Predicting osteoarthritis onset and progression with 3D texture analysis of cartilage MRI DESS: 6‐Year data from osteoarthritis initiative. Issue 11 (13th February 2022)

• Record Type: Journal Article
• Authors: Väärälä, Ari; Casula, Victor; Peuna, Arttu; Panfilov, Egor; Mobasheri, Ali; Haapea, Marianne; Lammentausta, Eveliina; Nieminen, Miika T.
• Is Part Of: Journal of orthopaedic research. Volume 40:Issue 11(2022)
• Page Start: 2597
• Page End: 2608
• Publication Date: 2022-02-13
• Subjects: cartilage -- DESS -- magnetic resonance imaging (MRI) -- osteoarthritis (OA) -- texture analysis; Orthopedics -- Periodicals; Musculoskeletal system -- Periodicals; 616.7
• DOI: 10.1002/jor.25293
• Languages: English
• ISSNs: 0736-0266

Abstract

Abstract: In this study, we developed a gray level co‐occurrence matrix‐based 3D texture analysis method for dual‐echo steady‐state (DESS) magnetic resonance (MR) images to be used for knee cartilage analysis in osteoarthritis (OA) studies and use it to study changes in articular cartilage between different subpopulations based on their rate of progression into radiographically confirmed OA. In total, 642 series of right knee DESS MR images at 3T were obtained from baseline, 36‐ and 72‐month follow‐ups from the OA Initiative database. At baseline, all 214 subjects included in the study had Kellgren‐Lawrence (KL) grade <2. Three groups were defined, based on time of progression into radiographic OA (ROA) (KL grades ≥2): control (no progression), fast progressor (ROA at 36 months), and slow progressor (ROA at 72 months) groups. 3D texture analysis was used to extract textural features for femoral and tibial cartilages. All textural features, in both femur and tibia, showed significant longitudinal changes across all groups and tissue layers. Most of the longitudinal changes were observed in progressors, but significant changes were observed also in controls. Differences between groups were mostly seen at baseline and 72 months. The method is sensitive to cartilage changes before and after ROA. It was able to detect longitudinal changes in controls and progressors and to distinguish cartilage alterations due to OA and aging. Moreover, it was able to distinguish controls and different progressor groups before any radiographic signs of OA and during OA. Thus, texture analysis could be used as a marker for the onset and progression of OA.