This study aimed to evaluate the concept of using high-resolution optical coherence tomography (OCT) imaging to rapidly assess surgical specimens and determine if cancer positive margins were left behind in the surgical bed. A mouse model of breast cancer was used in this study. Surgical specimens from 30 animals were investigated with OCT and automated interpretation of the OCT images was performed and tested against histopathology findings. Specimens from 10 animals were used to build a training set of OCT images, while the remaining 20 specimens were used for a validation set of images. The validation study showed that automated interpretation of OCT images can differentiate tissue types and detect cancer positive margins with at least 81% sensitivity and 89% specificity. The findings of this pilot study suggest that OCT imaging of surgical specimens and automated interpretation of OCT data may enable in the future real-time feedback to the surgeon about margin status in patients with breast cancer, and potentially with other types of cancers. Currently, such feedback is not provided and if positive margins are left behind, patients have to undergo another surgical procedure. Therefore, this approach can have a potentially high impact on breast surgery outcome.