July 2019
Volume 60, Issue 9
Free
ARVO Annual Meeting Abstract  |   July 2019
Deep learning algorithm for patient-level prediction of diabetic retinopathy (DR) response to vascular endothelial growth factor (VEGF) inhibition
Author Affiliations & Notes
  • Filippo Arcadu
    Roche Informatics, Roche, Basel, Switzerland
    Roche Personalized Healthcare, Roche, Basel, Switzerland
  • Fethallah Benmansour
    Roche Informatics, Roche, Basel, Switzerland
    Roche Personalized Healthcare, Roche, Basel, Switzerland
  • Andreas Maunz
    Roche Informatics, Roche, Basel, Switzerland
    Roche Personalized Healthcare, Roche, Basel, Switzerland
  • Jeffrey Ryuta Willis
    Clinical Science Ophthalmology, Genentech, Inc, South San Francisco, California, United States
    Roche Personalized Healthcare, Genentech, Inc, South San Francisco, California, United States
  • Marco Prunotto
    Immunology, Infectious Disease & Ophthalmology, Roche, Basel, Switzerland
    Roche Personalized Healthcare, Roche, Basel, Switzerland
  • Zdenka Haskova
    Clinical Science Ophthalmology, Genentech, Inc, South San Francisco, California, United States
    Roche Personalized Healthcare, Genentech, Inc, South San Francisco, California, United States
  • Footnotes
    Commercial Relationships   Filippo Arcadu, Roche (E); Fethallah Benmansour, Roche (E), Roche (I); Andreas Maunz, Roche (E); Jeffrey Willis, Genentech, Inc (E); Marco Prunotto, Roche (E); Zdenka Haskova, Genentech, Inc (E)
  • Footnotes
    Support  Genentech, Inc., South San Francisco, CA, provided support for the study and participated in the study design; conducting the study; and data collection, management, and interpretation
Investigative Ophthalmology & Visual Science July 2019, Vol.60, 2806. doi:https://doi.org/
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Filippo Arcadu, Fethallah Benmansour, Andreas Maunz, Jeffrey Ryuta Willis, Marco Prunotto, Zdenka Haskova; Deep learning algorithm for patient-level prediction of diabetic retinopathy (DR) response to vascular endothelial growth factor (VEGF) inhibition. Invest. Ophthalmol. Vis. Sci. 2019;60(9):2806. doi: https://doi.org/.

      Download citation file:


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

      ×
  • Supplements
Abstract

Purpose : DR is the leading cause of blindness among working-age adults due to vision threatening complications such as diabetic macular edema (DME), and the risk of vision loss increases with DR worsening. Several large studies found that VEGF inhibition with ranibizumab regressed DR in a significant proportion of patients with DR with or without DME, and that baseline DR severity was predictive of DR response to this therapy. However, there is a lack of tools to predict patient-level DR treatment response. As part of Roche’s comprehensive initiative in Ophthalmology Personalized Healthcare, we have developed a predictive algorithm of individual treatment response to help inform the optimal timing for start of ranibizumab therapy in DR.

Methods : RIDE (NCT00473382) and RISE (NCT00473330) were identical double-masked phase 3 trials of ranibizumab in patients with DR and vision loss due to DME (N = 759). DR severity was prospectively graded on stereoscopic 7-field color fundus photographs (CFPs) by masked reading center evaluators using the Early Treatment Diabetic Retinopathy Study (ETDRS) Diabetic Retinopathy Severity Scale (DRSS). The 7-field CFPs from patients treated with monthly ranibizumab 0.3 mg or 0.5 mg were used to train and validate deep learning algorithms predictive of ≥2-step DRS improvement after 6, 12, and 24 months from baseline. The performance of the deep learning (DL) models was assessed through calculation of the area under the curve (AUC).

Results : Manually detected rates of ≥2-step improvement in treated eyes at month 6, 12, and 24 from baseline were 30.7%, 33.7%, and 40.1%, respectively. At 6 months, the best DL algorithm was able to predict ≥2-step DRSS improvement with an AUC of 0.82 ± 0.03, sensitivity 87% ± 8%, and specificity 72% ± 6%. At 12 months, the best DL algorithm had an AUC of 0.85 ± 0.05, sensitivity 81% ± 10%, and specificity 82% ± 13%. At 24 months, the best DL algorithm had an AUC of 0.87 ± 0.04, sensitivity 84% ± 9%, and specificity 82% ± 7%.

Conclusions : Our work demonstrated feasibility of a deep learning algorithm that can predict response to anti-VEGF therapy from the baseline color fundus photographs (CFPs) at an individual patient level. CFPs from DR patients treated with anti-VEGFs in the real world are needed for further validation and to expand the applicability of the current pilot algorithm.

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

×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×