Uncertainty quantification in the radiogenomics modeling of EGFR amplification in glioblastoma.

ABSTRACT: Radiogenomics uses machine-learning (ML) to directly connect the morphologic and physiological appearance of tumors on clinical imaging with underlying genomic features. Despite extensive growth in the area of radiogenomics across many cancers, and its potential role in advancing clinical decision making, no published studies have directly addressed uncertainty in these model predictions. We developed a radiogenomics ML model to quantify uncertainty using transductive Gaussian Processes (GP) and a unique dataset of 95 image-localized biopsies with spatially matched MRI from 25 untreated Glioblastoma (GBM) patients. The model generated predictions for regional EGFR amplification status (a common and important target in GBM) to resolve the intratumoral genetic heterogeneity across each indiv

SUBMITTER: Hu LS 

PROVIDER: S-EPMC7886858 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature