Repeatability and Reproducibility of Radiomic Features: A Systematic Review

Abstract

PURPOSE: An ever-growing number of predictive models used to inform clinical decision making have included quantitative, computer-extracted imaging biomarkers, or "radiomic features." Broadly generalizable validity of radiomics-assisted models may be impeded by concerns about reproducibility. We offer a qualitative synthesis of 41 studies that specifically investigated the repeatability and reproducibility of radiomic features, derived from a systematic review of published peer-reviewed literature. METHODS AND MATERIALS: The PubMed electronic database was searched using combinations of the broad Haynes and Ingui filters along with a set of text words specific to cancer, radiomics (including texture analyses), reproducibility, and repeatability. This review has been reported in compliance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. From each full-text article, information was extracted regarding cancer type, class of radiomic feature examined, reporting quality of key processing steps, and statistical metric used to segregate stable features. RESULTS: Among 624 unique records, 41 full-text articles were subjected to review. The studies primarily addressed non-small cell lung cancer and oropharyngeal cancer. Only 7 studies addressed in detail every methodologic aspect related to image acquisition, preprocessing, and feature extraction. The repeatability and reproducibility of radiomic features are sensitive at various degrees to processing details such as image acquisition settings, image reconstruction algorithm, digital image preprocessing, and software used to extract radiomic features. First-order features were overall more reproducible than shape metrics and textural features. Entropy was consistently reported as one of the most stable first-order features. There was no emergent consensus regarding either shape metrics or textural features; however, coarseness and contrast appeared among the least reproducible. CONCLUSIONS: Investigations of feature repeatability and reproducibility are currently limited to a small number of cancer types. Reporting quality could be improved regarding details of feature extraction software, digital image manipulation (preprocessing), and the cutoff value used to distinguish stable features.