Predicting molecular subtypes of breast cancer using pathological images by deep convolutional neural network from public dataset

Abstract

Breast cancer is a heterogeneously complex disease. A number of molecular subtypes with distinct biological features lead to different treatment responses and clinical outcomes. Traditionally, breast cancer is classified into subtypes based on gene expression profiles; these subtypes include luminal A, luminal B, basal like, HER2-enriched, and normal-like breast cancer. This molecular taxonomy, however, could only be appraised through transcriptome analyses. Our study applies deep convolutional neural networks and transfer learning from three pre-trained models, namely ResNet50, InceptionV3 and VGG16, for classifying molecular subtypes of breast cancer using TCGA-BRCA dataset. We used 20 whole slide pathological images for each breast cancer subtype. The results showed that our scale training reached about 78% of accuracy for validation. This outcomes suggested that classification of molecular subtypes of breast cancer by pathological images are feasible and could provide reliable results