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A typical workflow for a radiomics study includes cohort selection, radiomic feature extraction, feature and predictive model selection, and model training and validation. While there has been increasing attention given to radiomic feature extraction, standardization, and reproducibility, currently, there is a lack of rigorous evaluation of feature selection methods and predictive models. Herein, we review the published radiomics investigations in CT lung cancer and provide an overview of the commonly used radiomic feature selection methods and predictive models. We also compare limitations of various methods in clinical applications and present sources of uncertainty associated with those methods. This review is expected to help raise awareness of the impact of radiomic feature and model selection methods on the integrity of radiomics studies.<\/jats:p>","DOI":"10.1002\/acm2.13869","type":"journal-article","created":{"date-parts":[[2022,12,17]],"date-time":"2022-12-17T07:49:57Z","timestamp":1671263397000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":76,"title":["Feature selection methods and predictive models in CT lung cancer radiomics"],"prefix":"10.1002","volume":"24","author":[{"given":"Gary","family":"Ge","sequence":"first","affiliation":[{"name":"Department of Radiology University of Kentucky  Lexington Kentucky USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Radiology University of Kentucky  Lexington Kentucky 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