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.

骨关节炎 (OA) 是一种影响全世界人民的常见关节疾病，特别是由于关节疼痛和功能限制而影响生活质量。这项研究探讨了放射组学（定量图像分析与机器学习相结合）在增强膝关节 OA 诊断方面的潜力。使用 138 个膝盖的 MRI 和 CT 扫描多模态数据集，从软骨段中提取放射组学特征。采用机器学习算法根据放射学特征对退化和健康的膝盖进行分类。在相关性和重要性分析的指导下，特征选择揭示了与纹理和形状相关的特征作为关键预测因素。鲁棒性分析，评估跨分割变化的特征稳定性，进一步细化特征选择。结果表明，使用放射组学对膝关节 OA 进行分类具有很高的准确性，展示了其在早期疾病检测和个性化治疗方法方面的潜力。这项工作有助于推进 OA 评估，是旨在开发新 OA 疗法的欧洲 SINPAIN 项目的一部分。