Predicting cancer recurrence from spatial multi-parameter cellular and subcellular imaging data

A method of creating a system for predicting cancer recurrence risk, comprising:

• receiving spatial multi-parameter cellular and sub-cellular imaging data for a plurality of cancer patients;
• spatially dissecting the spatial multi-parameter cellular and sub-cellular imaging data into a plurality of intra-tumor spatial domains;
• generating a base feature set for each of the intra-tumor spatial domains;
• for each of the intra-tumor spatial domains, selecting a subset of features from the base feature set for the intra-tumor spatial domain;
• for each of the intra-tumor spatial domains, developing and training a spatial domain specific multivariate prognostic model for predicting cancer recurrence risk using the subset of features of the intra-tumor spatial domain;
• combining the spatial domain specific multivariate prognostic model of each of the intra-tumor spatial domains into a joint prognostic model for predicting cancer recurrence risk.