Somatic Mutations Drive Distinct Imaging Phenotypes in Lung Cancer.

ABSTRACT: Tumors are characterized by somatic mutations that drive biological processes ultimately reflected in tumor phenotype. With regard to radiographic phenotypes, generally unconnected through present understanding to the presence of specific mutations, artificial intelligence methods can automatically quantify phenotypic characters by using predefined, engineered algorithms or automatic deep-learning methods, a process also known as radiomics. Here we demonstrate how imaging phenotypes can be connected to somatic mutations through an integrated analysis of independent datasets of 763 lung adenocarcinoma patients with somatic mutation testing and engineered CT image analytics. We developed radiomic signatures capable of distinguishing between tumor genotypes in a discovery cohort (n = 3

SUBMITTER: Rios Velazquez E 

PROVIDER: S-EPMC5528160 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature