title: In silico analysis of nuclei in glioblastoma using large-scale microscopy images improves prediction of treatment response.
abstract: In this paper, we present a complete and novel workflow for quantitative nuclear feature analysis of glioblastoma using high-throughput whole-slide microscopy image processing as it relates to treatment response and patient survival. With a complete suite of computer algorithms, large numbers of micro-anatomical structures, in this case nuclei, are analyzed and represented efficiently from whole-slide digitized images with numerical features. With regard to endpoints of treatment response, the computerized analysis presents a better discrimination than traditional neuropathologic review. As a result, this analysis method shows potential to facilitate a better understanding of disease progression and patients' response to therapy for glioblastoma.
authors.affiliation: Emory University, Center for Comprehensive Informatics, Atlanta, GA 30322, USA. jun.kong@emory.edu
authors.forename: Jun
authors.initials: J
authors.lastname: Kong
authors.forename: Lee
authors.initials: L
authors.lastname: Cooper
authors.forename: Carlos
authors.initials: C
authors.lastname: Moreno
authors.forename: Fusheng
authors.initials: F
authors.lastname: Wang
authors.forename: Tahsin
authors.initials: T
authors.lastname: Kurc
authors.forename: Joel
authors.initials: J
authors.lastname: Saltz
authors.forename: Daniel
authors.initials: D
authors.lastname: Brat
mesh_terms: D000970:Antineoplastic Agents / Q000627:therapeutic use; D001932:Brain Neoplasms / Q000188:drug therapy* / Q000473:pathology*; D002467:Cell Nucleus / Q000473:pathology*; D005909:Glioblastoma / Q000188:drug therapy* / Q000473:pathology*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008853:Microscopy / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016896:Treatment Outcome
chemical_list: D000970:Antineoplastic Agents