Radiogenomic Analysis of Oncological Data: A Technical Survey

Abstract

In the last few years, biomedical research has been boosted by the technological development of analytical instrumentation generating a large volume of data. Such information has increased in complexity from basic (i.e., blood samples) to extensive sets encompassing many aspects of a subject phenotype, and now rapidly extending into genetic and, more recently, radiomic information. Radiogenomics integrates both aspects, investigating the relationship between imaging features and gene expression. From a methodological point of view, radiogenomics takes advantage of non-conventional data analysis techniques that reveal meaningful information for decision-support in cancer diagnosis and treatment. This survey is aimed to review the state-of-the-art techniques employed in radiomics and genomics with special focus on analysis methods based on molecular and multimodal probes. The impact of single and combined techniques will be discussed in light of their suitability in correlation and predictive studies of specific oncologic diseases.

Keywords: MR; NGS technologies; cancer; correlation matrix; data mining; microarray; molecular imaging; radiogenomics; texture analysis.

Figure 1

Schematic radiomic workflow. The process starts with the acquisition of common diagnostic images (Computed Tomography (CT), Positron Emission Tomography (PET), Magnetic Resonance (MR) see Section 2.1.1) and the identification of the lesions under investigation. The target regions are segmented (for the sake of simplicity, the process is shown for a single regions of interest (ROI) in the PET image only, as highlighted by the red circle) with the chosen approach (see Section 2.1.2). Finally, for each segmented region up to some hundreds of features, which are typically divided in shape-based, first-, second- and higher-order statistical features, can be computed (see Section 2.1.3).

Figure 2

Schematic workflow of the most used genomic approaches that could be applied in the field of radiogenomics. Methylated DNA immune precipitation sequencing (MeDIP Seq) and Chip sequencing (Chip Seq) provide information about DNA methylation, DNA/protein interactions, and histone modification. Microarray is a technique used to measure the expression levels of large numbers of genes. RNA sequencing (RNA Seq) allows performing an in-depth analysis of the transcriptome with the identification of novel transcripts, alternative splicing allele specific expression, gene fusions, and genetic variations. Moreover, RNA Seq can give information about the transcriptome dynamics such as RNA editing, small insertions/deletions, exon connections, non-coding RNAs, and small RNAs.

Figure 3

The figure shows a general workflow for radiogenomic study. The first step includes data acquisition (clinical information, imaging and genomic data). Subsequently, data are normalized and underwent an integrative analysis to characterize each radiomic feature and identify specific underlying molecular functions. The overall flow, here schematically depicted, could represent a novel integrated approach for cancer diagnosis and prognosis.