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Radiogenomic Analysis of Oncological Data: A Technical Survey. | LitMetric

AI Article Synopsis

  • - Biomedical research has advanced with new analytical tools, leading to complex data sets that integrate basic health information, genetics, and imaging features known as radiomic data.
  • - Radiogenomics explores the connection between imaging characteristics and gene expression, utilizing innovative data analysis methods for better cancer diagnosis and treatment decision-making.
  • - The review focuses on current techniques in radiomics and genomics, particularly how they can be combined for improved correlation and predictive studies in various cancers.

Article 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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5412389PMC
http://dx.doi.org/10.3390/ijms18040805DOI Listing

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