June 2017
Volume 58, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2017
How to Distill Disease-Differentiating, Quantitative Phenotypic Patterns from OCT Data
Author Affiliations & Notes
  • S Scott Whitmore
    Stephen A. Wynn Institute for Vision Research, The University of Iowa, Iowa City, Iowa, United States
    Ophthalmology & Visual Sciences, The University of Iowa, Iowa City, Iowa, United States
  • Kyungmoo Lee
    Iowa Institute for Biomedical Imaging, The University of Iowa, Iowa City, Iowa, United States
  • Adam P DeLuca
    Stephen A. Wynn Institute for Vision Research, The University of Iowa, Iowa City, Iowa, United States
    Ophthalmology & Visual Sciences, The University of Iowa, Iowa City, Iowa, United States
  • Young H Kwon
    Stephen A. Wynn Institute for Vision Research, The University of Iowa, Iowa City, Iowa, United States
    Ophthalmology & Visual Sciences, The University of Iowa, Iowa City, Iowa, United States
  • Jeremy M Hoffman
    Stephen A. Wynn Institute for Vision Research, The University of Iowa, Iowa City, Iowa, United States
    Ophthalmology & Visual Sciences, The University of Iowa, Iowa City, Iowa, United States
  • Jennifer A Halder
    Stephen A. Wynn Institute for Vision Research, The University of Iowa, Iowa City, Iowa, United States
    Ophthalmology & Visual Sciences, The University of Iowa, Iowa City, Iowa, United States
  • Robert F Mullins
    Stephen A. Wynn Institute for Vision Research, The University of Iowa, Iowa City, Iowa, United States
    Ophthalmology & Visual Sciences, The University of Iowa, Iowa City, Iowa, United States
  • Todd E Scheetz
    Stephen A. Wynn Institute for Vision Research, The University of Iowa, Iowa City, Iowa, United States
    Ophthalmology & Visual Sciences, The University of Iowa, Iowa City, Iowa, United States
  • Edwin M Stone
    Stephen A. Wynn Institute for Vision Research, The University of Iowa, Iowa City, Iowa, United States
    Ophthalmology & Visual Sciences, The University of Iowa, Iowa City, Iowa, United States
  • Michael David Abramoff
    Stephen A. Wynn Institute for Vision Research, The University of Iowa, Iowa City, Iowa, United States
    Iowa Institute for Biomedical Imaging, The University of Iowa, Iowa City, Iowa, United States
  • Footnotes
    Commercial Relationships   S Scott Whitmore, None; Kyungmoo Lee, None; Adam DeLuca, None; Young Kwon, None; Jeremy Hoffman, None; Jennifer Halder, None; Robert Mullins, None; Todd Scheetz, None; Edwin Stone, None; Michael Abramoff, IDx LLC (I), IDx LLC (C), The University of Iowa (P)
  • Footnotes
    Support  The Stephen A. Wynn Foundation, NIH Grant EY019112, NIH Grant EY027038, NIH Grant EY026087
Investigative Ophthalmology & Visual Science June 2017, Vol.58, 826. doi:
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    • Get Citation

      S Scott Whitmore, Kyungmoo Lee, Adam P DeLuca, Young H Kwon, Jeremy M Hoffman, Jennifer A Halder, Robert F Mullins, Todd E Scheetz, Edwin M Stone, Michael David Abramoff; How to Distill Disease-Differentiating, Quantitative Phenotypic Patterns from OCT Data. Invest. Ophthalmol. Vis. Sci. 2017;58(8):826.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : The widespread adoption of ocular coherence tomography (OCT) has provided retina clinics with large collections of three-dimensional image data; however, identifying clinically relevant, disease-specific phenotypic patterns remains challenging. We present a computational workflow for identifying phenotypic patterns from curated OCT datasets, applied to patients with molecularly confirmed mutations in CRB1 and RS1 (see figure).

Methods : Research-consented subjects were recruited for IRB-approved studies in accordance with the tenets of the Declaration of Helsinki. We retrospectively analyzed OCT data from subjects with CRB1 mutations (n=15), subjects with RS1 mutations (n=12), and control subjects (n=15). We segmented 11 retinal layers using the Iowa Reference Algorithms and computed mean layer thickness values across the 9-region ETDRS grid. Using the 99 layer-by-region values for each patient, we performed principal component analysis to identify major patterns of variation and constructed a set of multinomial logistic regression models to predict the genotype of every patient. To facilitate our computational workflow, we developed ‘heyexr’, a open-source software package for the R programming language with functions for importing, transforming, and visualizing Heidelberg data and Iowa Reference Algorithms segmentation files.

Results : The first principal component captures most of the variation in the outer zone of the outer segment and the ellipsoid zone of the inner segments, whereas the second principal component captures the variation in the thickness of the inner nuclear layer. Of the logistic regression models we tested, the combination of the first four principal components maximized the overall accuracy to predict patient group (accuracy = 93%; 95% confidence interval, 81-99%; p = 1.2×10−14) while minimizing the information lost in modeling.

Conclusions : We successfully distilled disease-differentiating patterns from OCT data, illustrating the utility of our computational workflow. Our ‘heyexr’ package is available on GitHub (https://github.com/barefootbiology/heyexr).

This is an abstract that was submitted for the 2017 ARVO Annual Meeting, held in Baltimore, MD, May 7-11, 2017.

 

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