Cancer genotypes prediction and associations analysis from imaging phenotypes: a survey on radiogenomics

Yao Wang, Yan Wang, Chunjie Guo, Xuping Xie, Sen Liang, Ruochi Zhang, Wei Pang, Lan Huang

Research output: Contribution to journalArticle

Abstract

In this paper, we present a survey on the progress of radiogenomics research, which predicts cancer genotypes from imaging phenotypes and investigates the associations between them. First, we present an overview of the popular technology modalities for obtaining diagnostic medical images. Second, we summarize recently used methodologies for radiogenomics analysis, including statistical analysis, radiomics and deep learning. And then, we give a survey on the recent research based on several types of cancers. Finally, we discuss these studies and propose possible future research directions. In conclusion, we have identified strong correlations between cancer genotypes and imaging phenotypes. In addition, with the rapid growth of medical data, deep learning models show great application potential for radiogenomics.

Original languageEnglish
Pages (from-to)1151-1164
Number of pages14
JournalBiomarkers in Medicine
Volume14
Issue number12
Early online date24 Sep 2020
DOIs
Publication statusPublished - Sep 2020

Keywords

  • cancer genotypes
  • deep learning
  • imaging phenotype
  • prediction and associations analysis
  • radiogenomics
  • radiomics

ASJC Scopus subject areas

  • Drug Discovery
  • Clinical Biochemistry
  • Biochemistry, medical

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