Introduction to Radiomics | RDL Research Database

Abstract

Radiomics is a rapidly evolving field of research concerned with the extraction of quantitative metrics-the so-called radiomic features-within medical images. Radiomic features capture tissue and lesion characteristics such as heterogeneity and shape and may, alone or in combination with demographic, histologic, genomic, or proteomic data, be used for clinical problem solving. The goal of this continuing education article is to provide an introduction to the field, covering the basic radiomics workflow: feature calculation and selection, dimensionality reduction, and data processing. Potential clinical applications in nuclear medicine that include PET radiomics-based prediction of treatment response and survival will be discussed. Current limitations of radiomics, such as sensitivity to acquisition parameter variations, and common pitfalls will also be covered.

Keywords

RadiomicsWorkflowComputer scienceArtificial intelligenceField (mathematics)Dimensionality reductionFeature selectionMedical physicsMachine learningData miningMedicineMathematicsDatabase

MeSH Terms

HumansImage ProcessingComputer-AssistedNuclear Medicine

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Publication Info

Year: 2020
Type: review
Volume: 61
Issue: 4
Pages: 488-495
Citations: 1518
Access: Closed

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APA Style

                            
                                
                                    Marius E. Mayerhoefer, 
                                
                                    Andrzej Materka, 
                                
                                    Georg Langs
                                
                                et al.
                            
                            (2020). 
                            Introduction to Radiomics. 
                            Journal of Nuclear Medicine
                            , 61
                            (4)
                            , 488-495.
                            https://doi.org/10.2967/jnumed.118.222893
                        

Identifiers

DOI: 10.2967/jnumed.118.222893
PMID: 32060219
PMCID: PMC9374044

Data Quality

Data completeness: 86%