ArticleEuropean radiology2020
CT texture analysis of acetabular subchondral bone can discriminate between normal and cam-positive hips.
Article in European radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Radiomics in FAI: current status and perspectives.Bone & joint research · 2025Article
- Effects of nonstationary system blur on radiomic texture features of trabecular bone in normal and ultra-high resolution CT.Medical physics · 2025Article
- Texture analysis combined with machine learning in radiographs of the knee joint: potential to identify tibial plateau occult fractures.Quantitative imaging in medicine and surgery · 2025Article
- Deep Learning-Assisted Identification of Femoroacetabular Impingement (FAI) on Routine Pelvic Radiographs.Journal of imaging informatics in medicine · 2024Article
- MRI texture analysis of acetabular cancellous bone can discriminate between normal, cam positive, and cam-FAI hips.European radiology · 2023Article
- Laser lithotripsy for proximal ureteral calculi in adults: can 3D CT texture analysis help predict treatment success?European radiology · 2021Article
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6 authors.
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Abstract
objectivesThe purpose of this study was to determine if the CT texture profile of acetabular subchondral bone differs between normal, asymptomatic cam-positive, and symptomatic cam-FAI hips. In addition, the utility of texture analysis to discriminate between the three hip statuses was explored using a machine learning approach.
methodsIRB-approved, case-control study analyzing CT images in subjects with and without cam morphology from August 2010 to December 2013. Sixty-eight subjects were included: 19 normal controls, 26 asymptomatic cam, and 23 symptomatic cam-FAI. Acetabular subchondral bone was contoured on the sagittal oblique CT images using ImageJ ®. 3D histogram texture features (mean, variance, skewness, kurtosis, and percentiles) were evaluated using MaZda software. Groupwise differences were investigated using Kruskal-Wallis tests and Mann-Whitney U tests. Gradient-boosted decision trees were created and trained to discriminate between control and cam-positive hips.
resultsBoth asymptomatic and symptomatic cam-FAI hips demonstrated significantly higher values of texture variance (p = 0.0007, p < 0.0001), 90th percentile (p = 0.007, p = 0.006), and 99th percentile (p = 0.009, p = 0.009), but significantly lower values of skewness (p = 0.0001, p = 0.0013) and kurtosis (p = 0.0001, p = 0.0001) compared to normal controls. There were no differences in texture profile between asymptomatic cam and symptomatic cam-FAI hips. Machine learning models demonstrated high classification accuracy for discriminating control hips from asymptomatic cam-positive (82%) and symptomatic cam-FAI (86%) hips.
conclusionsTexture analysis can discriminate between normal and cam-positive hips using conventional descriptive statistics, regression modeling, and machine learning algorithms. It has the potential to become an important tool in compositional analysis of hip subchondral trabecular bone in the context of FAI, and possibly serve as a biomarker of joint degeneration. KEY POINTS: • The CT texture profile of acetabular subchondral bone is significantly different between normal and cam-positive hips. • Texture analysis can detect changes in subchondral bone in asymptomatic cam-positive hips that are equal to that of symptomatic cam-FAI hips. • Texture analysis has the potential to become an important tool in compositional analysis of hip subchondral bone in the context of FAI and may serve as a biomarker in the study of joint physiology and biomechanics.
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