August 2019
Volume 60, Issue 11
Free
ARVO Imaging in the Eye Conference Abstract  |   August 2019
A machine learning approach to predict response to anti-VEGF treatment in patients with neovascular age-related macular degeneration using SD-OCT
Author Affiliations & Notes
  • Jayashree Nair Sahni
    Pharma Research and Early Development, F. Hoffmann-La Roche , Basel, Switzerland
  • Andreas Maunz
    Pharma Research and Early Development, F. Hoffmann-La Roche , Basel, Switzerland
  • Filippo Arcadu
    Pharma Research and Early Development, F. Hoffmann-La Roche , Basel, Switzerland
  • Yan-Ping Zhang_Schaerer
    Pharma Research and Early Development, F. Hoffmann-La Roche , Basel, Switzerland
  • Yvonna Li
    Pharma Research and Early Development, F. Hoffmann-La Roche , Basel, Switzerland
  • Thomas Albrecht
    Pharma Research and Early Development, F. Hoffmann-La Roche , Basel, Switzerland
  • Andreas Thalhammer
    Pharma Research and Early Development, F. Hoffmann-La Roche , Basel, Switzerland
  • Fethallah Benmansour
    Pharma Research and Early Development, F. Hoffmann-La Roche , Basel, Switzerland
  • Footnotes
    Commercial Relationships   Jayashree Nair Sahni, F. Hoffmann-La Roche AG (E); Andreas Maunz, F. Hoffmann-La Roche AG (E); Filippo Arcadu, F. Hoffmann-La Roche AG (E); Yan-Ping Zhang_Schaerer, F. Hoffmann-La Roche AG (E); Yvonna Li, F. Hoffmann-La Roche AG (E); Thomas Albrecht, F. Hoffmann-La Roche AG (E); Andreas Thalhammer, F. Hoffmann-La Roche AG (E); Fethallah Benmansour, F. Hoffmann-La Roche AG (E)
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science August 2019, Vol.60, PB094. doi:https://doi.org/
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      Jayashree Nair Sahni, Andreas Maunz, Filippo Arcadu, Yan-Ping Zhang_Schaerer, Yvonna Li, Thomas Albrecht, Andreas Thalhammer, Fethallah Benmansour; A machine learning approach to predict response to anti-VEGF treatment in patients with neovascular age-related macular degeneration using SD-OCT. Invest. Ophthalmol. Vis. Sci. 2019;60(11):PB094. doi: https://doi.org/.

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

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Abstract

Purpose : The purpose of this study was to identify baseline spectral-domain optical coherence tomography (SD-OCT) features for predicting response to anti-VEGF therapy in patients with neovascular AMD, and to build a model capable of providing individual patient predictions.

Methods : Clinical trial data of patients (N=793) with neovascular AMD treated with ranibizumab from all arms of the HARBOR study (NCT00891735) was used to train and validate the models to predict response. Response to anti-VEGF therapy was defined as Best Corrected Visual Acuity (BCVA) ≥20/40 at month 12 in those subjects with BCVA ≤ 20/40 at baseline. Baseline age, sex, SD-OCT, and BCVA data were included. Automated segmentation of retinal layers and fluid-filled regions over a 6 X 6mm cube of SD-OCT images centered on the fovea was used to extract 62 SD-OCT features: 44 layer related features in 2-D space, 9 layer related in a 3D space and 9 fluidic area related features in 3-D space. To gain insight into the CatBoost model trained from the data, SHAP (SHapley Additive exPlanations) method was used to interpret patient-level model predictions. Model performance was assessed in terms of area under the receiver operating curve (AUROC) using 5-fold cross validation.

Results : AUROC to predict response at month 12 using only baseline data was 0.77 (95% CI 0.73 – 0.82). Baseline BCVA, central subfield thickness, central subfield volume, and intra-retinal fluidic volume were among the most impactful measurements to predict response according to SHAP analysis, driving predictions, up, down, down, and up, respectively, in favor of reaching BCVA ≥20/40.

Conclusions : We proposed and evaluated a machine learning methodology to predict probability of achieving functional BCVA from SD-OCT scans taken at treatment initiation. In the new era of therapies targeting multiple pathways in the management of neovascular AMD, the results of this retrospective analysis allow identification of patients who are likely to be good responders to anti-VEGF, thus enabling selection of appropriate patient population for the novel therapies and a precision medicine approach.

This abstract was presented at the 2019 ARVO Imaging in the Eye Conference, held in Vancouver, Canada, April 26-27, 2019.

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