Angelone, FrancescaCiliberti, Federica KiyomiTobia, Giovanni PaoloJónsson, HalldórPonsiglione, Alfonso MariaGislason, Magnus KjartanTortorella, FrancescoAmato, FrancescoGargiulo, Paolo2025-11-172025-11-172024-09-12Angelone, F, Ciliberti, F K, Tobia, G P, Jónsson, H, Ponsiglione, A M, Gislason, M K, Tortorella, F, Amato, F & Gargiulo, P 2024, 'Innovative Diagnostic Approaches for Predicting Knee Cartilage Degeneration in Osteoarthritis Patients : A Radiomics-Based Study', Information Systems Frontiers, vol. 27, no. 1. https://doi.org/10.1007/s10796-024-10527-51387-332622925765205559471-8a3c-4f35-929e-a966f28dbf7f85203696305https://hdl.handle.net/20.500.11815/6066Publisher Copyright: © The Author(s) 2024.Osteoarthritis (OA) is a common joint disease affecting people worldwide, notably impacting quality of life due to joint pain and functional limitations. This study explores the potential of radiomics — quantitative image analysis combined with machine learning — to enhance knee OA diagnosis. Using a multimodal dataset of MRI and CT scans from 138 knees, radiomic features were extracted from cartilage segments. Machine learning algorithms were employed to classify degenerated and healthy knees based on radiomic features. Feature selection, guided by correlation and importance analyses, revealed texture and shape-related features as key predictors. Robustness analysis, assessing feature stability across segmentation variations, further refined feature selection. Results demonstrate high accuracy in knee OA classification using radiomics, showcasing its potential for early disease detection and personalized treatment approaches. This work contributes to advancing OA assessment and is part of the European SINPAIN project aimed at developing new OA therapies.5608465eninfo:eu-repo/semantics/openAccessosteoarthritis, knee cartilage, imaging, segmentation, radiomics, machine learningTheoretical Computer ScienceSoftwareInformation SystemsComputer Networks and Communications/dk/atira/pure/researchoutput/researchoutputtypes/contributiontojournal/article10.1007/s10796-024-10527-5