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Abstract

BackgroundInfrapatellar fat pad (IPFP) quality has been implicated as a marker for predicting knee osteoarthritis (KOA); however, no valid quantification for subtle IPFP abnormalities has been established.PurposeThe purpose of this study was to investigate whether MRI-based three-dimensional texture analysis of IPFP abnormalities could help predict incident radiographic KOA.Materials and MethodsIn this prospective nested case-control study, 690 participants whose knees were at risk for KOA were included from the Pivotal Osteoarthritis Initiative MRI Analyses incident osteoarthritis cohort. All knees had a Kellgren-Lawrence grade of 1 or less at baseline. During the 4-year follow-up, case participants were matched 1:1 to control participants, with incident radiographic KOA as the outcome. MRI scans were segmented at the incident time point of KOA (hereafter, P0), 1 year before P0 (hereafter, P-1), and baseline. MRI-based three-dimensional texture analysis was performed to extract IPFP texture features. Least absolute shrinkage and selection operator and multivariable logistic regressions were applied in the development cohort and evaluated in the test cohort. The area under the receiver operating characteristic curve (AUC) was used to evaluate the discriminative value of the clinical score, IPFP texture score, and MRI Osteoarthritis Knee Score.ResultsParticipants were allocated to development (n = 500, 340 women; mean age, 60 years) and test (n = 190, 120 women; mean age, 61 years) cohorts. In both cohorts, IPFP texture scores (AUC ≥0.75 for all) showed greater discrimination than clinical scores (AUC ≤0.69 for all) at baseline, P-1, and P0, with significant differences in pairwise comparisons (P ≤ .002 for all). Greater predictive and concurrent validities of IPFP texture scores (AUC ≥0.75 for all) compared with MRI Osteoarthritis Knee Scores (AUC ≤0.66 for all) were also demonstrated (P ConclusionMRI-based three-dimensional texture of the infrapatellar fat pad was associated with future development of knee osteoarthritis.

Item Type:	Article
Authors/Creators:	Li, J and Fu, S and Gong, Z and Zhu, Z and Zeng, D and Cao, P and Lin, T and Chen, T and Wang, X and Lartey, R and Kwoh, CK and Guermazi, A and Roemer, FW and Hunter, DJ and Ma, J and Ding, C
Keywords:	MRI, prediction, knee osteoarthritis
Journal or Publication Title:	Radiology
Publisher:	Radiological Soc North America
ISSN:	0033-8419
DOI / ID Number:	https://doi.org/10.1148/radiol.212009
Copyright Information:	© RSNA, 2022.
Related URLs:	Google Scholar: Ding, C UTAS Profile: Ding, C
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