Evidence map›Paper›PMID 38379423›Full record

ArticleThe British journal of radiology2024

Fully automated CT imaging biomarkers for opportunistic prediction of future hip fractures.

Daniel Liu, John W Garrett, Alberto A Perez, Ryan Zea, Neil C Binkley, Ronald M Summers, Perry J Pickhardt

Abstract read
In one paragraph

Article in The British journal of radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
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

11 citing papers in PubMed.

  1. Article
  2. Multi-center validation of automated CT-based L1 vertebral Hounsfield unit.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2026
    Article
  3. Improved prediction of hip fracture using multi-faceted biomechanical computed tomography.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2026
    Article
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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

7 authors.

Daniel LiuDepartment of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, WI, 53792, United States.
John W GarrettDepartment of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, WI, 53792, United States.
Alberto A PerezDepartment of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, WI, 53792, United States.
Ryan ZeaDepartment of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, WI, 53792, United States.
Neil C BinkleyDepartment of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, WI, 53792, United States.
Ronald M SummersNational Institutes of Health Clinical Center, Potomac, MD, 20892, United States.ORCID 0000-0001-8081-7376
Perry J PickhardtDepartment of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, WI, 53792, United States.ORCID 0000-0002-5534-8202

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveAssess automated CT imaging biomarkers in patients who went on to hip fracture, compared with controls.

methodsIn this retrospective case-control study, 6926 total patients underwent initial abdominal CT over a 20-year interval at one institution. A total of 1308 patients (mean age at initial CT, 70.5 ± 12.0 years; 64.4% female) went on to hip fracture (mean time to fracture, 5.2 years); 5618 were controls (mean age 70.3 ± 12.0 years; 61.2% female; mean follow-up interval 7.6 years). Validated fully automated quantitative CT algorithms for trabecular bone attenuation (at L1), skeletal muscle attenuation (at L3), and subcutaneous adipose tissue area (SAT) (at L3) were applied to all scans. Hazard ratios (HRs) comparing highest to lowest risk quartiles and receiver operating characteristic (ROC) curve analysis including area under the curve (AUC) were derived.

resultsHip fracture HRs (95% CI) were 3.18 (2.69-3.76) for low trabecular bone HU, 1.50 (1.28-1.75) for low muscle HU, and 2.18 (1.86-2.56) for low SAT. 10-year ROC AUC values for predicting hip fracture were 0.702, 0.603, and 0.603 for these CT-based biomarkers, respectively. Multivariate combinations of these biomarkers further improved predictive value; the 10-year ROC AUC combining bone/muscle/SAT was 0.733, while combining muscle/SAT was 0.686.

conclusionOpportunistic use of automated CT bone, muscle, and fat measures can identify patients at higher risk for future hip fracture, regardless of the indication for CT imaging. ADVANCES IN KNOWLEDGE: CT data can be leveraged opportunistically for further patient evaluation, with early intervention as needed. These novel AI tools analyse CT data to determine a patient's future hip fracture risk.

Indexed as

Hip FracturesTomography, X-Ray ComputedAbsorptiometry, PhotonAgedAged, 80 and overBiomarkersBone DensityCase-Control StudiesFemaleHumansMaleMiddle AgedRetrospective StudiesBiomarkersAIBMDCThip fracturesosteoporosis

Identifiers

PMID38379423
PMCPMC11027263

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Registered trials

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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.