Literature DB >> 27221563

Prediction of medial tibiofemoral compartment joint space loss progression using volumetric cartilage measurements: Data from the FNIH OA biomarkers consortium.

Nima Hafezi-Nejad1, Ali Guermazi2, Frank W Roemer3, David J Hunter4, Erik B Dam5, Bashir Zikria6, C Kent Kwoh7, Shadpour Demehri8.   

Abstract

OBJECTIVES: Investigating the association between baseline cartilage volume measurements (and initial 24th month volume loss) with medial compartment Joint-Space-Loss (JSL) progression (>0.7 mm) during 24-48th months of study.
METHODS: Case and control cohorts (Biomarkers Consortium subset from the Osteoarthritis Initiative (OAI)) were defined as participants with (n=297) and without (n=303) medial JSL progression (during 24-48th months). Cartilage volume measurements (baseline and 24th month loss) were obtained at five knee plates (medial-tibial, lateral-tibial, medial-femoral, lateral-femoral and patellar), and standardized values were analysed. Multivariate logistic regression was used with adjustment for known confounders. Artificial-Neural-Network analysis was conducted by Multi-Layer-Perceptrons (MLPs) including baseline determinants, and baseline (1) and interval changes (2) in cartilage volumes.
RESULTS: Larger baseline lateral-femoral cartilage volume was predictive of medial JSL (OR: 1.29 (1.01-1.64)). Greater initial 24th month lateral-femoral cartilage volume-loss (OR: 0.48 (0.27-0.84)) had protective effect on medial JSL during 24-48th months of study. Baseline and interval changes in lateral-femoral cartilage volume, were the most important estimators for medial JSL progression (importance values: 0.191(0.177-0.204), 0.218(0.207-0.228)) in the ANN analyses.
CONCLUSIONS: Cartilage volumes (both at baseline and their change during the initial 24 months) in the lateral femoral plate were predictive of medial JSL progression. KEY POINTS: • Baseline lateral femoral cartilage volume is directly associated with medial JSL progression. • 24-month lateral femoral cartilage loss is inversely associated with medial JSL progression. • Lateral femoral cartilage volume is most important in association with medial JSL progression.

Entities:  

Keywords:  Cartilage; Knee osteoarthritis; Logistic models; Magnetic resonance imaging; Neural networks

Mesh:

Substances:

Year:  2016        PMID: 27221563     DOI: 10.1007/s00330-016-4393-4

Source DB:  PubMed          Journal:  Eur Radiol        ISSN: 0938-7994            Impact factor:   5.315


  49 in total

1.  Compartment differences in knee cartilage volume in healthy adults.

Authors:  Flavia M Cicuttini; Anita E Wluka; Yuanyuan Wang; Susan R Davis; Judith Hankin; Peter Ebeling
Journal:  J Rheumatol       Date:  2002-03       Impact factor: 4.666

2.  Radiographic progression of knee osteoarthritis is associated with MRI abnormalities in both the patellofemoral and tibiofemoral joint.

Authors:  B J E de Lange-Brokaar; J Bijsterbosch; P R Kornaat; E Yusuf; A Ioan-Facsinay; A-M Zuurmond; H M Kroon; I Meulenbelt; J L Bloem; M Kloppenburg
Journal:  Osteoarthritis Cartilage       Date:  2015-10-22       Impact factor: 6.576

3.  Nottingham knee osteoarthritis risk prediction models.

Authors:  Weiya Zhang; Daniel F McWilliams; Sarah L Ingham; Sally A Doherty; Stella Muthuri; Kenneth R Muir; Michael Doherty
Journal:  Ann Rheum Dis       Date:  2011-05-25       Impact factor: 19.103

4.  Neural networks for nodal staging of non-small cell lung cancer with FDG PET and CT: importance of combining uptake values and sizes of nodes and primary tumor.

Authors:  Lauren K Toney; Hubert J Vesselle
Journal:  Radiology       Date:  2013-10-28       Impact factor: 11.105

5.  What comes first? Multitissue involvement leading to radiographic osteoarthritis: magnetic resonance imaging-based trajectory analysis over four years in the osteoarthritis initiative.

Authors:  Frank W Roemer; C Kent Kwoh; Michael J Hannon; David J Hunter; Felix Eckstein; Tomoko Fujii; Robert M Boudreau; Ali Guermazi
Journal:  Arthritis Rheumatol       Date:  2015-05       Impact factor: 10.995

Review 6.  What is the predictive value of MRI for the occurrence of knee replacement surgery in knee osteoarthritis?

Authors:  J-P Pelletier; C Cooper; C Peterfy; J-Y Reginster; M-L Brandi; O Bruyère; R Chapurlat; F Cicuttini; P G Conaghan; M Doherty; H Genant; G Giacovelli; M C Hochberg; D J Hunter; J A Kanis; M Kloppenburg; J-D Laredo; T McAlindon; M Nevitt; J-P Raynauld; R Rizzoli; C Zilkens; F W Roemer; J Martel-Pelletier; A Guermazi
Journal:  Ann Rheum Dis       Date:  2013-07-25       Impact factor: 19.103

Review 7.  Conventional and novel imaging modalities in osteoarthritis: current state of the evidence.

Authors:  Shadpour Demehri; Nima Hafezi-Nejad; John A Carrino
Journal:  Curr Opin Rheumatol       Date:  2015-05       Impact factor: 5.006

8.  The effect of the knee adduction moment on tibial cartilage volume and bone size in healthy women.

Authors:  B D Jackson; A J Teichtahl; M E Morris; A E Wluka; S R Davis; F M Cicuttini
Journal:  Rheumatology (Oxford)       Date:  2003-12-16       Impact factor: 7.580

9.  Tibiofemoral joint osteoarthritis: risk factors for MR-depicted fast cartilage loss over a 30-month period in the multicenter osteoarthritis study.

Authors:  Frank W Roemer; Yuqing Zhang; Jingbo Niu; John A Lynch; Michel D Crema; Monica D Marra; Michael C Nevitt; David T Felson; Laura B Hughes; George Y El-Khoury; Martin Englund; Ali Guermazi
Journal:  Radiology       Date:  2009-07-27       Impact factor: 11.105

10.  Assessment of cartilage changes over time in knee osteoarthritis disease-modifying osteoarthritis drug trials using semiquantitative and quantitative methods: pros and cons.

Authors:  Lukas Martin Wildi; Johanne Martel-Pelletier; François Abram; Thomas Moser; Jean-Pierre Raynauld; Jean-Pierre Pelletier
Journal:  Arthritis Care Res (Hoboken)       Date:  2013-05       Impact factor: 4.794

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  11 in total

1.  Superolateral Hoffa's fat pad (SHFP) oedema and patellar cartilage volume loss: quantitative analysis using longitudinal data from the Foundation for the National Institute of Health (FNIH) Osteoarthritis Biomarkers Consortium.

Authors:  Arya Haj-Mirzaian; Ali Guermazi; Nima Hafezi-Nejad; Christopher Sereni; Michael Hakky; David J Hunter; Bashir Zikria; Frank W Roemer; Shadpour Demehri
Journal:  Eur Radiol       Date:  2018-04-12       Impact factor: 5.315

2.  Unifying the seeds auto-generation (SAGE) with knee cartilage segmentation framework: data from the osteoarthritis initiative.

Authors:  Hong-Seng Gan; Khairil Amir Sayuti; Muhammad Hanif Ramlee; Yeng-Seng Lee; Wan Mahani Hafizah Wan Mahmud; Ahmad Helmy Abdul Karim
Journal:  Int J Comput Assist Radiol Surg       Date:  2019-03-11       Impact factor: 2.924

Review 3.  Imaging of osteoarthritis-recent research developments and future perspective.

Authors:  Daichi Hayashi; Frank W Roemer; Ali Guermazi
Journal:  Br J Radiol       Date:  2018-01-19       Impact factor: 3.039

4.  Deep learning risk assessment models for predicting progression of radiographic medial joint space loss over a 48-MONTH follow-up period.

Authors:  B Guan; F Liu; A Haj-Mirzaian; S Demehri; A Samsonov; T Neogi; A Guermazi; R Kijowski
Journal:  Osteoarthritis Cartilage       Date:  2020-02-06       Impact factor: 6.576

5.  Conventional MRI-derived subchondral trabecular biomarkers and their association with knee cartilage volume loss as early as 1 year: a longitudinal analysis from Osteoarthritis Initiative.

Authors:  Farhad Pishgar; Amir Ashraf-Ganjouei; Mahsa Dolatshahi; Ali Guermazi; Bashir Zikria; Xu Cao; Mei Wan; Frank W Roemer; Erik Dam; Shadpour Demehri
Journal:  Skeletal Radiol       Date:  2022-04-02       Impact factor: 2.128

6.  3T MRI of the knee with optimised isotropic 3D sequences: Accurate delineation of intra-articular pathology without prolonged acquisition times.

Authors:  Osamah M Abdulaal; Louise Rainford; Peter MacMahon; Eoin Kavanagh; Marie Galligan; James Cashman; Allison McGee
Journal:  Eur Radiol       Date:  2017-04-21       Impact factor: 5.315

7.  Deep learning approach to predict pain progression in knee osteoarthritis.

Authors:  Bochen Guan; Fang Liu; Arya Haj Mizaian; Shadpour Demehri; Alexey Samsonov; Ali Guermazi; Richard Kijowski
Journal:  Skeletal Radiol       Date:  2021-04-09       Impact factor: 2.128

8.  Multi-classifier prediction of knee osteoarthritis progression from incomplete imbalanced longitudinal data.

Authors:  Paweł Widera; Paco M J Welsing; Christoph Ladel; John Loughlin; Floris P F J Lafeber; Florence Petit Dop; Jonathan Larkin; Harrie Weinans; Ali Mobasheri; Jaume Bacardit
Journal:  Sci Rep       Date:  2020-05-21       Impact factor: 4.379

Review 9.  Practical Implementation of Artificial Intelligence-Based Deep Learning and Cloud Computing on the Application of Traditional Medicine and Western Medicine in the Diagnosis and Treatment of Rheumatoid Arthritis.

Authors:  Shaohui Wang; Ya Hou; Xuanhao Li; Xianli Meng; Yi Zhang; Xiaobo Wang
Journal:  Front Pharmacol       Date:  2021-12-23       Impact factor: 5.810

10.  Conventional MRI-based subchondral trabecular biomarkers as predictors of knee osteoarthritis progression: data from the Osteoarthritis Initiative.

Authors:  Farhad Pishgar; Ali Guermazi; Frank W Roemer; Thomas M Link; Shadpour Demehri
Journal:  Eur Radiol       Date:  2020-11-25       Impact factor: 7.034

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