It is possible to use deep learning models to predict hormonal status and tumor proliferation from H&E-stained images. However, it is important to note that such predictions would likely be based on patterns or features in the images that are correlated with hormonal status or tumor proliferation, rather than on a direct measurement of these factors.

To predict hormonal status or tumor proliferation from H&E-stained images using deep learning, it would be necessary to first collect a large dataset of annotated images that include information about the hormonal status or tumor proliferation of the tissue being examined. The deep learning model could then be trained to recognize patterns or features in the images that are associated with specific hormonal status or tumor proliferation levels.

For example, certain patterns or features in the images might be correlated with higher levels of estrogen or progesterone, which are hormones that can affect the growth and development of breast tissue. Similarly, certain patterns or features might be correlated with higher levels of tumor proliferation, which is the rapid growth and division of cancer cells.

Once trained, the deep learning model could be used to make predictions about the hormonal status or tumor proliferation of new images by inputting the images into the model and using the output to identify patterns or features that are associated with specific hormonal status or tumor proliferation levels.

It’s important to note that predicting hormonal status or tumor proliferation from H&E-stained images is a complex task and may be affected by a variety of factors, including the quality and resolution of the images, the specific deep learning model being used, and the limitations of the approach. It is always important to carefully evaluate the performance of a deep learning model and consider the limitations of the approach when making predictions about biological processes or diseases.

Our team is working on a project to automatically identify breast tumor subtypes and hormonal status from H&E slides using deep learning technology.