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Assessment of Orbital Computed Tomography (CT) Imaging Biomarkers in Patients with Thyroid Eye Disease

  • Shikha ChagantiEmail author
  • Kevin Mundy
  • Michael P. DeLisi
  • Katrina M. Nelson
  • Robert L. Harrigan
  • Robert L. Galloway
  • Bennett A. Landman
  • Louise A. Mawn
Article
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Abstract

To understand potential orbital biomarkers generated from computed tomography (CT) imaging in patients with thyroid eye disease. This is a retrospective cohort study. From a database of an ongoing thyroid eye disease research study at our institution, we identified 85 subjects who had both clinical examination and laboratory records supporting the diagnosis of thyroid eye disease and concurrent imaging prior to any medical or surgical intervention. Patients were excluded if imaging quality or type was not amenable to segmentation. The images of 170 orbits were analyzed with the developed automated segmentation tool. The main outcome measure was to cross 25 CT structural metrics for each eye with nine clinical markers using a Kendall rank correlation test to identify significant relationships. The Kendall rank correlation test between automatically calculated CT metrics and clinical data demonstrated numerous correlations. Extraocular rectus muscle metrics, such as the average diameter of the superior, medial, and lateral rectus muscles, showed a strong correlation (p < 0.05) with loss of visual acuity and presence of ocular motility defects. Hertel measurements demonstrated a strong correlation (p < 0.05) with volumetric measurements of the optic nerve and other orbital metrics such as the crowding index and proptosis. Optic neuropathy was strongly correlated (p < 0.05) with an increase in the maximum diameter of the superior muscle. This novel method of automated imaging metrics may provide objective, rapid clinical information. This data may be useful for appreciation of severity of thyroid eye disease and recognition of risk factors of visual impairment from dysthyroid optic neuropathy from CT imaging.

Keywords

CT Multi-atlas Thyroid eye disease Optic nerve 

Notes

Acknowledgments

This work was conducted in part using the resources of the Advanced Computing Center for Research and Education at Vanderbilt University, Nashville, TN.

Funding/Support

This study is supported in part by an unrestricted grant from the Vanderbilt Eye Institute and Physician Scientist Award from Research to Prevent Blindness, New York, NY. This project was supported by the NIH 1R03EB012461 and the National Center for Research Resources, Grant UL1 RR024975-01 (now at the National Center for Advancing Translational Sciences, Grant 2 UL1 TR000445-06). This research was supported by NSF CAREER 1452485 and NIH grants 5R21EY024036. This research was conducted with the support from Intramural Research Program, National Institute on Aging, NIH. This project was supported in part by ViSE/VICTR. This work was also supported by the National Institutes of Health in part by the National Institute of Biomedical Imaging and Bioengineering training grant T32-EB021937.

Compliance with Ethical Standards

Institutional Review Board approval was obtained at Vanderbilt University prospectively to evaluate both the clinical and imaging data of the 381 patients and store them in RedCAP and XNAT databases.

Conflict of Interest

The authors declare that they have no conflict of interest.

Disclaimer

The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

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Copyright information

© Society for Imaging Informatics in Medicine 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceVanderbilt UniversityNashvilleUSA
  2. 2.Vanderbilt UniversityNashvilleUSA
  3. 3.Vanderbilt Eye InstituteVanderbilt University School of MedicineNashvilleUSA
  4. 4.Department of Biomedical EngineeringVanderbilt UniversityNashvilleUSA
  5. 5.Department of Electrical EngineeringVanderbilt UniversityNashvilleUSA

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