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

• Published in: Information systems frontiers Vol. 27; no. 1; pp. 51 - 73
• Main Authors: Angelone, Francesca, Ciliberti, Federica Kiyomi, Tobia, Giovanni Paolo, Jónsson, Halldór, Ponsiglione, Alfonso Maria, Gislason, Magnus Kjartan, Tortorella, Francesco, Amato, Francesco, Gargiulo, Paolo
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.02.2025; Springer Nature B.V
• Subjects: Algorithms; Artificial intelligence; Business and Management; Cartilage; Clinical medicine; Computed tomography; Control; Degeneration; Disease; Feature selection; Image analysis; Information systems; IT in Business; Joints (anatomy); Knee; Machine learning; Magnetic resonance imaging; Management of Computing and Information Systems; Operations Research/Decision Theory; Osteoarthritis; Pain; Quality of life; Radiomics; Risk factors; Stability analysis; Surgery; Systems Theory; Tumors; X-rays; osteoarthritis, knee cartilage, imaging, segmentation, radiomics, machine learning
• ISSN: 1387-3326; 1572-9419
• DOI: 10.1007/s10796-024-10527-5

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.