Abstract: Disclosed herein are methods for determining a subject level risk of metastatic cancer involving the training and/or deployment of models to determine 1) a lymph node level risk of individual lymph node involvement and/or 2) a subject level risk of lymph node involvement. Thus, the methods can identify patients who are high or low risk for having nodal disease and optionally enable the guided intervention of cancer patients, for example, via treatment.

Abstract: Systems and methods for creating therapeutic response predictions based on a synthetic tumor model generated based on micro-scale data associated with one or more parameters representative of one or more biological characteristics of tumors, where synthetic CT images constructed via back projection of the synthetic tumor model and distribution/response of one or more therapeutic therapies predicted for the synthetic tumor model are used to train an unsupervised learning model for determining a personalized treatment plan for a patient's tumor.

Abstract: Systems and methods for creating therapeutic response predictions based on a synthetic tumor model generated based on micro-scale data associated with one or more parameters representative of one or more biological characteristics of tumors, where synthetic CT images constructed via back projection of the synthetic tumor model and distribution/response of one or more therapeutic therapies predicted for the synthetic tumor model are used to train an unsupervised learning model for determining a personalized treatment plan for a patient's tumor.