Abstract Early stage estrogen receptor positive (ER+) breast cancer (BCa) treatment is based on the presumed aggressiveness and likelihood of cancer recurrence. Oncotype DX (ODX) and other gene expression tests have allowed for distinguishing the more aggressive ER+ BCa requiring adjuvant chemotherapy from the less aggressive cancers benefiting from hormonal therapy alone. However these tests are expensive, tissue destructive and require specialized facilities. Interestingly BCa grade has been shown to be correlated with the ODX risk score. Unfortunately Bloom-Richardson (BR) grade determined by pathologists can be variable. A constituent category in BR grading is tubule formation. This study aims to develop a deep learning classifier to automatically identify tubule nuclei from whole slide images (WSI) of ER+ BCa, the hypothesis being that the ratio of tubule nuclei to overall number of nuclei (a tubule formation indicator - TFI) correlates with the corresponding ODX risk categories. This correlation was assessed in 7513 fields extracted from 174 WSI. The results suggests that low ODX/BR cases have a larger TFI than high ODX/BR cases ( p < 0.01). The low ODX/BR cases also presented a larger TFI than that obtained for the rest of cases ( p < 0.05). Finally, the high ODX/BR cases have a significantly smaller TFI than that obtained for the rest of cases ( p < 0.01).