One of the main causes of breast cancer related death is its recurrence. In this study, we investigate the association of gene expression and pathological image features to understand breast cancer recurrence. A total of 172 breast cancer patient data was downloaded from the TCGA-BRCA database. The dataset contained diagnostic whole slide images and RNA-seq data of 80 recurrent and 92 disease-free breast cancer patients. We performed genomic analysis on RNA-seq data to obtain the hub genes related to recurrent breast cancer. We extracted relevant pathomic features from histopathology images. The discriminative ability of the hub genes and pathomic features were evaluated using machine learning classifiers. We used Spearman rank correlation analysis to find statistically significant association between gene expression and pathomic features. We identified that, genes which are related to breast cancer progression is significantly associated (adjusted p-value<0.05) with several pathomic features.Clinical Relevance- Histopathology is the gold standard for cancer detection. It provides us with cellular level information. A strong association between a pathomic feature and a gene expression will help clinicians understand the cellular and molecular mechanism of cancer for better prognosis.