Cartilage T2 mapping-based radiomics in knee osteoarthritis research: Status, progress and future outlook

•T2 mapping is easier to achieve by deep learning and computational methods.•Deep learning and automatic algorithm make T2 map segmentation easier.•OA Diagnostic or predictive models based on T2 mapping have shown promising results. Osteoarthritis (OA) affects more than 500 millions people worldwide and places an enormous economic and medical burden on patients and healthcare systems. The knee is the most commonly affected joint. However, there is no effective early diagnostic method for OA. The main pathological feature of OA is cartilage degeneration. Owing to the poor regenerative ability of chondrocytes, early detection of OA and prompt intervention are extremely important. The T2 relaxation time indicates changes in cartilage composition and responds to alterations in the early cartilage matrix. T2 mapping does not require contrast agents or special equipment, so it is widely used. Radiomics analysis methods are used to construct diagnostic or predictive models based on information extracted from clinical images. Owing to the development of artificial intelligence methods, radiomics has made excellent progress in segmentation and model construction. In this review, we summarize the progress of T2 mapping radiomics research methods in terms of T2 map acquisition, image postprocessing, and OA diagnosis or predictive model construction.