Abstract: 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.

Abstract: 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.

Abstract: A computational pathology method includes receiving multi-parameter cellular and/or sub-cellular imaging data for an image of a tissue sample, and locating and segmenting a plurality of tissue components of the tissue sample in the multi- parameter cellular and sub-cellular imaging data to generate segmented multi¬ parameter cellular and sub-cellular imaging data.

Abstract: A method (and system) of segmenting one or more histological structures in a tissue image represented by multi-parameter cellular and sub-cellular imaging data includes receiving coarsest level image data for the tissue image, wherein the coarsest level image data corresponds to a coarsest level of a multiscale representation of first data corresponding to the multi-parameter cellular and sub-cellular imaging data. The method further includes breaking the coarsest level image data into a plurality of non-overlapping superpixels, assigning each superpixel a probability of belonging to the one or more histological structures using a number of pre-trained machine learning algorithms to create a probability map, extracting an estimate of a boundary for the: one or more histological structures by applying a contour algorithm to the probability map, and using the estimate of the boundary to generate a refined boundary for the one or more histological structures.

Abstract: A method of characterizing cellular phenotypes includes receiving multi-parameter cellular and sub-cellular imaging data for a number of tissue samples from a number of patients or a number of multicellular in vitro models, performing cellular segmentation on the multi-parameter cellular and sub-cellular imaging data to create segmented multi-parameter cellular and sub-cellular imaging data, and performing recursive decomposition on the segmented multi-parameter cellular and subcellular imaging data to identify a plurality of computational phenotypes. The recursive decomposition includes a plurality of levels of decomposition with each level of decomposition including soft/probabilistic clustering and spatial regularization, and each cell in the segmented multi-parameter cellular and subcellular imaging data is probabilistically assigned to one or more of the plurality of computational phenotypes.

Abstract: 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 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.

Abstract: 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.

Abstract: 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.

Abstract: In aspects, the subject innovation can comprise systems and methods capable of automatically labeling cell nuclei (e.g., epithelial nuclei) in tissue images containing multiple cell types. The enhancements to standard nuclei segmentation algorithms of the subject innovation can enable cell type specific analysis of nuclei, which has recently been shown to reveal novel disease biomarkers and improve diagnostic accuracy of computational disease classification models.

Abstract: In aspects, the subject innovation can comprise systems and methods capable of automatically labeling cell nuclei (e.g., epithelial nuclei) in tissue images containing multiple cell types. The enhancements to standard nuclei segmentation algorithms of the subject innovation can enable cell type specific analysis of nuclei, which has recently been shown to reveal novel disease biomarkers and improve diagnostic accuracy of computational disease classification models.