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

Almhdie-Imjabbar, Ahmad; Toumi, Hechmi; Harrar, Khaled; Pinti, Antonio; Lespessailles, Éric

doi:10.1038/s41598-022-12083-x

Cited by 4 publications

(12 citation statements)

References 53 publications

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“…Total knee replacement or arthroplasty (TKA) is often considered an important clinical outcome due to the lack of disease-modifying osteoarthritis drugs (DMOADs) [ 63 ] for KOA. Besides the prediction of KOA incidence and progression, radiographic biomarkers have also been investigated for the prediction of TKA risk [ 47 , 48 , 49 , 50 , 51 ]. In this regard, two TBT-based methods were detected in this review [ 47 , 51 ].…”

Section: Resultsmentioning

confidence: 99%

“…Besides the prediction of KOA incidence and progression, radiographic biomarkers have also been investigated for the prediction of TKA risk [ 47 , 48 , 49 , 50 , 51 ]. In this regard, two TBT-based methods were detected in this review [ 47 , 51 ].…”

Section: Resultsmentioning

confidence: 99%

“…Very recently, the ability of radiographic TBT features measured using the VAR method was evaluated, for the first time, for the prediction of TKA risk [ 51 ]. Associated with radiographic severity descriptors in a set of 4382 participants (375 cases) from the OAI cohort, the TBT-based prediction model achieved an AUC of 0.92 (0.90–0.93) while a reference model based on standard clinical covariates and radiological KL grades achieved an AUC of 0.86 (0.84–0.86).…”

Section: Resultsmentioning

confidence: 99%

“…Three out of five studies provided acceptable AUC scores for the prediction of TKA risk. Theoretically, the prediction performance related to TKA should be lower than that related to KOA progression because there are more parameters to be considered to simulate the complexity of TKA as an outcome of KOA, including, for example the opinion of doctors and patients in the final decision of TKA [ 51 ]. Surprisingly, the overall AUC scores were better for the prediction of TKA than KOA progression.…”

Section: Discussionmentioning

confidence: 99%

“…Surprisingly, the overall AUC scores were better for the prediction of TKA than KOA progression. However, including knees with KL = 4 was found to be highly associated with TKA risk [ 50 , 51 ].…”

Section: Discussionmentioning

confidence: 99%

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Radiographic Biomarkers for Knee Osteoarthritis: A Narrative Review

Almhdie-Imjabbar¹,

Toumi

Lespessailles

2023

Life

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Conventional radiography remains the most widely available imaging modality in clinical practice in knee osteoarthritis. Recent research has been carried out to develop novel radiographic biomarkers to establish the diagnosis and to monitor the progression of the disease. The growing number of publications on this topic over time highlights the necessity of a renewed review. Herein, we propose a narrative review of a selection of original full-text articles describing human studies on radiographic imaging biomarkers used for the prediction of knee osteoarthritis-related outcomes. To achieve this, a PubMed database search was used. A total of 24 studies were obtained and then classified based on three outcomes: (1) prediction of radiographic knee osteoarthritis incidence, (2) knee osteoarthritis progression and (3) knee arthroplasty risk. Results showed that numerous studies have reported the relevance of joint space narrowing score, Kellgren–Lawrence score and trabecular bone texture features as potential bioimaging markers in the prediction of the three outcomes. Performance results of reviewed prediction models were presented in terms of the area under the receiver operating characteristic curves. However, fair and valid comparisons of the models’ performance were not possible due to the lack of a unique definition of each of the three outcomes.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Radiographic Biomarkers for Knee Osteoarthritis: A Narrative Review

Almhdie-Imjabbar¹,

Toumi

Lespessailles

2023

Life

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Computerized diagnosis of knee osteoarthritis from x‐ray images using combined texture features: Data from the osteoarthritis initiative

Messaoudene,

Harrar

2024

Int J Imaging Syst Tech

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The prevalence of knee osteoarthritis (KOA) cases has witnessed a significant increase on a global scale in recent years, emphasizing the need for automated diagnostic computer systems to aid in early‐stage osteoarthritis (OA) diagnosis. The accurate characterization of knee KOA stages through feature extraction poses significant research challenges due to the complexity of identifying relevant attributes. In this study, the development of a KOA diagnostic system is presented, leveraging a combination of Gabor, and Tamura parameters using the Canonical Correlation Analysis algorithm. Two feature selection algorithms, namely Principal Component Analysis and Relief, were employed for KOA classification. Furthermore, various classifiers, including K‐Nearest Neighbors, AdaBoost, Bagging, and Random Forest, were used to assess the proposed feature extraction approach. The diagnostic system was assessed using a dataset comprising 688 x‐ray images sourced from the OA initiative (OAI) dataset, consisting of 344 images from healthy subjects (Grade 0) and 344 images from pathological patients (Grade 2). To mitigate overfitting, a 10‐fold cross‐validation method was utilized. The experimental results indicate that the combination of Tamura and Gabor parameters with the Random Forest classifier achieved remarkable performance in KOA diagnosis, yielding an accuracy of 94.59%, and an area under the curve of 98.3%. Notably, the combined Gabor and Tamura models exhibited superior performance compared to individual models, as well as existing techniques reported in the literature.

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Short-term variations in trabecular bone texture parameters associated to radio-clinical biomarkers improve the prediction of radiographic knee osteoarthritis progression

Almhdie-Imjabbar,

Toumi,

Lespessailles

2023

Sci Rep

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The present study aims to examine whether the short-term variations in trabecular bone texture (TBT) parameters, combined with a targeted set of clinical and radiographic data, would improve the prediction of long-term radiographic knee osteoarthritis (KOA) progression. Longitudinal (baseline, 24 and 48-month) data, obtained from the Osteoarthritis Initiative cohort, were available for 1352 individuals, with preexisting OA (1 < Kellgren–Lawrence < 4) at baseline. KOA progression was defined as an increase in the medial joint space narrowing score from the 24-months to the 48-months control point. 16 regions of interest were automatically selected from each radiographic knee and analyzed using fractal dimension. Variations from baseline to 24 months in TBT descriptors as well as selected radiographic and clinical readings were calculated. Different logistic regression models were developed to evaluate the progression prediction performance when associating TBT variations with the selected clinical and radiographic readings. The most predictive model was mainly determined using the area under the receiver operating characteristic curve (AUC). The proposed prediction model including short-term variations in TBT parameters, associated with clinical covariates and radiographic scores, improved the capacity of predicting long-term radiographic KOA progression (AUC of 0.739), compared to models based solely on baseline values (AUC of 0.676, p-value < 0.008).

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

Cited by 4 publications

References 53 publications

Radiographic Biomarkers for Knee Osteoarthritis: A Narrative Review

Radiographic Biomarkers for Knee Osteoarthritis: A Narrative Review

Computerized diagnosis of knee osteoarthritis from x‐ray images using combined texture features: Data from the osteoarthritis initiative

Short-term variations in trabecular bone texture parameters associated to radio-clinical biomarkers improve the prediction of radiographic knee osteoarthritis progression

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