Radiogenomic Analysis of Oncological Data: A Technical Survey

• Published in: International journal of molecular sciences Vol. 18; no. 4; p. 805
• Main Authors: Incoronato, Mariarosaria, Aiello, Marco, Infante, Teresa, Cavaliere, Carlo, Grimaldi, Anna, Mirabelli, Peppino, Monti, Serena, Salvatore, Marco
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 12.04.2017; MDPI
• Subjects: Algorithms; Cancer; Computational Biology - methods; Data Mining - methods; Diagnostic Imaging; Gene expression; Genomics - methods; Genotype & phenotype; High-Throughput Nucleotide Sequencing; Humans; Image Interpretation, Computer-Assisted - methods; Image Processing, Computer-Assisted - methods; Magnetic Resonance Imaging; Medical diagnosis; Neoplasms - diagnostic imaging; Neoplasms - genetics; Positron-Emission Tomography; Radiomics; Review; Tomography, X-Ray Computed; microarray; MR; data mining; cancer; correlation matrix; radiogenomics; molecular imaging; NGS technologies; texture analysis
• ISSN: 1422-0067; 1661-6596; 1422-0067
• DOI: 10.3390/ijms18040805

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.