Predicting patient outcomes related to cancer

A non-transitory computer-readable medium storing instructions that, when executed on one or more processors, cause the one or more processors to:

• obtain a histological sample of a cancer tumor of a patient;
• determine a feature set for the histological sample by applying a deep learning module trained on a population of histological samples of cancer tumors of the same type as the obtained histological sample of the cancer tumor;
• and
• generate an outcome set for the patient by applying a second model to the determined feature set.

A system for predicting outcomes related to a cancer, the system comprising:

• at least one server communicatively coupled to a user device by a network, wherein the at least one server further comprises a non-transitory memory storing computer-readable instructions and at least one processor;
• the execution of the computer-readable instructions causing the at least one server to:
• train a deep learning module on a population of histological samples of cancer tumors, wherein the deep learning module comprises a U-net model;
• train a second model on feature set data and outcomes data, wherein the second model comprises a multivariate model;
• obtain a histological sample of a cancer tumor of a patient, wherein the cancer tumor is of the same type as the population of histological samples of cancer tumors;
• determine a feature set for the histological sample by applying the trained deep learning module;
• and
• generate an outcome set for the patient by applying the trained multivariate model to the determined feature set.