# US 2022/0044401 A1 · Computational systems pathology spatial analysis platform for in situ or in vitro multi-parameter cellular and subcellular imaging data > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2022/0044401 A1 - Document ID: US20220044401A1 - Status: Published application - Kind: A1 - Application number: 17/413,971 - Filed: 2019-12-16 - Published: 2022-02-10 - Earliest claimed: 2018-12-19 - Assignees: University of Pittsburgh—Of the Commonwealth System of Higher Education - Applicants: University of Pittsburgh-of the Commonwealth System of Higher Education - Inventors: Srinivas C. Chennubhotla; Filippo Pullara; Douglass L. Taylor - Web page: https://patentgrep.com/patent/US20220044401A1 ## CPC - G06V 20/698: {Matching; Classification} - A61B 5/00: Measuring for diagnostic purposes (radiation diagnosis A61B6/00; diagnosis by ultrasonic, sonic or infrasonic waves A61B8/00); Identification of persons - G01N 33/48: Biological material, e.g. blood, urine (G01N33/02, G01N33/26, G01N33/44, G01N33/46 take precedence); Haemocytometers (counting blood corpuscules distributed over a surface by scanning the surface G06M11/02) - G01N 33/575: for cancer - G06F 18/22: Matching criteria, e.g. proximity measures - G06T 7/0012: {Biomedical image inspection} - G06V 10/426: Graphical representations - G06V 10/761: {Proximity, similarity or dissimilarity measures} - G06T 2207/30024: Cell structures in vitro; Tissue sections in vitro - G06T 2207/30096: Tumor; Lesion - G06V 2201/03: Recognition of patterns in medical or anatomical images - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ## 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. ## Claims ### Claim 1 A method of analyzing a progression of a disease from multi-parameter cellular and subcellular imaging data obtained from a plurality of tissue samples from a plurality of patients or a number of multicellular in vitro models, comprising: generating a global quantification of a spatial heterogeneity among cells of certain varying predetermined phenotypes in the multi-parameter cellular and subcellular imaging data; identifying a plurality of microdomains for a plurality of tissue sections from the multi-parameter cellular and subcellular imaging data based on the global quantification, each microdomain being associated with a respective one of the tissue sections; and constructing a weighted graph for the multi-parameter cellular and subcellular imaging data, the weighted graph being indicative of a progression of the predetermined phenotypes and 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, such that the weighted graph may be analyzed to understand the progression of the disease. ### Claim 2 (depends on 1) The method according to claim 1, further comprising for each pair of microdomains in the plurality of microdomains, determining a weighted similarity between the microdomains of the pair, wherein the edge between each pair of microdomains in the weighted graph is the weighted similarity determined for the pair of the microdomains. ### Claim 3 (depends on 2) The method according to claim 2, further comprising determining a local quantification of a spatial heterogeneity among cells for each of the identified microdomains, wherein the determining of the weighted similarity between the microdomains of each pair is based on the local quantification of each microdomains of the pair. ### Claim 4 (depends on 1) The method according to claim 1, wherein the global quantification is a spatial map for the multi-parameter cellular and subcellular imaging data. ### Claim 5 (depends on 4) The method according to claim 4, wherein the spatial map is a pointwise mutual information (PMI) map. ### Claim 6 (canceled) ### Claim 7 (depends on 1) The method according to claim 1, wherein the predetermined phenotypes comprise a predetermined set of dominant biomarker intensity patterns. ### Claim 8 (depends on 1) The method according to claim 1, wherein a number of the identified microdomains are associated with an outcome specific variable that is in the form of time to recurrence or that is indicative of disease progression. ### Claim at position 9 9-10. (canceled) ### Claim 11 (depends on 8) The method according to claim 8, wherein the tissue samples are tumor samples and wherein the outcome specific variable is indicative of metastatic potential. ### Claim 12 (depends on 1) A non-transitory computer readable medium storing one or more programs, including instructions, which when executed by a computer, causes the computer to perform the method of claim 1. ### Claim 13 A computerized system for analyzing a progression of a disease from multi-parameter cellular and subcellular imaging data obtained from a plurality of tissue samples from a plurality of patients or a number of multicellular in vitro models, comprising: a processing apparatus, wherein the processing apparatus includes: a spatial heterogeneity quantification component configured for generating a global quantification of a spatial heterogeneity among cells of certain varying predetermined phenotypes in the multi-parameter cellular and subcellular imaging data; a microdomain identification component configured for identifying a plurality of microdomains for a plurality of tissue sections from the multi-parameter cellular and subcellular imaging data based on the global quantification, each microdomain being associated with a respective one of the tissue sections; and a weighted graph component configured for constructing a weighted graph for the multi-parameter cellular and subcellular imaging data, the weighted graph being indicative of a progression of the predetermined phenotypes and 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, such that the weighted graph may be analyzed to understand the progression of the disease. ### Claim 14 (depends on 13) The system according to claim 13, wherein the weighted graph component is further configured for determining, for each pair of microdomains in the plurality of microdomains, a weighted similarity between the microdomains of the pair, wherein the edge between each pair of microdomains in the weighted graph is the weighted similarity determined for the pair of the microdomains. ### Claim 15 (depends on 14) The system according to claim 14, wherein the weighted graph component is further configured for determining a local quantification of a spatial heterogeneity among cells for each of the identified microdomains, wherein the determining of the weighted similarity between the microdomains of each pair is based on the local quantification of each microdomains of the pair. ### Claim 16 (depends on 13) The system according to claim 13, wherein the global quantification is a spatial map for the multi-parameter cellular and subcellular imaging data. ### Claim 17 (depends on 16) The system according to claim 16, wherein the spatial map is a pointwise mutual information (PMI) map. ### Claim 18 (canceled) ### Claim 19 (depends on 13) The system according to claim 13, wherein the predetermined phenotypes comprise a predetermined set of dominant biomarker intensity patterns. ### Claim 20 (depends on 13) The system according to claim 13, wherein a number of the identified microdomains are associated with an outcome specific variable that is in the form of time to recurrence or that is indicative of disease progression. ### Claim at position 20 21-22. (canceled) ### Claim 23 (depends on 20) The system according to claim 20, wherein the tissue samples are tumor samples and wherein the outcome specific variable is indicative of metastatic potential. ### Claim 24 (depends on 13) The system according to claim 13, further comprising a component configured for guiding therapeutic strategy based on identifying cell types and states of activation and evolution based on the weighted graph. ### Claim at position 23 25-57. (canceled)