Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging

Abstract

Tumour heterogeneity in cancers has been observed at the histological and genetic levels, and increased levels of intra-tumour genetic heterogeneity have been reported to be associated with adverse clinical outcomes. This review provides an overview of radiomics, radiogenomics, and habitat imaging, and examines the use of these newly emergent fields in assessing tumour heterogeneity and its implications. It reviews the potential value of radiomics and radiogenomics in assisting in the diagnosis of cancer disease and determining cancer aggressiveness. This review discusses how radiogenomic analysis can be further used to guide treatment therapy for individual tumours by predicting drug response and potential therapy resistance and examines its role in developing radiomics as biomarkers of oncological outcomes. Lastly, it provides an overview of the obstacles in these emergent fields today including reproducibility, need for validation, imaging analysis standardisation, data sharing and clinical translatability and offers potential solutions to these challenges towards the realisation of precision oncology.

Personalizing Medicine Through Hybrid Imaging and Medical Big Data Analysis

“…In this case two main groups can be identified, such as shallow learning methods building on engineered (or handcrafted) features [8,53] and deep learning (DL) methods building on automatically comprehended, multi-layer representation of the data [54]. Several related works utilize machine learning built on engineered features [55][56][57][58][59]. These approaches typically employ feature selection [60] in the form of feature redundancy reduction [61] or feature ranking [8].…”

Background, current role, and potential applications of radiogenomics

“…Another future direction is the incorporation of the whole spectrum of “omics” technologies, ie, transcriptomics, proteomics, and metabolomics in radio‐“omics” research. It can be expected that the integration of multiple “omics” technologies—genomics, transcriptomics, proteomics, metabolomics—with advanced imaging techniques will further open up new avenues in the diagnosis and treatment of diseases, with initial data being promising …”

“…Whereas radiogenomics investigates relationships between imaging features and genomics, radiomics refers to the methodology behind the conversion of digital medical images with various data of interest including patient characteristics, outcomes, and 'omics data for an improved decision support . For a detailed review of the process of radiomics, ie, image acquisition, volume of interest identification, segmentation, feature extraction and quantification, database building, classifier modeling, data sharing, and its challenges, refer to recent review articles by Gillies et al, Sala et al, and Lambin et al…”

“…Parameters derived from advanced image processing and analysis can reflect the underlying phenotypic and genotypic characteristics of the tissue . Radiogenomics is thus designed to facilitate a deeper understanding of tumor biology and capture the intrinsic tumor heterogeneity with relevant implications for patient care . Ultimately, the goal is to develop imaging biomarkers for outcome that incorporate both phenotypic and genotypic metrics.…”

Radiomics in Head and Neck Cancer: Extracting Valuable Information from Data beyond Recognition

“…5 Based on the available data sets, databases can be built and shared. Once these steps are performed, informatics analyses can be performed [1,6,7] .…”