University of Pittsburgh

Douglass L Taylor

Srinivas C Chennubhotla

Shikhar Uttam Fnu

Filippo Pullara

Andrew Michael Stern

Michael J Becich

Albert H Gough

A method of predicting cancer recurrence risk for an individual includes receiving patient spatial multi-parameter cellular and sub-cellular imaging data for a tumor of the individual, and analyzing the patient spatial multi-parameter cellular and sub-cellular imaging data using a prognostic model for predicting cancer recurrence risk to determine a predicted cancer recurrence risk for the individual, wherein the joint prognostic model is based on spatial correlation statistics among features derived for a plurality of intra-tumor spatial domains from spatial multi-parameter cellular and sub-cellular imaging data obtained from a plurality of cancer patients.

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

A computational systems pathology spatial analysis platform includes: (i) a spatial heterogeneity quantification component configured for generating a global quantification of spatial heterogeneity among cells of varying phenotypes in multi-parameter cellular and subcellular imaging data; (ii) a microdomain identification component configured for identifying a plurality of microdomains for tissue samples based on the global quantification, each microdomain being associated with a tissue sample; and (iii) a weighted graph component configured for constructing a weighted graph for the multi-parameter cellular and subcellular imaging data, the weighted graph having a plurality of nodes and a plurality of edges each being located between a pair of the nodes, wherein in the weighted graph each node is a particular one of the microdomains and the edge between each pair of microdomains in the weighted graph is indicative of a degree of similarity between the pair of the microdomains.

Computational systems pathology spatial analysis platform for in situ or in vitro multi-parameter cellular and subcellular imaging data

A method of generating a plurality of spatially co-registered data elements, each spatially co-registered data element being associated with and generated from a pair of co-registered tissue sections obtained from adjacent positions of a core taken from a tissue sample and including an image data section and a genomic data section. The method includes, for each pair of co-registered tissue sections: (i) obtaining and storing as part of a data element a plurality of multi to hyperplexed images from the imaging data section of the co-registered tissue section, (ii) generating and storing as part of the data element image data from the plurality of multi to hyperplexed images, and (iii) generating and storing as part of the data element genomic data from the genomic data section of the associated co-registered tissue section.

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Pittsburgh, PA, United States of America

Douglass L Taylor