Evidence map›Paper›PMID 37231069›Full record

ArticleEuropean radiology2023

MRI texture analysis of acetabular cancellous bone can discriminate between normal, cam positive, and cam-FAI hips.

Taryn Hodgdon, Rebecca E Thornhill, Nick D James, Gerd Melkus, Paul E Beaulé, Kawan S Rakhra

Abstract read
PubMed Publisher
In one paragraph

Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
2.5field-weighted citation impact, top 11% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
  2. Canadian radiology: 2025 update.Japanese journal of radiology · 2025
    Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 1 institution in 1 country.

Taryn HodgdonOttawa Hospital Research Institute, The Ottawa Hospital - General Campus, 501 Smyth Road, Ottawa, ON, K1H 8L6, Canada.
Rebecca E ThornhillOttawa Hospital Research Institute, The Ottawa Hospital - General Campus, 501 Smyth Road, Ottawa, ON, K1H 8L6, Canada.
Nick D JamesDepartment of Information Services, The Ottawa Hospital - General Campus, 501 Smyth Road, Ottawa, ON, K1H 8L6, Canada.
Gerd MelkusOttawa Hospital Research Institute, The Ottawa Hospital - General Campus, 501 Smyth Road, Ottawa, ON, K1H 8L6, Canada.
Paul E BeauléDepartment of Orthopaedic Surgery, The Ottawa Hospital - General Campus, 501 Smyth Road, Ottawa, ON, K1H 8L6, Canada.
Kawan S RakhraOttawa Hospital Research Institute, The Ottawa Hospital - General Campus, 501 Smyth Road, Ottawa, ON, K1H 8L6, Canada. krakhra@toh.ca.ORCID http://orcid.org/0000-0002-1787-9096
Ottawa Hospital · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo compare the MRI texture profile of acetabular subchondral bone in normal, asymptomatic cam positive, and symptomatic cam-FAI hips and determine the accuracy of a machine learning model for discriminating between the three hip classes.

methodsA case-control, retrospective study was performed including 68 subjects (19 normal, 26 asymptomatic cam, 23 symptomatic cam-FAI). Acetabular subchondral bone of unilateral hip was contoured on 1.5 T MR images. Nine first-order 3D histogram and 16 s-order texture features were evaluated using specialized texture analysis software. Between-group differences were assessed using Kruskal-Wallis and Mann-Whitney U tests, and differences in proportions compared using chi-square and Fisher's exact tests. Gradient-boosted ensemble methods of decision trees were created and trained to discriminate between the three groups of hips, with percent accuracy calculated.

resultsSixty-eight subjects (median age 32 (28-40), 60 male) were evaluated. Significant differences among all three groups were identified with first-order (4 features, all p ≤ 0.002) and second-order (11 features, all p ≤ 0.002) texture analyses. First-order texture analysis could differentiate between control and cam positive hip groups (4 features, all p ≤ 0.002). Second-order texture analysis could additionally differentiate between asymptomatic cam and symptomatic cam-FAI groups (10 features, all p ≤ 0.02). Machine learning models demonstrated high classification accuracy of 79% (SD 16) for discriminating among all three groups.

conclusionNormal, asymptomatic cam positive, and cam-FAI hips can be discriminated based on their MRI texture profile of subchondral bone using descriptive statistics and machine learning algorithms. CLINICAL RELEVANCE STATEMENT: Texture analysis can be performed on routine MR images of the hip and used to identify early changes in bone architecture, differentiating morphologically abnormal from normal hips, prior to onset of symptoms. KEY POINTS: • MRI texture analysis is a technique for extracting quantitative data from routine MRI images. • MRI texture analysis demonstrates that there are different bone profiles between normal hips and those with femoroacetabular impingement. • Machine learning models can be used in conjunction with MRI texture analysis to accurately differentiate between normal hips and those with femoroacetabular impingement.

Indexed as

Femoracetabular ImpingementHip JointAcetabulumAdultCancellous BoneHumansMagnetic Resonance ImagingRetrospective StudiesCancellous boneFemoroacetabular impingementHip osteoarthritisMagnetic resonance imaging

Identifiers

PMID37231069
OpenAlexW4378188583

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.