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. 2021 Jan;39(1):74-85.
doi: 10.1002/jor.24811. Epub 2020 Jul 27.

T2 analysis of the entire osteoarthritis initiative dataset

Alaleh Razmjoo  1 , Francesco Caliva  1 , Jinhee Lee  1 , Felix Liu  2 , Gabby B Joseph  1 , Thomas M Link  1 , Sharmila Majumdar  1 , Valentina Pedoia  1
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Free article

T2 analysis of the entire osteoarthritis initiative dataset

Alaleh Razmjoo et al. J Orthop Res. 2021 Jan.
Free article

Abstract

While substantial work has been done to understand the relationships between cartilage T2 relaxation times and osteoarthritis (OA), diagnostic and prognostic abilities of T2 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 T2 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 T2 values, OA risk factors, radiographic OA, and pain were analyzed in the entire OAI dataset. The performance of T2 values in predicting the future incidence of radiographic OA as well as total knee replacement (TKR) were also explored. Automatic T2 values were comparable with manual ones. Significant associations between T2 relaxation times and demographic and clinical variables were found. Subjects in the highest 25% quartile of tibio-femoral T2 values had a five times higher risk of radiographic OA incidence 2 years later. Elevation of medial femur T2 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 T2 for future incidence OA and TKR. The inclusion of T2 averages from the automatic segmentation model improved several evaluation metrics when compared to only using demographic and clinical variables.

Keywords: T2 relaxometry; deep learning; early osteoarthritis; imaging biomarkers.

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References

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