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T 2 analysis of the entire osteoarthritis initiative dataset
Author(s) -
Razmjoo Alaleh,
Caliva Francesco,
Lee Jinhee,
Liu Felix,
Joseph Gabby B.,
Link Thomas M.,
Majumdar Sharmila,
Pedoia Valentina
Publication year - 2021
Publication title -
journal of orthopaedic research®
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.041
H-Index - 155
eISSN - 1554-527X
pISSN - 0736-0266
DOI - 10.1002/jor.24811
Subject(s) - medicine , magnetic resonance imaging , osteoarthritis , radiography , quartile , nuclear medicine , knee pain , segmentation , incidence (geometry) , radiology , mathematics , artificial intelligence , pathology , computer science , confidence interval , alternative medicine , geometry
While substantial work has been done to understand the relationships between cartilage T 2 relaxation times and osteoarthritis (OA), diagnostic and prognostic abilities of T 2 on a large population yet need to be established. Using 3921 manually annotated 2D multi‐slice multi‐echo spin‐echo magnetic resonance imaging volume, a segmentation model for automatic knee cartilage segmentation was built and evaluated. The optimized model was then used to calculate T 2 values on the entire osteoarthritis initiative (OAI) dataset composed of longitudinal acquisitions of 4796 unique patients, 25 729 magnetic resonance imaging studies in total. Cross‐sectional relationships between T 2 values, OA risk factors, radiographic OA, and pain were analyzed in the entire OAI dataset. The performance of T 2 values in predicting the future incidence of radiographic OA as well as total knee replacement (TKR) were also explored. Automatic T 2 values were comparable with manual ones. Significant associations between T 2 relaxation times and demographic and clinical variables were found. Subjects in the highest 25% quartile of tibio‐femoral T 2 values had a five times higher risk of radiographic OA incidence 2 years later. Elevation of medial femur T 2 values was significantly associated with TKR after 5 years (coeff = 0.10; P = .036; CI = [0.01,0.20]). Our investigation reinforces the predictive value of T 2 for future incidence OA and TKR. The inclusion of T 2 averages from the automatic segmentation model improved several evaluation metrics when compared to only using demographic and clinical variables.