Literature DB >> 28935435

Subchondral tibial bone texture predicts the incidence of radiographic knee osteoarthritis: data from the Osteoarthritis Initiative.

T Janvier1, R Jennane1, H Toumi2, E Lespessailles3.   

Abstract

OBJECTIVES: To evaluate whether trabecular bone texture (TBT) parameters measured on computed radiographs (CR) could predict the onset of radiographic knee osteoarthritis (OA).
MATERIALS AND METHODS: Subjects from the Osteoarthritis Initiative (OAI) with no sign of radiographic OA at baseline were included. Cases that developed either a global radiographic OA defined by the Kellgren-Lawrence (KL) scale, a joint space narrowing (JSN) or tibial osteophytes (TOS) were compared with the controls with no changes after 48 months of follow-up. Baseline bilateral fixed flexion CR were analyzed using a fractal method to characterize the local variations. The prediction was explored using logistic regression models evaluated by the area under the receiver operating characteristic curves (AUC).
RESULTS: From the 344 knees, 79 (23%) developed radiographic OA after 48 months, 44 (13%) developed progressive JSN and 59 (17%) developed osteophytes. Neither age, gender and BMI, nor their combination predicted poorer KL (AUC 0.57), JSN or TOS (AUC 0.59) scores. The inclusion of the TBT parameters in the models improved the global prediction results for KL (AUC 0.69), JSN (AUC 0.73) and TOS (AUC 0.71) scores.
CONCLUSIONS: Several differences were found between the models predictive of three different outcomes (KL, JSN and TOS), indicating different underlying mechanisms. These results suggest that TBT parameters assessed when radiographic signs are not yet apparent on radiographs may be useful in predicting the onset of radiological tibiofemoral OA as well as identifying at-risk patients for future clinical trials.
Copyright © 2017 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Fractal analysis; Knee osteoarthritis; Radiographs; Subchondral bone; Trabecular bone texture

Mesh:

Year:  2017        PMID: 28935435     DOI: 10.1016/j.joca.2017.09.004

Source DB:  PubMed          Journal:  Osteoarthritis Cartilage        ISSN: 1063-4584            Impact factor:   6.576


  9 in total

1.  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

2.  MRI-based Texture Analysis of Infrapatellar Fat Pad to Predict Knee Osteoarthritis Incidence.

Authors:  Jia Li; Shuai Fu; Ze Gong; Zhaohua Zhu; Dong Zeng; Peihua Cao; Ting Lin; Tianyu Chen; Xiaoshuai Wang; Richard Lartey; C Kent Kwoh; Ali Guermazi; Frank W Roemer; David J Hunter; Jianhua Ma; Changhai Ding
Journal:  Radiology       Date:  2022-05-31       Impact factor: 29.146

3.  Subchondral tibial bone texture of conventional X-rays predicts total knee arthroplasty.

Authors:  Ahmad Almhdie-Imjabbar; Hechmi Toumi; Khaled Harrar; Antonio Pinti; Eric Lespessailles
Journal:  Sci Rep       Date:  2022-05-18       Impact factor: 4.996

4.  Study of the Efficacy of Artificial Intelligence Algorithm-Based Analysis of the Functional and Anatomical Improvement in Polynucleotide Treatment in Knee Osteoarthritis Patients: A Prospective Case Series.

Authors:  Ji Yoon Jang; Ji Hyun Kim; Min Woo Kim; Sung Hoon Kim; Sang Yeol Yong
Journal:  J Clin Med       Date:  2022-05-18       Impact factor: 4.964

5.  Bone Density and Texture from Minimally Post-Processed Knee Radiographs in Subjects with Knee Osteoarthritis.

Authors:  Jukka Hirvasniemi; Jaakko Niinimäki; Jérôme Thevenot; Simo Saarakkala
Journal:  Ann Biomed Eng       Date:  2019-02-14       Impact factor: 3.934

6.  Bone Structure Analysis of the Radius Using Ultrahigh Field (7T) MRI: Relevance of Technical Parameters and Comparison with 3T MRI and Radiography.

Authors:  Mohamed Jarraya; Rafael Heiss; Jeffrey Duryea; Armin M Nagel; John A Lynch; Ali Guermazi; Marc-André Weber; Andreas Arkudas; Raymund E Horch; Michael Uder; Frank W Roemer
Journal:  Diagnostics (Basel)       Date:  2021-01-12

7.  Prediction of knee osteoarthritis progression using radiological descriptors obtained from bone texture analysis and Siamese neural networks: data from OAI and MOST cohorts.

Authors:  Ahmad Almhdie-Imjabbar; Khac-Lan Nguyen; Hechmi Toumi; Rachid Jennane; Eric Lespessailles
Journal:  Arthritis Res Ther       Date:  2022-03-08       Impact factor: 5.156

8.  Multimodal Machine Learning-based Knee Osteoarthritis Progression Prediction from Plain Radiographs and Clinical Data.

Authors:  Aleksei Tiulpin; Stefan Klein; Sita M A Bierma-Zeinstra; Jérôme Thevenot; Esa Rahtu; Joyce van Meurs; Edwin H G Oei; Simo Saarakkala
Journal:  Sci Rep       Date:  2019-12-27       Impact factor: 4.379

9.  A machine learning approach to distinguish between knees without and with osteoarthritis using MRI-based radiomic features from tibial bone.

Authors:  Jukka Hirvasniemi; Stefan Klein; Sita Bierma-Zeinstra; Meike W Vernooij; Dieuwke Schiphof; Edwin H G Oei
Journal:  Eur Radiol       Date:  2021-04-21       Impact factor: 5.315

  9 in total

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