Generally speaking, there are two different ways to use deep learning to derive prognostic information from histologic slides. (A) Both methods begin in a similar fashion, with digitization of stained tumor samples from a patient to create WSIs. The tissue in each WSI is then divided into smaller image patches. (B) The direct approach uses a CNN or similar deep-learning model that has been trained using tumor patches as input and patient outcomes as labels. This process permits the model to directly predict patient outcomes but is not easily explainable using current methods. (C) The indirect method is illustrated with a simplified representation of the GAN-based method used by Koyuncu et al., but other approaches might use fully convolutional networks or other types of CNNs to accomplish the same task. Two generators (G_MN and G_EP) translate the patches into segmentation masks, and these masks are combined to identify tumor nuclei (black) and multinucleated tumor nuclei (red). The MuNI is calculated for all tumor patches and serves as an intermediate value that can then be used, along with other clinical data, for prognostication.