Subchondral tibial bone texture analysis predicts knee osteoarthritis progression: data from the Osteoarthritis Initiative

Janvier, Thomas; Jennane, Rachid; Valéry, Antoine; Harrar, Khaled; Delplanque, Maurice; Lelong, Charles; Lœuille, Damien; Toumi, Hechmi; Lespessailles, Éric

doi:10.1016/j.joca.2016.10.005

Cited by 50 publications

(83 citation statements)

References 37 publications

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“…As there were no previous data for the optimal location of ROIs for bone texture analysis in the hip, we decided to cover the whole femoral ROI and also incorporate the acetabulum in our analyses. As shown in a previous study for the knee, areas distal from the subchondral bone might also include relevant texture information [14]. Interestingly, the relevant ROIs for the prediction of rHOA in our analyses were either at or next to the principal compressive trabeculae or close to the joint space (Figure 3).…”

Section: Discussionsupporting

confidence: 55%

“…However, in OA research, the majority of studies analyzing bone texture are concentrated on the knee, using mostly fractal-based texture analysis methods [1,[9][10][11][12][13]. There is evidence that tibial trabecular bone texture can be used to predict both development and progression of OA as well as total knee replacement [14][15][16][17][18][19][20][21]. Only one study applied fractal signature analysis (FSA) on hip radiographs to quantify trabecular bone changes in subjects with prevalent hip OA and reported changes in fractal dimension of femoral head between baseline and 18 months followup [22].…”

Section: Introductionmentioning

confidence: 99%

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Bone texture analysis for prediction of incident radiographic hip osteoarthritis using machine learning: data from the Cohort Hip and Cohort Knee (CHECK) study

Hirvasniemi

Gielis

Arbabi

et al. 2019

Osteoarthritis and Cartilage

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Our aim was to assess the ability of radiography-based bone texture parameters in proximal femur and acetabulum to predict incident radiographic hip osteoarthritis (rHOA) over a 10 years period. Pelvic radiographs from CHECK (Cohort Hip and Cohort Knee) at baseline (987 hips) were analyzed for bone texture using fractal signature analysis in proximal femur and acetabulum. Elastic net (machine learning) was used to predict the incidence of rHOA (Kellgren-Lawrence grade (KL) ≥ 2 or total hip replacement (THR)), joint space narrowing score (JSN, range 0-3), and osteophyte score (OST, range 0-3) after 10 years. Performance of prediction models was assessed using the area under the receiver operating characteristic curve (ROC AUC). Of the 987 hips without rHOA at baseline, 435 (44%) had rHOA at 10-year follow-up. Of the 667 hips with JSN grade 0 at baseline, 471 (71%) had JSN grade ≥ 1 at 10-year follow-up. Of the 613 hips with OST grade 0 at baseline, 526 (86%) had OST grade ≥ 1 at 10-year follow-up. AUCs for the models including age, gender, and body mass index to predict incident rHOA, JSN, and OST were 0.59, 0.54, and 0.51, respectively. The inclusion of bone texture parameters in the models improved the prediction of incident rHOA (ROC AUC 0.66 and 0.71 when baseline KL was also included in the model) and JSN (ROC AUC 0.62), but not incident OST (ROC AUC 0.53). Bone texture analysis provides additional information for predicting incident rHOA or THR over 10 years.

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Section: Discussionsupporting

confidence: 55%

Section: Introductionmentioning

confidence: 99%

Bone texture analysis for prediction of incident radiographic hip osteoarthritis using machine learning: data from the Cohort Hip and Cohort Knee (CHECK) study

Hirvasniemi

Gielis

Arbabi

et al. 2019

Osteoarthritis and Cartilage

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“…These results are in line with previous data shown by Podsiadlo et al 19 where they found better results with a larger scale in CR. In our previous study 9 we obtained better results when running the analyses in the vertical direction for the progression of pre-existing knee OA. This implies that TBT analysis is relevant in the horizontal direction for early diagnosis and then in the vertical one for late follow-up.…”

Section: Details Of the Tbt Analysismentioning

confidence: 77%

“…Our approach included the whole proximal tibial trabecular bone, mapping the area with 16 different ROIs to cover the global aspect of knee OA as defined by Loeser 3 . We have already demonstrated that relevant information to detect the risk of OA progression is provided by both lateral and distal subchondral bone regions at the tibia and not only by the medial one 27,46 .…”

Section: Discussionmentioning

confidence: 97%

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

Janvier

Jennane

Toumi

et al. 2017

Osteoarthritis and Cartilage

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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.

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Predictive Validity of Radiographic Trabecular Bone Texture in Knee Osteoarthritis

Kraus

Collins

Charles

et al. 2017

Arthritis & Rheumatology

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Objective. To evaluate radiographic subchondral trabecular bone texture (TBT) as a predictor of clinically relevant osteoarthritis (OA) progression (combination of symptom and structural worsening).Methods. The Foundation for the National Institutes of Health (FNIH) OA Biomarkers Consortium undertook a study of progressive knee OA cases (n = 194 knees with both radiographic and pain progression over 24-48 months) and comparators (n = 406 OA knees not meeting the case definition). TBT parameters were extracted from a medial subchondral tibial region of interest by fractal signature analysis of radiographs using validated semiautomated software. Baseline TBT and timeintegrated values over 12 and 24 months were evaluated for association with case status and separately with radiographic and pain progression status, adjusted for age, sex, body mass index, race, baseline Kellgren/Lawrence grade, baseline joint space width, Western Ontario and McMaster Universities Osteoarthritis Index pain score, and pain medication use. C statistics were generated from receiver operating characteristic curves.Results. Relative to comparators, cases were characterized by thinner vertical and thicker horizontal trabeculae. The summed composite of 3 TBT parameters at baseline and over 12 and 24 months best predicted case status (odds ratios 1.24-1.43). The C statistic for predicting case status using the TBT composite score (0.633-0.649) was improved modestly but statistically significantly over the use of covariates alone (0.608). One TBT parameter, reflecting thickened horizontal trabeculae in cases, at baseline and over 12 and 24 months, predicted risk of any progression (radiographic and/or pain progression).Conclusion. Although associations are modest, TBT could be an attractive means of enriching OA trials for progressors since it can be generated from screening knee radiographs already standard in knee OA clinical trials.

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Subchondral tibial bone texture analysis predicts knee osteoarthritis progression: data from the Osteoarthritis Initiative

Abstract: Our findings indicate that TBT parameters assessed in different locations in the joint provided a good predictive ability to detect knee OA progression.

Cited by 50 publications

References 37 publications

Bone texture analysis for prediction of incident radiographic hip osteoarthritis using machine learning: data from the Cohort Hip and Cohort Knee (CHECK) study

Bone texture analysis for prediction of incident radiographic hip osteoarthritis using machine learning: data from the Cohort Hip and Cohort Knee (CHECK) study

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

Predictive Validity of Radiographic Trabecular Bone Texture in Knee Osteoarthritis

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