{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/stable-prediction-with-radiomics-data","title":"Stable prediction with radiomics data","arxiv_id":"1903.11696","date":"2019-03-27","proceeding":null,"authors":["Carel F. W. Peeters","Caroline Übelhör","Steven W. Mes","Roland Martens","Thomas Koopman","Pim de Graaf","Floris H. P. van Velden","Ronald Boellaard","Jonas A. Castelijns","Dennis E. te Beest","Martijn W. Heymans","Mark A. van de Wiel"],"abstract":"Motivation: Radiomics refers to the high-throughput mining of quantitative\nfeatures from radiographic images. It is a promising field in that it may\nprovide a non-invasive solution for screening and classification. Standard\nmachine learning classification and feature selection techniques, however, tend\nto display inferior performance in terms of (the stability of) predictive\nperformance. This is due to the heavy multicollinearity present in radiomic\ndata. We set out to provide an easy-to-use approach that deals with this\nproblem.\n  Results: We developed a four-step approach that projects the original\nhigh-dimensional feature space onto a lower-dimensional latent-feature space,\nwhile retaining most of the covariation in the data. It consists of (i)\npenalized maximum likelihood estimation of a redundancy filtered correlation\nmatrix. The resulting matrix (ii) is the input for a maximum likelihood factor\nanalysis procedure. This two-stage maximum-likelihood approach can be used to\n(iii) produce a compact set of stable features that (iv) can be directly used\nin any (regression-based) classifier or predictor. It outperforms other\nclassification (and feature selection) techniques in both external and internal\nvalidation settings regarding survival in squamous cell cancers.","url_abs":"http://arxiv.org/abs/1903.11696v1","url_pdf":"http://arxiv.org/pdf/1903.11696v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"stable-prediction-with-radiomics-data","repo_url":"https://github.com/CFWP/FMradio","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"feature-selection","task_name":"feature selection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}