Prediction of Geographic Atrophy Lesion Area and Growth Rate

From Multimodal Imaging Using Deep Learning

1 Genentech, Inc., South San Francisco, CA

Neha Anegondi, MTech1

Simon S. Gao, PhD1; Verena Steffen, MSc1;

Christina Rabe, PhD1; Daniela Ferrara, MD, MS, PhD1;

and Qi Yang, PhD1

Presented at EURETINA 2021 Virtual Meeting | 9–12 September 2021

2

Disclosures

Financial Disclosures

• NA, SSG, VS, CR, DF, QY: Employee: Genentech, Inc.

Study Disclosures

• This presentation includes retrospective analyses of data from 3 studies conducted in patients

• Institutional Review Board approval was obtained prior to study initiation

• Funding was provided by Genentech, Inc., a member of the Roche Group, for the study and third-party

writing assistance, which was provided by Nibedita Gupta, PhD, of Envision Pharma Group

a An example case from Chroma (NCT02247479).

1. Wong W et al. Lancet Glob Health. 2014;2(2):e106-e116.

AMD, age-related macular degeneration; FAF, fundus autofluorescence; GA, geographic atrophy; OCT, optical coherence tomography. 3

Background and Objective

GA growth rate over timea

OCT

en face

Baseline Week 24 Week 48 Week 96

FAF

Objective

To predict GA growth rate from baseline FAF and OCT using a

multimodal multitask deep learning approach

Fundus autofluorescence (FAF)

• Gold standard for GA diagnosis and measurement

• Basis for the primary endpoint in clinical trials

Optical coherence tomography (OCT) allows:

• Depth-resolved assessment of the retina

• Identification of GA precursors

Geographic atrophy (GA)

• Advanced form of AMD and a major cause of severe vision

impairment in developed countries worldwide1

• GA lesion is characterised by complete loss of retinal pigment

epithelium, photoreceptors and choriocapillaris

FAF, fundus autofluorescence; GA, geographic atrophy; OCT, optical coherence tomography. 4

Methods: Lampalizumab Datasets

1722 study eyes of patients with bilateral GA enrolled in natural history and lampalizumab clinical trials

(images on Heidelberg Spectralis)

Proxima A (NCT02479386) | Chroma (NCT02247479) | Spectri (NCT02247531)

Cohort

Input Data

Training dataset

n = 1279

Holdout dataset

n = 443

5-fold nested cross-validation

All splits balanced for baseline factors

GA lesion area (mm2) measured from FAF images by an independent reading centre

GA lesion growth rate (mm2/year) was estimated with a linear model fitted using all available GA lesion area

measurements within 2 years

Baseline macular OCT volumes of 496 × 1024 × 49 voxels

Baseline macular 30-degree FAF images of 768 × 768 pixels

3 multitask convolutional neural network (CNN) models were trained to simultaneously predict

GA lesion area and GA lesion growth rate:

Reference model: linear model using baseline GA lesion area, lesion contiguity, lesion distance to

fovea and low-luminance deficit (LLD) measurements to derive GA lesion growth rate prediction

Performance: evaluated by calculating the in-sample coefficient of determination (R2), defined as

the square of Pearson correlation coefficient between true and predicted GA area and growth rate

a Prior work published: Anegondi N et al. Proc SPIE 11634, Multimodal Biomedical Imaging XVI. Published online March 5, 2021. doi:10.1117/12.2575898.

CNN, convolutional neural network; FAF, fundus autofluorescence; GA, geographic atrophy; OCT, optical coherence tomography; R2, coefficient of determination. 5

Methods: CNN Models to Predict GA Area and GA Growth Rate

FAFa OCT Multimodal (FAF + OCT)

1. Optimal hyperparameter set was chosen from the 5-fold nested cross-validation

results on the training data

2. Model retrained with the selected hyperparameter set on all the training data

3. Evaluated on holdout data

For CNN

models:

BM, Bruch’s membrane; OCT, optical coherence tomography.

Methods: OCT Preprocessing

Histogram

matching

Volume

flattening

along BM

En face

maps

Full depth

Sub_BM

Above_BM

Example Output

3 channels of en face maps

OCT volume

After

Histogram

Matching

Before

Histogram

Matching

OCT en face 28th B-scan 29th B-scan

OCT en face 28th B-scan 29th B-scan

6

CNN, convolutional neural network; FAF, fundus autofluorescence; GA, geographic atrophy; OCT, optical coherence tomography.

Methods: Architecture of CNN Models

Single Modality

Model

Multimodal

Model

Input image PreprocessingCNN:

Inception V3

Global

average

pooling

Dense 256

Dense 256

GA lesion

area

GA lesion

growth rate

OCT PreprocessingCNN:

Inception V3

FAFCNN:

Inception V3

Global

average

pooling

Dense 256

Dense 256

GA lesion

area

GA lesion

growth rate

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a GA lesion area measured in mm2. b GA lesion growth rate measured in mm2/year.

FAF, fundus autofluorescence; GA, geographic atrophy; N/A, not applicable; OCT, optical coherence tomography; R2, coefficient of determination.

Results: Performance Comparison of Different Models

Cross-Validation and Holdout Set Performance Using linear model, FAF only, OCT only and multimodal (FAF + OCT) multitask models

Model

Performance

Mean (SD) 5-Fold Cross-Validation R2

(n = 1279)

Holdout Set R2 (Bootstrap 95% CI)

(n = 443)

Model Input GA Areaa GA Growth Rateb GA Areaa GA Growth Rateb

Linear model N/A 0.18 N/A 0.16 (0.10, 0.24)

FAF only 0.93 (0.03) 0.48 (0.05) 0.96 (0.95, 0.97) 0.48 (0.41, 0.55)

OCT only 0.91 (0.03) 0.42 (0.04) 0.91 (0.87, 0.95) 0.36 (0.29, 0.43)

FAF + OCT multimodal 0.93 (0.02) 0.52 (0.05) 0.94 (0.92, 0.96) 0.47 (0.40, 0.54)

8

GA, geographic atrophy.

Results: All Data Points in the Holdout Set Were From the Multimodal Approach

GA Area GA Growth Rate

Measured versus predicted GA area and GA growth rate

from the multimodal approach on holdout data (n = 443)

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Tru

e B

aselin

e G

A A

rea

Predicted Values for GA Area

Tru

e G

A G

row

th R

ate

Predicted Values for GA Growth Rate

FAF, fundus autofluorescence; GA, geographic atrophy; OCT, optical coherence tomography.

Conclusions

A multimodal approach (FAF + OCT) showed comparable performance to FAF-only approach

Further validation in additional datasets is needed to confirm robust performance

This work has the potential to improve confidence in clinical development by informing clinical trial design,

implementation and analysis, specifically:

o Trial adjustment

o Patient prescreening, enrichment and stratification

o Post hoc data analysis

These findings show the feasibility of using baseline FAF and/or OCT images to predict individual GA lesion area and GA lesion growth rate with a

multitask deep learning approach

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