Directional fractal signature methods for trabecular bone texture in hand radiographs: Data from the Osteoarthritis Initiative

Wolski, Marcin; Podsiadło, Paweł; Stachowiak, Gwidon

doi:10.1118/1.4890101

Cited by 14 publications

(13 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It was suggested that hand joints with radiographic OA exhibit lower distal FDs as compared to the joints without OA 4 . In our previous study, mean ± standard deviation values of 2.640 ± 0.062 and 2.691 ± 0.061 were reported for FD Sta for OA and non-OA joints respectively 4 . These values fall into the lowest tertile group (T1) of FD Sta .…”

Section: Discussionmentioning

confidence: 99%

“…Each TB texture ROI was quantified by means of four fractal texture parameters calculated using the AVOT method 4…”

Section: Methodsmentioning

confidence: 99%

“…Directional fractal signature (DFS) analysis of trabecular bone (TB) texture regions selected on hand radiographs is a promising approach. Our previous study suggested that our recently developed novel DFS method, called an augmented variance orientation transform (AVOT) method, can detect minute differences in TB selected on distal and proximal phalanges of subjects with and without radiographic hand OA 4 .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Trabecular bone texture detected by plain radiography is associated with MRI-defined osteophytes in finger joints of women without radiographic osteoarthritis

Wolski

Podsiadło

Stachowiak

et al. 2018

Osteoarthritis and Cartilage

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Discussionmentioning

confidence: 99%

“…Each TB texture ROI was quantified by means of four fractal texture parameters calculated using the AVOT method 4…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Trabecular bone texture detected by plain radiography is associated with MRI-defined osteophytes in finger joints of women without radiographic osteoarthritis

Wolski

Podsiadło

Stachowiak

et al. 2018

Osteoarthritis and Cartilage

Self Cite

View full text Add to dashboard Cite

show abstract

“…(5) A particular type of fractal analysis is fractal signature analysis (FSA), which is a computerized textural analysis method used to measure vertical and horizontal trabeculae based on the fractal dimensions of the bone structure over a range of trabecular widths. (6-8) FSA has been previously applied for bone architecture measurements and disease progression using radiographs in cases of knee osteoarthritis, (9)(10)(11)(12)(13)(14) osteoporosis treatment response after administration of risedronate, (15) hand osteoarthritis, (16) and hip osteoarthritis. (17) Furthermore, differences in fractal signatures in RA radiographs among three types of bone conditions, namely normal, osteopenic, and eroded bone, have also been assessed.…”

Section: Introductionmentioning

confidence: 99%

Radiographic Bone Texture Analysis Using Deep Learning Models for Early Rheumatoid Arthritis Diagnosis

Huang

Miao²,

Zheng³

et al. 2020

Preprint

View full text Add to dashboard Cite

BackgroundRheumatoid arthritis (RA) is characterized by altered bone microarchitecture (radiographically referred to as ‘texture’) of periarticular regions. We hypothesize that deep learning models can quantify periarticular texture changes to aid in the classification of early RA.MethodsThe second, third, and fourth distal metacarpal areas from hand radiographs of 892 early RA and 1236 non-RA patients were segmented for the Deep Texture Encoding Network (Deep-TEN; texture-based) and residual network-50 (ResNet-50; texture and structure-based) models to predict the probability of RA. The performances were measured using the area under the curve of the receiver operating characteristics curve (AUROC). Multivariate logistic regression was used to estimate the odds ratio (OR) with 95% confidence intervals (CIs) for RA.ResultsThe AUROC for RA was 0.69 for the Deep-TEN and 0.73 for the ResNet-50 model. The positive predictive values of a high texture score to classify RA using the Deep-TEN and ResNet-50 models were 0.64 and 0.67, respectively. High mean texture scores were associated with age- and sex-adjusted ORs (95% CI) for RA of 3.42 (2.59–4.50) and 4.30 (3.26–5.69) using the Deep-TEN and ResNet-50 models, respectively. The moderate and high RA risk groups determined by the Deep-TEN model were associated with adjusted ORs (95% CIs) of 2.48 (1.78–3.47) and 4.39 (3.11–6.20) for RA, respectively, and those using the ResNet-50 model were 2.17 (1.55–3.04) and 6.91 (4.83–9.90), respectively.ConclusionFully automated quantitative assessment for periarticular texture by deep learning models can help the classification of early RA.

show abstract

“…However, measures of MRI-detected cartilage thickness was demonstrated superior in sensitivity to change compared to the two radiographic methods [13], further supporting the need to include MRI in longitudinal OA studies. OA, which could be potentially useful for early detection and prediction of hand OA [18].…”

Section: Conventional Radiographymentioning

confidence: 99%

Imaging of osteoarthritis (OA): What is new?

Mathiessen

Cimmino

Hammer

et al. 2016

Best Practice & Research Clinical Rheumatology

View full text Add to dashboard Cite

Directional fractal signature methods for trabecular bone texture in hand radiographs: Data from the Osteoarthritis Initiative

Abstract: The AVOT method is well suited for the analysis of bone texture in hand radiographs and it could be potentially useful for early detection and prediction of hand OA.

Cited by 14 publications

References 59 publications

Trabecular bone texture detected by plain radiography is associated with MRI-defined osteophytes in finger joints of women without radiographic osteoarthritis

Trabecular bone texture detected by plain radiography is associated with MRI-defined osteophytes in finger joints of women without radiographic osteoarthritis

Radiographic Bone Texture Analysis Using Deep Learning Models for Early Rheumatoid Arthritis Diagnosis

Imaging of osteoarthritis (OA): What is new?

Contact Info

Product

Resources

About