| Literature DB >> 32060219 |
Marius E Mayerhoefer1,2, Andrzej Materka3, Georg Langs2, Ida Häggström4, Piotr Szczypiński3, Peter Gibbs5, Gary Cook6,7.
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.Entities:
Keywords: PET; artificial intelligence; machine learning; radiomics; single-photon emission tomography
Mesh:
Year: 2020 PMID: 32060219 PMCID: PMC9374044 DOI: 10.2967/jnumed.118.222893
Source DB: PubMed Journal: J Nucl Med ISSN: 0161-5505 Impact factor: 11.082