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ABSTRACT
PurposeTo determine the feasibility of MRI texture analysis as a method of quantifying subchondral bone architecture in knee osteoarthritis (OA).
MethodsAsymptomatic subjects aged 20-30 (group 1, n=10), symptomatic patients aged 40-50 (group 2, n=10) and patients scheduled for knee replacement aged 55-85 (group 3, n=10) underwent high spatial resolution T1 weighted coronal 3T knee MRI.Regions of interest were created in the medial (MT) and lateral (LT) tibial subchondral bone from which 20 texture parameters were calculated. T2 mapping of the tibial cartilage was performed in groups 1 & 2. Mean parameter values were compared between groups using ANOVA. Linear discriminant analysis (LDA) was used to evaluate the ability of texture analysis to classify subjects correctly.
ResultsSignificant differences in 18/20 and 12/20 subchondral bone texture parameters were demonstrated between groups at the MT and LT respectively. There was no significant difference in mean MT or LT cartilage T2 values between group 1 and group 2.LDA demonstrated subject classification accuracy of 97% (95% CI 91-100%).
ConclusionMRI texture analysis of tibial subchondral bone may allow detection of alteration in subchondral bone architecture in OA. This has potential applications in understanding OA pathogenesis and assessing response to treatment.
PURPOSETo compare two different methods of quantitative assessment of tibial subchondral bone in osteoarthritis (OA): statistical texture analysis (sTA) and trabecular microarchitecture analysis (tMA).
METHODSAsymptomatic controls aged ͮͬ-ͯͬ (n=ͭͬ), patients aged Ͱͬ-ͱͬ with chronic knee pain but without established OA (n=ͭͬ) and patients aged ͱͱ-ʹͱ with advanced OA scheduled for knee replacement (n=ͭͬ) underwent knee MR imaging at ͯT with a ͯD gradient echo (GRE) sequence to allow sTA and tMA.tMA and sTA features were calculated using ROI creation in the medial (MT) and lateral (LT) tibial subchondral bone. Features were compared between groups using one-way ANOVA.The two most discriminating tMA and sTA features were used to construct exploratory discriminant functions to assess the ability of the two methods to classify participants.
RESULTSNo tMA features were significantly different between groups at either MT or LT. ͭͳ/ͮͬ and ͭͭ/ͮͬ sTA features were significantly different between groups at the MT/LT respectively (p<ͬ.ͬͬͭ).Discriminant functions created using tMA features classified ͭͮ/ͯͬ participants correctly (Ͱͬ% accuracy, ͵ͱ% CI ͮͮ-ͱʹ%) based on MT data and ͵/ͯͬ correctly (ͯͬ%, ͭͰ-ͰͲ) based on
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