July 2019
Volume 60, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2019
Evaluation of shape-descriptive and texture features as potential prognostic variables in progression of geographic atrophy.
Author Affiliations & Notes
  • Neha Anegondi
    Genentech Clinical Imaging Group, Genentech, South San Francisco, California, United States
    Roche Personalized Healthcare, Roche, Switzerland
  • Simon S. Gao
    Genentech Clinical Imaging Group, Genentech, South San Francisco, California, United States
    Roche Personalized Healthcare, Roche, Switzerland
  • Jasmine Patil
    Genentech Clinical Imaging Group, Genentech, South San Francisco, California, United States
  • Alexandre Fernandez Coimbra
    Genentech Clinical Imaging Group, Genentech, South San Francisco, California, United States
  • Footnotes
    Commercial Relationships   Neha Anegondi, Genentech, Inc. (E); Simon Gao, Genentech, Inc. (E); Jasmine Patil, Genentech, Inc. (E); Alexandre Coimbra, Genentech, Inc. (E)
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science July 2019, Vol.60, 1906. doi:
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      Neha Anegondi, Simon S. Gao, Jasmine Patil, Alexandre Fernandez Coimbra; Evaluation of shape-descriptive and texture features as potential prognostic variables in progression of geographic atrophy.. Invest. Ophthalmol. Vis. Sci. 2019;60(9):1906.

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

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Abstract

Purpose : To assess the prognostic potential of multiple shape-descriptive (Pfau, M., et al. Retina. 2018) and textural features in the progression of geographic atrophy (GA).

Methods : A retrospective analysis was performed using the monthly spectral domain optical coherence tomography (SD-OCT) image data from patients in the MAHALO (NCT01229215) study. OCT images with significant motion artifacts were excluded. Thirty-three eyes [14 fellow eyes (FE) and 19 sham study eyes (SE)] were evaluated for this analysis. The GA lesion areas were derived from en face choroidal images at baseline and 18-month using a semi-automated algorithm (Figure 1A to 1D). Shape-descriptive [lesion area (mm2), perimeter (mm), circularity, feretmax (mm), feretmin (mm), square-root of lesion area, perimeter and circularity] and textural (contrast, correlation, energy and homogeneity) parameters were calculated based on the algorithm output and correlated with the change in GA lesion area using simple and multiple linear regression models. Coefficient of determination (R2) values were reported.

Results : Linear regression of each shape-descriptive parameter with lesion growth (determined by taking absolute difference between baseline and 18-month lesion areas) showed high R2 values of 0.28, 0.24 and 0.23 for square-root area, feretmin and square-root circularity, respectively (Table 1). Similar analysis with textural parameters showed energy to have the highest R2 value of 0.1 (Table 1). Multiple regression of all shape-descriptive parameters with lesion growth showed R2 value of 0.36. Similarly, all textural parameters showed R2 value of 0.14.

Conclusions : These findings suggest that shape-descriptive and textural factors may have potential as prognostic variables for GA progression. Combinations of shape-descriptive and textural factors will need to be further explored in larger datasets to evaluate their validity in predicting GA lesion growth.

This abstract was presented at the 2019 ARVO Annual Meeting, held in Vancouver, Canada, April 28 - May 2, 2019.

 

Table 1: Mean (standard error of mean) of shape-descriptive and textural parameters at baseline and 18-month, respectively. R2 is the coefficient of determination obtained from linear regression between baseline value of each parameter and lesion growth (absolute difference between baseline and 18-month lesion areas).Sqrt., square-root

Table 1: Mean (standard error of mean) of shape-descriptive and textural parameters at baseline and 18-month, respectively. R2 is the coefficient of determination obtained from linear regression between baseline value of each parameter and lesion growth (absolute difference between baseline and 18-month lesion areas).Sqrt., square-root

 

Figure 1: En face choroidal images from a patient at (A) Baseline and (B) 18-month with corresponding GA segmentation (C-D).

Figure 1: En face choroidal images from a patient at (A) Baseline and (B) 18-month with corresponding GA segmentation (C-D).

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