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. 2020 Feb;47(2):282-289.
doi: 10.3899/jrheum.180541. Epub 2019 Apr 15.

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

Michael Antony Bowes  1   2 , Gwenael Alain Guillard  1   2 , Graham Richard Vincent  1   2 , Alan Donald Brett  1   2 , Christopher Brian Hartley Wolstenholme  1   2 , Philip Gerard Conaghan  3   4
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Free article

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

Michael Antony Bowes et al. J Rheumatol. 2020 Feb.
Free article

Abstract

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 2 manual segmentation datasets: 379 magnetic resonance images manually segmented in-house (training set), and 582 from the Osteoarthritis Initiative with data available at 0, 1, and 2 years (biomarkers set). Agreement of mean thickness was assessed using Bland-Altman plots, and change with pairwise Student t test in the central medial femur (cMF) and tibia regions (cMT). Repeatability was assessed on a set of 19 knees imaged twice on the same day. Responsiveness was assessed using standardized response means (SRM).

Results: Agreement of manual versus automated methods was excellent with no meaningful systematic bias (training set: cMF bias 0.1 mm, 95% CI ± 0.35; biomarkers set: bias 0.1 mm ± 0.4). The smallest detectable difference for cMF was 0.13 mm (coefficient of variation 3.1%), and for cMT 0.16 mm(2.65%). Reported change using manual segmentations in the cMF region at 1 year was -0.031 mm (95% CI -0.022, -0.039), p < 10-4, SRM -0.31 (-0.23, -0.38); and 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); and at 2 years was -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 cartilage thickness measurements comparable to those of careful manual segmentation, but with improved responsiveness.

Keywords: CARTILAGE; KNEE; MAGNETIC RESONANCE IMAGING; OSTEOARTHRITIS.

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