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Equine veterinary journal2020; 53(2); 277-286; doi: 10.1111/evj.13321

A radiomics platform for computing imaging features from µCT images of Thoroughbred racehorse proximal sesamoid bones: Benchmark performance and evaluation.

Abstract: Proximal sesamoid bone (PSB) fractures are the most common fatal musculoskeletal injury in North American racehorses. Computed tomography has the potential to detect morphological changes in bone structure but can be challenging to analyse reliably and quantitatively. Objective: To develop a radiomics platform that allows the comparison of features from micro-CTs (µCT) of PSBs in horses that sustained catastrophic fractures with horses that did not. To compare features calculated with a radiomics approach with features calculated from a previously published study that used quantitative µCT in the same specimens. Methods: Retrospective study of cadaver specimens of µCT images of PSBs using prospectively applied radiomics. Methods: Radiomics features were computed on standardised CT datasets to benchmark the software. Features from µCT images of PSBs from eight horses that sustained PSB fracture and eight controls were computed using the contralateral, intact forelimb from horses sustaining PSB fracture (cases, n = 19) and all available forelimbs for controls (n = 30). Two-hundred and fifteen radiomic features were calculated, and similar or comparable features were compared with those reported in a previous study that used the same specimens. Results: Morphologic features computed with the radiomics approach, such as volume, minor axis dimensions and anisotropy were highly correlated with previously published data. A high number of imperceptible radiomic features, such as entropy, coarseness and histogram features were also found to be significantly different (P < .01). The extent of the differences in image features for the cases and controls PSBs depends on radiomic calculation settings. Conclusions: Only datasets obtained from cadaver specimens were included in the study. Conclusions: A radiomics approach for analysing µCT images of PSBs was able to identify and reproduce differences in image features in cases and controls. Furthermore, radiomics revealed many more imperceptible image features between cases and control PSBs.
© 2020 EVJ Ltd.
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Publication Date: 2020-08-07 PubMed ID: 32654167DOI: 10.1111/evj.13321Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

This research focuses on developing a radiomics platform to analyze µCT images of Proximal Sesamoid Bones (PSBs) in thoroughbred race horses, with the goal of detecting bone structure changes that might indicate problematic fractures. The article details the methodology, results, and conclusions of this new platform’s benchmark performance evaluation and comparison with a previous study.

Methodology

The researchers conducted a retrospective study of µCT image-based cadaver specimens of PSBs, using a radiomics method to compute data features. Specifically:

  • A collection of µCT images from eight horses that had suffered PSB fractures and eight horses that hadn’t were used to calculate the radiomics features.
  • The intact forelimb of the same horses was used as a benchmark to have a complete data set.
  • The software computed 215 unique radiomics features for analysis and comparison.
  • These computed features were compared to the ones identified in a previous study of the same specimens.

Results

The results indicated that:

  • Morphological features such as volume, minor axis dimensions, and anisotropy computed by the radiomics platform were highly correlated with the data from the preceding study.
  • A high number of radiomic features – entropy, coarseness and histogram features, among others – were not visible to the naked eye (imperceptible) but were shown by the platform to be significantly different.
  • Differentiation in image features between PSBs in the case group and the control group was dependent on the settings used for the radiomic calculations.

Conclusions

Only datasets obtained from cadaver specimens were used in this study. Despite this limitation, the team concluded that:

  • The radiomics platform can effectively analyze µCT images of PSBs and identify differences between data features in the test and control groups
  • The platform identified numerous imperceptible image feature differences between the two groups. These features can provide new, valuable insights for horses’ PSBs health.

Cite This Article

APA
Basran PS, Gao J, Palmer S, Reesink HL. (2020). A radiomics platform for computing imaging features from µCT images of Thoroughbred racehorse proximal sesamoid bones: Benchmark performance and evaluation. Equine Vet J, 53(2), 277-286. https://doi.org/10.1111/evj.13321

Publication

Equine veterinary journal
ISSN: 2042-3306
NlmUniqueID: 0173320
Country: United States
Language: English
Volume: 53
Issue: 2
Pages: 277-286

Researcher Affiliations

Basran, Parminder S
  • Clinical Sciences, Cornell University, Ithaca, NY, USA.
Gao, Jonathan
  • Clinical Sciences, Cornell University, Ithaca, NY, USA.
Palmer, Scott
  • Population Medicine and Diagnostic Sciences, Cornell University, Ithaca, NY, USA.
Reesink, Heidi L
  • Clinical Sciences, Cornell University, Ithaca, NY, USA.
  • Equine and Farm Animal Hospital, Cornell University, Ithaca, NY, USA.

MeSH Terms

  • Animals
  • Benchmarking
  • Forelimb / diagnostic imaging
  • Fractures, Bone / diagnostic imaging
  • Fractures, Bone / veterinary
  • Horse Diseases / diagnostic imaging
  • Horses
  • Retrospective Studies
  • Sesamoid Bones / diagnostic imaging

Grant Funding

  • Harry M. Zweig Memorial Fund for Equine Research

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Citations

This article has been cited 7 times.
  1. Noordwijk KJ, Chen L, Ruspi BD, Schurer S, Papa B, Fasanello DC, McDonough SP, Palmer SE, Porter IR, Basran PS, Donnelly E, Reesink HL. Metacarpophalangeal Joint Pathology and Bone Mineral Density Increase with Exercise but Not with Incidence of Proximal Sesamoid Bone Fracture in Thoroughbred Racehorses. Animals (Basel) 2023 Feb 24;13(5).
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  2. Costa da Silva RG, Mishra AP, Riggs CM, Doube M. Classification of racehorse limb radiographs using deep convolutional neural networks. Vet Rec Open 2023 Jun;10(1):e55.
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  3. Basran PS, McDonough S, Palmer S, Reesink HL. Radiomics Modeling of Catastrophic Proximal Sesamoid Bone Fractures in Thoroughbred Racehorses Using μCT. Animals (Basel) 2022 Nov 4;12(21).
    doi: 10.3390/ani12213033pubmed: 36359157google scholar: lookup
  4. Bouhali O, Bensmail H, Sheharyar A, David F, Johnson JP. A Review of Radiomics and Artificial Intelligence and Their Application in Veterinary Diagnostic Imaging. Vet Sci 2022 Nov 8;9(11).
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