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Abstract Number: 1130

Change In Knee Osteophyte Volume By Semi-Automated Method In Knee Osteoarthritis Over Four Years Using 3T DESS 3D MRI

Michael Hakky1, Charles Ratzlaff2, Ali Guermazi3, Mohamed Jarraya4 and Jeffrey W. Duryea5, 1Radiology, Lahey Clinic, Burlington, MA, 2Harvard Medical School / Brigham and Women's Hospital, Boston, MA, 3Boston University, Boston, MA, 4Department of Radiology, Boston University School of Medicine, Boston, MA, 5Radiology, Brigham and Women's Hospital, Boston, MA

Meeting: 2013 ACR/ARHP Annual Meeting

Keywords: Knee, MRI and osteophytosis

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Session Information

Title: Imaging of Rheumatic Diseases II: Imaging in Spondyloarthritis and Osteoarthritis

Session Type: Abstract Submissions (ACR)

Background/Purpose: The development and growth of osteophytes are considered a radiographic surrogate for osteoarthritis (OA) at the knee. Methods to determine osteophyte burden are largely semi-quantitative and subjective. Quantifying osteophyte volume could potentially provide an improved measure of change for OA trials and observational research. We have developed a software method for semi-automated osteophyte segmentation on MRI. Our objective was to validate the responsiveness of this method to osteophyte volume change over four years in subjects with established knee OA according to Kellgren and Lawrence (KL) grade. We assume that OA is a progressive process, thus osteophyte burden should increase over time.

Methods:  Ninety subjects (51 KL 2 and 39 KL 3) were selected from the Osteoarthritis Initiative (OAI) Progression Cohort. Double echo steady state 3D sagittal images were obtained on a 3T Siemens Trio MR system. Measurements were performed on coronal reformatted series. A reader used the software method to segment marginal osteophytes of the knee at baseline and 48-month follow up. Readings were performed paired but blinded to order of visit. The reader identified the first and last slice of the central weight bearing region and an edge detection algorithm demarcated the bone edges. The reader marked the expected normal bone contour at the base of the osteophytes (Figure 1a). The software calculated the area of the osteophyte (Figure 1b) on each slice and generated volume measurements.

The primary outcome was change in osteophyte volume (ΔV) from baseline to follow-up. Statistics used were the average change in osteophyte volume from baseline to 48 months (ΔV), the standard deviation (SD) of ΔV, the standardized response mean (SRM) ΔV/SD of ΔV, and the percentage of subjects with net increase in osteophyte burden.

Results: The average change in osteophyte volume (ΔV) was 1240 mm3, the SD = 1557 mm3, and the SRM was 0.80. A net increase in osteophyte volume from baseline to 48 months was observed for 83% (75/90, 40 KL 2 and 35 KL 3) of the subjects. The average reading time was approximately 10 minutes per knee.

Net Increase

Mean ΔV

SD ΔV

SRM

KL 2 + KL 3 (90)

75/90 (83%)

1240 mm3

1557 mm3

0.80

KL 2  (51)

40/51 (78%)

1004 mm3

1477 mm3

0.68

KL 3 (39)

35/39 (90%)

1549 mm3

1624 mm3

0.95

Table 1 – Responsiveness to change over 48 months

Conclusion: The results confirm that quantitative measure of osteophyte volume can be performed efficiently and is responsive to change over time. This method has the potential be a powerful tool for monitoring longitudinal osteophyte change, and could assess a large study cohort rapidly while potentially reducing study costs and allow for evaluation of large scale trials such as the OAI.

Figure 1a and b

This study was funded by NIH/NIAMS R01AR056664


Disclosure:

M. Hakky,
None;

C. Ratzlaff,
None;

A. Guermazi,

Boston Imaging Core Lab,

1,

Merck Serono,

5,

Sanofi-Aventis Pharmaceutical,

5,

TissueGene,

5;

M. Jarraya,
None;

J. W. Duryea,
None.

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