Precision, Reliability, and Responsiveness of a Novel Automated Quantification Tool for Cartilage Thickness: Data from the Osteoarthritis Initiative

Objective; Accurate automated segmentation of cartilage should provide rapid reliable outcomes for both epidemiological studies and clinical trials. We aimed to assess the precision and responsiveness of cartilage thickness measured with careful manual segmentation or a novel automated technique.

Methods; Agreement of automated segmentation was assessed against two manual segmentation datasets: 379 MR images manually segmented in-house (Training set), and 582 from the OAI with data available at 0, 1, and 2 years (Biomarkers set). Agreement of mean thickness was assessed using Bland-Altman plots, change with pairwise Students t-test, in the central medial femur and tibia regions (cMF, cMT). Repeatability was assessed on a set of 19 knees imaged twice on the same day. Responsiveness was assessed using standardised response means (SRMs).

Results; Agreement of manual vs automated methods was excellent with no meaningful systematic bias (Training set cMF bias 0.1mm 95%CI ±0.35, Biomarkers set bias 0.1mm ±0.4). The smallest detectable difference (SDD) for cMF was 0.13mm, coefficient of variation (CoV) 3.1%; cMT 0.16 mm, 2.65%. Reported change using manual segmentations in the cMF region at 1 year was -0.031mm, confidence limit (-0.022, -0.039), p<10-4, SRM -0.31 (-0.23,-0.38); at 2 years was -0.071 (-0.058,-0.085), p<10-4, SRM -0.43(-0.36,-0.49). Reported change using automated segmentations in the cMF at 1 year was - 0.059 (-0.047, -0.071), p<10-4, SRM -0.41(-0.34,-0.48) ; 2 years: -0.14 (-0.123,-0.157), p<10-4, SRM -0.67 (-0.6,-0.72).

Conclusion; A novel cartilage segmentation method provides highly accurate and repeatable measures with comparable cartilage thickness measurements to careful manual segmentation, but with improved responsiveness.