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ArticleEuropean radiology2020

CT texture analysis of acetabular subchondral bone can discriminate between normal and cam-positive hips.

Taryn Hodgdon, Rebecca E Thornhill, Nick D James, Paul E Beaulé, Andrew D Speirs, Kawan S Rakhra

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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.

0numbers the graph read from it
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6citing papers in PubMed
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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

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3 · Its place in the literature

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6 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Taryn HodgdonDepartment of Radiology, University of Ottawa, 451 Smyth Road, Ottawa, Ontario, K1H 8M5, Canada.
Rebecca E ThornhillDepartment of Radiology, University of Ottawa, 451 Smyth Road, Ottawa, Ontario, K1H 8M5, Canada.
Nick D JamesDepartment of Information Services, The Ottawa Hospital, 501 Smyth Road, Ottawa, Ontario, K1H 8L6, Canada.
Paul E BeauléDivision of Orthopedic Surgery, The Ottawa Hospital, 501 Smyth Road, Ottawa, Ontario, K1H 8L6, Canada.
Andrew D SpeirsDepartment of Mechanical and Aerospace Engineering, Carleton University, Ottawa, ON, Canada.
Kawan S RakhraDepartment of Radiology, University of Ottawa, 451 Smyth Road, Ottawa, Ontario, K1H 8M5, Canada. krakhra@toh.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

AcetabulumAdultCase-Control StudiesFemaleFemoracetabular ImpingementHip JointHumansMaleReproducibility of ResultsTomography, X-Ray ComputedArthritisBoneFemoroacetabular impingementHipMachine learning

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.