{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Ren M"],"funding":["Natural Science Foundation of Beijing Municipality","Pazhou Lab","National Natural Science Foundation of China","NCI NIH HHS","National Institutes of Health","Yale Cancer Center Pilot Award","Higher Education Discipline Innovation Project","National Science Foundation"],"pagination":["1579-1591"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8995088"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["78(4)"],"pubmed_abstract":["In cancer research, supervised heterogeneity analysis has important implications. Such analysis has been traditionally based on clinical/demographic/molecular variables. Recently, histopathological imaging features, which are generated as a byproduct of biopsy, have been shown as effective for modeling cancer outcomes, and a handful of supervised heterogeneity analysis has been conducted based on such features. There are two types of histopathological imaging features, which are extracted based on specific biological knowledge and using automated imaging processing software, respectively. Using both types of histopathological imaging features, our goal is to conduct the first supervised cancer heterogeneity analysis that satisfies a hierarchical structure. That is, the first type of imagin"],"journal":["Biometrics"],"pubmed_title":["Hierarchical cancer heterogeneity analysis based on histopathological imaging features."],"pmcid":["PMC8995088"],"funding_grant_id":["1916251","510320","12171454","CA204120","11971404","CA241699","Basic Scientific Project 71988101","Z190004","R03 CA241699","R01 CA204120","12026604","71988101","B13028","P50 CA196530","CA196530"],"pubmed_authors":["Ma S","Huang J","Zhong T","Ren M","Zhang S","Zhang Q"],"additional_accession":[]},"is_claimable":false,"name":"Hierarchical cancer heterogeneity analysis based on histopathological imaging features.","description":"In cancer research, supervised heterogeneity analysis has important implications. Such analysis has been traditionally based on clinical/demographic/molecular variables. Recently, histopathological imaging features, which are generated as a byproduct of biopsy, have been shown as effective for modeling cancer outcomes, and a handful of supervised heterogeneity analysis has been conducted based on such features. There are two types of histopathological imaging features, which are extracted based on specific biological knowledge and using automated imaging processing software, respectively. Using both types of histopathological imaging features, our goal is to conduct the first supervised cancer heterogeneity analysis that satisfies a hierarchical structure. That is, the first type of imagin","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Dec","modification":"2025-04-21T23:16:23.613Z","creation":"2025-04-05T19:02:54.451Z"},"accession":"S-EPMC8995088","cross_references":{"pubmed":["34390584"],"doi":["10.1111/biom.13544"]}}