Quantitation of Oncologic Image Features for Radiomic Analyses in PET

Travis L Williams; Mithat Gonen; Rick Wray; Richard K G Do; Amber L Simpson

doi:10.1007/978-1-0716-3499-8_23

Quantitation of Oncologic Image Features for Radiomic Analyses in PET

Methods Mol Biol. 2024:2729:409-421. doi: 10.1007/978-1-0716-3499-8_23.

Authors

Travis L Williams¹, Mithat Gonen¹, Rick Wray², Richard K G Do², Amber L Simpson³

Affiliations

¹ Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
² Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
³ School of Computing and Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada. amber.simpson@queensu.ca.

PMID: 38006509
DOI: 10.1007/978-1-0716-3499-8_23

Abstract

Radiomics is an emerging and exciting field of study involving the extraction of many quantitative features from radiographic images. Positron emission tomography (PET) images are used in cancer diagnosis and staging. Utilizing radiomics on PET images can better quantify the spatial relationships between image voxels and generate more consistent and accurate results for diagnosis, prognosis, treatment, etc. This chapter gives the general steps a researcher would take to extract PET radiomic features from medical images and properly develop models to implement.

Keywords: Analysis; Imaging features; Machine learning; Oncology; PET; Radiomics.

MeSH terms

Image Processing, Computer-Assisted* / methods
Positron-Emission Tomography*