) could be used to distinguish various BRCA molecular subtypes. This study retrospectively collected a dataset of 922 BRCA patients with MRIs and experimental genomic profiles. A genetic algorithm is then employed to select the optimal MRI features for each subproblem. Subsequently, stacking ensemble learning is implemented to learn these features and generate the prediction outcomes. Our model showed a significant performance of 0.700, 0.732, and 0.642 (area under the curve; AUC) in predicting ER, PR, and HER‐2 statuses. For multiclassification of Luminal A, Luminal B, HER2, and TNBC, the AUCs reached 0.672, 0.624, 0.639, and 0.669, respectively. Our model is superior in most subtypes compared to the state‐of‐the‐art predictors on the same dataset. In conclusion, genetic algorithm and ensemble learning can be suitable for BRCA subtype classification with high performance.