{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["He B"],"funding":["National Natural Science Foundation of China","NCI NIH HHS","National Institutes of Health","NIH HHS"],"pagination":["1397-1408"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9367644"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["77(4)"],"pubmed_abstract":["Heterogeneity is a hallmark of cancer. For various cancer outcomes/phenotypes, supervised heterogeneity analysis has been conducted, leading to a deeper understanding of disease biology and customized clinical decisions. In the literature, such analysis has been oftentimes based on demographic, clinical, and omics measurements. Recent studies have shown that high-dimensional histopathological imaging features contain valuable information on cancer outcomes. However, comparatively, heterogeneity analysis based on imaging features has been very limited. In this article, we conduct supervised cancer heterogeneity analysis using histopathological imaging features. The penalized fusion technique, which has notable advantages-such as greater flexibility-over the finite mixture modeling and other techniques, is adopted. A sparse penalization is further imposed to accommodate high dimensionality and select relevant imaging features. To improve computational feasibility and generate more reliable estimation, we employ model averaging. Computational and statistical properties of the proposed approach are carefully investigated. Simulation demonstrates its favorable performance. The analysis of The Cancer Genome Atlas (TCGA) data may provide a new way of defining/examining breast cancer heterogeneity."],"journal":["Biometrics"],"pubmed_title":["Histopathological imaging-based cancer heterogeneity analysis via penalized fusion with model averaging."],"pmcid":["PMC9367644"],"funding_grant_id":["R03 CA241699","R01 CA204120","CA204120","11971404","CA241699"],"pubmed_authors":["Liu Y","Ma S","Huang J","Zhong T","He B","Zhang Q"],"additional_accession":[]},"is_claimable":false,"name":"Histopathological imaging-based cancer heterogeneity analysis via penalized fusion with model averaging.","description":"Heterogeneity is a hallmark of cancer. For various cancer outcomes/phenotypes, supervised heterogeneity analysis has been conducted, leading to a deeper understanding of disease biology and customized clinical decisions. In the literature, such analysis has been oftentimes based on demographic, clinical, and omics measurements. Recent studies have shown that high-dimensional histopathological imaging features contain valuable information on cancer outcomes. However, comparatively, heterogeneity analysis based on imaging features has been very limited. In this article, we conduct supervised cancer heterogeneity analysis using histopathological imaging features. The penalized fusion technique, which has notable advantages-such as greater flexibility-over the finite mixture modeling and other techniques, is adopted. A sparse penalization is further imposed to accommodate high dimensionality and select relevant imaging features. To improve computational feasibility and generate more reliable estimation, we employ model averaging. Computational and statistical properties of the proposed approach are carefully investigated. Simulation demonstrates its favorable performance. The analysis of The Cancer Genome Atlas (TCGA) data may provide a new way of defining/examining breast cancer heterogeneity.","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Dec","modification":"2025-04-05T09:43:47.628Z","creation":"2025-04-05T09:43:47.628Z"},"accession":"S-EPMC9367644","cross_references":{"pubmed":["32822084"],"doi":["10.1111/biom.13357"]}}