Evidence map›Paper›PMID 30179924›Full record

ArticleClinical orthopaedics and related research2019

Which Two-dimensional Radiographic Measurements of Cam Femoroacetabular Impingement Best Describe the Three-dimensional Shape of the Proximal Femur?

Penny R Atkins, YoungJae Shin, Praful Agrawal, Shireen Y Elhabian, Ross T Whitaker, Jeffrey A Weiss, Stephen K Aoki, Christopher L Peters, Andrew E Anderson

Abstract read
In one paragraph

Article in Clinical orthopaedics and related research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
5.7field-weighted citation impact, top 4% 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

27 citing papers in PubMed, 51 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. MASSM: An End-to-End Deep Learning Framework for Multi-Anatomy Statistical Shape Modeling Directly From Images.Shape in medical imaging : International Workshop, ShapeMI 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024, Proceedings. ShapeMI (Workshop) (2024 : Marrakech, Morocco) · 2025
    Article
  9. Article
  10. Particle-Based Shape Modeling for Arbitrary Regions-of-Interest.Shape in medical imaging : International Workshop, ShapeMI 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings. ShapeMI (Workshop) (2023 : Vancouver, B.C.) · 2023
    Article
  11. Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. MRI-based synthetic CT shows equivalence to conventional CT for the morphological assessment of the hip joint.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2022
    Article
  17. Article
  18. Article
  19. Article
  20. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors at 2 institutions in 3 countries.

Penny R AtkinsP. R. Atkins, J. A. Weiss, S. K. Aoki, C. L. Peters, A. E. Anderson, Department of Orthopaedics, University of Utah, Salt Lake City, UT, USA P. R. Atkins, Y. Shin, R. T. Whitaker, J. A. Weiss, C. L. Peters, A. E. Anderson, Department of Bioengineering, University of Utah, Salt Lake City, UT, USA P. Agrawal, S. Y. Elhabian, R. T. Whitaker, J. A. Weiss, A. E. Anderson, Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA P. Agrawal, S. Y. Elhabian, R. T. Whitaker, J. A. Weiss, School of Computing, University of Utah, Salt Lake City, UT, USA.
YoungJae Shin
Praful Agrawal
Shireen Y Elhabian
Ross T Whitaker
Jeffrey A Weiss
Stephen K Aoki
Christopher L Peters
Andrew E Anderson
Herat University · AFUniversity of Utah · US

Funding

TRD - VisualizationP41GM103545 · NIGMS · UNIVERSITY OF UTAH · PI JOHNSON, CHRISTOPHER R., MACLEOD, ROB S. · 2012 to 2019
$9.7M
Finite Elements For Biomechanics And BiophysicsR01GM083925 · NIGMS · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · PI GERARD A. ATESHIAN, JEFFREY A. WEISS · 2008 to 2026
$7.6M
Population-Based Shape and Biomechanical Analysis of Hip PathoanatomyR01EB016701 · NIBIB · UNIVERSITY OF UTAH · PI ANDERSON, ANDREW EDWARD · 2013 to 2023
$3.6M
Musculoskeletal and Finite Element Modeling of Femoroacetabular ImpingementR21AR063844 · NIAMS · UNIVERSITY OF UTAH · PI ANDERSON, ANDREW EDWARD · 2013 to 2014
$349k
NIAMS NIH HHS R21 AR063844NIBIB NIH HHS R01 EB016701NIGMS NIH HHS P41 GM103545NIGMS NIH HHS R01 GM083925
6 · The paper itself

Abstract

backgroundMany two-dimensional (2-D) radiographic views are used to help diagnose cam femoroacetabular impingement (FAI), but there is little consensus as to which view or combination of views is most effective at visualizing the magnitude and extent of the cam lesion (ie, severity). Previous studies have used a single image from a sequence of CT or MR images to serve as a reference standard with which to evaluate the ability of 2-D radiographic views and associated measurements to describe the severity of the cam lesion. However, single images from CT or MRI data may fail to capture the apex of the cam lesion. Thus, it may be more appropriate to use measurements of three-dimensional (3-D) surface reconstructions from CT or MRI data to serve as an anatomic reference standard when evaluating radiographic views and associated measurements used in the diagnosis of cam FAI. QUESTIONS/PURPOSES: The purpose of this study was to use digitally reconstructed radiographs and 3-D statistical shape modeling to (1) determine the correlation between 2-D radiographic measurements of cam FAI and 3-D metrics of proximal femoral shape; and 2) identify the combination of radiographic measurements from plain film projections that were most effective at predicting the 3-D shape of the proximal femur.

methodsThis study leveraged previously acquired CT images of the femur from a convenience sample of 37 patients (34 males; mean age, 27 years, range, 16-47 years; mean body mass index [BMI], 24.6 kg/m, range, 19.0-30.2 kg/m) diagnosed with cam FAI imaged between February 2005 and January 2016. Patients were diagnosed with cam FAI based on a culmination of clinical examinations, history of hip pain, and imaging findings. The control group consisted of 59 morphologically normal control participants (36 males; mean age, 29 years, range, 15-55 years; mean BMI, 24.4 kg/m, range, 16.3-38.6 kg/m) imaged between April 2008 and September 2014. Of these controls, 30 were cadaveric femurs and 29 were living participants. All controls were screened for evidence of femoral deformities using radiographs. In addition, living control participants had no history of hip pain or previous surgery to the hip or lower limbs. CT images were acquired for each participant and the surface of the proximal femur was segmented and reconstructed. Surfaces were input to our statistical shape modeling pipeline, which objectively calculated 3-D shape scores that described the overall shape of the entire proximal femur and of the region of the femur where the cam lesion is typically located. Digital reconstructions for eight plain film views (AP, Meyer lateral, 45° Dunn, modified 45° Dunn, frog-leg lateral, Espié frog-leg, 90° Dunn, and cross-table lateral) were generated from CT data. For each view, measurements of the α angle and head-neck offset were obtained by two researchers (intraobserver correlation coefficients of 0.80-0.94 for the α angle and 0.42-0.80 for the head-neck offset measurements). The relationships between radiographic measurements from each view and the 3-D shape scores (for the entire proximal femur and for the region specific to the cam lesion) were assessed with linear correlation. Additionally, partial least squares regression was used to determine which combination of views and measurements was the most effective at predicting 3-D shape scores.

resultsThree-dimensional shape scores were most strongly correlated with α angle on the cross-table view when considering the entire proximal femur (r = -0.568; p < 0.001) and on the Meyer lateral view when considering the region of the cam lesion (r = -0.669; p < 0.001). Partial least squares regression demonstrated that measurements from the Meyer lateral and 90° Dunn radiographs produced the optimized regression model for predicting shape scores for the proximal femur (R = 0.405, root mean squared error of prediction [RMSEP] = 1.549) and the region of the cam lesion (R = 0.525, RMSEP = 1.150). Interestingly, views with larger differences in the α angle and head-neck offset between control and cam FAI groups did not have the strongest correlations with 3-D shape.

conclusionsConsidered together, radiographic measurements from the Meyer lateral and 90° Dunn views provided the most effective predictions of 3-D shape of the proximal femur and the region of the cam lesion as determined using shape modeling metrics. CLINICAL RELEVANCE: Our results suggest that clinicians should consider using the Meyer lateral and 90° Dunn views to evaluate patients in whom cam FAI is suspected. However, the α angle and head-neck offset measurements from these and other plain film views could describe no more than half of the overall variation in the shape of the proximal femur and cam lesion. Thus, caution should be exercised when evaluating femoral head anatomy using the α angle and head-neck offset measurements from plain film radiographs. Given these findings, we believe there is merit in pursuing research that aims to develop the framework necessary to integrate statistical shape modeling into clinical evaluation, because this could aid in the diagnosis of cam FAI.

Indexed as

AdolescentAdultCadaverCase-Control StudiesFemaleFemoracetabular ImpingementFemurHumansImaging, Three-DimensionalMagnetic Resonance ImagingMaleMiddle AgedPredictive Value of TestsRadiographic Image Interpretation, Computer-AssistedReproducibility of ResultsSeverity of Illness Index

Identifiers

PMID30179924
PMCPMC6345307
OpenAlexW2889239390

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.