Fully Automated Prediction of Geographic Atrophy Growth Using Quantitative Spectral-Domain Optical Coherence...

To develop a predictive model based on quantitative characteristics of geographic atrophy (GA) to estimate future potential regions of GA growth.

Progression study and predictive model.

One hundred eighteen spectral-domain (SD) optical coherence tomography (OCT) scans of 38 eyes in 29 patients.

Imaging features of GA quantifying its extent and location, as well as characteristics at each topographic location related to individual retinal layer thickness and reflectivity, the presence of pathologic features (like reticular pseudodrusen or loss of photoreceptors), and other known risk factors of GA growth, were extracted automatically from 118 SD OCT scans of 38 eyes from 29 patients collected over a median follow-up of 2.25 years. We developed and evaluated a model to predict the magnitude and location of GA growth at given future times using the quantitative features as predictors in 3 possible scenarios.

Potential regions of GA growth.

In descending order of out-of-bag feature importance, the most predictive SD OCT biomarkers for predicting the future regions of GA growth were thickness loss of bands 11 through 14 (5.66), reflectivity of bands 11 and 12 (5.37), thickness of reticular pseudodrusen (5.01), thickness of bands 5 through 11 (4.82), reflectivity of bands 7 through 11 (4.78), GA projection image (4.73), increased minimum retinal intensity map (4.59), and GA eccentricity (4.49). The predicted GA regions in the 3 tested scenarios resulted in a Dice index mean ± standard deviation of 0.81±0.12, 0.84±0.10, and 0.87±0.06, respectively, when compared with the observed ground truth. Considering only the regions without evidence of GA at baseline, predicted regions of future GA growth showed relatively high Dice indices of 0.72±0.18, 0.74±0.17, and 0.72±0.22, respectively. Predictions and actual values of GA growth rate and future GA involvement in the central fovea showed high correlations.

Experimental results demonstrated the potential of our predictive model to predict future regions where GA is likely to grow and to identify the most discriminant early indicator (thickness loss of bands 11 through 14) of regions susceptible to GA growth.