Our results outperformed theirs (0.69 vs. 0.649, 0.658 vs. 0.622, and 0.701 vs. 0.500 for ER, PR, and HER2), which could be due to various reasons, including (i) the deployment of deep learning, leading to a lower level of uncertainty in region of interest selection, segmentation, feature extraction, etc., and (ii) analyzing the whole image instead of exclusively selecting the tumor, including the peripheral regions and neighboring tissues, which could result in improvements in prediction. Here, ERBB2 is linked to neoplasm.