Radiogenomics and Histomics in Glioblastoma: The Promise of Linking Image-Derived Phenotype with Genomic Information

Abstract

Intra-tumor heterogeneity is the fundamental challenge in finding a cure for late-stage cancers. Physical biopsies do not sufficiently cover the diversity of molecular phenotypes within the tumor. Treatments are only effective on a subset of vulnerable tumor cells due to the prevalence of tumor stem-like cells. GBM tumors exemplify these general properties of late-stage cancers, with heterogeneous molecular profiles, histology, and radiology. Radiomics aims to characterize disease phenotypes from radiology scans in order to provide an alternative view of tumor heterogeneity, enabling models built from retrospective analysis of radiology scan data, and their integration with clinical data and molecular profiles. Computational histology (histomics) follows a workflow analogous to that of radiomics, with pre-processing, segmentation, feature extraction and analytics. The goal of histomics is to compute cellular morphometry and heterogeneity features from histology datasets. Genomic traits can potentially be inferred from histologic features by analysis of large, linked pathology-genomic data sets. There is also an active investigation of computer vision and machine learning applications to classify gliomas using radiology and histology images. The potential of radiomics, radiogenomics and histomics studies is to advance personalized cancer treatment by enabling interpretation of biological mechanisms underlying imaging phenotypes. These efforts aim to make personalized therapies more accessible. Results from preliminary imaging could direct administration of precision assays to guide treatment, measure treatment response and identify targetable genetic alterations from image-derived phenotype data, across biological scale. Radiomics and histomics promises to revolutionize the practice of personalized medicine, by providing an important complement to molecular strategies.