Semi-Automated Quantification of Cartilage Loss in Knee Osteoarthritis: Responsiveness and Concurrent Validity

Abstract

Purpose: Cartilage volume change on Magnetic Resonance Imaging (MRI) is a potential surrogate endpoint in osteoarthritis (OA) research. Current scoring systems have limited sensitivity to change and require expensive and time-intensive radiology expertise. Our Local- Area Cartilage Segmentation (LACS) software for knee OA has been demonstrated to be fast, responsive, and associated with radiographic and pain progression. We aimed to extend LACS to the lateral femur, medial tibia, and lateral tibia, and evaluate responsiveness to change as well as correlation with existing cartilage measurements using an established independent manual segmentation method. Methods: 115 participants with symptomatic knee OA were selected from the Osteoarthritis Initiative progression sub-cohort. Cartilage volume in fixed weight-bearing areas was measured with LACS on unilateral knee MRIs at the baseline and 24 month visit, paired and blinded to image date, using the sagittal 3D double-echo steady-state (DESS) sequence. Change in cartilage volume was calculated between the baseline and the 24-month visit. Responsiveness and concurrent validity were quantified. Results: The standardized response mean was –0.54 for the medial femoral region and lower in the other sub-regions. Correlation with Chondrometrics measures of cartilage thickness ranged between 0.67 and 0.88. The expert reader time was under 5 minutes and total reader time less than 10 minutes total per compartment. Conclusions: We updated the LACS software for knee cartilage segmentation to not only include the medial femur, but also medial tibia, lateral tibia, and lateral femur. The automated steps increase efficiency compared to manual segmentation, and limit the need and cost of an expert reader. We found favorable SRMs for cartilage volume change over two years with default regions of interest. The software correlated well against established measurements of cartilage thickness based on manual segmentation. The updated LACS software represents a powerful tool for fast segmentation of knee cartilage in larger cohorts.