The article "Radiomics Signature of Osteoarthritis: Current Status and Perspective" by Tianshu Jiang et al. reviews the current state and future prospects of radiomics in the diagnosis and prognosis of osteoarthritis (OA). OA is a prevalent chronic degenerative joint disease affecting millions worldwide, causing significant socioeconomic burdens. Traditional radiological examinations are limited in their ability to diagnose early OA, detect progression, or predict outcomes. Radiomics, a novel approach that extracts high-dimensional imaging features from medical images, has emerged as a promising tool for precise diagnosis and prognosis in various diseases, including OA. The authors highlight the limitations of current radiological methods, such as X-rays, CT scans, and MRI, in detecting subtle changes and early signs of OA. Radiomics, by transforming qualitative interpretations into quantitative data, offers a more accurate and comprehensive analysis of medical images. The review covers the history and evolving applications of radiomics, emphasizing its potential in OA diagnosis and prognosis. Recent studies have demonstrated the effectiveness of radiomics in detecting early OA, tracking progression, and assessing treatment efficacy. For instance, a study using BoneFinder and convolutional neural networks achieved superior performance in classifying patellofemoral OA of the knee joint. Another study using CT images of the temporomandibular joint (TMJ) showed high accuracy in diagnosing TMJ OA when combining radiomics features with clinical biomarkers. The article also discusses the challenges and limitations of current radiomics applications in OA, such as the need for standardized protocols, the impact of image acquisition parameters, and the importance of feature selection and reproducibility. It emphasizes the importance of large-scale image databases and cross-institutional validations to ensure the reliability and generalizability of radiomics findings. Finally, the authors provide recommendations for future research, including the harmonization of imaging protocols, ensuring sufficient sample sizes, and carefully selecting regions of interest (ROI) for feature extraction. They conclude that while radiomics shows promise in OA diagnosis and prognosis, further research is needed to address clinical requirements and enhance its clinical applicability.The article "Radiomics Signature of Osteoarthritis: Current Status and Perspective" by Tianshu Jiang et al. reviews the current state and future prospects of radiomics in the diagnosis and prognosis of osteoarthritis (OA). OA is a prevalent chronic degenerative joint disease affecting millions worldwide, causing significant socioeconomic burdens. Traditional radiological examinations are limited in their ability to diagnose early OA, detect progression, or predict outcomes. Radiomics, a novel approach that extracts high-dimensional imaging features from medical images, has emerged as a promising tool for precise diagnosis and prognosis in various diseases, including OA. The authors highlight the limitations of current radiological methods, such as X-rays, CT scans, and MRI, in detecting subtle changes and early signs of OA. Radiomics, by transforming qualitative interpretations into quantitative data, offers a more accurate and comprehensive analysis of medical images. The review covers the history and evolving applications of radiomics, emphasizing its potential in OA diagnosis and prognosis. Recent studies have demonstrated the effectiveness of radiomics in detecting early OA, tracking progression, and assessing treatment efficacy. For instance, a study using BoneFinder and convolutional neural networks achieved superior performance in classifying patellofemoral OA of the knee joint. Another study using CT images of the temporomandibular joint (TMJ) showed high accuracy in diagnosing TMJ OA when combining radiomics features with clinical biomarkers. The article also discusses the challenges and limitations of current radiomics applications in OA, such as the need for standardized protocols, the impact of image acquisition parameters, and the importance of feature selection and reproducibility. It emphasizes the importance of large-scale image databases and cross-institutional validations to ensure the reliability and generalizability of radiomics findings. Finally, the authors provide recommendations for future research, including the harmonization of imaging protocols, ensuring sufficient sample sizes, and carefully selecting regions of interest (ROI) for feature extraction. They conclude that while radiomics shows promise in OA diagnosis and prognosis, further research is needed to address clinical requirements and enhance its clinical applicability.