Innovative Diagnostic Approaches for Predicting Knee Cartilage Degeneration in Osteoarthritis Patients: A Radiomics-Based Study

Francesca Angelone (), Federica Kiyomi Ciliberti (), Giovanni Paolo Tobia (), Halldór Jónsson (), Alfonso Maria Ponsiglione (), Magnus Kjartan Gislason (), Francesco Tortorella (), Francesco Amato () and Paolo Gargiulo ()
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Francesca Angelone: University of Naples, ’Federico II’
Federica Kiyomi Ciliberti: Reykjavik University
Giovanni Paolo Tobia: University of Salerno
Halldór Jónsson: Reykjavik University
Alfonso Maria Ponsiglione: University of Naples, ’Federico II’
Magnus Kjartan Gislason: Reykjavik University
Francesco Tortorella: University of Salerno
Francesco Amato: University of Naples, ’Federico II’
Paolo Gargiulo: Reykjavik University

Information Systems Frontiers, 2025, vol. 27, issue 1, No 4, 73 pages

Abstract: Abstract 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.

Keywords: osteoarthritis; knee cartilage; imaging; segmentation; radiomics; machine learning (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10796-024-10527-5

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